ADVANCES IN FLOW RESEARCH [second ed.] 9783030534677, 3030534677

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Corinna Peifer Stefan Engeser  Editors

Advances in Flow Research Second Edition

Advances in Flow Research

Corinna Peifer • Stefan Engeser Editors

Advances in Flow Research Second Edition

Editors Corinna Peifer Department of Psychology University of Lübeck Lübeck, Germany

Stefan Engeser Department of Psychology University of Trier Trier, Germany

ISBN 978-3-030-53467-7 ISBN 978-3-030-53468-4 https://doi.org/10.1007/978-3-030-53468-4

(eBook)

© The Editor(s) (if applicable) and The Author(s) 2012, 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

Since Flow was originally published in 1990, it has been translated into many languages. This includes practically all of the European languages, plus Chinese (traditional as well as modernized), Japanese, Korean, and some Indonesian languages that I once did not know existed. At least three Prime Ministers have asked how flow could help their countries avoid alienation from work, or emigration to countries that offered more interesting challenges. The Mayor of Seoul has shared his concern that the 40,000 employees of his Metropolitan Government might become too bureaucratic, as civil servants have a tendency to be. CEOs of some of the major companies worldwide have implemented flow in various aspects of their operations and, in at least two cases, have reported startling jumps not only in revenues but also in profits. All of this started over 50 years ago, when I decided to teach a senior seminar at Lake Forest College, in Illinois. The dozen or so undergraduate students in that class were not particularly academically oriented, and they were not even majoring in psychology—most of them came from the department of sociology and anthropology. I decided to teach the seminar on the topic of play—but what I had in mind was not children’s play, on which there was huge literature, but on the kind of play adults engage in. Although I was in my 30s by then, I still “played” chess, climbed rocks, and did many other things that did not have to be done, just for the sheer enjoyment of doing it. As I started to read the psychological literature on play, I felt a sense of mounting incredulity and dismay. First of all, almost all the articles (or books) were about the play of children. But it was much worse; even children’s play was described in strictly functional terms, without reference to the experience of play itself, which, as far as I was concerned, was the only reason that made play interesting in the first place. True, playing football could be good training for a healthy lifestyle and for working on a team (as an adult), and playing chess might be a good way to develop intellectual skills that a child could use (as an adult). But I was quite sure that children did not play football or chess to prepare themselves for a later job.

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It occurred to me then that most of the literature (even the work of such luminaries as Jean Piaget or Erick Erickson) had been trying to explain the distal outcomes of play, not the proximal ones. They wanted to know “what good is it?” not “how does it feel like?” Having been exposed to the phenomenology of Husserl, Heidegger, and Merleau-Ponty, I realized that what was missing from the literature was a consideration of the phenomenology of play. The next major realization was that the phenomenology of play seemed indistinguishable when the person felt it in a play situation like a game, or in settings that are not usually thought of as play—like music, painting, and even in work. When I started publishing the results of our first interview-based studies, I called the peculiar state people reported when they were “playing” the autotelic experience, Greek for something you might be doing primarily for the sake of the experience itself. Later, to use more accessible language, I called it flow, borrowing from the language of the people we interviewed, which often used the image of flowing waters as an analogy to the feeling they were describing. The initial reception to my first book, Beyond Boredom and Anxiety, and to the research articles that ensued, could be best described as benign neglect. In private, some colleagues congratulated me for having given a name to something very obvious that they had known forever. Others congratulated me for having had the courage to write about something so fascinating, but that unfortunately was not amenable to scientific investigation. Even though patronizing, such responses were more comforting than the deep silence that otherwise surrounded my work. The only formal recognition in the first dozen years or so came from a brief, and not exactly encouraging review of the book, by Edward Deci in Psych Abstracts. Interestingly, the first sparks of academic interest came from anthropologists, followed by sociologists, and finally by psychologists of sports and leisure. Then, slowly, the field began to grudgingly accept flow as something that might have some relevance to the central issues of psychology. The first intellectual contribution from a psychologist came from Fausto Massimini, a physician who became Professor of Psychology at the University of Milan. He and his lively lab have contributed more to the development of research on flow than could be summarized in a few pages. His insight in the role that flow plays in cultural evolution was a brilliant extension of the theory, and the many cross-cultural studies done under his aegis are now part of history. So now, more than a generation later, as I am paging through the various chapters of this volume, I am reminded of an anecdote I read—a long time ago—about the last days of Leonardo da Vinci. In 1519, the old master was ailing and despondent on his sickbed at the Clos Lucé, a noble manor house Francis I of France had placed at his disposal near his royal chateau at Amboise. The staff let the King know that they were worried about his guest. Francis hastened to the old man’s bedside, and seeing how depressed he appeared to be, said something like: “Maestro, you who have achieved more in your life than all other artists and scientists put together, how can you be so sad?” to which Leonardo is supposed to have answered: “Thank you Sire, for your kindness—but no master can die happy until at least one of his disciples surpass him.”

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If Leonardo was right, then—mutatis mutandis—I ought to die happy (but hopefully not for some time yet). The chapters in this book show that there are quite a few directions in which the work I was able to accomplish is being stretched, continued, and improved upon, and they show that new directions, unexpected and unimagined before, are being opened up for investigation. Of course, Leonardo could also have been wrong—as a student of human nature he did not shine as well as in other fields—and being surpassed might not make one as happy as he thought. After all, do you really believe that if some connoisseur of art had written: “Andrea Salieri, who learned to paint in Leonardo da Vinci’s studio, just painted a young lady’s portrait he calls Monna Vanna, and it leaves his master’s Monna Lisa in the dust . . .” Leonardo would have been happy? I do not think so, either. So it is a bittersweet experience to read these excellent scientific essays, each one of which adds something new and important to what has been written about flow so far. And, of course, I realize that whether I am happy or not is besides the point; what counts is that the ideas are carried forward into the future—it is their survival that matters, and the consequences they will have for the lives of the next generation. In this respect, the present volume guarantees that the contribution of flow to an understanding of human behavior will grow and prosper in the years to come. At the end of the day, I am grateful to all those who have contributed to it. Claremont, CA 2019

Mihaly Csikszentmihalyi

Foreword

Seven years ago, the first edition of Advances in Flow Research was published, a work which was both important and challenging at the same time. Important because back then, a great deal had already been published on the theme of flow, but certainly not always in the manner called for by researchers who are committed to the methodological standards of empirical research. Of course, this should by no means diminish the value of publications that offer up an exploratory, everyday perspective. Indeed, it is always possible to approach meaningful and interesting phenomena along paths other than the methodologically strictly regulated one of empirical research. Rather, it simply means that with a growing stock of certainties, plausibilities, and presumptions, at some point one wishes to know what can be seen as safely replicable knowledge, and under which conditions. And then there is no way around resorting to the sometimes stony field of empirical research, in which insights cannot be so easily gained as when gathering and classifying cleverly observed everyday phenomena. And it is precisely this gap into which Advances in Flow Research stepped. The book had set itself the goal of addressing the research on the flow experience which satisfied the methodological standards of empirical research, especially those standards applicable to psychology. For only through such research will it be possible to ground the flow experience within the body of established concepts of psychology and its neighboring disciplines in perpetuity. With such aspirations, a publication can quickly slip into the small niche of books which are only received and appreciated by a handful of specialists. The challenge was therefore to present the contents, despite the scientific orientation, in a way that was comprehensible, interesting, and stimulating for a broader readership. The fact that a resolutely research-oriented book is now experiencing its second edition demonstrates that this challenge was mastered. From a personal perspective, this fact is all the more gratifying given that in my own time of active research, I became interested relatively early on in phenomena that we nowadays address under the fitting term of “flow.”

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“There’s a guy in Chicago with an unpronounceable name who recently described exactly what you’re reporting.” This is what my teacher, Heinz Heckhausen, told me back in 1978, when, in a research colloquium of our motivational psychology workgroup, I reported on a “new” activity incentive that I was unable to categorize. In the course of the cognitive revolution, our research group in Bochum had become accustomed to analyzing motivational phenomena according to the perspective that individuals constantly act with a view to the attainable outcomes and the value of their likely consequences. Of course, this purpose-centered analytical perspective was not erroneous, but it was constricted. We quickly notice this if we look at leisure activities, in which it is irrefutably apparent that besides the incentives linked to the consequences of the outcome, there are also incentives that lie directly in the performance of the activity itself (activity-related incentives). And here, one can find a colorful diversity of the most varied of perceptions, states, and feelings, which people like to have and for which reason they carry out certain activities time and time again and for as long as possible—even if these activities have no valuable outcome consequences and at times even bring with them foreseeable costs. Back then, I wanted to find out about these things as comprehensively as possible and to describe and classify them precisely. A lot of things could be quickly understood or were already known: the experience of “thrill and adventure,” joy in the interaction with nice people, feeling how one’s own movements are functioning perfectly, experiencing how a piece of music is succeeding to an ever greater degree, the sense of well-being in nature, and much more. For one thing, however, no theoretically introduced category of experiences could yet be found at first glance: that good feeling of becoming so completely absorbed in a smoothly running activity that one loses track of time forgets the original purpose of the activity and indeed even forgets oneself. Even if one is working to one’s full capacity, it is not experienced as a burden, but rather as a pleasant state in which one is happy to become immersed. Undoubtedly, this too was an incentive, which lies in the execution of the activity and not in the consequences of its outcome. But with which motivational systems might it be associated? Or had something entirely new been discovered here? No, it was nothing new. As is well known, the man with the difficult to pronounce name, Mihaly Csikszentmihalyi, had already termed this as the flow experience three years previously in his highly fruitful book Beyond Boredom and Anxiety (1975) and had described it in detail. His strongly phenomenological research, and the way in which he described it in his book was in no way consistent with the mainstream of empirical-experimental psychology of the time. However, there was a clear sense that somebody had hit upon a phenomenon that had not yet been described in this structure. Certainly, there had been forerunners, but the configuration of flow components which he presented was new as a concept. This flow concept had a very strong ally in academia, namely that of selfexperience, for academics in particular, who are practically obsessive in their search for opportunities to delve undisturbed and entirely into their thought systems and

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research findings, and who then describe all of this in articles and books, are very well aware of this flow state. Even if, as an empirical psychologist, one might criticize details in research methodology or in theory formulation, one can hardly deny the existence of a state which is undoubtedly present in one’s own experience. I believe that this indisputability of self-experienced flow was and is a first, and perhaps the most important, guarantor for flow to remain alive as an object of research. The second guarantor is the fact that even after a good four decades of flow research and many exciting findings, there still remains so much to be clarified. And this is what this book deals with in an impressively clear and, where necessary, critical manner. On a theoretical level, attempts to elaborate more sharply on the functional particularities of the flow state are worthwhile. What exactly is happening differently in us when we are operating in flow rather than generating a result in a conscious and willful way? How, from this, can we explain the better performances which are sometimes reported for the flow state? From a motivational perspective, it needs to be clarified more precisely what leads us to repeatedly do things that bring about the flow state. Does the flow state itself act as an effective incentive or is it merely an attendant phenomenon of the accomplishment of well-mastered activities which we love anyway? Moreover, it remains to be clarified whether and what flow has to do with that which people describe as “happiness.” Even though public attention for the flow concept might be on the increase, it would not be particularly helpful to rashly link together flow and happiness (or even equate them with one another). Independently of this, it remains to be clarified whether the flow state perhaps has an affinity with certain motivational systems. For instance, the balance between demand and skills is a condition that is conducive both to the flow experience and to competence-oriented achievement motivation. In accordance with this, flow elements can clearly be found in the description of the achievement-motivational activity incentive. Should one therefore postulate a particular closeness between flow and achievement motivation? In that case, with a sufficient degree of freedom of lifestyle, highly (success-confident) achievementmotivated people should find themselves in a state of flow particularly frequently. Is this really the case? After all, flow can also arise in action contexts that have nothing to do with the theme of achievement. These are but a few of the many questions that need to be empirically clarified and which are dealt with in this book. When it comes to the empirical clarification of these types of questions or similar, then, with flow, we are faced with the particular problem that the measurement itself can change that which is being measured. Do classical survey methods thus become basically unsuitable because we interrupt the flow state through the survey? Or do surveys provide us with data that we can nevertheless use, albeit with certain limitations, as indicators of the state that has just been interrupted? And what, precisely, do we have to ask in order to be able to conclude with sufficient certainty from the survey data that flow has occurred? Is the ratio of skill to challenge, which is frequently used as an indicator of flow, really sufficient? Ideal, of course, would be possibilities to capture flow quasi “online” and without an interrupting survey, for instance through observable features of the activity performance or perhaps through

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psychophysiological indicators. And how should we picture the relation between the measurement values and the state of flow? Do we think of flow as an alternative characteristic, in which this state only exists above a certain threshold and not below it—just like flicking a switch? Or should we conceive of flow as a kind of continuum, on which this state, in its components, varies in strength and completeness? This, of course, has considerable impacts on how we may measure or diagnose flow. Once we are on methodologically secure ground, broad fields of questions open up, which will make the flow experience a fascinating object of investigation for basic and applied research for a long time to come. Under which situational conditions and with which activities do flow states occur especially frequently? Particularly for applied research, the question becomes important of where, with a view to its consequences, the flow state is desired and where it should be avoided if possible. According to the action context, both possibilities appear to be given. But how do we go about fostering or avoiding flow? With the focus of the first edition lying in the methodological and conceptual area, thus dealing with a kind of “general psychology of flow,” this second edition happily adds many application chapters. Although, obviously, not everything has yet been clarified in the basic research area (and probably never will be), it is now time to examine how flow can be specifically grasped and utilized in contexts such as work, sports, music, and human–computer interaction, but also more generally defined fields such as “social interaction” or “development.” This second edition also deals with the arising basic and applied research questions, addressed by colleagues who possess a high level of expertise in the area of empirical flow research. We find here a remarkable work which is not content to merely reproduce the current state of research. Of course, it does also do this, but it reaches further and sets itself the goal of making the flow experience a conceptually clear-cut study variable that is as methodologically “tough” as possible. The current volume provides a solid foundation for this purpose for all those who are interested in a fascinating phenomenon, namely a state in which we are entirely, and without self-reflection, absorbed in a smoothly running activity which, despite a high level of demand, we still have well under control. I am certain that, also with the second edition of Advances in Flow Research, the editor and authors have once again created a standard work on flow research. Gladbeck, Germany 2019

Falko Rheinberg

Preface

This second edition of Advances in Flow Research aims to provide a broad and balanced overview on the current state of flow research—from the basics to specific contexts of application. While the chapters of the first edition have been updated and/or substantially revised (Chaps. 1, 2, 3, 4, 5, 7, 8, 9, and 16), seven new chapters targeting different contexts of application have been added (Chaps. 6, 10, 11, 12, 13, 14, and 15). The authors of this book present what has been learned since the beginning of flow research, what is still open to further investigation, and how the mission to understand and foster flow should continue. Accordingly, we address researchers, teachers, and students from diverse professions, such as psychology, sociology, education, sports, the arts, IT, and economics. Particularly with the new applied chapters, we also address practitioners who seek for sound research on flow in their field of expertise to foster flow in contexts such as groups (Chap. 10), work (Chap. 11), development (Chap. 12), sports (Chap. 13), arts and music (Chap. 14), and human–computer interaction (Chap. 15). The book can be used as a textbook for advanced courses on motivation, attention, flow experience, intrinsic motivation, optimal motivation and excellence, daily experience, well-being, and similar topics. Study questions at the end of each chapter can help students to test their knowledge. A preceding abstract of each chapter offers an overview of the topic. Text boxes provide more details, and figures are used to illustrate the topic. We aimed at integrating different theoretical perspectives rather than advertising a single point of view. What will you read in this book? In the introduction (Engeser, Schiepe-Tiska, and Peifer, Chap. 1), we will talk about the definition of flow, draw the historical lines, and present a short review of current flow research, which is becoming more complex and is following various lines of inquiry. Flow research could be regarded as a mission to understand enjoyment in human life, and many researchers have joined in studying flow experience to understand its conditions and consequences in more detail. However, there is not yet a consensus about the measurement of flow, and the potential of existing measures is often not used to its full potential. Therefore, xiii

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the book dedicates one chapter to the methodological aspects in flow research (Moneta, Chap. 2). Following this, Barthelmäs and Keller (Chap. 3) take a close look at the antecedents, boundary conditions, and consequences of flow. While flow is often described in the achievement context, flow can be experienced in nearly any kind of activity. Thus, an entire chapter will be devoted to flow in non-achievement situations, including their special aspects and conceptual difficulties (Schiepe-Tiska and Engeser, Chap. 4). The authors propose that personal preferences structure the situation and, consequently, support the experience of flow: when a person’s motives correspond to the motive-specific incentives in a situation, flow is more likely to occur. At the heart of flow research is the motivational aspect of this experience. Flow motivates people to carry out the activity again and to seek challenges and thereby improve their skills and abilities. Abuhamdeh (Chap. 5) discusses similarities and differences of another prominent theory of intrinsic motivation—the self-determination theory—in comparison with flow theory. He argues that the two theories hold explanatory power in contrasting, largely non-overlapping contexts. This contrasts with the prevailing understanding that each theory represents different levels of analysis of the same contexts. A repeatedly debated question in flow research is whether enjoyment is an integral part of flow experience. Abuhamdeh (Chap. 6) disentangles prominent arguments regarding that question and finally answers this question in the affirmative. The fact that flow experiences might also have negative consequences has been almost entirely neglected so far, but some researchers have begun to discuss and study this issue (cf. Zimanyi and Schüler, Chap. 7). In this respect, research examines the assumption that flow is so rewarding that activities are still executed regardless of potential negative consequences, as in the case of addiction, high-risk activities, or antisocial activities. In Chap. 8, Peifer and Tan enter into the psychophysiology of flow. The psychophysiological processes during flow provide new insights for a deeper understanding of the phenomenon, while a lot of research is still needed in this field. The next chapter reflects on the search for a “flow personality” or “autotelic personality” (Baumann, Chap. 9). The chapter discusses how different personality aspects relate to flow, suggests a measure of the “autotelic personality”, and offers a dynamic perspective on personality and flow. In Chap. 10, Walker addresses the phenomenon of social flow, distinguishes flow in social situations from interactive flow, and argues that it differs from solitary flow. Chapter 11 (Peifer and Wolters) is dedicated to flow in the context of work. Peifer and Wolters outline the importance of flow for well-being and performance and further provide a model that systematically captures the conditions of flow in the work context. Flow is of relevance in all fields of human development, from infancy to old age. Chapter 12 (Freire, Gissubel, Tavares, and Teixeira) provides an overview of flow research over the life span, addressing effects of flow in healthy development as well as in psychopathology. From the beginnings of flow research, flow was investigated in the sports context. Stoll and Ufer report in Chap. 13 the

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current state of affairs of this vein of research. They look at flow in exercise, in competitive sports, as well as in recreational sports. Similarly, research on flow in the arts has a long tradition. Harmat, de Manzano, and Ullèn wrap up research on flow in music, dance, and the visual arts. Despite the long tradition, relatively little research has been conducted in this application context, and the authors outline gaps for future investigation. Finally, Triberti, Di Natale, and Gaggioli (Chap. 15) look at a future-oriented topic of flow application: its use in human–computer interaction. This new area of research is highly relevant in the age of digitalization. The chapter makes suggestions on how to use technologies to improve well-being. Our book ends with a theoretical integration of the different paths being taken within the increasingly more complex field of flow research (Peifer and Engeser, Chap. 16). It also examines what has been learned since the beginning of flow research, what is still open to further investigation, and how the mission to understand and foster flow experience should continue. While we have now completed this book after a long period of intensive work and a lot of helpful feedback, we already have new ideas for an extended third edition. These ideas include chapters on the role of flow in education and on flow in cultural development. Also, as interest in flow research is growing, we look forward to the advancements that will be made in the coming years. For now, we wish our readers a good and fruitful engagement with this book and hope that it will have a positive impact on the various fields of research and application. Lübeck, Germany Trier, Germany

Corinna Peifer Stefan Engeser

Contents

1

Historical Lines and an Overview of Current Research on Flow . . . Stefan Engeser, Anja Schiepe-Tiska, and Corinna Peifer

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2

On the Conceptualization and Measurement of Flow . . . . . . . . . . . Giovanni B. Moneta

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Antecedents, Boundary Conditions and Consequences of Flow . . . . Michael Barthelmäs and Johannes Keller

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Flow in Nonachievement Situations . . . . . . . . . . . . . . . . . . . . . . . . . 109 Anja Schiepe-Tiska and Stefan Engeser

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Flow Theory and Cognitive Evaluation Theory: Two Sides of the Same Coin? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Sami Abuhamdeh

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On the Relationship Between Flow and Enjoyment . . . . . . . . . . . . . 155 Sami Abuhamdeh

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The Dark Side of the Moon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Zsuzsanna Zimanyi and Julia Schüler

8

The Psychophysiology of Flow Experience . . . . . . . . . . . . . . . . . . . 191 Corinna Peifer and Jasmine Tan

9

Autotelic Personality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Nicola Baumann

10

Social Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Charles J. Walker

11

Flow in the Context of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Corinna Peifer and Gina Wolters

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Flow Experience in Human Development: Understanding Optimal Functioning Along the Lifespan . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 Teresa Freire, Keith Gissubel, Dionísia Tavares, and Ana Teixeira

13

Flow in Sports and Exercise: A Historical Overview . . . . . . . . . . . . 351 Oliver Stoll and Michele Ufer

14

Flow in Music and Arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 László Harmat, Örjan de Manzano, and Fredrik Ullén

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Flowing Technologies: The Role of Flow and Related Constructs in Human-Computer Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Stefano Triberti, Anna Flavia Di Natale, and Andrea Gaggioli

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Theoretical Integration and Future Lines of Flow Research . . . . . . 417 Corinna Peifer and Stefan Engeser

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441

Contributors

Sami Abuhamdeh Department of Psychology, Marmara University, Istanbul, Turkey Michael Barthelmäs Institute of Psychology and Education, Ulm University, Ulm, Germany Nicola Baumann Department of Psychology, University of Trier, Trier, Germany Mihaly Csikszentmihalyi Quality of Life Research Center, Claremont Graduate University, Claremont, CA, USA Stefan Engeser Institute of Psychology, University of Trier, Trier, Germany Teresa Freire School of Psychology, University of Minho, Minho, Portugal Andrea Gaggioli Department of Psychology, Università Cattolica del Sacro Cuore, Milano, MI, Italy Applied Technology for Neuro-Psychology Lab, I.R.C.C.S. Istituto Auxologico Italiano, Milano, MI, Italy Keith Gissubel School of Psychology, University of Minho, Minho, Portugal László Harmat Department of Psychology, Faculty of Health and Life Sciences, Linnaeus University, Växjö, Sweden Johannes Keller Institute of Psychology and Education, Ulm University, Ulm, Germany Örjan de Manzano Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden Giovanni B. Moneta School of Psychology, London Metropolitan University, London, UK Anna Flavia Di Natale Department of Psychology, University of Milan-Bicocca, Milano, MI, Italy xix

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Corinna Peifer Department of Psychology, University of Lübeck, Lübeck, Germany Falko Rheinberg Institute of Psychology, Universität Potsdam, Potsdam, Germany Anja Schiepe-Tiska Center for International Student Assessment (ZIB), TUM School of Education, Munich, Germany Universität München, Munich, Germany Julia Schüler Department of Sports Science, Sport Psychology, University of Konstanz, Konstanz, Germany Oliver Stoll Institute of Sports Science, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany Jasmine Tan Goldsmith University of London, London, UK Dionísia Tavares School of Psychology, University of Minho, Minho, Portugal Ana Teixeira School of Psychology, University of Minho, Minho, Portugal Stefano Triberti Department of Oncology and Hemato-Oncology, University of Milan, Milano, MI, Italy Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milano, MI, Italy Michele Ufer Institute of Sports Science, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany Fredrik Ullén Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden Charles J. Walker Department of Psychology, St. Bonaventure University, St. Bonaventure, NY, USA Gina Wolters Faculty of Psychology, Ruhr-University Bochum, Bochum, Germany Zsuzsanna Zimanyi Department of Sports Science, Sport Psychology, University of Konstanz, Konstanz, Germany

Chapter 1

Historical Lines and an Overview of Current Research on Flow Stefan Engeser

, Anja Schiepe-Tiska

, and Corinna Peifer

Abstract This chapter introduces the flow concept by listing the components of flow as provided by Csikszentmihalyi. We will show that these components constitute the widely shared definitional ground of researchers in the field, with only minor variation between research groups and time periods. Next, we try to clarify some lingering ambiguities regarding the components of flow, and then talk about flow as an optimal experience as well as discussing the relationship between flow and happiness. Subsequently, we trace the history of flow. We take time to describe the beginnings of flow research by Csikszentmihalyi and a similar research program by Rheinberg in Germany. Following the description of flow and qualitative analyses, we will present the quantitative approach of Experience Sampling Method (ESM), which has greatly influenced research on flow. Then we will look at current lines of research on flow, identifying and describing topics of increasing interest in the last years. Creativity (e.g., in music and arts) and well-being remain an important part of flow research, but flow research has entered many other areas, spanning from the emerging research on flow in teams or psychophysiological correlates of flow to flow in sports, learning (education), development, work, and human computer interaction—all topics that will be addressed in more detail in the chapters of this book. Finally, we complete this first chapter by discussing methodological aspects of the research on flow.

S. Engeser (*) Institute of Psychology, University of Trier, Trier, Germany e-mail: [email protected] A. Schiepe-Tiska Center for International Student Assessment (ZIB), TUM School of Education, Munich, Germany Universität München, Munich, Germany e-mail: [email protected] C. Peifer Department of Psychology, University of Lübeck, Lübeck, Germany e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_1

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The Concept of Flow Flow is a state in which an individual is completely absorbed in activity without reflective self-consciousness but with a deep sense of control.

The concept of flow has become a widely known experience since Csikszentmihalyi (1975) systematically described this “optimal experience” in his book “Beyond Boredom and Anxiety”. He observed that artists were entirely caught up in their projects, working feverishly to finish them and then lost all interest in their work after completion. Obviously, the incentive for engaging in that activity laid in the performance of the activity itself. Determining what makes an activity valuable and satisfying became the focus of Csikszentmihalyi’s work. The title of his book “Beyond Boredom and Anxiety” can be seen as the title for a mission to accomplish this. Also, this title might be seen as a contrast to Skinner (1971), who some years earlier had controversially advocated systematic conditioning as a way of social development in his book “Beyond Freedom and Dignity”. Flow research, with its implications on well-being and enjoyment, was a constitutional part of a new direction in psychology called “Positive Psychology” (cf. Snyder & Lopez, 2009). Since Csikszentmihalyi (1975) first described the flow concept more than 40 years ago, it has become a widely studied and popular concept with broad implications. Bearing this in mind, we still recognize a basic agreement on the definition of flow itself (there is more dispute regarding how flow could and should be measured; cf. Moneta, Chap. 2). The original definition provided by Csikszentmihalyi (1975) was only marginally modified over the years (e.g., Nakamura & Csikszentmihalyi, 2014). Moreover, the definition of flow entails different components, which provide the flexibility to pronounce a particular component or add new components without completely changing the definition. Furthermore, it taps into a scientifically meaningful concept and is at the same time intuitively understood on the basis of one’s own experience.

Definition of Flow: Components of Flow In defining flow, Csikszentmihalyi (1975) described six components of flow experience. These components are listed in Box 1.1 with additional citations given by the participants who were interviewed by Csikszentmihalyi. They shall illustrate why individuals are highly engaged in activities without extrinsic rewards (see paragraph “historical lines” below). The first three components listed in Box 1.1—merging of action and awareness, centering of attention and the loss of self-consciousness—represent aspects of the total absorption (or immersion) into the activity. Besides this, a person experiencing flow has a strong feeling of control (fourth component listed in Box 1.1), while the demands are clear and non-contradictory and the next steps of the action feel natural

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and occur without consciously thinking about the action itself, the pursuit of the action or about distant goals (fifth component listed in Box 1.1). The action runs smoothly without the feeling of effort or will, and it is experienced as enjoyable and rewarding in itself. This is why Csikszentmihalyi speaks of an autotelic nature of flow, which means that the experience of enacting is the reason for the action itself (from the Greek “auto”, meaning self, and “telos”, meaning goal or purpose). Box 1.1Components of Flow Based on Csikszentmihalyi (1975) • Merging of action and awareness; a person is aware of his/her actions but not of the awareness itself; “You don’t see yourself as separate from what you are doing” (p. 39). • Centering of attention on a limited stimulus field; high degree of concentration; “When the game is exciting, I don’t seem to hear anything-the world seems to be cut off from me and all there is to think about is my game” (p. 40). • Loss of self-consciousness; considerations about self become irrelevant; this could be described as ‘the loss of ego’, ‘self-forgetfulness’, ‘transcendence of individuality’ or ‘fusion with the world’ (p. 42); “You yourself are in an ecstatic state to such a point that you feel as though you almost don’t exist. . . . I just sit there watching it in a state of awe and wonderment. And it just flows out by itself” (p. 44). • The feeling of control of one’s action and the feeling of control over the demands of the environment; “I get a tyrannical sense of power. I feel immensely strong, as though I have the fate of another human in my grasp” (p. 44). • Coherent, non-contradictory demands for action and clear, unambiguous feedback; goals and means of achieving them are logically ordered; action and reaction are automatic; “I think it’s one of the few sorts of activities in which you don’t feel you have all sorts of different kinds of demands, often conflicting, upon you. . .” (p. 46). • Autotelic nature; no need for external goals or rewards; “The act of writing justifies poetry. Climbing is the same: recognizing that you are a flow. The purpose of the flow is to keep on flowing . . .” (p. 47). Later, other researchers in the field and Csikszentmihalyi himself published small variations of the components of flow. For example, Nakamura and Csikszentmihalyi (2005; Csikszentmihalyi & Csikszentmihalyi, 1988a, 1988b) additionally listed the characteristic of “distortion of temporal experience of time”, which typically means the feeling of time passing faster than normal. In the context of sports, Jackson and Marsh (1996) listed nine components of flow. They considered “time transformation” as an additional component, too. Furthermore, the authors divided the component of “coherent, non-contradictory demands” into the two components “clear goals” and “unambiguous feedback”.

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Additionally, they included the “challenge-skill balance” as a component. Based on these components, they developed a state and a trait scale to assess flow (cf. Moneta, Chap. 2). These scales have also been used in other contexts than sports (e.g., Fullagar & Kelloway, 2009). Similarly, also Rheinberg based his measure of flow on the components listed in Box 1.1 (cf. Rheinberg & Engeser, 2018), and the Flow Short Scale was designed to assess them (cf. Engeser & Rheinberg, 2008). Rheinberg did not include the “autotelic nature” of flow as a definitional part, as was occasionally done by Csikszentmihalyi and others (e.g., Csikszentmihalyi & Schiefele, 1994). We discuss the topic of autotelic nature in more detail below. Furthermore, we discuss some lingering ambiguities regarding other components and aspects of the definition of flow.

Flow as a Multifaceted Experience Conceptual and empirical evidence showed that the components of flow are highly correlated. Factor analyses of instruments assessing the components of flow warranted that the components represent a single dimension (Beard & Hoy, 2010; Engeser & Rheinberg, 2008; Jackson & March, 1996; cf. Moneta, Chap. 2). As a conclusion, the components of flow could be represented by one dimension only. However, this conclusion is premature. To illustrate this, imagine a pilot in an airplane, with speed and height displayed on his control panel. Although the two measures are highly correlated (e.g., because the airplane is flying faster at a higher altitude), we would never recommend the pilot to pay attention to only one measure. The same applies to the components of flow. They could be highly correlated but at times dissociated. For example, “centering of attention on a limited stimulus field” is not only characteristic for flow, but also characteristic for a state of high anxiety (Eysenck, 1992). However, both states are highly different emotional states for a person. Therefore, taking only this component as a single indicator for flow would be misleading. Similarly, the “feeling of control” is a central characteristic of flow, but it can also be present when someone performs a very simple, routine task, during which a person is rather bored than in flow. Nevertheless, more research is still needed to specify under which conditions components are associated or dissociated. This includes research attempting to find out whether single components are triggered exclusively by certain conditions and whether the components differ in their consequences. Should all components have the same conditions and consequences, they would never dissociate and thus, the components to describe flow could be reduced. Csikszentmihalyi (1975, p. 38) speculated that the merging of action and awareness is the clearest sign of the experience, and absorption might, in fact, represent a more central aspect than the other components. Perhaps this state is very closely associated with all other components, but, we indicate that flow is an experience with different components, which, in their interplay, represent the experience of flow. As a result, flow cannot be reduced to a single component, and all attempts to take one component of flow as the

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definitional aspect of flow will consequently disregard essential parts. As Csikszentmihalyi put it, flow is the “holistic sensation that people feel when they act with total engagement” (Csikszentmihalyi, 1975, p. 36), and this holistic sensation is comprised of the components just described. This does not exclude research that pays special attention to only one component (cf. Moneta & Csikszentmihalyi, 1999). However, one should keep in mind that this component does not fully represent the flow experience. One more aspect is important with respect of the multiple facets of flow: Some of the listed components are sometimes regarded as conditions rather than as components of flow or the other way around. This particularly applies to the “challengeskill balance”: Take, for example, the component “feeling of control” which has become especially important in flow research (e.g., Nakamura & Csikszentmihalyi, 2014; cf. Moneta, and Barthelmäs & Keller, Chaps. 2 and 3). The individual is assumed to have the feeling of control if challenge and skills are in balance. Therefore, challenge skill balance is regarded as a condition to foster the experience. We will discuss this aspect in the next paragraph were we argue to clearly differentiate between conditions and experience of flow.

Flow as a Subjective Experience It is important to note that not necessarily the objective balance of challenge and skill would lead to the experience of control, but rather a persons’ individual, subjective evaluation of the situation and his or her skills. Presumably, two persons will experience a feeling of control at different levels of challenge (cf. Engeser & Rheinberg, 2008; Abuhamdeh, Chap. 5). Thus, the objective condition itself does not determine the experience of control, as it does not determine any human experience. “In any case, a sense of control is definitely one of the most important components of the flow experience, whether or not an ‘objective’ assessment justifies such feelings” (Csikszentmihalyi, 1975, p. 46) and “it is the subjectively perceived opportunities and capacities for action that determine experiences” (Nakamura & Csikszentmihalyi, 2005, p. 91). Accordingly, while the objective challenge skill balance can be regarded as a condition that influences flow, the subjective experience of control can be regarded as a component of flow. The same can be illustrated with the component of coherent, non-contradictory demands. The activity can provide a clear goal and immediate feedback. Flow activities such as playing the piano, climbing or playing chess have such clear rules and goals. They also provide immediate feedback, which makes the experience of flow more likely—in the case of playing the piano, every sound created by the musician entails an auditive feedback to its originator. But once again, the person has to adopt this goal and perceive the feedback provided, which depends on the individual’s history and expertise. We can also see things the other way around and think of a less well structured situation providing no such clear feedback. The individual may nevertheless exploit this minimal feedback and find a way to structure the situation (cf. Schiepe-Tiska & Engeser, Chap. 4, for an analogous

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perspective). Therefore, it is important to see the component “coherence and non-contradictory demands” as a subjective experience that is part of the flow experience rather than a condition. This does not exclude that non-contradictory demands can act as a condition, as they can strongly influence the subjective experience, but to exclude it as a component would be shortcoming.

Flow as an Autotelic Experience Csikszentmihalyi (1975) also listed “the autotelic nature”as a component of flow. It refers to the assumption that flow is experienced as being highly rewarding and that individuals strive to attain this state over and over again. During flow, the incentive lies purely in the engagement in an activity itself (Schüler & Engeser, 2009). In this respect, flow can be called an autotelic or intrinsically rewarding experience. As such, including it as a component of flow helps us to understand human motivation better, as it explains why we do things without any obvious external rewards. This rewarding nature of flow experience has many implications and consequences, and suggests that flow can have positive consequences for the individual and the society (Csikszentmihalyi, 1997). On the other hand, in some respects, defining the autotelic nature as a component of flow is problematic. First, the construct of flow was originally used to explain autotelic or intrinsically motivated behavior (see history below). If the autotelic or intrinsically motivated behavior is part of the definition of flow itself, it poses a risk of circular explanations. Csikszentmihalyi originally used the term “autotelic experience” instead of flow. “In calling an experience ‘autotelic,’ we implicitly assume that it has no external goals or external rewards; such an assumption is not necessary for flow” (Csikszentmihalyi, 1975, p. 36). This means that flow could at least be triggered by external goals (cf. Csikszentmihalyi, 1975, p. 41ff). Moreover, flow could be experienced in any activity, and not only in activities with a distinct “intrinsic” nature. In a working context, for example, an individual could be assigned to a task and become completely absorbed in this activity whilst carrying it out. This would mean that an initially extrinsically motivated behavior can become intrinsic during its performance.

Flow as an Optimal Experience Csikszentmihalyi calls flow the “optimal experience” in the sense that “Flow is defined as a psychological state in which the person feels simultaneously cognitively efficient, motivated, and happy” (Moneta & Csikszentmihalyi, 1996, p. 277). Indeed, many empirical studies have found positive associations of flow with positive affect (cf. Abuhamdeh, Chap. 6), motivation, and performance enhancement (cf. Barthelmäs & Keller, Chap. 3) in creative activities (cf. Harmat, de Manzano & Ullén, Chap. 14), at work (cf. Peifer & Wolters, Chap. 11), during

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learning, and in sports (cf. Stoll & Ufer, Chap. 13). Flow also contributes to developing skills and personal growth (cf. Freire, Gissubel, Tavares, & Teixeira, Chap. 12): It fosters the engagement in challenging activities, in which skills can improve with increased practice. As a consequence, in order to further perceive a challenge skill balance and thus, to stay in flow, a person has to set continuously higher standards. In this respect, Csikszentmihalyi assumes flow to be the key to a rich, productive life (Csikszentmihalyi, 1996) and even to cultural evolution (Massimini, Csikszentmihalyi, & Delle Fave, 1988). Thus, flow—as optimal experience—is a positively valenced experience and is associated with feelings of enjoyment, high motivation and enhanced efficiency (cf. Barthelmäs & Keller, Chap. 3 and Abuhamdeh, Chaps. 5 and 6). However, based on some empirical results, there are reasons to question the general conclusion that flow is an optimal experience. Take for example the context of sports. In general, flow has been reported to be associated with high performance (e.g., Jackson & Roberts, 1992). However, in a marathon, Schüler and Brunner (2009) found that although flow was associated with higher training motivation before the marathon, it was not associated with higher performance during the marathon. Perhaps, during a marathon it is necessary for the self to be a vivid “dictator”, forcing the body to run and flow would hinder this (see Schüler & Langens, 2007). In this respect, flow would be an optimal experience for the motivation to run and, thus, to exercise, but not for a higher performance in the marathon itself. Regardless of whether this interpretation of Schüler’s results is accurate, it is worth thinking about when flow is optimal to foster performance. More specifically: how does flow impact the informational and motivational processes, and for what kind of tasks is this optimal? Barthelmäs and Keller (Chap. 3) will provide conceptual work and empirical data to tackle this question. Additionally, empirical studies suggest that rather a fluctuation of different states of consciousness may be optimal instead of being in flow all the time in order to achieve high performance. Like sleeping and waking, flow experience should alternate with phases of relaxation (cf. Peifer & Tan, Chap. 8 for the relationship between stress and flow). Therefore, the optimal balance between flow experiences and other states of consciousness should be more closely examined (e.g., Baumann, Lürig, & Engeser, 2016). This becomes even more relevant when thinking about the sustainability of performance or about what makes a successful and happy life (cf. Nakamura & Csikszentmihalyi, 2005, p. 97).

Flow and Happiness Flow is a rewarding experience, and loosely speaking, rewarding experiences make us happy, as punishing experiences make us unhappy. However, flow is not the experience of happiness itself. “When we are in flow, we are not happy . . . if a rock climber takes time out to feel happy while negotiating a difficult move, he might fall to the bottom of the mountain” (Csikszentmihalyi, 1997, p. 32). Hence, the

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experience of happiness would distract attention from the action and prevent flow. Empirical data support that flow and happiness are not experienced at the same time. As expected by Csikszentmihalyi (1997), flow experience while climbing has been found to be associated with the feeling of happiness (i.e. positive valence) afterwards, and with feelings of high positive activation (i.e. energetic arousal) and medium levels of negative activation (i.e. tense arousal) during the task (Aellig, 2004; cf. Rheinberg & Engeser, 2018). In line with that, Schallberger and Pfister (2001) also showed that flow is more strongly associated with emotional states indicated by high “activation” than with emotions of happiness (cf. Rogatko, 2009). Furthermore correlational data from an Experience Sampling Method (ESM) study by Rheinberg, Manig, Kliegl, Engeser, & Vollmeyer (2007; cf. Rheinberg & Engeser, 2018) also showed that flow and happiness correlated only at a low level. In addition, the work of Silvia (2008) points in a similar direction, stating that flow is not strongly associated with happiness. Instead, he sees close links of the emotion of interest to the concept of flow. Similarly, Bricteux and colleagues outline the link between interest and flow (Bricteux, Navarro, Ceja, & Fuerst, 2017; cf. Abuhamdeh, Chap. 6). It is also important to point out that flow itself is not defined through an affective state. The components of flow listed above (Box 1.1) do not include any description of an affective state. Of course, it does not rule out that flow can be highly correlated with some affect: One can at least imply some positive affective tone of flow because of its autotelic nature (i.e. being self-rewarding, see section ‘Flow as an autotelic experience’), and based on findings of positive activation during, and increased positive valence after the experience of flow. Further conceptual work and empirical research need to be conducted to relate flow experience and affect. As just mentioned, this line of flow research has already begun, and Barthelmäs and Keller (Chap. 3) as well as Abuhamdeh (Chap. 6) will present further data and discussion on this aspect.

Historical Lines and Current Flow Research The Beginning The term flow was created by Csikszentmihalyi in 1975 after several years of research on play activities, creativity, and artists’ personality (Csikszentmihalyi & Bennett, 1971; Csikszentmihalyi & Getzels, 1973; Getzel & Csikszentmihalyi, 1966). Csikszentmihalyi was seeking to understand what makes activities inherently motivating. Investigating what makes an activity enjoyable has broad implications for our lives and on the level of society as a whole, as Csikszentmihalyi vividly expressed in the following quotation: If we continue to ignore what makes us happy, what makes our life enjoyable, we shall actively help perpetrate the dehumanizing forces which are gaining momentum day by day (Csikszentmihalyi, 1975, p. 197).

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The quotation also gives the impression that searching for the enjoyment of activities was not in the focus of psychology at that time, and Csikszentmihalyi articulated the danger that life could become worse in a technocratic world, with behaviorism being the main paradigm in psychology. Subsequently, he tried to start a research program to counter this potential danger and Csikszentmihalyi himself mentioned the following starting point of his research program: In a world supposedly ruled by the pursuit of money, power, prestige, and pleasure, it is surprising to find certain people who sacrifice all those goals for no apparent reason: people who risk their lives climbing rocks, who devote their lives to art, who spend their energy playing chess. By finding out why they are willing to give up material rewards for the elusive experience of performing enjoyable acts, we hope to learn something that will allow us to make everyday life more meaningful. At present, most of the institutions that take up our time-schools, offices, factories-are organized around the assumption that serious work is grim and unpleasant. Because of this assumption, most of our time is spent doing unpleasant things. By studying enjoyment, we might learn how to redress this harmful situation (Csikszentmihalyi, 1975, p. 1).

To study enjoyment, Csikszentmihalyi argues that neither the dominant paradigm of behaviorism, nor a psychoanalytic approach can provide a full answer. Instead of calling the activities a means to get a reward or to satisfy libidinal needs, he shifted his view to see the activity itself “as an autonomous reality that has to be understood in its own terms” (Csikszentmihalyi, 1975, p. 10). He approached the understanding of enjoyment by investigating individuals’ subjective experience and the subjective reasons why they perform such activities, which were assumed to be autotelic. Csikszentmihalyi started his studies by conducting interviews with hockey and soccer players, spelunkers and explorers, a mountain climber, a handball player and a long distance swimmer, which provided the basis for questionnaires and more structured interview forms. A first quantitative survey conducted with rock climbers, composers, modern dancers, chess players, and basketball players revealed eight possible reasons for enjoying an activity. The reasons with the highest ranking were “enjoyment of the experience and use of skills” and “the activity itself: the pattern, the action, the world it provides”, followed by “development of personal skills”. “Prestige, regard, glamor” was placed at the end of the list (Csikszentmihalyi, 1975, p. 15). In an in-depth analysis of interviews (accompanied by quantitative data similar to that just listed) relating to chess, rock climbing, rock dancing, and enjoyment of work among surgeons, Csikszentmihalyi identified the recurrent experiences, which are listed in Box 1.1. In his analysis, he recognized these recurrent experiences and outlined their importance for understanding the motivational significance.

Flow Theory The shift to analyze the experience of the activity in its own right and to recognize the recurrent patterns was essential for flow research. However, an additional major

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step was to formulate a model that explained why activities are enjoyable in themselves. Altogether, this allowed researchers to go beyond the description of single activities and provided the necessary generalizations to stimulate scientific progress. As described in Chaps. 2 (Moneta) and Chap. 3 (Barthelmäs & Keller), the Flow Model of 1975 proposes that flow is experienced when the challenges and skills are in balance (see also Box 1.2). When there is an imbalance, and challenges are too demanding for an individual’s skills, worry and anxiety will result. If skills exceed opportunities, boredom is experienced. Many aspects of the model were already outlined in the article “An exploratory model of play” (Csikszentmihalyi & Bennett, 1971). The model of play is grounded in the assumption that play provides a perfect structure for action. The individual has to decide on a definite number of alternatives and does not have to think about potential indefinite possibilities. Games thus provide the right number of possibilities and opportunities for action (compare the “coherent, non-contradictory demands” as one component of flow in Box 1.1). In respect to the understanding of the development of flow theory, it is important to point out that challenges are seen as opportunities for action and skills as action capabilities (Csikszentmihalyi, 1975, p. 49). This means a quite broad understanding of challenge and skills, which can be understood when keeping in mind the basic assumption of “an exploratory model of play” (see above). Historically, this broad understanding of challenge and skills later diminished, but still allows to extend flow theory to activities without a clear achievement character (i.e. in which challenge and skills are not inherent and natural aspects; cf. Schiepe-Tiska & Engeser, Chap. 4). On the other hand, such a broad understanding clearly poses conceptual and methodological problems, too. Box 1.2 One “Curiosity” of Csikszentmihalyi’s 1975 Model of the Flow State In the depiction of the Flow Model of 1975, we point to what, at first glance, appears to be a curiosity: “And finally, a person with great skills and few opportunities for applying them will pass from that of boredom again into that of anxiety” (p. 50). Csikszentmihalyi does not explain why this should be the case (later presentations of the model do not include anxiety when skill extensively exceeds challenges). We suppose that Csikszentmihalyi assumed that humans need structure, and when opportunities for action are not given, this will lead to the experience of chaos and anxiety. Experiments on sensory deprivation (cf. Solomon, 1961), or prisoners who are kept in isolation may be prototypical for such a state. Both are highly aversive states after quite short durations of time.

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Piaget Play Hebb, Berlyn Optimal

Caillois Play White Competence

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Groos, Bühler Funktionslust DeCharms Personal causation

Maslow Peak Experience

Csikszentmihalyi Autotelic Experience / Flow Experience Fig. 1.1 Theoretical precursors of Csikszentmihalyi’s flow theory

Theoretical Precursors of the Flow Theory Hebb (1955) and Berlyne (1960) proposed that the right amount of stimulation is essential for explaining behavior that is not extrinsically rewarded (i.e., intrinsically motivated behavior). Animals including men strive for an optimal level of stimulation. Novel stimuli provide an enjoyable sensation; however, too much novelty leads to anxiety and too little novelty leads to the exploration for stimulation. There are clear parallels of the work of both authors to the flow model here, and their ideas influenced the development of the flow model, as explicitly stated by Csikszentmihalyi (1975). Both are included as precursers of flow theory in Fig. 1.1. Similarly, White (1959) outlined that novelty and variety are enjoyable for their own sake and proposed the term “effectance” motivation. This term refers to the assumption that individuals like to have an effect on the environment, which is perceived as enjoyable and they consequently learn to deal with the environment and develop new competences. This leads to a subjective feeling of efficacy or competence, which is enjoyable and rewarding in itself (compare the “feeling of control” as one component of flow in Box 1.1). Csikszentmihalyi also referred to DeCharms (1968), who proposed that the feeling of being the origin of an action is an important aspect of enjoyment. Both White and DeCharms also influenced Self-Determination Theory (SDT; Deci & Ryan, 1985), which, like the flow theory, originated in the 1970s to explain intrinsically motivated behavior (cf. Abuhamdeh, Chap. 5 for a comparison of flow theory and Cognitive Evaluation Theory—a subtheory within SDT). White and DeCharms were also influential in terms of research on achievement motivation, which is the theoretical background for Heckhausen’s (Heckhausen & Rheinberg, 1980) and subsequently Rheinberg’s work (see below; cf. Engeser & Rheinberg, 2008). Other precursors of flow theory were Piaget (1951) and Caillois (1958), who both tried to understand the motivational aspects of play. Caillois believed that individuals find pleasure in play by competing against others, controlling things, testing

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limits, extending skills, having new experiences, seeking danger and altering their consciousness. Csikszentmihalyi used prototypical examples of Caillois’ postulated needs (competition, control, transcend limitation, “vertigo”) underlying the rewarding experience of activities in order to ascertain whether these needs are present in activities of interest (e.g., rock climbing, chess). Empirical results revealed that Caillois’ needs are important but not exclusively for enacting these activities. At the same time, Csikszentmihalyi (1975) concluded that the empirical results made clear that “the underlying similarity that cuts across these autotelic activities, regardless of their formal differences . . . they all give participants a sense of discovery, exploration, problem solution–in other words, a feeling of novelty and challenge” (Csikszentmihalyi, 1975, p. 30). We also listed Groos (1899) and Bühler (1922) as precursors (also mentioned by Csikszentmihalyi), who described the pleasurable sensation (“Lust”) when an individual functions (“Funktion”) effectively. The focus on the sensation or experience of the activity was the focus of Csikszentmihalyi’s work, too: “. . . my first concern was about the quality of subjective experience that made a behavior intrinsically rewarding. How did the intrinsic rewards feel?” (Csikszentmihalyi & Csikszentmihalyi, p. 7). According to Bühler, the enjoyment lies in the sensation of effectance and control of action, and this is an important motivational aspect for understanding human behavior. It is a kind of innate reward mechanism provided by evolutionary means to ensure the development of skills (Bühler, 1922, p. 456). Last but not least, Csikszentmihalyi described parallels of flow to peak experiences as described by Maslow (1968) and in many respects to the experience in rituals, meditations and any other religious experience (Csikszentmihalyi, 1975). The component of flow in Box 1.1 “Loss of self-consciousness” has the strongest parallel with such peak experience in religious contexts (for the relationship of flow and meditation cf. Peifer & Tan, Chap. 8). Possibly, such religious activities, rituals and meditation might be seen as “flow activities”, with clear rules and goals, allowing the individual to become completely absorbed.

Similar Research in Germany In the 1970s, Rheinberg tried to predict exam preparation of students with a model adopted from Heckhausen (1977; cf. Rheinberg & Engeser, 2018). The model aimed to capture all relevant aspects of motivation, but frequently predicted the exam preparation incorrectly for some students (Rheinberg, 1982, 1989). When asking these students why the model did not make correct predictions, it became clear that the model missed an important aspect: the incentives of the learning activities themselves. The model merely looked for incentives that lay in the consequences of learning activities (e.g., being proud, grades, avoiding blame), as it was the case for the prevalent motivational theories at that time (cf. Heckhausen & Rheinberg, 1980; Weiner, 1972).

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Having recognized this desideratum, Rheinberg concluded that the incentives of activities could be studied best in leisure activities and began with an analogous research question to that of Csikszentmihalyi: Why are individuals highly engaged in activities with no obvious rewards or even with high costs? In addition to the analogous research question, the methodological approach of Rheinberg’s and Csikszentmihalyi’s work was very similar. Rheinberg conducted in-depth interviews with individuals engaging in motorcycling and windsurfing (Rheinberg, 1982, 1986), and later he interviewed musicians and skiers (Rheinberg, 1993; cf. Rheinberg & Engeser, 2018). These activities have in common that the motivation to engage in the activity is hardly understood if the incentives of the activity itself are not considered. In Box 1.3, we list examples of verbalizations of activity-specific incentives. Some of them sound familiar as compared to the components of flow experience listed above (see Box 1.1). This includes being completely absorbed in the activity, forgetting about everything else and the feeling of control. One quotation listed in Box 1.3 points to an activity incentive that is related to sensation seeking in high-risk sports (Rheinberg, 1986; cf. Zuckerman, 1994), another highlights the immediate enjoyment of being in nature and one states that self-expression is part of the personal identity. We want to point out, that in a similar vein, Csikszentmihalyi (1975, p. 15) also mentioned other activity incentives he did not subsume under the concept of flow (this implies that flow is “just” one of other possible activity incentives). Box 1.3 Examples of Verbalizations of Activity-Specific Incentives in leisure Activities (Rheinberg, 1993, Translations by the Authors; cf. Rheinberg, 1982 and Rheinberg & Engeser, 2018) • “The most important thing is that when I sit on the motorbike, everything else is gone—no troubles with the company, with the children, just driving, driving, driving” (motorcycling, p. 10) • “To feel how the board, the rig and one’s own movements become an entity which deals with the wind and waves” (windsurfing, p. 7) • “To have just mastered a threatening and anxiety-inducing situation” such as a storm (windsurfing, p. 12) • “To experience nice and elegant movements; the perfect interplay between the skis and one’s own movements” (skiing, p. 9) • “To enjoy the atmosphere of the mountains” (skiing, p. 10) • “When I play music, I am totally concentrated on what I am playing. No disturbing thoughts, the environment, even pain, I do not feel them anymore” (music, p. 10). • “Through music I can express myself: I put all my personality into it” (music, p. 13)

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Rheinberg (1993) started with a systematic classification of all aspects that were mentioned in the interviews, but unfortunately, this had not attracted further systematic research. Although not enough systematic work has been conducted, there seems to be a limited number of possible activity-related incentives and a wide overlap of incentives between activities. The limited number and the wide overlap of incentives provided ideal grounds upon which to understand engagement in activities that, from the perspective of outsiders, initially appear insane. For example, an analysis of graffiti spraying (Rheinberg & Manig, 2003) showed that the engagement in this risky and costly activity offers well known incentives. These young people enjoy experiencing and improving their painting skills, being in the company of others, and state that “when you’re out spraying, you completely forget all the stress you have at home and at school” (p. 228; translations by the authors).1 Although Rheinberg and Csikszentmihalyi had a similar research question, similar methods, and obtained comparable results, they looked at these results differently. Rheinberg studied the incentives discovered in in-depth interviews separately. He did not focus on the commonalities of the activities he had studied. Csikszentmihalyi, on the other hand, focused on incentives that were mentioned regularly in different activities and ended up with the description of flow experience. One advantage of his research strategy is that it allows researchers to look at any activity to find out whether flow is experienced. This brings us back to the starting point of Rheinberg’s research. He recognized how important task-specific incentives are in respect to learning motivation and engagement in learning activities. The prediction of the learning activity in Rheinberg’s early work has increased considerably when considering task incentives (Rheinberg, 1989; cf. Rheinberg & Engeser, 2018)2. This implies that flow and other task incentives are important factors in the endeavor to understand the motivational aspects of activities. Rheinberg’s research revealed that task-specific incentives are important even for activities that are conducted for purpose-related incentives (e.g., learning in order to pass the exam) or for “extrinsic” reasons (e.g., money for successful task fulfillment). If activity incentives are in line with the purpose-related reasons (e.g., enjoy learning activities and wanting to pass the exam), the activity should run smoothly on motivational grounds. On the other hand, if an activity is motivated by purposerelated incentives only and the task incentives are not given or even aversive, motivation becomes fragile. This would be the case for a student who is preparing for a math exam but who hates even opening a math book. In this case, volitional control is needed to deal with the aversive nature involved in conducting the activities (Engeser, 2009; Rheinberg & Engeser, 2010).

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Compare the description of Csikszentmihalyi’s work on the basis of Caillois, above. When individual differences in the incentive focus were considered, the predictions were almost perfect. The incentive focus can be seen as a construct similar to Csikszentmihalyi’s autotelic personality (cf. Baumann, Chap. 9). A questionnaire to measure the incentive focus is presented by Rheinberg et al., (1997). 2

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The fact that task-related incentives in general and flow in particular can be relevant in any kind of activity brings us to the next step of flow research, which is described in the following: studying flow experience in daily actions.

Flow in Daily Experience Csikszentmihalyi (1975) recognized the importance of flow experience while being engaged in activities. He had already studied a great variety of activities, based on retrospective or summative measures. What could be more progressive than to study flow while the person is actually conducting the activity? And moreover, to study a comprehensive wide range of activities? In an advancement of Csikszentmihalyi’s (1975) diary method to study flow patterns in everyday life, Csikszentmihalyi, Larson and Prescott (1977) developed and used the Experience Sampling Method (ESM), which provided an appropriate tool for such an enterprise. Subjects receive signals at random times during waking hours of a normal week. Each time when the subject gets beeped, he or she answers a short questionnaire, i.e. the experience sampling form (ESF; cf. Moneta, Chap. 2 or Schiepe-Tiska & Engeser, 2017). Among other things, subjects indicate where they are and what they are doing, rate how they feel on a list of 13 adjectives (e.g., friendly, happy), state whether they are in control of the action, and provide information on the “challenge in the activity” and “skills in the activity”. It is noteworthy that the ESF did not include indicators of all components of flow (cf. “methodological approaches” below and Moneta, Chap. 2). The ESM provided a rich database on what people were doing in their daily life and how they experienced it, for example, how often people watch TV and how they feel about it compared to other leisure activities or work (cf. Csikszentmihalyi & LeFevre, 1989). As the ESM, along with the ESF, was used soon by other researchers in different countries (e.g., Massimini & Carli, 1988), it also allowed cross-cultural comparisons (cf. Moneta, 2004). Furthermore, the ESM has been advertising the concept of flow, which should not be underestimated. The possibilities for conducting research with the ESM (and similar methods) also established it as a standard tool not only for research on flow (e.g., Hektner, Schmidt, & Csikszentmihalyi, 2007; Trull & Ebner-Priemer, 2009). Box 1.4 ESM and Measuring a State of the Loss of Self-Consciousness A standard criticism of ESM with regard to measuring flow is that the beep signal ruins this state of consciousness. This is clearly the case, but it does not mean that the measure of flow will not be valid. Imagine that the subject gets beeped when he or she is totally absorbed in an activity. The beep brings him or her out of this state, but the subject will still remember what he or she was (continued)

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Box 1.4 (continued) doing and feeling some seconds ago. Strictly speaking, the person is not unconscious or insensible when in flow. It may be compared to a dream; when we wake up, we stop dreaming, but we can still remember the contents and feelings of the dream and can report them. In Box 1.4 we discuss a repeatedly presented criticism of the ESM method, and there are other potential weaknesses besides its strengths (cf. Scollon, Kim-Prieto, & Diener, 2003; Schiepe-Tiska & Engeser, 2017). We also want to highlight an important conceptional finding of the Csikszentmihalyi et al. (1977) ESM Study, which gives an impression that task incentives are important for motivation. The ESF asked a person “Why were you doing this?” The results revealed that for nearly 80% of the actions the response was “I wanted to do it”. In these cases, we would expect that the person enjoys the activity, or at least does not feel bad while doing it. This interpretation fits in with an early study on daily experience by Rheinberg (1989). Here, only 12% of the actions were experienced as aversive and 61% as positive, and the rest being neutral in this respect. This shows that we predominantly enjoy things we are doing, and the hedonistic principle leads to the expectation that we will actively seek such activities and avoid things we do not enjoy. This cannot be ignored when attempting to understand human striving.

Well-Being, Creativity, and Cultural Development Csikszentmihalyi (1975) explored the implications of flow experience by relating it to “big questions” of human happiness, well-being and creativity. What does it take to live a happy, meaningful and creative life? In this chapter, it is reiterated that flow is a rewarding experience. Experiencing it more often makes life more satisfying, and should prevent a person from living a dull life. It is a thrill that stands out from routine and uneventful times (Csikszentmihalyi, 1997 p. 97). Moreover, flow can be experienced in a wide variety of activities. Where and how people find flow depends on the opportunities an individual has. These opportunities at least partially depend on the cultural or socio-cultural context in which an individual lives. For example, in a secular society, there may be fewer opportunities to experience it in a religious vocation, but more options to find it in leisure activities. Or, in a well-educated home, learning and discussion about science may provide a means of becoming completely absorbed. Massimini et al. (1988) used this as a basic principle to outline a theory about bio-cultural evolution. Society offers opportunities to experience flow and activities in which individuals experience flow will be more likely to be selected. Subsequently, these opportunities are reinforced and become an even more central part of culturally offered opportunities for action. “Clearly, enjoyment is the main reason for the selection of most artistic cultural forms. Painting, music, drama, and

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even the mere ability to write are symbolic skills adopted because they produce positive states of consciousness” (Massimini et al., 1988, p. 62; cf. Harmat et al., Chap. 14 on flow in music and arts). The rewarding nature of flow has a further implication, which Csikszentmihalyi emphasizes in relation to what makes a happy and meaningful life (Csikszentmihalyi, 1996, 1997). Flow provides means to help one live up to one’s individual potential. A person actively searching for challenging situations that stretch his or her skills will increasingly develop more skills (cf. Barthelmäs & Keller, Chap. 3 on consequences of flow, Baumann, Chap. 9 on autotelic personality and Freire et al., Chap. 12 on flow and development). The person evolves into a more and more complex individual. Csikszentmihalyi (1996) also outlined this pattern in his book on creativity, in which he interviewed nearly one hundred outstanding personalities from around the world who had made substantial contributions “to a major domain of culture” (p. 12; e.g., science, art). Once again, he found that such people got a thrill out of their work, dedicated much of their energy to it, and increasingly developed skills and expertise. In general, this means that a society should provide opportunities for action that allow individuals to enjoy an activity, experience flow and live up to their potential (cf. Rheinberg & Engeser, 2010). Modern societies should make culturally valued activities like studying science prone to enjoyment in order to ensure the motivational basis for excellence in these areas. The broad implications drawn from Csikszentmihalyi for a better life are inspiring and of great value. On the other hand, such broad implications are difficult to test empirically and risk overstretching the construct. Moreover, we have to keep in mind that “Flow is a powerful motivator, but it does not guarantee virtue” and that “a culture that enhances flow is not necessarily ‘good’ in any moral sense” (Csikszentmihalyi & Csikszentmihalyi, 1988a, 1988b, p. 186). Csikszentmihalyi and Csikszentmihalyi take an extreme example: the Nazi fascist regime. They speculated that a game plan was provided here, which “set simple goals, clarified feedback, and allowed a renewed involvement with life that many found to be a relief from prior anxieties and frustrations” (p. 186; see Schüler, Chap. 7).

Current Developments Flow research has gained increasing interest during the years. In order to provide an overview on recent flow research, the European Flow-Researchers’ Network3 (EFRN) has elaborated a scoping review (Peifer et al., 2018). The review provides an initial impression of the quantitative coverage of the flow concept. A literature research was conducted, consulting the platforms PsychInfo, PubMed, PubPsych,

3 The European Flow-Researchers’ Network (EFRN) was founded in 2012 with the aim to reach a common understanding of the concept of flow, its antecedents and consequences.

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Web of Science and Scopus, thereby using the search terms “flow/optimal experience/challenge-skill balance” and “Csikszentmihalyi”. In addition, flow researchers from the EFRN could add missing studies. Studies were only included if they contained original data on flow and if they were published in English language between 2000 and 2016. All hits were checked manually, ending up with 200 references dealing with flow as one main topic. This search should cover a great deal of the work on flow and should provide at least a rough estimate of the lower limit of quantitative coverage. The number of references shows that the flow concept is intensively recognized and studied, and will possibly even attract more research in the future, as the number of references has visibly increased over the period of literature search. In the following, we provide a summary of the topics within flow research as identified by the scoping review and additional analyses conducted with that data.

Current Topics and Concerns More than 40 years of research have provided the time to study various topics. As presented above, intensively practiced leisure activities, daily experience and the implications for creativity and well-being have been examined from the beginning of flow research. These topics are still of interest, however, the number of studies increased and a wider range of topics has been studied. One lingering concern is the deeper understanding of the characteristics of flow, its predictors, and its consequences (for an overview cf. Barthelmäs & Keller, Chap. 3). Regarding the characteristics of flow, researchers started to include attentional processes during flow, for example the concept of absorption as a central characteristic of flow (“effortless attention”, Bruya, 2010), and sustained attention as a concept being associated to flow (cf. Barthelmäs & Keller, Chap. 3). Furthermore, researchers started to examine physiological correlates of flow in body and brain (cf. Peifer & Tan, Chap. 8). Regarding the predictors of flow, studies looked at the influence of personality variables (20% of the articles), as well as at various contextual factors (about 30% of the articles), providing information on flowconducive characteristics of the context. Examples of those contextual factors are clear goals, feedback, autonomy or organizational climate. In earlier research, flow was mainly the explained variable and did not serve as an independent variable. This has changed recently and is more balanced in that the outcomes of flow are more intensively studied now. Looking at the outcomes in more detail, we found that 25% of the 200 identified studies explicitly deal with wellbeing or related (affective/ emotional) constructs. At the same time, we identified another 25% of studies, which investigated behavioral outcomes of flow, with performance being by far the most researched outcome, but also creativity, customer oriented behavior or consumption behavior were investigated as consequences of flow. Furthermore, flow has been investigated with respect to different contexts of application: Sports and learning are major areas of flow research, together making up

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over a third of the references. In sports (cf. Stoll & Ufer, Chap. 13), besides examining the task-specific incentive of flow itself as a state of being “in the zone”, flow has been also investigated as a predictor of higher performance, (e.g., Engeser & Rheinberg, 2008; Schüler & Brunner, 2009; Swann et al., 2017). Understanding its conditions and consequences give trainers, athletes, teachers and students an idea on how to enjoy and thereby improve the respective activities. In another line of research in the context of sports it is investigated how flow in physical activities can help to improve health (e.g., Persson, 1996; Rebeiro & Miller-Polga, 1999; Reinhardt et al., 2008; Riva, Castelnuovo, & Mantovani, 2006; cf. Stoll & Ufer, Chap. 13 and Triberti, Di Natale & Gaggioli, Chap. 15 on flow in rehabilitation). Similarly prevalent is research in the area of work and the endeavor to understand the working conditions under which flow can be fostered (e.g., Debus, Sonnentag, Deutsch, & Nussbeck, 2014; Salanova, Bakker, & Llorens, 2006; Schüler, Sheldon, Prentice, & Halusic, 2016). Research on the consequences of flow in work contexts mostly concern outcomes on performance, but also on motivation, wellbeing, work satisfaction and on the social environment (for an overview see Peifer & Wolters, 2017; cf. Peifer & Wolters, Chap. 11). Another major and evolving topic is human-computer interaction (see Triberti, Di Natale, & Gaggioli, Chap. 15), game-based learning and media use, which make together about 20% of the studies published since the year 2000. Of special interest is the understanding when interaction with a computer runs smoothly and the user is highly concentrated on the task (e.g., Liu, Liao, & Pratt, 2009). A derivate of this research relates to how flow will influence consumer behavior and might tap into marketing purposes (e.g., Drengner, Gaus, & Jahn, 2008). However, flow in humancomputer interaction can also be used in rehabilitation settings (e.g., Pedroli et al., 2018; cf. Triberti et al., Chap. 15). A smaller proportion of the identified studies deals with developmental and clinical implications of flow and how flow could be used to improve therapeutic settings or to offer new and rewarding experiences (e.g., Teixeira & Freire, 2020; Wanner, Ladouceur, Auclair, & Vitaro, 2006; see also Freire et al., Chap. 12). Last but not least, some of these studies are concerned with the negative consequences of flow, a topic that has fortunately attracted some research interest. Schüler (Chap. 7) focuses on this topic in detail. Classical areas of flow research such as music, and arts are still attracting a reasonable amount of interest from researchers, making more than 10% of the studies published in the new millenium (cf. Harmat et al., Chap. 14). Research, which addresses personality aspects (cf. Baumann, Chap. 9), makes part of about 20% of the detected articles since the year 2000. A particular trend in flow research can be seen in the investigation of flow in social situations, called social flow, shared flow, collective flow, team flow, group flow and the like. While yet only 14 of the identified 200 articles deal with flow in social situations, the number of studies in this area increased in the last few years and is likely to further increase in the close future (see e.g., Bakker, 2005; Graham, 2008;

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Mugford, 2006; van den Hout, Davis, & Weggeman, 2018; see also Ackerman & Bargh, 2010; cf. Walker, Chap. 10).

Methodological Approaches When conducting empirical studies, researchers have to find ways to measure flow somehow. Initially, (semi-)structured interviews served this purpose and led to the description and definition of flow (see Box 1.1 and section ‘The beginning’). In today’s research, interviews are still a substantial part of the research conducted (representing roughly 10%). They are mainly used to explore new aspects in sports (e.g., Seifert & Hedderson, 2010; Swann, Crust, Keegan, Piggott, & Hemmings, 2015), flow in groups (Mugford, 2006; Sutton, 2005) and possible new aspects of antecedences and consequences of flow at work (Wright, Sadlo, & Stew, 2007). The vast majority of studies use questionnaires to assess flow. The measurement of flow with questionnaires can be differentiated in (1) measuring all components of flow experience or (2) capturing some components. Other measures (3) ask for the experience of flow in a global manner (i.e., give a description of flow) or (4) infer to flow when conditions of flow according to a flow model are met (e.g., a balance of demands and skills). Moneta (Chap. 2) will address in detail the measurement and its implications for the conceptualization of flow (see also Barthelmäs & Keller, Chap. 3 and Schiepe-Tiska & Engeser, 2017). Measuring flow with all or some of its components is dominant in current research. Probably accelerated by a widespread measuring of all components of flow in sports (e.g., Andrew & Jackson, 2008; Jackson & Marsh, 1996), the first approach (measuring all components) is becoming more prevalent and is gaining momentum in areas other than sport (e.g., Cermakova, Moneta, & Spada, 2010; Fullagar & Kelloway, 2009; Peifer, Schächinger, Engeser, & Antoni, 2015; see also Engeser & Rheinberg, 2008). The advantage of measuring all components becomes clear if we recall the description of “flow as a multifaceted experience” provided above. The most typical example for the second approach—measuring some components of flow—is the Experience Sampling Form (ESF), in which the components of concentration, self-consciousness and the feeling of control are measured as indicators of flow (e.g., Delle Fave & Bassi, 2009). Other researchers use the perception of time as an indicator of concentration (Keller & Bless, 2008). Keller and Bless additionally asked about the feeling of control and autotelic nature of flow. Other researchers rely heavily on the intrinsic or autotelic aspect of flow (e.g., Bakker, 2008). However, measuring flow with only some of its components risks to reduce the validity of the measure (see section ‘Flow as an autotelic experience’; Schiepe-Tiska & Engeser, 2017). The third approach of questionnaires—giving a description of flow—has been used rarely (e.g., Asakawa, 2010) and goes back to the research on talented teachers (Csikszentmihalyi, Rathunde, & Whalen, 1993). Individuals have to indicate how often they experience “. . . something where your concentration is so intense, your

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attention so undivided and wrapped up in what you are doing . . .” (p. 275). In interview studies, such descriptions are sometimes used to start with, or complement the qualitative data with a quantitative measure (Seifert & Hedderson, 2010). The fourth approach—inferring to flow based on a flow model—is typically applied in ESM studies. The ESF questionnaire additionally asks for the level of challenge and skills and uses the ratio of both as an indicator of flow. Although the ESF questionnaire does also measure components of flow, researchers take this ratio as the indictor of flow and do not rely on a direct measure of components. However, as indicated above, recently the measurement of all components is also beginning to occur in ESM studies (e.g., Fullagar & Kelloway, 2009; Nielsen & Cleal, 2010; Rheinberg et al., 2007). Alternative measures besides asking subjects in interviews and standardized questionnaires arise from psychobiological correlates of the experience of flow as measured with psychophysiological or neuroimaging approaches (cf. Peifer & Tan, Chap. 8). Another approach to measure flow is the use of observation methods. However, we know of only one study, which has so far applied such an approach (Custodero, 2005). Moneta (Chap. 2) discusses further potential alternative measures of flow. Apart from how flow is measured, most of the studies are correlational, with a cross-sectional design. A great proportion of these studies correlate questionnaires of flow with questionnaires of other constructs of interest. This allows to include a variety of constructs, and to study these interrelations with high numbers of respondents and different populations in an economical manner. However, correlational data do not allow to test causal relationships, and this is a weakness of this approach (cf. Barthelmäs & Keller, Chap. 3). A second weakness is that questionnaires share common method variance (e.g., due to social desirability) and correlations between flow and other measures might be attributed to this (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Correlational data between flow and behavioral data (e.g., records of behavior, performance measures) try to overcome the second weakness and they have been found across most areas of research (e.g., Abuhamdeh & Csikszentmihalyi, 2009; Schüler & Brunner, 2009). Although correlational, this clearly reduces the problem of common method variance. One particular correlational approach, which has a high potential to overcome some of the limitations, is using the ESM. This method provides rich data in a variety of contexts and gives a good description of daily experiences. It allows the researcher to identify conditions and consequences of flow and to look at the flow concept in greater detail. ESM data are predominantly analyzed as cross-sectional correlational data. However, they do provide repeated measures and longitudinal data for the period studied. The potential of this data format has rarely been used (Engeser & Baumann, 2016; Fullagar & Kelloway, 2009; Graham, 2008) even though it offers a good opportunity to test causal relationships more effectively. Experimental designs were virtually absent in the first 25 years of flow research (cf. Moller, Meier, & Wall, 2010). Possibly, researchers were convinced that it is too difficult to induce flow in the lab or to manipulate conditions appropriately in natural settings. However, recent research showed that games are very suitable to induce

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flow in the lab (cf. Barthelmäs & Keller, Chap. 3), and it has been demonstrated that this is not restricted to games alone (e.g., Christandl, Mierke, & Peifer, 2018; cf. Schüler 2007). In the area of sports, Reinhardt, Lau, Hottenrott, and Stoll (2006) manipulated conditions for runners on a treadmill in order to gain an understanding of the conditions for flow in running. Game-based learning and media do also provide a good framework for experimental analysis, and research shows that this could be used in experimental designs to tap into causal relationships (Van Schaik & Ling, 2012). In line with the development of these new approaches, it is not surprising that experimental research increased in recent years. We hope that this introduction will facilitate the understanding of theoretical, empirical and methodological aspects of the chapters in this book. Furthermore, we hope that it provides a framework for understanding flow research in general and how it became such an interesting research topic.

Study Questions • What are the components of flow? Are some components more important than others? Could flow be reduced to a single component? Csikszentmihalyi (1975) listed six components of flow. These components are widely shared and adopted by other research, with only minimal differences and changes over the decades. The components describe an experience of total absorption into the activity, the feeling of control and of knowing what to do. The action runs smoothly and is experienced as enjoyable. All components (see Box 1.1) constitute flow and—based on what we know today—no component could be regarded as more important than another. This also means that flow cannot be reduced to one single component. Other researchers see some of the components as conditions of flow rather than components, as described in the next chapters of the book. In the section ‘Flow as a subjective experience’, we argued that all components are a constitutional part of the flow experience. The main argument for this is that flow is a subjective experience that is only partly dependent on objective conditions. • What are the pros and cons of including the “autotelic nature” of flow as one definitional component of flow? Flow is a rewarding experience and individuals would strive to attain experience in its own right regardless of the consequences. Csikszentmihalyi speaks of an autotelic nature because the experience of enacting is the reason for action itself (from the Greek “auto”, meaning self, and “telos”, meaning goal or purpose). Therefore, it is self-evident to include it in a componential description of flow. However, flow has been, and is, used as a construct to describe the positive aspects of the experience and to gain a better understanding of the quality of experiences. The aim was to explain the autotelic or intrinsic aspect of the activity. To integrate

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the variable which “has to be explained” poses the problem of circular explanations when integrating this variable into the definition. • Why should flow be associated with higher motivation and performance? Is flow supposed to be an optimal experience in every instance? Due to the rewarding nature of the experience of flow, individuals are motivated to do this activity again. Doing an activity all over again should, in general, lead to better skills. In order to experience flow again, they will set themselves more challenging goals. Thus, flow is a motivating force for excellence. Second, flow could be regarded as a highly functional state (e.g., high concentration) and should therefore foster performance for most activities. (See also Barthelmäs and Keller, Chap. 3 on the discussion of flow and performance.) However, based on some empirical results, there are reasons to question the general conclusion that flow is an optimal experience. Take for example the context of sports. In general, flow has been reported to be associated with high performance (e.g., Jackson & Roberts, 1992). For example it was found that flow was beneficial for the motivation to run and, thus, to exercise, but not for a higher performance in the marathon itself (Schüler & Brunner, 2009). More research is needed on how flow impacts the informational and motivational processes, and for what kind of tasks is this optimal? (cf. Barthelmäs and Keller, Chap. 3). Additionally, empirical studies suggest that rather a fluctuation of different states of consciousness may be optimal instead of being in flow all the time in order to achieve high performance. Thus, the optimal balance between flow experiences and other states of consciousness should be more closely examined (e.g., Baumann et al., 2016). • How are flow and happiness related? The relationship between happiness and flow is somewhat complicated and delicate. Flow is not happiness, but it is related to happiness. A person in flow does not have the conscious experience of being happy. This would even terminate the total absorption in the activity. Flow is not defined by affective means and is possibly correlates more strongly with interest and positive activation than with happiness. However, flow is a rewarding experience, which subsequently leads to happiness and satisfaction. In general, it also provides fulfillment for the person who experiences flow, and lends structure and meaning to life, even to the point of being part of the personal identity. • What do we call the “mission” of Csikszentmihalyi’s work? What was the starting question of his work? In our understanding, his mission was to explain human enjoyment and what makes an activity valuable and satisfying. This mission is still alive today, and a “new branch” of psychology, called Positive Psychology, has been stimulated and inspired by the work on flow. Related to this mission, Csikszentmihalyi asked himself what makes an activity enjoyable in its own right, instead of understanding the experience in relation to its means He therefore started to study activities where

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the enjoyment of the action seems to be highly important, such as rock climbing or playing chess. (See also Chap. 6, Abuhamdeh on flow and enjoyment.) • Please mention the major contributions of Csikszentmihalyi’s and Rheinberg’s work on the concept of flow. Both regarded the experience of an activity as an important aspect of human motivation and began a research agenda that provided insights into this respect. Csikszentmihalyi focused on the recurrent experiences in activity and ended up defining flow experience. He also provided a model that provided an explanation of when flow should occur. Finally, he started to study daily experience with the methodologically innovative approach of the Experience Sampling Method (ESM). Rheinberg studied the incentives discovered in in-depth interviews separately (i.e., he did not focus on the commonalities of all the activities which he studied). His research implies a limited number of possible activity-related incentives and a wide overlap of incentives between activities. The limited number and the wide overlap of incentives provide ideal grounds upon which to understand engagement in activities that, from the perspective of outsiders, initially appear insane. • Please name the basic ideas of the precursors of the flow concept. Understanding intrinsically motivated behavior was a vivid research question at the time Csikszentmihalyi started his research on flow. Hebb and Berlyne proposed that the right amount of stimulation will explain intrinsic motivation. If humans or other animals are “bored”, they look for stimulation, and too much stimulation is experienced as aversive and is avoided. White expanded this idea and proposed that an individual aims to learn about how to influence the environment (“effectance” motivation). DeCharms emphasized that individuals want the feeling of being the origin of their own actions. This wish for self-determination was adopted by Deci and Ryan and incorporated in their Self Determination Theory, along with the effectance motivation of White. Both aspects are also important aspects of the research tradition in achievement motivation, with McClelland and Heckhausen as prominent figures. Piaget and Caillois both see play as an intrinsic motivation to develop skills, test limits and gain new experiences to better adapt to the challenges of life. Caillois also suggested that seeking danger and altered states of consciousness are rewarding in their own right. Groos and Bühler concentrated more on the experience itself. Similar to the flow research, the experience of an activity is regarded as rewarding in its own right, although it basically functions through evolutionary means similar to White, Piaget and Caillois. Finally, Maslow is mentioned as a precursor, as he described peak experience as especially joyous and exciting moments of life, such as sudden feelings of intense happiness, transcendental experiences of unity or knowledge of higher truth. • What are current topics of flow research and what topics are expected to gain more research interest in the coming years? Studying flow experience in leisure activities, daily experiences and the implications for creativity and well-being has been, and remains, an important part of flow

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research. Major areas of research are sports and learning in educational settings as well as flow at work. Upcoming areas of flow research are human-computer interaction, game-based learning, media use and flow under the perspective of marketing aspects. A small proportion of the work deals with clinical implications. Research on possible negative aspects has rarely been undertaken, but we expect this to be an upcoming aspect (but not a main focus). Classical areas of flow research such as music and arts are still attracting a reasonable amount of interest from researchers, as do personality aspects. We expect that flow in a social context and psychophysiological aspects are topics that will attract much research interest in the future.

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Chapter 2

On the Conceptualization and Measurement of Flow Giovanni B. Moneta

Abstract This chapter introduces in chronological order the three main measurement methods—the Flow Questionnaire, the Experience Sampling Method, and the standardized scales of the componential approach—that researchers developed and used in conducting research on the flow state. Each measurement method and underlying conceptualization is explained, and its strengths and limitations are then discussed in relation to the other measurement methods and associated conceptualizations. The analysis reveals that, although the concept of flow remained stable since its inception, the models of flow that researchers developed in conjunction with the measurement methods changed substantially over time. Moreover, the findings obtained by applying the various measurement methods led to corroborations and disconfirmations of the underlying models, and hence provided indications on how to interpret and possibly modify flow theory. The chapter then analyzes the emerging process approach, which conceptualizes and measures flow as a dynamic path rather than an object, and highlights its potential for integrating flow and creativity within the same conceptual framework. The final section outlines new directions for developing more valid and useful measurement methods that can help to advance the understanding of flow, its antecedents, and its consequences.

Theory, Models, and Measurement Methods Engeser, Schiepe-Tiska, and Peifer (Chap. 1) argued that the definition of flow has changed very little since Csikszentmihalyi’s (1975/2000) original formulation in 1975, and that there is strong agreement among researchers on the definition itself. Yet, they pointed that that there is a certain level of disagreement among researchers as to how flow should be measured. Indeed, over the past 35 years, researchers have kept developing and validating new measurement tools for flow, and modifying and re-validating established ones, which indicates that a gold measurement standard for G. B. Moneta (*) School of Psychology, London Metropolitan University, London, UK e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_2

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flow has yet to be achieved. How is it possible to have agreement on a concept and disagreement on how to go about measuring it? This apparent paradox is not uncommon in the history of psychology, and can be understood by recognizing that the path from the theoretical definition to the operationalization of a construct goes through the intermediate process of modeling. A theory, such as flow theory, essentially is a set of interrelated constructs— including their definitions—and propositions that describe systematically the relationships among the constructs with the purpose of explaining and predicting a range of measurable outcomes. A measurement method, such as the Experience Sampling Method, is an apparatus and a technique for using it that is designed to measure some—but not necessarily all—theoretical constructs in order to test some predictions made by the theory. When researchers use a measurement method in order to test specific predictions derived from a theory they typically simplify the theory, and condense it into a simpler and more precise model. The model can be an authentic mathematical model, which states relationships among constructs in the form of equations, or simply a graphic representation, such as a conceptual diagram, a path diagram, or a flow chart. Modeling is helpful because it reduces the gap between words and numbers, and hence allows testing abstract relationships expressed in natural language on real-world data using statistics. Yet, because it implies a somewhat arbitrary interpretation and simplification of the underlying theory, researchers may end up adopting different models in their research and hence disagreeing on how certain constructs should be measured. To some extent, this is what has happened in the field of flow research. Therefore, a historical approach is adopted in this chapter. In the following three sections, each major measurement method and underlying conceptualization (i.e. the modeling) is explained, and its strengths and limitations are then discussed in relation to prior measurement methods and conceptualizations. The last section outlines some novel directions of methodological research that will hopefully lead to a more accurate, complete, and integrated modeling and hence a gold measurement standard for flow.

Capturing Flow in Special Endeavors Description of the Measurement Method The interviews that Csiksentmihalyi (1975/2000) conducted with participants from a wide range of occupations produced a wealth of textual descriptions of the flow experience in various domains of human endeavor. Some of the most insightful and clear descriptions of flow were then selected and condensed to create the first measurement method for flow, the Flow Questionnaire (FQ; Csikszentmihalyi & Csikszentmihalyi, 1988). The FQ proposes definitions of flow and asks respondents to recognize them, describe the situations and activities in which they experience flow, and rate their subjective experience when they are engaged in flow-conducive

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activities. Understanding how this is achieved requires entering the “nuts and bolts” of the instrument. Box 2.1 shows the key sections of the FQ. Section “Theory, Models, and Measurement Methods” presents three quotes that vividly describe the flow experience. Section “Capturing Flow in Special Endeavors” requires just a yes/no answer, and hence allows classifying participants into flow-ers (i.e. those who experienced flow in their lives) and non-flow-ers (i.e. those who did not experience flow in their lives). The following sections are directed only to flow-ers. Section “Capturing Flow in Daily Experience” asks them to freely list their flow-conducive activities. Section “The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable” asks participants who reported two or more flow-conducive activities to select one activity that best represents the experience described in the quotes, that is, the best flow-conducive activity. Section “The Process Approach: Capturing Flow as a Pathway to Flow” asks respondents to rate their subjective experience when they are engaged in the best flow-conducive activity and in other activities, such as work or being with family, using Likert-like scales. The scales include expressions that had emerged from interviews, such as ‘I get involved’ and ‘I enjoy the experience and the use of my skills’, and the two cornerstone variables of flow theory, ‘challenges of the activity’ and ‘your skills in the activity’. Box 2.1 The Key Sections of the Flow Questionnaire (Adapted from Csikszentmihalyi & Csikszentmihalyi, 1988, p. 195) 1. Please read the following quotes: My mind isn’t wandering. I am not thinking of something else. I am totally involved in what I am doing. My body feels good. I don’t seem to hear anything. The world seems to be cut off from me. I am less aware of myself and my problems. My concentration is like breathing I never think of it. When I start, I really do shut out the world. I am really quite oblivious to my surroundings after I really get going. I think that the phone could ring, and the doorbell could ring or the house burn down or something like that. When I start I really do shut out the world. Once I stop I can let it back in again. I am so involved in what I am doing. I don’t see myself as separate from what I am doing. 2. Have you ever felt similar experiences? 3. If yes, what activities were you engaged in when you had such experiences? 4. Please write here the name of the activity—among those you quoted, if any—which best represents the experience described in the three quotations, i.e. the activity where you feel this experience with the highest intensity. 5. On the next pages there are a number of items referring to the ways people could feel while doing an activity (e.g. ratings on the activity quoted in (continued)

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Box 2.1 (continued) section “The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable”, work or study, or spending time with the family). For each item please tell us how you feel doing each of these activities.

The First Model of the Flow State The FQ is a way to approach the empirical study of flow as represented by the first graphic model of the flow state (Csikszentmihalyi, 1975/2000, p. 17), which is reproduced with some additions in Fig. 2.1a. The model partitions the world of experience in three main states—flow, anxiety, and boredom—that are represented as non-overlapping areas of a challenge by skill Cartesian space. The flow state is posited to occur when there is an equivalent ratio of perceived challenges from the activity to perceived skills in carrying out the activity. This can occur when both challenges and skills are low, when both are medium, and when both are high: in all these cases there is a balance of challenges and skills and hence a person should be in flow. Yet, not all flow states are the same: when achieved in high-challenge/highskill situations flow will be more intense, ordered, and complex than when it is achieved in low-challenge/low-skill situations (Csikszentmihalyi, personal communication, 1987). The anxiety state is posited to occur when the perceived challenges from the activity exceed the perceived skills in carrying out the activity, whereas the boredom state is posited to occur when the perceived skills in carrying out the activity exceed the perceived challenges from the activity. As Engeser and colleagues pointed out (see Box 2.2 in Chap. 1), Csikszentmihalyi later revised the model as shown in Fig. 2.1b. He removed the ‘anxiety’ label for situations in which skills are very high and challenges are very low, and no longer referred to ‘worry’ for situations in which skills are very low and challenges are medium; so that, the original model of Fig. 2.1a was simplified into the threefold partition flow-anxiety-boredom. Finally, in the second edition of his Csikszentmihalyi 1975/2000 book, Csikszentmihalyi renamed ‘boredom’ as ‘boredom/relaxation’, indicating that a situation of over-control may be either aversive or mildly hedonic depending on personal and situational factors. Finally, Csikszentmihalyi (1975/2000) viewed the model as the experiential map through which a person “walks” in the quest of flow of ever growing complexity: the shown trajectories represent the hypothetical walk of a person who starts an endeavor in a state of low-complexity flow, crosses into the anxiety and boredom states, and eventually reaches a state of high-complexity flow.

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Fig. 2.1 (a) The first model of the flow state (adapted from Csikszentmihalyi, 1975) and (b) the revised model of the flow state (adapted from Csikszentmihalyi, 2000)

Strengths and Weaknesses The potential for application of the FQ can be evaluated in respect to the model of Fig. 2.1. The FQ has four main strengths. First, it provides a single and clear definition of flow that identifies with no ambiguity the diagonal region of the model and can be used to estimate the prevalence of flow (i.e. the percentage of people in specific populations that experience flow in their lives) as a single construct, and hence it allows studying differences in prevalence across genders, age groups, occupations, or cultures. The flow quotes capture directly merging of action and awareness (e.g. “I don’t see myself as separate from what I am doing”), centering ofattention (e.g. “my concentration is like breathing I never think of it”), and loss of self-consciousness (e.g. “I am less aware of myself and my problems”) and implicitly autotelic nature, feeling of control, and coherent,

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non-contradictorydemandsand feedback. In all, the quotes seem to capture the kernel of the construct, as defined by Csikszentmihalyi (1975/2000) in 1975. Second, unlike the approaches presented in sections “Capturing Flow in Daily Experience” and “The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable” of this chapter, the FQ does not “impose” flow to respondents, that is, it does not arbitrarily assume that everybody experiences flow in general or in a specific context. An important implication is that participants who would be classified as non-flow-ers based on the FQ because they did not recognize the proposed flow quotes, could obtain an artificial flow score on standardized flow questionnaires simply because they reported some level of concentration or absorption—which per se do not signify flow—when engaged in the target activity. Therefore, the FQ may be considered a more valid method for measuring the prevalence of flow. Third, because it asks respondents to freely list the activities in which they experienced flow, the FQ can be used to estimate the prevalence of flow in specific contexts. For example, Moneta (2010, 2012) used a two-step procedure to assess the prevalence of flow in work: in the first step, independent judges coded the listed activities into either “work” or “leisure”; in the second step, participants were classified into those who (a) do not experience flow (non-flow-ers), (b) best experience flow when engaged in a work activity (work flow-ers), and (c) best experience flow when engaged in a leisure activity (leisure flow-ers). Finally, by virtue of asking flow-ers to rate various facets of subjective experience as well as the levels of challenge and skill perceived when they were engaged in their best flow-conducive activity, the FQ allows testing whether flow occurs when challenges and skills are in relative balance with each other, and whether subjective experience is more positive in the flow state than in the anxiety and boredom states. The FQ has three main weaknesses. First, do the flow quotes constitute a single description of the flow state? In a study (Moneta, 2010, 2012), the original flow quotes were streamlined and divided in two separate sections of the FQ, one designed to measure a shallower flow and the other a deeper flow1, as shown in Box 2.2. Box 2.2 Quotes Used to Capture “Shallow” and “Deep” Flow (Moneta, 2010, 2012) “Shallow” flow: • “My mind isn’t wandering. I am totally involved in what I am doing and I am not thinking of anything else. My body feels good... the world seems to be cut off from me... I am less aware of myself and my problems”. (continued)

1 The separation of quotes was suggested by Antonella Delle Fave in 1997 (personal communication).

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Box 2.2 (continued) • “My concentration is like breathing... I never think of it... When I start, I really do shut out the world”. • “I am so involved in what I am doing... I don’t see myself as separate from what I am doing”. “Deep” flow: • “I am really quite oblivious to my surroundings after I really get doing in this activity”. • “I think that the phone could ring, and the doorbell could ring or the house burn down or something like that...” • “Once I stop I can let it back in again”. The quotes of “deep” flow differ from those of “shallow” flow in that they emphasize the condition of isolation from the environment that is central to the construct of flow. A sample of 393 workers located in the United Kingdom and from a wide range of occupations were cross-classified according to whether they had both types of flow, only one type, or neither one, as shown in Table 2.1. Although the majority of participants (n ¼ 250, 63.6%) provided concordant answers, a third of the sample (n ¼ 130, 33.1%) experienced shallow flow but did not experience deep flow, and a small group (n ¼ 13, 0.3%) experienced deep flow but did not experience shallow flow. As such, the quotes seem to constitute a reasonably homogeneous set, with the caveat that a flow state characterized by a strong sense of isolation from the environment is less prevalent than, and perhaps qualitatively different from an ordinary flow state. Yet, because deep flow and shallow flow appear to be somewhat distinct phenomena, mixing shallow flow quotes with deep flow quotes creates uncertainty as to exactly what a respondent’s yes/no answer refers to. Second, the FQ does not allow measuring the intensity or level of flow in specific endeavors, except for the shallow-deep distinction on a nominal measurement scale. Although, it is possible to infer whether a flow-er experienced flow in a specific activity (e.g. work) by checking whether that activity appears in the list of flowconducive activities, the FQ does not allow measuring how intense flow was in that activity. Section “The Process Approach: Capturing Flow as a Pathway to Flow” of the FQ contains scales measuring intensity of experience when engaged in a flowconducive activity, irrespective of whether one experiences flow while engaged in

Table 2.1 Cross-classification of 393 workers in the United Kingdom by whether they experienced shallow flow and deep flow

Shallow flow No Yes Total

Deep flow No 115 130 245

Yes 13 135 148

Total 128 265 393

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that activity; because people experience flow only a percentage of times when they perform a flow-conducive activity, such intensity measures do not specifically tap flow intensity in that activity. Moreover, if a flow-er reports no flow-conducive activity in the target category (e.g. work), it is still possible that the participant experienced flow in the target activity but simply forgot listing the activity. Therefore, the FQ is useful primarily for assessing prevalence of flow in general, and it is open to the risk of false negatives when used to estimate prevalence of flow in specific contexts of activity. Finally, the FQ does not allow a straightforward assessment of how perceived challenges of the activity, perceived skills in the activity, and the ratio of the two variables influence the occurrence of the flow state. This is because participants are asked to indicate their average challenge and skill levels in the best flow-conducive activity, and hence they are not necessarily reporting challenge and skill levels when in the flow state. The problem is that an average rating also is affected by the frequency with which flow—versus other states, such as anxiety and boredom, which are associated with other challenge/skill ratios—is experienced in the best flow-conducive activity. Therefore, the FQ is not a method of choice for testing the core tenet of flow theory and for investigating the dynamic “walks” in the challenge by skills Cartesian space that are represented in the model of Fig. 2.1.

Overall Assessment In all, the FQ is a good measurement method for studying the prevalence of flow, but it is a limited measurement method for investigating the effects of challenges and skills on subjective experience, and it cannot measure the intensity of flow in general and in specific endeavors. The measurement methods presented in the next two sections can be viewed as attempts to overcome such limitations.

Capturing Flow in Daily Experience Description of the Measurement Method The empirical test of flow theory in respect to everyday life experience became possible with the introduction of the Experience Sampling Method (ESM; Csikszentmihalyi, Larson, & Prescott, 1977; Csikszentmihalyi & Larson, 1987). The ESM is a measurement method designed to infer the time budget (i.e the sequence and times in which individuals are in specific states) in everyday life and the associated variation of subjective experience. The ESM seeks a random sampling of the population of experiences in respect to activities and contexts of action and associated subjective feelings. The ESM pursues the goal of ecological validity by studying subjective experience while participants are acting in their natural

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Fig. 2.2 Selected sections and items of the Experience Sampling Form (ESF) (adapted from Csikszentmihalyi & Larson, 1987, p. 536)

environments. The ESM consists of administering a questionnaire to a sample of participants repeatedly over random time intervals during their daily activities. The ESM is designed to overcome mnemonic distortions and post hoc rationalizations by asking appropriate questions just when the participants are engaged in their daily activities. The original form of the ESM (Csikszentmihalyi & Larson, 1987) gathers eight self reports per day in response to electronic signals randomly generated by pagers that respondents wear for a week. After each signal, participants provide their answers on the Experience Sampling Form (ESF). Figure 2.2 shows sample sections and items of the ESF. The core idea underlying the introduction of the ESM in flow research was that flow could be operationalized using the ‘Challenges of the activity’ and ‘Your skills in the activity’ items in such a way that flow would be any state in which challenges and skills simultaneously exceeded their weakly averages. The ESF contains 13 categorical items and 29 scaled items. The categorical items serve to reconstruct the activity (main activity, concurrent activities, and content of thought), the context (date, time beeped, time filled out, place, companionship, and influential facts which have occurred since the last pager signal), and some aspects related to motivation and interest (reasons for the activity, sources of physical discomfort, wished activity and companionship if different from the current ones, and comments). Except for reasons for the activity and companionship, the categorical items are open-ended and have to be coded by the researcher after collecting the data. The scaled items are designed to measure the intensity of a range of subjective

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feelings. Sixteen items are ten-point scales coded from zero (not at all or low) to nine (very or high); they measure the following variables: concentration, difficulty in concentrating, feeling good, feeling self-conscious, feeling in control, living up to the person’s expectations, living up to the expectations of others, physical discomfort, challenges from the activity, skills in the activity, importance of the activity to the person, importance of the activity to others, and importance of the activity to the person’s overall goals, success in the activity, wish to be doing something different, and satisfaction. The remaining thirteen scaled variables are Likert scales, coded from one to seven, with the following positive poles: alert, happy, cheerful, strong, active, sociable, proud, involved, excited, open, clear, relaxed, and cooperative. The ESM is a more complex measurement method than typical standardized questionnaires that are administered in a single occasion. This has both positive and negative consequences. On the positive side, the ESM allows investigating a wider range of phenomena. On the negative side, the data collected using the ESM are prone to biases that need to be carefully controlled for in the statistical analysis. Box 2.3 examines two important sources of bias affecting the ESM data and statistical strategies used to control them. Box 2.3 Potential Biases of the ESM Data and Strategies Used to Control Them The data gathered using the ESM have the structure of person-specific streams of experiential data points. These streams exhibit two potential sources of bias that have to be controlled for in data analysis. First, the scaling of the experiential variables differ between participants, so that, a value of 5 on a 1–9 scale may represent a high score for a participant who tends to give low ratings across situations and times, and a low score for a participant who tends to give high ratings across situations and times. Csikszentmihalyi and Larson (1984) addressed this problem using individual standardization, an approach that many other researchers adopted in their ESM studies. For example, consider the variable challenge. Each participant’s vector of raw scores of challenge is individually standardized as follows: (a) the mean value and standard deviation of the vector is computed, (b) the mean value is then subtracted from each raw score and the difference is divided by the standard deviation. As such, a value of z-challenge for an observation represents the extent—measured in standard deviation units—to which that observation departs from the weakly mean of challenge for that participant. Using z-scores in lieu of raw scores removes individual differences in scaling under the assumption that participants experienced the same overall level of challenge throughout the week of the study. Second, because participants are allowed to defer filling out an ESF after receiving a signal or not to fill it out at all if the activity they are engaged in at the time of signal does not allow, the number of data points differs between (continued)

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Box 2.3 (continued) participants; so that, traditional techniques for the analysis of repeated measures cannot be used on the beep-level data. The majority of studies addressed this problem using individual aggregation (for a comprehensive explanation see Larson & Delespaul, 1992; Hektner, Schmidt, & Csikszentmihalyi, 2007). For example, consider again the variable challenge as measured in two contexts, work and leisure. Each participant’s vector of raw scores of challenge is individually aggregated by calculating the mean of z-challenge for those observations that occurred when the participant was working and the mean of z-challenge for those observations that occurred when the participant was engaged in leisure activities. As such, each participant has just one aggregate score for work (i.e. mean z-challenge of work) and one aggregate score for leisure (i.e. mean z-challenge of leisure). Using individually aggregated scores in lieu of beep-level scores removes individual differences in number of observations, and hence allows the use of standard statistical techniques for repeated measures at the expense of loss of information from the data.

The Quadrant Model and the Experience Fluctuation Model In the first large-scale application, Csikszentmihalyi and Larson (1984) administered the ESM to a sample of 75 high-school students in the Chicago area, and analyzed how the quality of subjective experience varies as a function of four contexts of activity: life in the family, companionship with friends, solitude, and life in class. They found that those contexts yield quite different patterns of average values of subjective experience variables. Life in the family is associated with feeling happy but aggravated by lack of concentration and involvement; companionship with friends yields higher happiness and involvement but still a low concentration; solitude yields poor experience in respect to happiness and involvement but higher concentration; school life yields unhappiness but high concentration and average involvement. Csikszentmihalyi and Larson interpreted these patterns in terms of flow theory, that is, by analyzing the types of activities that are carried out within each of these contexts in respect to the levels of challenges and skills that they involve, but they could not test the theory because they had not included the challenge and skill items in the ESF of that study. Nevertheless, the provided interpretations were so interesting that stimulated researchers to find ways to use the ESM to test the core predictions made by flow theory. Csikszentmihalyi and LeFevre (1989) administered the ESM to a sample of 78 workers in Chicago with the main aim of disentangling the effects on the quality of subjective experience that are due to being in flow from those that are imputable to being engaged in work or leisure. They pursued the goal by introducing a new model and operationalization of the flow state, the quadrant model, which is shown in

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Fig. 2.3 (a) The quadrant model of the flow state (adapted from Csikszentmihalyi & LeFevre, 1989) and (b) the experience fluctuation model of the flow state (adapted from Massimini, Csikszentmihalyi, & Carli, 1987)

Fig. 2.3a. The model partitions the world of experience in four main states—flow, anxiety, boredom, and apathy—that are represented as quadrants of a challenges by skill Cartesian space in which both axis variables are standardized with the 0 value representing the weakly mean. The model represents flow as a state in which a participant perceives challenge and skill greater then the weekly average and in relative balance with each other. In an attempt to provide a more detailed classification system, Massimini and colleagues (Massimini et al., 1987; Massimini & Carli, 1988) proposed the Experience Fluctuation Model (which is often referred to as the ‘channel model’ or the ‘octant model’), which is shown in Fig. 2.3b. The model partitions the world of experience in eight main states that are represented as arc-sectors (‘channels’) of 45 each of a challenge by skill Cartesian space in which both axis variables are standardized with the 0 value representing the weakly mean. Similar to the quadrant model (see Fig. 2.3), the model represents flow as a state in which a participant

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Fig. 2.4 The model of the flow state emerging from the quadrant and experience fluctuation models

perceives challenge and skill greater than the weekly average and in relative balance with each other. The main differences from the quadrant model are that the channel model provides a narrower operationalization of the construct of challenge/skill balance and a more detailed characterization of the non-flow states. The main difference between the quadrant and experience fluctuation models, on the one hand, and the 1975/2000 models of the flow state shown in Fig. 2.1, on the other hand, is the addition of the ‘apathy’ state, which is posited to be the least positive state. Therefore, the original claim that flow occurs when challenges and skills are in relative balance with each other independently of their level was abandoned in favor of a more complex representation. In order to achieve flow two conditions need to be satisfied: (a) there is balance between challenges and skills, and (b) both challenges and skills are greater than their weakly average. As such, both the quadrant model and the experience fluctuation model conform to the new model of the flow state shown in Fig. 2.4. The figure shows that flow is expected to occur when both challenge and skill reach highest levels.

Strengths and Weaknesses of the Quadrant and Experience Fluctuation Models The quadrant model has two main strengths: it is a simple classification system and it allows performing disarmingly simple tests of the core predictions made by flow theory. For example, Csikszentmihalyi and LeFevre (1989) estimated an ANOVA model in which subjective experience was the dependent variable, and flow (flow vs. non-flow, including boredom, anxiety, and apathy) and activity (work vs. leisure) were the within-participants factors. Flow turned out to explain considerably more variance in subjective experience than activity, thus corroborating the hypothesis that the quality of subjective experience is more influenced by flow than by context of activity. The main strength of the channel model stems from the rich and robust empirical findings it generated. Massimini et al. (1987) administered the ESM to a sample of 47 Italian high-school students in Milan in order to investigate the variation of

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subjective experience across channels. The eight channels were considered as eight levels of one within-participants factor. Eighteen facets of experience were treated as dependent variables, each in a separate analysis: Concentration, Ease of concentration, Unselfconscious, Control, Alert, Happy, Cheerful, Strong, Friendly, Active, Sociable, Involved, Free, Excited, Open, Clear, Wish doing this, and Wish to be here. Univariate F-testing was used to ascertain whether the variation of the mean z-score of each dependent variable across the eight challenge/skillconditions was overall significant. Flow theory was substantially corroborated in that: (a) the F-test was significant for each of the 18 dependent variables, showing that the challenge/skill ratio is influential for all measured facets of experience; (b) for 13 dependent variables (72%) the maximum occurred in the condition highchallenge/high-skill ( flow), whereas for the remaining variables the maximum occurred in the condition high-challenge/medium skill (arousal; Concentration), the condition medium-challenge/high-skill (control; Friendly) or the condition low-challenge/high-skill (relaxation; Ease of concentration, Unselfconscious, and Sociable); (c) for nine dependent variables (50%) the minimum occurred in the condition low-challenge/low-skill (apathy), whereas for the remaining variables the minimum occurred in either the condition medium-challenge/low-skill (worry; Ease of concentration, Control, Happy, Cheerful, Friendly, Sociable, Free, and Clear) or the condition high-challenge/low-skill (anxiety; Unselfconscious). Furthermore, t-testing was performed to detect, for each dependent variable, the conditions in which the mean z-score was greater than the week average. The t-test relative to the condition high-challenge/high-skill reached significance in the predicted direction for 12 dependent variables (67%) (the exceptions being Ease of concentration, Unselfconscious, Alert, Cheerful, Friendly, and Sociable) further supporting the hypothesis that in the situations defined as high-challenge/high-skill the quality of subjective experience is significantly better than average. These findings were substantially replicated across age groups, cultures, and life domains (Carli, Delle Fave, & Massimini, 1988; Csikszentmihalyi, 1990, 1997a; Delle Fave & Bassi, 2000; Delle Fave & Massimini, 2005; Haworth & Evans, 1995). Although more detailed than the quadrant model, the channel model shares with it two key limitations. First, there are problems with the operationalization of flowconducive situations as characterized by ‘above average’ levels of challenge and skill. Such operalization rests on the strong assumption that participants would rate the challenges and skills perceived while doing a specific activity with reference to a global standard of measurement that is common to all activities. Barthelmäs and Keller (Chap. 3) discuss in depth this assumption and question its tenability on conceptual and empirical ground. Second, both the quadrant model and the channel model are classification systems, and hence they do not allow testing the implicit assumptions underlying the classification itself. In general, this is because both models measured flow indirectly as high-challenge/high-skill condition and did not measure flow directly. In particular, the superiority of the flow channel over the other channels was universally

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interpreted as being due to the equivalent ratio of perceived challenges from the activity to perceived skills in carrying out the activity. Yet, is the balance of challenges and skills needed to explain the pattern of findings? A number of researchers addressed this question, somewhat independently of each other, by adopting a regression modeling approach.

The Regression Modeling Approach In order to assess whether the balance of challenge and skill has an independent and positive effect on experience, researchers (Moneta & Csikszentmihalyi, 1996, 1999; Pfister, 2002) first considered the additive model in which experience is the dependent variable and challenge and skill are the predictors: Experience ¼ ß0 þ ß1 challenge þ ß2 skill

ð2:1Þ

If the regression coefficients of challenge (ß1) and skill (ß2) are both positive and of equal size, then experience is an inclined plane over the challenge by skill Cartesian space as shown in Fig. 2.5a. The figure shows that flow varies from low (blue), medium (green), and high (red) levels as a function of challenges and skills. The figure shows that the quality of experience will be highest in the flow channel and lowest in the apathy channel, and will decrease as one rotates, either clockwise or anti-clockwise, from the flow channel to the apathy channel. Thus, such model and its simple variants—obtained by changing the relative size of the two coefficients—would account for all the findings gathered using the quadrant and channel models. This raises a problem: the regression model 1 considers challenge and skill as two independent predictors, each contributing to experience independently of the other; therefore, there is no need to invoke the concept of balance in order to explain the findings. Thus, all the interpretations of the findings obtained using the quadrant and experience fluctuation models were speculative at the time they were put forth. Once it became clear that neither the quadrant model nor the experience fluctuation model could be used to test key predictions made by flow theory, researchers set out to develop a regression modeling approach with three aims: (a) to ascertain if the balance of challenges and skills matters, (b) to identify a model of subjective experience that is estimated using the ESM data, as opposed to a classification model that somewhat arbitrarily allocates observations to channels or quadrants, and (c) to use the estimated model, as opposed to an imposed model, in order to identify the optimal challenge/skill ratio and the extent to which the effects that challenges, skills, and their balance have on subjective experience vary between individuals. The concomitant development of multilevel or hierarchical linear modeling (e.g. Bryk & Raudenbush, 1992; Goldstein, 1995) made possible to estimate the models more efficiently than previously done. Because the technique allows to control for incomplete streams of repeated observations and individual differences in scaling [for comprehensive explanations of how this is achieved with ESM data see Moneta &

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Fig. 2.5 The three-dimensional representation of the (a) additive, (b) cross-product, and (c) absolute-difference regression models of the flow state (adapted from Moneta & Csikszentmihalyi, 1996, 1999; Pfister, 2002)

Csikszentmihalyi (1999), and Conti (2001)], the regression models were estimated on raw, beep-level scores without having to resort to individual standardization and aggregation (see Box 2.3).

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Fig. 2.5 (continued)

The first aim was addressed by adding the challenge by skill cross-product (Ellis, Voelkl, & Morris, 1994; Moneta, 1990) or the absolute difference of challenge and skill to the regression model 1 (Moneta & Csikszentmihalyi, 1996; Pfister, 2002), or using quadratic terms of challenge and skill following a rotation of the predictor axes (Moneta, 1990; Moneta & Csikszentmihalyi, 1999). Because these different models have comparable statistical fit to the data, only the two simplest models are considered here. The cross-product model is an extension of the additive model: Experience ¼ ß0 þ ß1 challenge þ ß2 skill þ ß3 challenge  skill

ð2:2Þ

The predictor challenge*skill is the cross-product of challenge by skill, which can be equal to 0 (if both challenge and skill equal zero) or greater than 0 (if challenge and skill are greater than zero). Its coefficient ß3 represents the effect of the balance of challenge and skill on experience. The model is fully consistent with the theory if the following conditions are all satisfied: (a) ß1 > 0, (b) ß2 > 0, and (c) ß3 > 0. The first two conditions imply that both challenge and skill have a positive linear effect on experience. The third condition implies that the balance of challenge and skill has a positive linear effect on experience. Figure 2.5b provides a graphic representation and interpretation of the model. The figure shows that the quality of experience will be highest in the flow channel and lowest in the apathy channel, and will decrease as one rotates, either clockwise or anti-clockwise, from the flow channel to the apathy channel. Moreover, there is a premium in experience—represented by a saddle running from low to high balanced challenge-skill levels.

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The absolute difference model also is an extension of the additive model: Experience ¼ ß0 þ ß1 challenge þ ß2 skill þ ß3 jchallenge—skillj

ð2:3Þ

The predictor |challenge—skill| is the absolute difference between challenge and skill, which can be equal to 0 (if challenge equals skill) or greater than 0 (if challenge and skill differ in any way). Its coefficient ß3 represents the effect of the imbalance of challenge and skill on experience. The model is fully consistent with the theory if the following conditions are all satisfied: (a) ß1 > 0, (b) ß2 > 0, and (c) ß3 < 0. The first two conditions imply that both challenge and skill have a positive linear effect on experience. The third condition implies that the imbalance of challenge and skill has a negative linear effect on experience. Figure 2.5c provides a graphic representation and interpretation of the model. The surface will look like a roof, as shown in Fig. 2.5. The edge of the roof (i.e. the line where the two sloped planes of the roof intersect each other) represents the optimal challenge/skill ratio. In this ideal case, the edge of the roof is perpendicular to the diagonal line of balance of the challenge by skill plane (i.e. each point of the edge corresponds to an observation in which challenge equals skill). If the linear effect of challenge is greater than that of skill (ß1 > ß2), the edge of the roof will rotate horizontally towards the challenge axis, whereas if the linear effect of skill is greater than that of challenge (ß1 < ß2), the edge of the roof will rotate horizontally towards the skill axis. The effect of the imbalance is represented by the slope of the roof: the steeper the slope, the greater the negative effect of the imbalance of challenge and skill. If the slope of the roof is null, then the roof will just be an inclined plane with no edge, and hence there would be no optimal challenge/skill ratio. The ideal flow state can be operationalized as the absolute maximum of the surface, which in this case is on the edge of the roof, perpendicular to the observation for which both challenge and skill achieve their maximum.

Strengths and Weaknesses of the Regression Modeling Approach The main strength of the regression approach stems from the specific empirical findings it generated, which could not be generated using the quadrant and channel models. First, it was found that many facets of subjective experience—such as concentration, interest in the activity, enjoyment of the activity, or happiness—are predicted by challenge and skill independently as well as by their relative balance; therefore, balance has an effect on the quality of experience over and above the effects of challenge and skill, although the effect of balance is small compared to the independent effects of challenge and skill (Moneta & Csikszentmihalyi, 1996). Second, the regression coefficients of challenge, skill, and the balance of the two were found to differ between facets of experience in such a way that the optimal ratio was about 1:1 for some facets (e.g. involvement), biased towards higher levels of challenge for others (e.g. concentration), and biased towards higher levels of skill for

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yet other variables (e.g. happiness) (Moneta & Csikszentmihalyi, 1996); therefore, there seem to be different optimal challenge/skill ratios, and hence optimization of experience requires trade-offs between facets of experience. Third, the model fitted better and was more consistent with theoretical predictions in achievement contexts than in non-achievement contexts (Moneta & Csikszentmihalyi, 1996); therefore, the theory would appear to be more applicable when achievement goals and opportunities are salient. Fourth, the effects of challenge, skill, and the balance of the two differed across individuals (Moneta & Csikszentmihalyi, 1996, 1999); so that, for example, balance has a strong, positive effect on some individuals, and no effect or even a negative effect on other individuals; therefore, the theory would appear to be fully applicable only to some individuals. Finally, the effects of challenge, skill, and balance were found to be linked to personalitytraits—such as traitintrinsic motivation and interdependent self-construal (Moneta, 2004b), situational variables—such as goals, interests, importance of the activity, and state intrinsic motivation (Csikszentmihalyi, Abuhamdeh, & Nakamura, 2005; Ellis et al., 1994; Rheinberg, Manig, Kliegl, Engeser, & Vollmeyer, 2007), and culture (Moneta, 2004a); therefore, the theory would need to be expanded to account for conceptual relationships with other psychological theories. In all, these studies corroborated the kernel assumptions of flow theory and provided indications on how to further develop the theory. Although the regression models constitute advancement in respect to the quadrant and channel models, they share with them three key limitations. First, as Ellis et al. (1994) pointed out, many of the investigated facets of experience are not clearly connected to the flow construct, and hence cannot be regarded as indicators of flow. In particular, variables like ‘wish to do the activity’, ‘active’, or ‘sad-happy’ have never been theorized to be an integral part of the flow experience. Moreover, the construct validity of the scales used to tap the investigated facets of experience has never been assessed by standard psychometric methods, such as exploratory and confirmatory factor analysis. Second, in all applications the key predictors challenge and skill were measured by only one item each. This is obviously unacceptable from a psychometric stand. Finally, there is a conceptual problem with the construct of challenge. Rheinberg et al. (2007; cf. Rheinberg, 2008) argued that, in addition to challenge and skill, also the perceived difficulty level or ‘demands’ of the activity should be assessed because challenge implies a compound of difficulty and skill. For example, an easy task can be very challenging to a novice, and a difficult task can be unchallenging to an expert. Although, Pfister (2002) found similar effects of the difficulty/skill and challenge/skill ratios on the quality of experience, the construct of difficulty may be relevant when achievement motivation is taken into account. According to Atkinson’s (1957) model, people with more achievement motivation prefer tasks of medium difficulty, in which there should be a balance between difficulty and skill, whereas people with less achievement motivation prefer tasks of low difficulty, in which skill should be greater than difficulty. Therefore, difficulty is an item that, together with others tapping the constructs of challenge and skill, should be considered for inclusion in future developments of the ESM.

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Overall Assessment In all, the ESM proved to be superior to the FQ for the purpose of measuring the flow state in daily life and for testing hypotheses concerning the effects that challenge, skill, and their balance have on flow. Yet, the ESM somehow “imposes” flow on respondents and hence is inferior to the FQ for the purpose of measuring prevalence of flow. Finally, the ESM scales developed to date do not achieve satisfactory levels of content validity, and their construct validity is largely unknown. The measurement methods presented in the next section can be viewed as attempts to overcome the latter limitation.

The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable Description of the Measurement Method The methods for measuring flow presented in the previous sections were original and proved to be innovative in generating many insightful and robust findings. However, they are far from being psychometrically sound. For this reason, some researchers set out to construct and validate questionnaires that would measure flow to the standards required by traditional test theory. Several scales were developed pursuing essentially the same aim (e.g. Engeser & Rheinberg, 2008; Keller & Bless, 2008; Moneta, 2017; Schüler, 2010). This section will focus primarily on the scales developed by Jackson and Eklund (2002, 2004). These scales are consistent with Csikszentmihalyi’s (Jackson & Csikszentmihalyi, 1999) componential view of flow, they measure flow both as a state and as a trait, and are the most frequently used in research and practice, particularly in the sports context. Jackson and Marsh (1996) and Jackson and Csikszentmihalyi (1999) described flow as a state characterized by nine components: focused concentration on the present activity (concentration), sense of control over one’s actions (control), merging of action and awareness (merging), autotelic experience (autotelic), loss of selfconsciousness (self-consciousness), loss of time-awareness or time acceleration (time), clear proximal goals (goals), unambiguous feedback (feedback), dynamic balance between challenge and skill (balance). These components can be regarded as correlated dimensions of the flow construct that can trade-off in determining the intensity or level of flow. If the level of all components is highest, a person will be in a most intense, complex, and ordered flow state. If some components reach highest level whereas others reach only medium or low levels, the contributions to flow of the different components will trade off in producing a flow state that will be overall less intense, complex, and ordered than the ideal flow state. Jackson and Eklund (2002, 2004) applied the componential view of flow to measure flow as a state, a broad trait (i.e. the tendency to experience flow frequently

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and intensely across a wide range of situations), and a domain-specific trait (i.e. the tendency to experience flow frequently and intensely in specific contexts of activity). They developed, refined, and validated two standardized questionnaires: the Flow State Scale-2, which measures intensity of flow as a state, and the Dispositional Flow Scale-2, which measures intensity of flow as either a general trait or as a domainspecific trait. The item content of the two questionnaires is similar. As is it is customary in test construction, the main difference between the state and trait questionnaires resides in the initial instructions given to participants: the state questionnaire asks participants to answer the questions thinking of the specific activity they just completed, whereas the trait questionnaire asks participants to answer the questions thinking of their general experience across situations and times or of their average experience when they are engaged in a context of activity (e.g. work or leisure). Both the state and the trait questionnaires have good psychometric properties (Jackson & Eklund, 2002, 2004).

The Componential Model Construct validity is a key property of any measurement method, and it is customarily assessed using confirmatory factor analysis (CFA). The specific way CFA is applied fully clarifies the model that was used to construct the measurement method. Jackson and Eklund (2002, 2004) estimated two CFA models, and they used the same pair of CFA models for the data provided by the state questionnaire and the data provided by the trait questionnaire. The first model is the nine-factor model with correlated factors shown in Fig. 2.6. This is a classical test theory model in which nine intercorrelated latent facets of the construct of flow cause responses on the measured indicators; that is, the behaviors described by the items of the questionnaire are manifestations of nine latent facets. This model represents flow as a multi-faceted construct. The second model is the single-factor model shown in Fig. 2.7. This is a classical test theory model in which the latent construct of flow causes responses on the measured indicators; that is, the behaviors described by the items of the questionnaire are manifestations of a single latent construct. This model represents flow as a single construct. Which of the two models should be adopted? Jackson and Eklund (2002, 2004) found that both models have good statistical fit, but the nine-factor model fits better than the single-factor model. Therefore, they recommended using nine sub-scale scores, each measuring a somewhat distinct component of flow, in research. Yet, they acknowledged the parsimony and theoretical usefulness of an overall scale score to measure flow as a single construct.

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Fig. 2.6 The nine-factor measurement model for the Jackson and Eklund (2002, 2004) Flow State Scale-2 and Dispositional Flow Scale-2. e ¼ measurement error

Strengths and Weaknesses The componential approach has two main strengths. First, it provides a comprehensive characterization of flow that is by far more complete than that provided by the FQ and the ESM. Second, it provides measures of flow that are psychometrically more valid and reliable than those provided by the FQ and the ESM. In all, the componential approach achieves the psychometric standards that flow research needs in order to earn full recognition in the field of psychology. The componential approach has three main and interrelated weaknesses. First, like the ESM, it “imposes” flow on all respondents, even if some would be classified as non-flow-ers using the FQ. As such, both the componential approach and the ESM are inferior to the FQ for the purpose of estimating the prevalence of flow. Second, the componential approach as implemented in the FSS-2 and DFS-2 assumes a model of flow that contradicts the various models that researchers have adopted in conjunction with the FQ and the ESM, in that it has to date ignored the distinction between antecedents of flow (i.e. factors that can, under some circumstances, cause flow) and indicators of flow (i.e. experiences and behaviors that are,

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Fig. 2.7 The one-factor measurement model for the Jackson and Eklund (2002, 2004) Flow State Scale-2 and Dispositional Flow Scale-2. e ¼ measurement error

under some circumstances, caused by flow). In particular, the balance of challenge and skill was consistently regarded as an antecedent of flow in the regression modeling approach using the ESM, whereas it is considered a component of flow in the model that drove the development of the FSS-2 and DFS-2. In general, there is an ongoing debate (see review by Swann, Keegan, Piggott, & Crust, 2012) on two interlinked issues: (a) the set of components of flow, and (b) the separation between components of flow and other variables that may be functionally related to flow but are not indicators of flow. Regarding the first issue, positions range from assuming only one component of flow (Schiefele & Raabe, 2011) to assuming all nine listed above (Jackson & Csikszentmihalyi, 1999). Regarding the second issue, in 2009, Hoffman and Novak had already identified thirty definitional models of flow, each proposing a somewhat different partition of the nine components into antecedents of flow, expressions of flow, and effects of flow. As such, the componential approach is in need of major development. Finally, the componential approach can hardly handle what can be called the paradoxes of attention. Csikszentmihalyi (1978) pointed out that states of heightened and focused attention occur in two different contexts: when a person is in flow, and

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when a person is facing an overwhelming threat. Building on this distinction, Engeser and colleagues (Chap. 1) consider a hypothetical state in which a person would score high on concentration and low on all other components of flow, and argue that such a state could not be called flow. How does the componential approach deal with that case? If one adopts the single-factor measurement model of Fig. 2.7, the overall flow score for that state would be the sum (or the mean) of all the item scores. Because only a small number of items measure concentration, the overall flow score for that hypothetical state would be low. Hence, the impact of this paradox on the componential model is not severe. Yet, consider the diametrical paradox, a hypothetical state in which a person would score low on concentration and high on all other components of flow. That could be the case of a hallucinogenic or even a near-death experience, but arguably not a flow state, because attention is an essential component of executive functioning (Mathews, Yiend, & Lawrence, 2004). How does the componential approach deal with that case? Because only a small number of items measure concentration, the overall flow score for that hypothetical state would be high. Hence, the componential model cannot handle this paradox. There have also been attempts to develop componential models of flow that are not based on Jackson and Csikszentmihalyi’s (1999) nine-component model, such as the WOrk-reLated Flow scale (WOLF; Bakker, 2008), or that include only some components and add new ones, such as EduFlow (Heutte, Fenouillet, Boniwell, Martin-Krumm, & Csikszentmihalyi, 2014, 2016a, 2016b), a measure of flow in study contexts. A problem inherent in such variants of the componential model of flow is the enhanced risk of low discriminant validity. As a case in point, WOLF measures three correlated components of flow—absorption, workenjoyment, and intrinsic workmotivation—whereas the Utrecht Work Engagement Scale (UWES; Schaufeli, Salanova, González-Romá, & Bakker, 2002) measures three components of work engagement—absorption, dedication, and vigour. Due to the content overlap between the two scales, Fullagar and Kelloway (2013) recommended not using WOLF, and Moneta (2017) argued that every componential flow scale should undergo thorough tests of discriminant validity particularly against similar constructs such as work engagement and positive affect. Finally, a number of componential scales have been developed to fit specific domains and types of activities, such as Ghani and Deshpande’s (1994) scale designed to measure human-computer interaction, Novak, Hoffman, and Yung’s (2000) scale designed to measure online customer experience, and Fu, Su, and Yu’s (2009) EGameFlow scale designed to measure flow in e-learning games. In general, such variants of the componential model do not include core components of flow and include new components, such as interactivity and exploratory behavior, that may be related to flow but are not indicators of flow. As such, these scales have been criticized on the ground that they capture flow-related phenomena but not flow (Fullagar & Kelloway, 2013; Hoffman & Novak, 2009).

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Overall Assessment In all, the componential approach has generated methods for measuring intensity or level of flow that are more complete and psychometrically sound than the FQ and the ESM. Yet, the componential approach cannot measure prevalence of flow, and hence is inferior to the FQ in that respect. Moreover, the componential models proposed to date have too simple a structure to account for the complexity of flow. Finally, attempts to develop componential scales that tap components of flow other than those included in the nine-component model appear to be at risk of low discriminant validity.

The Process Approach: Capturing Flow as a Pathway to Flow Description of the Measurement Method The models covered in the previous sections view flow as an object that can be measured independently of its underlying processes and dynamics. In a nutshell, this means that flow is defined statically as a set of characteristics irrespective of how it is achieved. Instead, the process model of flow views flow as a process leading to an optimal state of consciousness. In a nutshell, this means that flow is defined dynamically, in relation to how it is achieved. The process model is grounded in the regression modeling approach, and goes beyond by positing that the process through which an optimal state of consciousness is achieved has a nonlinear dynamics.

The Nonlinear Dynamic Model Ceja and Navarro (2009, 2011, 2012) proposed that the variations of subjective experience at work conform to nonlinear dynamic models, and provided empirical evidence in support of their claim estimating various forms of nonlinear models on ESM data. Linear models assume that the change of outcome variables (e.g. concentration, absorption, and merging of action and awareness) as a function of the change of predictor variables (i.e. challenges, skills, and their relative balance) is smooth and continuous; all the regression models of flow dealt with in section “The Regression Modeling Approach” are linear models. In contrast, nonlinear models assume that, as the system departs from an equilibrium point its behavior becomes increasingly unstable to the extent that change in the outcome variable as a function of predictor variables becomes abrupt and discontinuous. The simplest instance of such abrupt changes is provided by Ceja and Navarro’s (2012; Navarro & Ceja, 2011) cusp catastrophe model of flow, which is shown in Fig. 2.8.

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Fig. 2.8 Cusp catastrophe model of flow showing (a) the bifurcation edge, (b) the cusp zone, and (c) smooth and troublesome pathways to flow (adapted from Ceja & Navarro, 2012; Navarro & Ceja, 2011)

Figure 2.8 shows the bifurcation edge, which is the source of instability in this model. When “walking” on the edge of the cusp, a minimal change in levels of challenges and/or skills results in either a sharp enhancement (i.e. a climb on the surface) or a sharp deterioration (i.e. a descent on the surface) of subjective experience. This means that when in the cusp zone, the approach to flow is an inherently unstable process that could fail abruptly, and its instability is not due to random error but to a deterministic mechanism. In particular, being in the cusp zone implies both the highest probability of experiencing flow suddenly and the highest probability of experiencing the opposite of flow suddenly, and hence the greatest variability of outcomes. The nonlinearity of the cusp model of flow influences the way one can achieve flow. Figure 2.8 shows the two extreme cases: smooth pathway and troublesome pathway to flow. On the one hand, the smooth pathway begins with low challenges and low skills, proceeds by just increasing skills till the point one feels extremely skillful in handling low challenges, and finally proceeds by just increasing challenges to reach the high-challenge, high-skill state of flow. On the other hand, the troublesome pathway begins with high challenges and low skills, proceeds by just increasing skills till the point one can progress toward the flow state if and only if one somehow manages to “climb” the steep inner wall of the cusp. As such, the smooth

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passage to flow avoids the instability of the cusp, the troublesome pathway faces it fully, and any other path in between the two faces intermediate levels of instability. The two extreme pathways to flow impose differing requirements on cognitive and emotional processes. On the one hand, the troublesome pathway to flow requires the ability to “survive” in the cusp zone and manage to come out of it as a winner. The cusp experience essentially means that a problem solver recognizes that old tricks do not work for the task at end, and hence something new has to be figured out in order to succeed. In that context flow can be achieved only by conceiving and implementing a creative idea. As such, it is reasonable to assume that the cognitive processes that are required in the cusp zone are the provision of feedback on how one is doing, the ability and willingness to seek such feedback, problem finding (Getzels & Csikszentmihalyi, 1975, 1976) as well as all other cognitive processes underlying creativity, such as information gathering, incubation, idea generation, idea evaluation, and idea implementation (see review by Palermo & Moneta, 2016). Moreover, the emotional processes that are required in the cusp zone are the initial experience of negative affect derived from failure and frustration in problem solving, followed by an affective shift characterized by a decrease of negative affect and an increase of positive affect that supports creative ideation and idea implementation (e.g. Baumann, see Chap. 9; Bledow, Rosing, & Frese, 2013). Therefore, the cusp zone can be labeled as the creativity zone. On the other hand, the smooth pathway to flow requires ordinary learning processes and self-regulation that support understanding of the problem at end and step-by-step acquisition and deployment of the new skills that would allow solving the problem. This does not mean that creativity cannot occur, but rather that it is optional and limited by context to a lesser and more ordinary form often referred to as “little-c” (Davis, 2004), “everyday” (Richards, Kinney, Benet, & Merzel, 1988), “small” (Feldman, Csikszentmihalyi, & Gardner, 1994), and “inherent” (Runco, 1995) creativity. Moreover, the emotional processes that are required in the non-cusp zone are those that support any well-paced and progressive learning endeavor. Therefore, the non-cusp zone can be labeled as the non-creativity zone. Figure 2.8 identifies flow as the state that is most likely to occur when challenges and skills are matched and at their highest levels, and it does so without considering the path through which flow was achieved. This raises a key question: is flow operationalized as a high-challenge/high-skill state the same object whether it is reached through the troublesome or the smooth pathways? Based on Csikszentmihalyi’s (1997b) psychological and biographical analysis of major creative contributions to the fields of science, arts, and business one would conclude that flow is such only if it is achieved throughout the cusp zone. Instead, states of high concentration, absorption, and merging of action and awareness achieved through the non-cusp zone could be simply labeled as engagement. An alternative perspective is to link the pathways to flow to the types of flow that, as shown in section “Theory, Models, and Measurement Methods”, can be detected using alternative versions of the flow questionnaire. In particular, flow achieved through the cusp zone could be labeled deep flow, whereas flow achieved through the non-cusp zone could be labeled shallow flow. An additional implication of the model is that deep flow should take longer to achieve than shallow flow, and should be inherently more

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unstable, as failure is around the corner all along the troublesome path to flow, and each recognized failure is likely to interrupt automatic information processing and cause distress. For example, a student tackling a radically new mathematical problem may have to try a variety of approaches before finding one that works, whereas a student tackling a slightly new mathematical problem may adopt an already learned approach and adapt it with limited trial and error. Whether or not the term flow is used to characterize a state of highest concentration, absorption, and merging of action and awareness achieved through the cusp and non-cusp zones, the overall implication of the nonlinear dynamic model is that all previous and static operationalizations of flow as a single object mix apples with oranges and hence miss their target, i.e. flow. Finally, the nonlinear dynamic model of flow also opens a new perspective on the issue of determining whether flow is a universal experience, which is crucial to the measurement of flow. As seen in section “Theory, Models, and Measurement Methods”, when measured as a state using the flow questionnaire, a minority of respondents reports never having experienced flow. Moreover, when measured as a domain-specific disposition using Likert-like scales in twin studies, the heritability estimate of flow proneness is moderate in the domains of work, maintenance, and leisure, and is explained by the same genetic factors across the three domains (Mosing et al., 2012). This implies that not all individuals can experience flow. Figure 2.8 indicates that flow is a universal experience, as any person during an endeavor could reach a high-challenge/high-skill condition by following the troublesome pathway. However, Ceja and Navarro’s (2012; Navarro & Ceja, 2011) found that for a minority of participants the flow model has no cusp. For these participants experience conforms to one of the three linear models described in section “Capturing Flow in Daily Experience” and depicted in Fig. 2.5. For each one of those models there is no difference between pathways to flow in that no pathway crosses a cusp or other form of turbulence area. In turn, this implies that for “linear” individuals there is no troublesome pathway to flow, and hence they are structurally prevented from experiencing flow. The psychological interpretation for the absence of the cusp area is that an individual is unable and/or unwilling to appropriately recognize and assess feedback from the activity (e.g. failure in problem solving) and react accordingly by triggering cognitive and emotional processes that are involved in creative problem solving.

Strengths and Weaknesses The process approach has four main strengths. It can explain why many people report “suddenly I get into the zone” experiences when asked to describe flow, why and how flow and creativity are intertwined processes in the course of personally meaningful and high-stake endeavors, and why flow is a common but not universal experience. Moreover, it was supported both using the cross-product of challenge and skill (Ceja & Navarro 2012; Navarro & Ceja 2011) and the absolute difference of challenge and skill (Bricteux, Navarro, & Ceja, 2016) as operationalizations of the

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concept of balance. As such, it provides a more accurate characterization of flow that accounts for key theoretical and empirical findings. Finally, the cusp catastrophe model of flow avoids the pitfalls of the Quadrant and Octant models of flow in that it does not operationalize flow as a high-challenge/high-skill state. Although the prototypical model shown in Fig. 2.8 indicates that flow occurs only for high challenges and high skills, the shape of the surface, including the extent of its non-linearity, can vary greatly between persons and between tasks and contexts within the same person. For example, the model allows for flow to be experienced in a deficit state characterized by overwhelming challenges. The process approach has two main weaknesses. It has been tested only on small samples and it requires numbers of ESM observations in excess of 120 per participant, which makes any such study expensive and hard to implement. As such, it provides novel insights in the measurement of flow that need, however, further testing and development.

Overall Assessment In all, the process approach has generated methods for identifying and measuring flow with greater accuracy, accounting for the complexity of flow, and avoiding the pitfalls of static measurement methods that may erroneously apply the flow label to a large class of more ordinary states of consciousness. Yet, the process approach is at an early stage of development, has not been widely tested, and its development requires formidably complex and expensive study designs. Moreover, although the cusp catastrophe model of flow is flexible and hence capable to account for individual and situational differences, other, more complex non-linear models should be considered in future research to account for factors that influence flow over and beyond the challenge/skill ratio (e.g. Baumann, Lürig, & Engeser, 2016).

Directions for Future Conceptual-Methodological Research The analysis conducted in this chapter suggests five main directions for future research aimed at developing more valid measurement methods for flow. First, as Engeser and colleagues (Chap. 1) suggest, there is a need of integration and standardization of the existing measurement methods. Although it still needs conceptual development, the componential approach produced the most complete and psychometrically sound measures of flow, both as a state and as a trait, and hence should inform and guide an improvement of the FQ and the ESM. In particular, the quotes section of the FQ should be expanded to include quotes of all facets that are considered to be expression of flow (i.e. that are theorized to be caused by the latent construct of flow); moreover, section of the FQ (Box 1) should be modified to provide a systematic assessment of flow intensity in specific activities. By the same

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token, the ESM should contain scaled items that tap validly and reliably each facet of flow and each antedecent of flow (e.g. Rheinberg et al., 2007), i.e. each variable that is theorized to cause the latent construct flow. Finally, researchers may consider applying and further developing the Day Reconstruction Method (DRM; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004) for the purpose of assessing prevalence and intensity of flow. The DRM assesses systematically significant everyday life events that occurred the day before, with procedures designed to minimize recall bias. As such, the DRM has the potential of capturing brief but intense flow experiences that might instead be missed by the ESM due to its time-sampling structure. Second, all three main types of measurement methods need to be developed in order to ascertain whether flow is a single construct or a label for a constellation of constructs. Moreover, there is the need of providing evidence of convergent and discriminant validity of each measurement method for flow in relation to measurement methods that were designed to tap other types of optimal experience, such as ‘peak performance’ (Privette, 1983), ‘peak experience’ (Maslow, 1964), work engagement (Schaufeli et al., 2002), and positive affect. Section “Capturing Flow in Special Endeavors” showed some evidence supporting the idea that the quotes of the FQ may capture a shallow flow—which supports activities that require social interaction, such as teaching or football playing—and a deep flow—which supports activities for which social interaction would be detrimental, such as chess playing or proving mathematical theorems. Section “Capturing Flow in Daily Experience” reported evidence indicating that the optimal challenge/skill ratio differs across facets of experience, suggesting that there may be different types of optimal experiences, such as a high-challenge/medium-skill one that optimizes cognitive efficiency and a medium-challenge/high-skill one that optimizes hedonic tone. This finding has been recently supported by an experimental study (Baumann et al., 2016) that manipulated the challenge/skill ratio dynamically while participants where playing a computer game: a slight overload condition turned out to be conducive to flow but not to general enjoyment of the activity, indicating a divide between flow and other types of optimal experiences. This evidence suggests that all three main types of measurement methods for flow should be improved in order to enable them to test the tenet that there is one and only one flow state. Third, all three main types of measurement methods need to be developed in order to ascertain whether flow and its antecedents are substantially the same across cultures. The FQ was administered to samples from various cultures and provided evidence of cultural invariance (Delle Fave & Massimini, 2004). The ESM was administered to Japanese (Asakawa, 2004) and Chinese (Moneta, 2004b) university student samples and provided evidence of cultural variations in flow models that, however, could be explained based on cross-cultural theories of psychosocial development (Moneta, 2004a). The FSS-2 and DSF-2 were translated and validated in various languages (e.g. Kawabata, Mallett, & Jackson, 2008). Although these studies suggest that cultural variation is small, a more basic test has not yet been conducted. The key question is: if we were to repeat the whole process that led to the componential model of flow—starting with interviews and proceeding to the construction of the FQ and componential measurement scales—in a new culture (e.g. the Chinese

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or Indian cultures), would we identify exactly the same facets of flow and antecedents of flow? Fourth, the process approach to flow has shed new insights in the measurement of flow and questioned whether the other approaches have any real chance of success. The cusp catastrophe model of flow (Bricteux et al., 2016; Ceja & Navarro, 2012; Navarro & Ceja, 2011) indicates that “authentic flow”, as opposed to generic states of intense task absorption, can be detected only by looking at the path through which it is achieved: if the path crosses the turbulent cusp area characterized by perceived high challenge and low skill, then it is flow, otherwise it is something else and of a more ordinary nature. A static measurement of flow is similar to an ECG conducted in a resting state, whereas a dynamic measurement of flow is similar to an exercise electrocardiogram or stress ECG, which allows knowing how the heart responds to being pushed. By analogy, because it ignores the dynamic processes underlying the achievement of flow, the componential approach to the measurement of flow may fail capturing flow. Finally, the cusp catastrophe model of flow indicates that individuals may need a high level of awareness of flow and its antecedents and a strong self-regulation in order to achieve flow in natural environments that are not really designed to facilitate flow, such as open-plan offices and clustered cubicles. High levels of such awareness can be conceptualizeed as metacognition. Metacognition refers to the knowledge and beliefs about one’s own cognitive regulation and the capability to deconstruct and understand them through reflection and problem solving, which in turn enables selfregulation (Flavell, 1979). Two key types of metacognitions should be considered in flow research: general metacognitions supporting the correct interpretation of emotional cues and flexible goal re-structuring in facing challenges (Beer & Moneta, 2010) and specific metacognitions of flow for which we have initial definitions and measurement scales (Wilson & Moneta, 2016). In conclusion, this chapter has shown that, following the original formulation of flow theory, researchers developed four main methods for measuring flow, the FQ, the ESM, the standardized scales of the componential approach, and the dynamic use of those scales within the process approach. Researchers used each measurement method in conjunction with one or more models, which were somewhat arbitrary interpretations and simplifications of the theory. Researchers interpreted the empirical findings of their studies with reference to the model of flow they had adopted hypothetically, and the gathered evidence provided a mixture of corroboration and disconfirmation of their model, which in turn led to small but important modifications of the theory. This process had some chronological order, but was not always linear or perfectly logical. This pattern is common in science, and in the history of psychology in particular, although researchers may differ in the extent to which they are aware of it. The key message of this chapter is that no existing measurement method for flow and associated model is watertight, and that a gold standard for the modeling and measurement of flow is not at close reach. Hopefully, this chapter helped to convince young flow researchers that models and measurement methods go hand in hand, are paramount to the development and application of flow theory, and hence need continuous improvement.

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Study Questions • What are the main measurementmethods for flow? Is one of these methods better overall than the others? If yes, why? The main measurement methods for flow are the Flow Questionnaire, the Experience Sampling Method, and the standardized scales of the componential approach. The chapter suggests that none of these three main measurement methods is overall superior to the others: each one has pros and cons that trade off depending on the specific question the researcher is tackling (see sub-sections “Overall Assessment” in sections “Capturing Flow in Special Endeavors”, “Capturing Flow in Daily Experience”, “The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable”, and “The Process Approach: Capturing Flow as a Pathway to Flow”, and see section “Directions for Future Conceptual-Methodological Research” of this chapter). You may, of course, disagree with this conclusion; but if you do, you should state your rationale. For example, if you believe that construct validity is paramount, then the standardized scales of the componential approach would be the likely winners. • Think of one research question about flow and select a measurementmethod to test it. What criteria did you use in making your choice? Once you have listed the criteria, check them against the “Overall Assessment” sub-sections in sections “Capturing Flow in Special Endeavors”, “Capturing Flow in Daily Experience”, “The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable”, and “The Process Approach: Capturing Flow as a Pathway to Flow” of this chapter and determine by yourself if you have made a sensible choice. As a final check, read the first paragraph of section “The Process Approach: Capturing Flow as a Pathway to Flow” of this chapter and determine by yourself if your research question would require a modification or adaptation of the measurement method you have chosen. • What are the mainstrengthsand main limitations of the Flow Questionnaire? On one hand, the Flow Questionnaire is a good measurement method for assessing the prevalence of flow, that is, whether participants sampled from a population (e.g. students or workers) have ever experienced flow in their lives. This is because the Flow Questionnaire proposes a description of flow and asks respondents to freely report whether or not they had similar experiences. As such, it does not “impose” flow on respondents and does not lead to inflated prevalence rates (see section “Strengths and Weaknesses’ in section “Capturing Flow in Special Endeavors” of this chapter). For this reason, the Flow Questionnaire can be validly used to compare the prevalence rates of different populations, such as Chinese versus British college students or white-collar versus blue-collar workers. On the other hand, the Flow Questionnaire is a limited measurement method for investigating the effects of challenges and skills on subjective experience, and the intensity of flow in general and in specific endeavors (e.g. work and leisure).

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This is because the scaled items of section “The Process Approach: Capturing Flow as a Pathway to Flow” of the Flow Questionnaire refer to the experience while doing the best flow-conducive activity, but do not refer specifically to those instances in which a respondent experiences flow while doing that activity (see section “Strengths and Weaknesses” in section “Capturing Flow in Special Endeavors” of this chapter). However, these limitations could be overcome by modifying the Flow Questionnaire (see the first paragraph of section “Directions for Future Conceptual-Methodological Research” of this chapter). • What are the mainstrengthsand main limitations of the Experience Sampling Method? On one hand, the Experience Sampling Method is a good measurement method for studying the flow state in daily life and for testing hypotheses concerning the effects that perceived challenges from the activity, perceived skills in the activity, and the balance of the two perceptions have on the occurrence of flow while engaged in the activity. This is because the Experience Sampling Method gathers repeated measures of subjective experience at random times while participants are engaged in daily activities, minimizing memory bias. For this reason, the Experience Sampling Method can be validly used to test and compare alternative models of how various situational factors (e.g. type and context of activity or levels of challenges and skills and their balance) and various personal factors (e.g. personalitytraits, culture, gender, or occupation) conjointly influence the quality of daily experience, including intensity of the flow state (see section “Capturing Flow in Daily Experience” of this chapter). On the other hand, the Experience Sampling Method is a limited method for studying prevalence of flow. This is because it somehow “imposes” flow on respondents, as opposed to asking them explicitly to report whether or not they experienced flow at the time they were beeped. Moreover, the Experience Sampling Method uses scales for measuring flow intensity that lack content validity and have unknown construct validity. As such, it does not provide a sound measure of the construct of flow intensity. However, these limitations could be overcome by modifying the Experience Sampling Method (see the first paragraph of section “Directions for Future Conceptual-Methodological Research” of this chapter). • What are the mainstrengthsand main limitations of thecomponential approach? On one hand, the componential approach has generated methods for measuring intensity of flow that have good content and construct validity, and hence are the most psychometrically sound among the available measurement methods. On the other hand, the componential approach is not good for assessing prevalence of flow because it “imposes” flow on respondents and hence leads to inflated prevalence rates. Moreover, the componential approach, as implemented in the FSS-2 and DFS-2, does not distinguish antecedents and facets of flow (see next study question) and has too simple a structure to account for the complexity of the relationships between antecedents of flow and flow itself. Yet, these limitations could be overcome in future developments of the componential scales (see ‘Strengths and weaknesses’ in section “The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable” of this chapter).

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• What is the key difference between ‘antecedents’ of flow and ‘components’ or ‘facets’ of flow? Antecedents of flow are internal states and perceptions that precede and foster the flow state, but are not themselves expressions of flow. These include, for example, clarity of goals, unambiguous feedback, and perceptions of challenge and skill in carrying out an activity. These factors are theorized to have a causal impact on flow by either increasing the likelihood that flow occurs or by augmenting the intensity of flow. Components or facets of flow are internal states and perceptions that represent expressions of flow. These include, for example, merging of action and awareness and loss of time-awareness or time acceleration when carrying out an activity. These factors are theorized to be caused by flow (see “Strengths and Weaknesses” in section “The Componential Approach: Capturing Flow as a Multidimensional State-Trait Variable” of this chapter). • How many models of flow have been proposed to date and in which way(s) they differ from each other? Many models of flow have been proposed to date. The sub-set of models presented in this chapter includes the first model of the flow state (see Fig. 2.1), the quadrant model and experience fluctuation models (see Figs. 2.3 and 2.4), the cross-product and absolute-difference regression models (see Fig. 2.5), the ninefactor componential model (see Fig. 2.6), the one-factor componential model (see Fig. 2.7), and the cusp catastrophe model (Fig. 2.8). These seven models can be grouped into pairs of similar models. For example, let’s consider three pairs. The first model of the flow state and the absolutedifference regression model are similar, except for the latter is defined by a mathematical model and can be tested using regression analysis (Pair A). The quadrant model and the experience fluctuation model are similar, except for the latter is more detailed (Pair B). The one-factor componential model and the nine-factor componential model are similar, except for the former represents flow as a single construct whereas the latter represents flow as nine interrelated constructs (Pair C). Pair A and Pair B of models are similar to each other in that they explain the occurrence of flow as a function of challenge and skill, whereas Pair C of models measures intensity of flow without explaining the causal factors underlying it. Finally, Pair A and Pair B of models differ in that the latter assumes that flow is more likely to occur when challenge and skill exceed a person’s weekly average and do not operationalize—and hence allow testing—the construct of balance of challenge and skill. • How is the flow state represented in the various models of flow that have been proposed? For example, with reference to the previous question and answer, Pair A of models represent flow as a state that is more likely to occur when there is a balance of challenge and skill, and it is more intense as the sum of challenge and skill grows. Pair B of models represents flow as a state that is more likely to occur when both challenge and skill exceed a person’s weekly average. Finally, Pair C of models represents flow as either a single construct with nine facets (see Fig. 2.7) or as a nine-faceted construct (see Fig. 2.6).

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• Compare the original flow modelwith thequadrant model, pointing out similarities and differences. The most noticeable difference between the quadrant model (see Fig. 2.3a) and the first model of the flow state (see Fig. 2.1a) is that the former includes the ‘apathy’ state, which is posited to be the least positive state. Therefore, the claim made by the first flow model that flow occurs when challenges and skills are in relative balance with each other independently of their level is modified by the quadrant model as follows: in order to achieve flow two conditions need to be satisfied: (a) there is balance between challenges and skills, and (b) both challenges and skills are greater than their weakly average. • Compare thequadrant modeland the experience fluctuation modelwith the absolute difference regression model, pointing out similarities and differences. Similarities. The quadrant model and the experience fluctuation model are similar to the absolute difference regression model in that they all explain the occurrence of flow as a function of challenge and skill (see Figs. 2.3 and 2.5c). Differences. The quadrant model and the experience fluctuation model are classification systems, in which subjective experience is grouped into distinct states (see Figs. 2.3) as a function of levels of perceived challenge from an activity and perceived skill in conducting an activity. As such, these models assume that the balance of challenge and skill fosters flow, but do not allow testing this assumption. Moreover, these models assume that flow occurs only when challenge and skill levels exceed their weekly average, which is somewhat questionable on theoretical and empirical ground (see section “Strengths and Weaknesses of the Quadrant and Experience Fluctuation Models” of this chapter and Chap. 3) and in contradiction with the first model of the flow state (see Fig. 2.1a). The absolute difference regression model represents flow as a state that is more likely to occur when there is a balance of challenge and skill, and it is more intense as the sum of challenge and skill grows. This model assumes that the balance of challenge and skill fosters flow, and allows testing this assumption controlling for the effects that challenge and skill have on flow independently of each other. Moreover, this model does not assume that flow occurs only when challenge and skill levels exceed their weekly average, and in that it is consistent with the first model of the flow state (see Fig. 2.1a).

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Ellis, G. D., Voelkl, J. E., & Morris, C. (1994). Measurements and analysis issues with explanation of variance in daily experience using the Flow model. Journal of Leisure Research, 26, 337–356. Engeser, S., & Rheinberg, F. (2008). Flow, moderators of challenge-skill-balance and performance. Motivation and Emotion, 32, 158–172. Feldman, D. H., Csikszentmihalyi, M., & Gardner, H. (1994). Changing the world: A framework for the study of creativity. Westport, CT: Praeger. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34, 906–911. Fu, F. L., Su, R. C., & Yu, S. C. (2009). EGameFlow: A scale to measure learners’ enjoyment of e-learning games. Computers and Education, 52, 101–112. Fullagar, C., & Kelloway, E. K. (2013). Work-related flow. In A. B. Bakker & K. Daniels (Eds.), A day in the life of a happy worker (pp. 41–57). New York: Psychology Press. Getzels, J. W., & Csikszentmihalyi, M. (1975). From problem solving to problem finding. In I. A. Taylor & J. W. Getzels (Eds.), Perspectives in creativity (pp. 90–116). Chicago: Aldine. Getzels, J. W., & Csikszentmihalyi, M. (1976). The creative vision: A longitudinal study of problem finding in art. New York: Wiley. Ghani, J., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human-computer interaction. The Journal of Psychology, 128, 381–391. Goldstein, H. (1995). Multilevel statistical models. New York: Halsted. Haworth, J., & Evans, S. (1995). Challenge, skill and positive subjective states in the daily life of a sample of YTS students. Journal of Occupational and Organizational Psychology, 68, 109–121. Hektner, J., Schmidt, J., & Csikszentmihalyi, M. (2007). Experience sampling method. Measuring the quality of everyday life. Thousand Oaks, CA: Sage. Heutte, J., Fenouillet, F., Boniwell, I., Martin-Krumm, C., Csikszentmihalyi, M., (2014). Optimal learning experience in digital environments: Theoretical concepts, measure and modelisation. Symposium Digital Learning in 21st Century Universities, Georgia Institute of Technology (Georgia Tech), Atlanta, GA. Heutte, J., Fenouillet, F., Boniwell, I., Martin-Krumm, C., & Csikszentmihalyi, M. (2016a). The EduFlow model: A contribution toward the study of optimal learning environments. In L. Harmat, F. Ørsted Andersen, F. Ullén, J. Wright, & G. Sadlo (Eds.), The flow experience: Empirical research and applications (pp. 105–123). New York: Springer. Heutte, J., Fenouillet, F., Boniwell, I., Martin-Krumm, C., & Csikszentmihalyi, M., (2016b). Proposal for a conceptual evolution of the flow in education (EduFlow) model. European Conference on Positive Psychology (ECPP), Angers, France. Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23, 23–34. Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport & Experience Psychology, 18, 17–35. Jackson, S. A., & Csikszentmihalyi, M. (1999). Flow in sports: The keys to optimal experiences and performances. Champaign, IL: Human Kinetics. Jackson, S. A., & Eklund, R. C. (2002). Assessing flow in physical activity: The flow state scale-2 and dispositional flow scale-2. Journal of Sport and Exercise Psychology, 24, 133–150. Jackson, S. A., & Eklund, R. C. (2004). The flow scale manual. Morgantown, WV: Fitness Information Technology, Inc. Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science, 306, 1776–1780. Kawabata, M., Mallett, C. J., & Jackson, S. A. (2008). The flow state scale-2 and dispositional flow scale-2: Examination of factorial validity and reliability for Japanese adults. Psychology of Sport and Exercise, 9, 465–485.

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Chapter 3

Antecedents, Boundary Conditions and Consequences of Flow Michael Barthelmäs

and Johannes Keller

Abstract In this chapter, we analyze flow with respect to three aspects. First, we examine the basis for flow experiences to emerge. We focus our discussion on the situational antecedents of flow and emphasize the fact that the emergence of flow is basically dependent on a perceived fit of skills and task demands. Thereby we critically discuss the “above average” perspective and the related quadrant and octant models of flow highlighting the fact that the “above average” notion is based on problematic assumptions. Further, we discuss the concept of flow intensity and propose a revised flow model, which builds on the original notion of perceived fit of skills and task demands and includes the value attributed to the relevant activity as additional crucial factor. Second, we address boundary conditions of the flow experience, emphasizing the role of both personality and situational factors that qualify the relation between a perceived skills-demands fit and flow. Third, we critically review the available evidence on affective, cognitive and performancerelated consequences resulting from flow or a compatibility of skills and demands. In addition, we highlight obstacles in the research exploring these consequences of flow and discuss first starting points to circumvent these.

Introduction The state of flow has been studied academically for more than four decades (Csikszentmihalyi, 1975; see also Engeser, Schiepe-Tiska & Peifer, Chap. 1) and is present in the public mind, as well. Phrases like “I’ve been in a flow” or “I’ve been in the channel” found their way into everyday language and are used to describe This chapter is a revised and condensed version of the chapters “The Flow Model Revisited” (Keller & Landhäußer, 2012) and “Flow and Its Affective, Cognitive, and Performance-Related Consequences” (Landhäußer & Keller, 2012), published in the first edition of this book (Engeser, 2012). M. Barthelmäs · J. Keller (*) Institute of Psychology and Education, Ulm University, Ulm, Germany e-mail: [email protected]; [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_3

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experiences in various contexts, e.g., during a mountain bike trip, an intense period of work or while playing a challenging game. The aim of this chapter is to improve readers’ comprehension of the flow experience by discussing three subtopics. The first part of the chapter (antecedents of flow) is devoted to the question of what builds the basis for flow experiences to emerge. Here, we systematically discuss the conditions under which an individual engages in an activity that have to be met in order to enable individuals to enter a state of flow. While this discussion specifically focuses on the situational conditions under which flow emerges, in the second part (boundary conditions of flow) we debate the role of personality and situational factors that qualify the relation between skills-demands compatibility and the experience of flow. After discussing these boundary conditions of the flow experience, we critically review empirical evidence regarding affective, cognitive and performance-related consequences of flow (part three; consequences of flow and the skill-demands-compatibility). While doing so, obstacles in the empirical analysis of flow-consequences become apparent (e.g., most of the evidence is limited to correlational findings) which we will discuss with the aim of providing first starting points to circumvent these obstacles. This contribution is a revised and condensed version of the chapters from Johannes Keller (Keller & Landhäußer, 2012) and Anne Landhäußer (Landhäußer & Keller, 2012) published in the first edition of this book (Engeser, 2012). Consequently, the original chapters are more detailed and elaborated in some respect (e.g., in this edition we abbreviated paragraphs regarding the autotelic experience of flow, originally presented in Chap. 4; Landhäußer & Keller, 2012) and the interested reader can find additional information in the first edition of this book. However, we attempted to remain the crucial ideas of the original chapters while updating our discussion with new thoughts and recent empirical evidence. Before we enter into the details of the discussion, three clarifications have to be made. These are necessary for (1) the term “challenge”, (2) the term “flow experience” and (3) the measurement of flow experience. The term “challenge” is frequently mentioned in the flow literature in the discussion of the notion that a perceived fit of skills and challenge builds the basis for the emergence of flow experiences. We want to clarify that we consider the term “demands” much more appropriate than the term “challenge” (for a detailed discussion of this conceptual aspect, see Rheinberg & Engeser, 2018). Moreover, in line with the discussion of the flow phenomenon presented in Chap. 1 (Engeser, Schiepe-Tiska, & Peifer), we are referring to flow as a subjective experience that is characterized by the combination of distinct (experiential) states that co-occur during engagement in a skill-related activity, specifically (1) reduced reflective self-consciousness, (2) modified experience of time (“time stands still”), (3) involvement and enjoyment, (4) focused concentration, (5) a strong feeling of control, and (6) the activity is perceived as rewarding in and of itself. It is evident that each of these states can be experienced by individuals who are definitely not in a state of flow. For example, a person can experience a strong sense of control during routine activities (such as teeth brushing or setting the table) while none of the other flow-specific states are experienced. In addition, an individual sitting at the beach

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watching a sunset can experience a loss of self-consciousness and still be far from a state of flow given the lack of the characteristic strong sense of control that accompanies the execution of a skill-related activity in a state of flow. It is important to acknowledge the fact that flow experiences reflect a distinct combination of experiential states, particularly when one is considering the boundary conditions that enable (or prevent) the occurrence of the subjective experience. In addition, it is necessary to clarify that flow is an experiential state. From our perspective, this implies that some methods are more suitable than others to assess flow experience (see also Box 3.1). In our opinion the experience sampling method (ESM; e.g., Engeser & Baumann, 2016) and the day reconstruction method (DRM; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004) are generally appropriate to examine flow in everyday life, as these techniques allow to capture fluctuation of experience by surveying participants in situ (ESM) or on basis of an elaborated diary reconstruction process (DRM). Examining flow in a more controlled (i.e., laboratory) setting, its assessment during a specific activity seems valid when directly applied after the activity (e.g., Keller & Bless, 2008). However, some studies attempted to measure the state of flow (or a skills-demands compatibility) in a retrospective manner, instructing participants to bring past episodes of flow to mind (e.g., Eisenberger, Jones, Stinglhamber, Shanock, & Randall, 2005; Olčar, Rijavec, & Golub, 2017; Rijavec, Golub, & Olčar, 2016). As there is reliable evidence that such assessments are prone to mental biases (e.g., Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), in our view, this method is less appropriate to examine the state of flow. Consequently, we will not discuss literature applying this retrospective approach (see also Peifer & Tan, Chap. 8 for a discussion on the conceptualization of flow as state versus trait). Box 3.1 Restriction Regarding the Empirical Evidence We Consider in This Chapter In our view, some methods are more suitable than others to study the experience of flow. Resting upon findings that retrospective recalls are prone to mental biases (e.g., Podsakoff et al., 2003), we conclude that approaches instructing participants to recall past flow episodes in a rather unsystematic way are suboptimal for examining flow experience. Consequently, we report only on studies that either used techniques that capture flow directly after a potential flow episode (which is possible in both a controlled laboratory setting and ecologically valid ambulatory studies) or enabled a systematic reconstruction process (cf. Kahneman et al., 2004) with the aim to reduce the influence of recall biases.

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Part 1: Antecedents of Flow Antecedent Factors Outlined in Flow Theory Flow theory refers to three antecedents: (1) clear goals (in the sense of a clear understanding of the task structure, which is frequently based on clear task instructions), (2) immediate and unambiguous feedback (in terms of diagnostic information regarding one’s progress or success in executing the activity), and (3) a balance of perceived skills and perceived task demands (Nakamura & Csikszentmihalyi, 2009). One aspect in this context is noteworthy: the activity has to be skill-related for the emergence of flow experiences (given the fact that a balance of skills and task demands represents one crucial antecedent; cf. the discussion of flow in non-achievement situations in Schiepe-Tiska & Engeser, Chap. 4). That is, activities that are passive in character (such as watching a sunset or taking a relaxing bath) and do not involve a skill-component cannot be associated with a flow experience. Empirically, the important role of a perceived fit of skills and task demands for the experience of flow has been well documented in correlational research (cf. Bakker, 2005; Jackson & Marsh, 1996; Moneta & Csikszentmihalyi, 1996; Schiefele & Roussakis, 2006) and in experimental studies as well (cf. Baumann, Lürig, & Engeser, 2016; Engeser & Rheinberg, 2008; Harmat et al., 2015; Keller & Bless, 2008; Keller, Bless, Blomann, & Kleinböhl, 2011; Keller & Blomann, 2008; Yoshida et al., 2014). The relevance of a perceived fit becomes even more apparent when considering the interrelation of the three antecedent factors in some detail. Such an examination reveals that two of the antecedents (clear goals and feedback) are incorporated in the most crucial antecedent, the fit of perceived skills and perceived task demands. That is, we argue that the proposed antecedents of the flow experience can be simplified and reduced to a perceived skills-demandscompatibility which makes flow theory more parsimonious. This notion rests on the insight that individuals can only attain a meaningful subjective construal of their level of skill and the level of task demands involved in the relevant activity if (a) the structure of the task is clear to them (“clear goals” in the terminology used in the flow literature; the goal concept is typically used differently in the psychological literature; cf. Austin & Vancouver, 1996) and (b) they can diagnose the degree to which they are successful in the execution of the activity (based on a clear feedback). It is evident that a meaningful evaluation of one’s skill in executing an activity is hardly possible under conditions where the structure of the task is not clear. For example, how should one reasonably rate one’s level of skill in playing cricket or the level of demands one is confronted with in a game of cricket without knowledge on the structure of this game? In parallel, how should one reasonably rate one’s level of skill in playing cricket without diagnostic information (feedback) regarding the quality of one’s actions? It seems also hardly possible to construct a meaningful judgment regarding progress or success in an activity when the structure of the task remains obscure.

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In sum, we propose that the antecedents in the flow model can be reduced to the factor “perceived fit of skills and task demands” which implies “clear goals” and “immediate, unambiguous feedback” as crucial aspects that have to be met for flow experiences to emerge.

Antecedent Factors Beyond a Perceived Fit of Skills and Task Demands In addition to the crucial factor of a perceived fit of skills and demands, other determinants of the flow experience can be derived from a consideration of the defining elements of the flow experience. As noted above, flow reflects a distinct combination of experiential states and this suggests that factors related to the specific elements of the flow experience may function as antecedents, too. Regarding the reduced level of self-consciousness that is characteristic of flow experiences, we argue that situational influences that increase individuals’ selfconsciousness are likely to prevent flow experiences. For example, we suppose that the emergence of flow experiences should be hampered when individuals engage in the relevant activity in front of a mirror - a manipulation that is known to increase self-consciousness (Carver & Scheier, 1978; Wicklund & Duval, 1971). Similarly, we suppose that individuals suffering from depression, who are prone to an enhanced level of negative self-reflection in form of rumination (NolenHoeksema, 1991), should experience flow to a smaller extent. Further, it seems plausible to assume that depressive symptoms are not only detrimental to this flow element, but also to ‘enjoyment and involvement’. Given the loss of pleasure and interest as a cardinal symptom in depression (APA, 2013), it becomes apparent that individuals suffering from this disease should be less likely to experience flow. With respect to the strong sense of control that typically emerges under conditions of flow we suggest that factors triggering an experience reflecting lack of autonomy (a basis for the experience of control) are likely to reduce the chances that an individual enters a state of flow. For example, it seems plausible to argue that employees in a work context characterized by low autonomy are less likely to experience flow (under conditions of a perceived fit of skills and task demands) than those working under high autonomy conditions. Support for this idea comes from a study by Kowal and Fortier (1999). Their study on swimmers included the question whether there is a relation between athletes’ sense of autonomy in a specific training session and the experience of flow during this session. It turned out, that those who perceived the attendance at the training session as a free choice rather than an obligation, experienced more flow. Further, Bakker (2005) examined the relation of the job characteristics among music teachers and the flow experience among teacher and pupils. Besides evidence for a contagion of flow between pupils and teacher, teachers reported more flow experience when they experienced their job with a certain amount of autonomy.

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Further, imagine a work process that is characterized by frequent interruptions e.g. due to phone calls or e-mails. With reference to the flow element “intensely focused concentration”, it seems apparent, that under such circumstances the experience of flow is less likely. As flow episodes seem to be rewarding in and of itself, we suggest that a context which undermines this reward should be unconducive to the experience of flow. A potential scenario for inhibiting rewards is the occurrence of overjustification (e.g. Lepper, Greene, & Nisbett, 1973). Overjustification can occur when individuals are intrinsically motivated to perform a certain activity. When the activity is additionally rewarded by an extrinsic gratification this can result in a decreased intrinsic motivation to perform the activity (Lepper et al., 1973) as individuals may reattribute their motivation from intrinsic to extrinsic rewards (Bem, 1972). However, it seems, that overjustification occurs exclusively when the extrinsic reward is expectable beforehand and when a task-contingent (doing the task vs. not is the criteria for receiving the reward) and not a performance-contingent reward is applied (Harackiewicz, Manderlink, & Sansone, 1984). Finally, the experience of a modulated time-perception could also be a factor that hinders or facilitates the experience of flow. In a series of studies, Christandl, Mierke and Peifer (2018) manipulated the subjective time progression by altering the ratio between the announced amount and the actual amount of time to work on a certain task. Participants were given 10 min to work on an anagram task, while half of the participants were told they would have 15 min to work on the task (time-flies condition), and the other half got the information that they would have 5 min (time-drags condition). Participants in the time-flies condition experienced more flow during the task (Study 1, 3 and 4), and also experienced more flow in a subsequent task without time-perception manipulation (Study 4). The studies provide also relevant information on the relation of flow and performance. We will discuss these results in the respective section in the third part of the chapter. So far, these considerations suggesting additional antecedent factors beyond a skills-demands fit for the experience of flow come with empirical evidence only in part, while others stick to a theoretical level. A full empirical evaluation is lacking, but would be enlightening. Experimental tests of these potential antecedent factors would not only provide information on how to foster/hamper the experience of flow, but would also give further clarity concerning the identification of the defining elements of the flow experience.

Perceived Fit of Skills and Task Demands “Above Average” Coming back to the role of the perceived fit of skills and task demands for the experience of flow, some flow researchers proposed revisions to the original flow model, basically driven by empirical findings that seemed incompatible with the original flow channel model (Csikszentmihalyi & Csikszentmihalyi, 1991; Csikszentmihalyi & Rathunde, 1993; Massimini & Carli, 1988; see also Moneta,

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Chap. 2). The basic notion according to which flow is most likely to emerge when individuals perceive a fit of skills and task demands (see the respective Figure depicting the flow channel model in Moneta, Chap. 2) has been qualified. Specifically, the notion was added that a perceived fit of skills and task demands is only likely to result in a flow experience when skills and demands are located above the average level of skills and demands across various activities the individual is engaging in. In line with this specification, a quadrant model (see the respective Figure in Moneta, Chap. 2, cf. Csikszentmihalyi & Csikszentmihalyi, 1991) and an octant model (see the respective Figure in Moneta, Chap. 2; cf. Massimini & Carli, 1988) have been proposed. As depicted in the relevant figures in Chap. 2 (Moneta), both of the revised models differ from the original flow model in that the notion that a perceived fit of skills and task demands (challenge) is associated with flow experiences is substantially qualified. Both models assume that such a perceived fit is not associated with flow (but with apathy) when skills and demands are located below the average level of skills and demands across various activities the individual is engaging in. That is, these revised models introduce a further condition for flow experiences to emerge: perceived skills and demands have to be above the average level of skills and demands an individual experiences across the various activities he or she is engaging in. At first sight, the “above average thesis” and the related models seem plausible. In fact, it seems fairly reasonable to assume that individuals are likely to feel largely apathetic when washing the dishes or in other activities that are low in the perceived skills and in the perceived demands involved in the activity (“low” meaning that perceived skills and demands are lower than those typically experienced in other activities). However, the “above average thesis” and the revised flow models (quadrant and octant model) rest on several assumptions that can be questioned. First, it is questionable whether perceived demands (or “challenges”) and perceived skills can be considered to represent orthogonal (independent) constructs (note that this problem is also relevant regarding the original flow channel model; cf. Pfister, 2002). In our view, individuals have to take the demands of the task into account to arrive at an evaluation of their skills in the task (and vice versa). Note that a demanding task is typically defined as one that requires much skill (similarly, a challenging task is typically defined as one that is testing one’s abilities). That is, evaluating demandingness of a task requires a reference to skills (or abilities), with higher (lower) demandingness implying a higher (lower) level of skill. Stated differently, perceived skills and demands (or challenges) are confounded. Accordingly, we think it is not particularly meaningful to conceptualize the two constructs as orthogonal dimensions. We suggest to replace the “classic” flow channel concept with a uni-dimensional construct reflecting the perceived fit of skills and task demands (which can vary from low to high level; we will outline this idea in detail below). Second, it is an open question whether it is meaningful to compute average levels of perceived skills and perceived demands across the various activities an individual is engaging in. Such a comparison across activities implies that individuals evaluate

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the perceived skills and demands with an absolute standard in mind (e.g., the typical levels of skills and demands they experience when engaging in activities). The computation of an average level is only meaningful if one assumes that the ratings of various activities (e.g., on seven point scales with endpoints labeled “very low” and “very high”) are based on a general or absolute standard that the respondents have in their mind. We cannot be sure that this is actually the case when participants respond to questions regarding perceived skills and demands with respect to different activities. Given that we know from substantive research on survey methodology (cf. Sudman, Bradburn, & Schwarz, 1996) that responses are constructed on the spot and therefore heavily context dependent, it seems not particularly plausible to assume that questions regarding skills and demands levels and the related response scales are interpreted equivalently (i.e., with a general or absolute standard in mind) across activities. Third, the “above average thesis” is also questionable in view of recent experimental findings based on fairly trivial activities (e.g., playing the computer game “Tetris”; Keller & Bless, 2008). These studies show that flow experiences can emerge even in situations where it seems not particularly plausible to assume that the levels of skills and demands involved in the activity (a simple computer game) were “above average.” In sum, we are skeptical regarding the “above average thesis”. We suggest a different type of revision of the original flow channel model (diverging from the quadrant and octant model), which will be presented in the next paragraph.

The Revised Model of Flow Experiences Considering the flow models proposed so far, they do not allow for predictions regarding the intensity of flow experiences that emerge under conditions of a perceived fit of skills and task demands. Accordingly, we think that an extension of the original flow channel model is meaningful. The graphical representation of the revised flow model in Fig. 3.1 reveals that we propose that flow intensity is a function of two factors: We stick to the basic idea that the perceived fit of skills and demands is the essential condition for flow to emerge and suggest the inclusion of a second dimension representing the subjective value assigned to (or perceived in) the relevant activity. Diverging from the original flow channel model, we do not consider perceived demands (or challenges) and perceived skill as orthogonal constructs but simply refer to perceived fit as crucial factor. We suggest to measure perceived fit with questions such as “To what degree did the demands of the task fit with your skills in the task?” rather than to measure perceived demands and perceived skills separately—since the latter method neglects the fact that perceived skills and perceived demands can hardly be considered as independent constructs. According to this revised model of flow, individuals experience a higher intensity of flow under conditions of a perceived fit of skills and task demands the more they are subjectively attached to the activity. For example, a guitar player who loves to

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Fig. 3.1 The revised flow model: Flow intensity as a function of perceived fit of skills and demands and subjective value of the activity. Please see Box 3.2, for a detailed description of the assumed basis for the factor subjective value.

play (i.e., who perceives a large amount of value in guitar playing) experiences a higher intensity of flow under conditions of a perceived fit of skills and task demands than a guitar player who is not so enthusiastically attached to guitar playing (i.e., who perceives a lower amount of value in guitar playing). Based on the revised flow model it is possible to derive predictions regarding the intensity of flow experiences. Flow intensity is implicitly already considered in the literature as flow is often treated as a continuous, rather than a categorical concept. However, this assumption is neither explicitly nor systematically addressed in current flow-models. In fact, flow theory is largely silent regarding the question of how differences in the intensity of flow experiences can be explained. Although the terms “deep flow” and “micro flow” that refer to variations in flow intensity have been briefly discussed by flow theorists (Csikszentmihalyi, 1975, 1992), a systematic theoretical perspective addressing the potential bases of such variations is missing so far. Some flow researchers referred to the absolute level of skills and demands in discussing this question (Percival, Crous, & Schepers, 2003; Privette, 1983) suggesting that flow intensity is a function of the level of skills and demands involved in the activity (such that “deep flow” is possible at high levels of skills and demands relative to some kind of an absolute standard). Given our skepticism regarding the interpretation of respondents’ evaluations of perceived demands and skills across activities, we think it is problematic to answer the intensity question in this way. Instead, we propose that flow intensity is dependent on the subjective value individuals assign to an activity and/or the degree of the perceived fit of skills and demands in a specific situation (which could be higher or lower). To put it differently, the revised model implies the assumption that “deep flow” (i.e., high flow

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intensity) should be possible even in fairly trivial activities (e.g., when a person is setting the table) as long as the person perceives a fit between skills and demands or/and assigns a high level of subjective value to the activity (e.g., a passionate homemaker). It is important to note that we refer to a conceptualization of “subjective value” proposed by Higgins (2006). According to this theoretical account, value is “an experience of strength of motivational force. It is an experience of how intensely one is attracted to or repulsed from something.” (Higgins, 2006, p. 456). Value as motivational force is conceptualized as a result from two basic ingredients: (a) hedonic experience (pleasure/pain properties of the value target) and (b) engagement strength, which can be based on regulatory fit (cf. Higgins, 2000) or the use of proper means (for a detailed discussion of the various potential sources of engagement strength, see Higgins, 2006). Subjective value of an activity can be assessed based on free choice task engagement (higher subjective value is reflected in a stronger tendency to re-engage in a task with strong engagement, such as for a relatively long period of time, c.f., Higgins, Cesario, Hagiwara, Spiegel, & Pittman, 2010). It should be noted that we refer to the subjective value assigned to the activity, not to the consequences of the activity (see also Schiepe-Tiska & Engeser, Chap. 4 and Peifer & Tan, Chap. 8 for a discussion of this issue). That is, deep flow cannot simply be fostered by way of announcing a (material) reward for the successful completion of an activity. Such a reward enhances the subjective value of the consequences of the activity which has to be distinguished from the subjective value of the activity itself. It is also noteworthy that there is reason to assume a bi-directional relation between the value perceived in an activity and the intensity of flow experienced during engagement in the relevant activity. As outlined above, it is plausible to assume that individuals experience flow more intensely the more value they perceive in an activity. However, it is plausible to argue that a reverse causal pathway is possible as well. That is, individuals are likely to perceive more value in an activity the more intensely they experienced flow in previous episodes where they engaged in the relevant activity.

What Determines a Skill-Related Activity’s Subjective Value? Our revision of the original flow model which refers to the subjective value of activities raises the question of what determines an activity’s subjective value. Given that the flow model focuses exclusively on skill-related activities, the question can be focused more specifically on factors that determine the subjective value individuals perceive in the execution of skill-related activities. In addressing this question, we refer to the general notion of regulatory compatibility (cf. Keller & Bless, 2008) defined as the compatibility of person characteristics (e.g., habitual goal orientation, personal needs or standards) and structural settings or environmental characteristics

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(e.g., task framing, availability of distinct means, salience of specific outcomes or incentives). That is, regulatory compatibility can be described as “a phenomenological experience that arises when individuals experience a compatibility of (personal and situational) factors that are involved in performing a task or activity” (Keller & Bless, 2008, p. 197; see Box 3.2). An example illustrates this perspective. For example, starting from regulatory focus theory (Higgins, 2000), research on regulatory fit addresses the compatibility in the manner of goal pursuit (e.g., eager vs. vigilant strategies) and habitual or current regulatory orientations or goal standards (e.g., need for security or need for nurturance; ideals or oughts as relevant standards, gains or losses as relevant outcomes). Regulatory fit thus reflects a specific type of a regulatory compatibility that focuses on goal-related factors in the person and the environment. Regulatory fit has been studied extensively by Higgins and his colleagues as well as other researchers in the field (cf. Keller & Bless, 2006; for a review, see Higgins & Spiegel, 2004). In one exemplary study, Freitas and Higgins (2002, Study 3) activated distinct selfregulatory standards (ideals or oughts) and then asked participants to work on a visual search task that was framed with reference to either eagerness or vigilance. In the case of a regulatory fit (i.e., combining an ideal standard with eagerness framing and an ought standard with vigilance framing), participants reported significantly more task enjoyment than they did in other conditions. Box 3.2 Regulatory Compatibility and Subjective Value of an Activity Regulatory compatibility reflects “a phenomenological experience that arises when individuals experience a compatibility of (personal and situational) factors that are involved in performing a task or activity” (Keller & Bless, 2008). This experience can be based on various types of compatibilities, such as regulatory fit (Higgins, 2000), thematic endogeny (Kruglanski, 1975) or goal congruency (Harackiewicz & Sansone, 1991; please see Chap. 3 of the first version of this book for detailed discussion of these concepts). The flow experience reflects regulatory compatibility as well (compatibility of skills and task demands). Following the ideas proposed by Higgins (2006), we argue that the value assigned to an activity is not only determined by the hedonic quality (pleasure/pain; i.e. the direction of the motivational force) associated with the activity but reflects the repulsion or attraction force of the activity in a broader sense, which is also a function of the motivational force experience associated with the activity. We argue that regulatory compatibility is an important basis for the emergence of a pleasurable hedonic experience with a high level of motivational force. That is, regulatory compatibility can be understood as an important basis for the subjective value assigned to an activity. In addition, we want to highlight the fact that regulatory compatibility may also emerge in the context of skill-related activities in individuals characterized by personality traits that are linked to the execution of skills and competencies.

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Specifically, achievement motivation (McClelland, Atkinson, Clark, & Lowell, 1953), autonomy orientation (Deci & Ryan, 1985), internal locus of control (Rotter, 1966) as well as action orientation (Kuhl, 1994) seem to fit well with the competence aspect of skill-related activities. That is, we suppose that individuals with a strong (a) autonomy orientation, (b) internal locus of control or (c) action orientation are most likely to experience flow (given a perceived fit of skills and task demands) at a particularly high level of intensity based on the fact that these orientations are particularly well compatible with situations that require the execution of skills and competencies. First studies addressing these notions support this perspective (action orientation: Baumann et al., 2016; Keller & Bless, 2008; internal locus of control: Keller & Blomann, 2008). In sum, we propose a revised flow model that explicitly considers the intensity of flow. Implicitly, empirical research has already reflected flow as dimensional, rather than a categorical construct, while flow models have been largely silent about this factor. We suggest that flow intensity is dependent on (a) the degree of the perceived fit between individual skills and task demands and (b) the subjective value assigned to this task. Based on the work of Higgins and colleagues (e.g., Higgins et al., 2010), we suggest that a regulatory compatibility can be understood as an important basis of this subjective value. We suggest, that the adaption of paradigms from regulatory focus research (e.g., the manipulation of the fit between ideal/ought standards with eagerness/vigilance framings; Freitas & Higgins, 2002) can be fruitful to empirically test our proposed model. We would like to emphasize, that we are neither the first, nor the only ones who assume an additional compatibility factor besides the basic fit-perception of skills and demands that is relevant for the experience of flow. In short, Chap. 4 (SchiepeTiska & Engeser) outlines the idea that a perceived fit of skills and demands is only one way to satisfy basic human motives (like achievement, affiliation and power), i.e., in this case an achievement motive. The authors suggest and provide empirical evidence that only those individuals who have a high achievement motive do experience flow when a skills-demands compatibility is given. Further, they suggest that individuals holding a power motive or an affiliation motive should experience flow when the situation holds incentives that have the potential to meet the respective motive. In Chap. 8 (Peifer & Tan) a similar, but even more fine-grained motive concept is proposed to predict the experience of flow. This approach does not only distinguish between basic motives (like achievement, power and affiliation), but also considers the sub-dimensions of “approach—avoidance” and “self-determined— incentive focused” to qualify the dimensions of the autotelic personality. Both approaches contain appealing ideas, like a differentiation of implicit and explicit motives, which implies that some individuals might have a congruence of their implicit and their explicit motives, while others experience an incongruence. Schiepe-Tiska and Engeser (Chap. 4) provide first evidence that individuals with congruent motives are more likely to experience flow. However, the application of projective tests that are typically used to measure implicit motives is considered to be tricky (e.g., Lilienfeld, Wood, & Garb, 2000). One major concern about projective tests is the insufficient documentation of psychometric properties. No doubt, tests

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like the operant motive test (OMT; Kuhl & Scheffer, 1999) or the picture story exercise (PSE; Schultheiss & Pang, 2007) are more structured than the classic thematic apperception test (TAT; Murray, 1943) as they contain standardized task instructions and therefore mark an improvement. However, crucial psychometric aspects of these measurements seem not convincing to us, as commonly used tests to assess implicit motives do not show a convergent validity (Schüler, Brandstätter, Wegner, & Baumann, 2015; please note, this article also contains a discussion on the criterion-related validity of commonly used projective test). Taken together, an additional compatibility factor besides the classic fit-perception of skills and demands is discussed in flow literature. We approach this factor by drawing on a regulatory compatibility/subjective value idea proposed by Higgins (2006) while other scholars refer to the compatibility of implicit motives and the situational incentives to satisfy these motives (see Schiepe-Tiska & Engeser, Chap. 4 and Peifer & Tan, Chap. 8). As already mentioned, none of these approaches is free from critique. With regard to our approach, one could question whether subjective value and perceived fit of skills and demands are orthogonal factors, or whether they can be condensed into an abstract compatibility dimension. Preliminary, we suggest to keep the differentiation of subjective value and skills demands fit until empirical evidence is available to evaluate the validity of the model.

Part 2: Boundary Conditions of Flow As mentioned above, a fit of skills and demands is considered as the basic prerequisite of the experience of flow.1 However, as several findings suggest that this relation is qualified by different factors (Baumann et al., 2016; Engeser & Rheinberg, 2008; Keller & Bless, 2008; Keller & Blomann, 2008; Kocjan & Avsec, 2017), we will discuss this evidence in the following paragraphs. We first focus on the role of personality factors in this context, before we highlight the influence of situational factors.

Personality Factors as Boundary Condition for Flow By referring to the concept autotelic personality, it was postulated that certain personality qualities affect the frequency and intensity with that individuals experience flow (Csikszentmihalyi, Rathunde, & Whalen, 1993; see also Peifer & Tan, Chap. 8). For example, it was assumed that individuals characterized by a high level

As there is no systematic work on the revised flow model (see Fig. 3.1) available to date and consequently no evidence on potential moderators in this model, we limit this discussion on studies referring to the basic flow channel model. 1

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of openness to new challenges might be especially prone to experience flow. While empirical evidence for this specific idea is lacking, a study from Engeser and Rheinberg (2008) provided first evidence for the idea that another personality variable affects the experience of flow. The authors revealed that the assumed quadratic relation between a skills-demands fit and flow (i.e., the highest flowscores were assumed to appear when skills and demands were in balance, while a mismatch of skills and demands was expected to result in less flow) was limited to individuals characterized by a low level of fear of failure. The concept fear of failure describes a habitual tendency to avoid failure in achievement settings, as failure is maladaptively associated with shame for those individuals (Elliot & Thrash, 2004). In their study, individuals reporting a high level of fear of failure experienced more flow when their skills exceeded the demands (Engeser & Rheinberg, 2008), highlighting the rationale that the relation between skills-demands fit and flow is not deterministic, but rather dependent on specific personality attributes. In addition, individuals with a strong internal locus of control were found to experience enjoyment and involvement (i.e., central aspects of the flow experience) when skills and demands were in balance, while individuals with a weak internal locus of controls did not enter this state (Keller & Blomann, 2008). Locus of control describes the belief of how individuals’ effort and work affect their experienced outcome. A person who is rather convinced that his/her action barely affects experiential outcomes holds an external locus of control. However, holding an internal locus of control, an individual typically perceives that outcomes are contingent upon his/her action (Rotter, 1966) which was associated with an enhanced tendency to experience flow (Keller & Blomann, 2008). Further evidence suggests that action
/state-orientation also moderates the relation between skills-demands fit and the experience of flow. Action-state orientation (particularly the volatility-persistence component of action-orientation) describes individuals’ tendency to maintain the focus on a task and to stay engaged in a task until it is completed (Diefendorff, Hall, Lord, & Strean, 2000). A high level of action-orientation was associated with a tendency to experience enjoyment and involvement under a skills-demands fit, while individuals with a low level of action-orientation did not enter the state of flow (Keller & Bless, 2008). The important role of an action-orientation for the experience of flow was demonstrated in another more current study (Baumann et al., 2016). In an experimental flow-study three different conditions were established, all aiming for a fit of skills and demands in a computer-game. In the balance-condition a constant fit of skills and demands was realized. In the dynamic-medium-condition the pace of the game was increasing from a less demanding to a more demanding fit of skills and demands, and the game was interrupted by two short breaks. In the dynamic-highcondition the pace was hold at the maximum level, which was believed to still result in a fit of skills and demands. In this condition three short breaks were established. The authors hypothesized that a slight overload (i.e., the dynamic-high-condition) and a fluctuation of demands (i.e., the dynamic-medium-condition) result in more flow than a constant fit and found supporting evidence. Participants in the dynamic-high-condition reported the highest flow-scores, while participants in the

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dynamic-medium-condition reported the highest enjoyment scores. In contrast to the dynamic conditions, the balance-condition was interpreted as less optimal for experiencing flow and enjoyment. Interestingly, when individuals were holding a habitual action-orientation, they experienced flow regardless under which condition the game was played (Baumann et al., 2016). This result supports the view that action-orientation is a central personality factor that fosters the experience of flow, even under suboptimal conditions. In addition, the authors identified sensation seeking as another personality variable influencing the experience of flow. High (vs. low) sensation seekers experienced more flow in the dynamic-high-condition and showed less flow in the balance-condition. Possibly, the context of the dynamichigh-condition satisfied their urge for stimulation to a greater extent than the balance-condition.

Situational Factors as Boundary Conditions for Flow It seems plausible to assume that besides personality factors also situational aspects beyond a skills-demands fit could have an impact on the experience of flow. One aspect that could function as a situational boundary condition of flow is the perceived importance of an activity (see also Abuhamdeh, Chap. 5). Engeser and Rheinberg (2008) hypothesized that a fit of skills and demands should facilitate the experience of flow exclusively when the task is perceived as rather unimportant (e.g., playing a computer game could be interpreted as rather unimportant as typically little is at stake; cf. Tozman & Peifer, 2016). The authors suggested that when an activity is perceived as important, flow experience should be facilitated under conditions where skills exceed situational demands. Following this reasoning, preparing for an exam could be interpreted as rather important as this has a long-term impact (i.e., failing an exam forces students to repeat the exam, while passing an exam enables individuals to continue their curriculum). Engeser and Rheinberg (2008) found empirical support for their idea: Relatively high flow-scores were obtained when skills and demands were in balance, but only when the importance2 of the activity was evaluated as relatively low. However, and in line with their hypothesis, when the perceived importance was relatively high, highest flow-scores were reported when skills exceeded demands. Further evidence that the importance of an activity plays a role in experiencing flow was provided by a current ESM-study (Engeser & Baumann, 2016) which revealed that importance of an activity can partially explain differences in flow experience between work- and leisure-contexts.

The items “something important to me is at stake here”, “I won’t make any mistakes here”, and “I am worried about failing” were used to measure importance (Engeser & Baumann 2016; Engeser & Rheinberg 2008). Focusing on the negative consequences of the activity, the concept of ‘importance’ can be clearly distinguished from the concept ‘subjective value’ (i.e., the value a person attributes to the activity per se) that we introduced in the first part of this chapter. 2

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There is one other study that examined the influence of situational aspects beyond a perceived fit of skills and demands on the experience of flow. In this study,3 students rated their flow experience after participating in a 90 min long group-work (Kocjan & Avsec, 2017; Study 2). Afterwards, they filled out a Big 5 inventory and indicated how they perceived the situation, i.e., the group-work, with regard to the “situational 8 DIAMONDS: Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception and Sociability”. Rauthmann et al. (2014) proposed this taxonomy in order to measure so-called situation characteristics (i.e., psychologically meaningful aspects of situations). Intellect, for example, captures the extent to which a situation is cognitively demanding, entails deep reflection and enables to show intellectual abilities. When flow was regressed on the Big 5 personality traits and the 8 DIAMONDS in a hierarchical regression, only the predictors pOsitivity and Intellect significantly explained variance in the experience of flow. However, these results must be treated with caution: The dimension pOsitivity is conceptionally extremely similar to the enjoyment/involvement element of flow. It seems not surprising that a scale comprising items like “the situation is enjoyable” and “the situation is playful” shows strong relations to flow, especially when the characteristics of the situation and the experience of the person are rated by the person involved in the activity (see Rauthmann, Sherman, & Funder, 2015, for a detailed discussion of this latter issue). One possibility to circumvent this problem in subsequent studies could be a research design that allows to gather information about situation characteristics not only by an in situ rater, but also from an ex situ rater who rates the situation characteristics on basis of written descriptions or videos of the situation. This approach would allow to clearly distinguish between the perceived potential of a situation to entail e.g., enjoyment (i.e., situation characteristic) and the actually experienced enjoyment (i.e., flow experience). In summary, it is possible that no effect of personality traits on flow was found in the present study, because the dimension pOsitivity accounted (due to its measurement) for an excessive amount of variance in flow. Even though the validity of the presented study is limited in this respect, the main idea to use a standardized framework to examine the influence of situation characteristics on the experience of flow is quite innovative and should be explored in further studies.

3 The article from Kocjan and Avsec (2017) contained another study examining the relation between situational characteristics and the experience of flow. However, the assessment of flow was applied in a retrospective manner, therefore we do not discuss the study here. Problems concerning this approach of measuring flow are discussed in the introduction of this chapter.

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Fig. 3.2 Preconditions, boundary conditions, components, and consequences of the flow experience

Part 3: Consequences of Flow and a Skills-Demands Compatibility We will now discuss possible consequences of flow experiences. On the one hand, a positive effect of flow experiences on performance is postulated (cf. Engeser & Rheinberg, 2008). On the other hand, flow should have an effect on affective, cognitive as well as physiological factors (see Fig. 3.2). Since the physiological consequences of flow are covered in Chap. 8 (Peifer & Tan), they will not be discussed here. Taking a close look into the literature on flow reveals that many studies do not investigate correlates or consequences of flow experience but those of a skills-demands-compatibility - which is its curial precondition. Further, there is a cognizable trend to equalize the precondition of flow with the experience itself (e.g., Csikszentmihalyi & LeFevre, 1989; Hektner & Csikszentmihalyi, 1996; Heo, Lee, Pedersen, & McCormick, 2010; Ilies et al., 2017; Nakamura, 1988; Wells, 1988). In their empirical work, these authors measure perceived challenges and skills and infer that participants experience flow in case both are above the individuals’ mean and in balance (please note that the first part of this chapter contains a critical discussion of this “above average” perspective). This is problematic as the association between the precondition of flow and the experience itself is not deterministic (for further discussion see Keller & Landhäußer, 2011; Rheinberg & Engeser, 2018) and moderated by situational (Moneta & Csikszentmihalyi, 1996) as well as personality factors (Baumann et al., 2016; Engeser & Rheinberg, 2008; Keller & Bless, 2008;

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Keller & Blomann, 2008). Consequently, a measure of skills-demands-balance should not be used (or interpreted) as a measure of the flow experience per se. As depicted in Fig. 3.2. when discussing possible consequences of flow experiences, it is essential to differentiate clearly between consequences of a specific skillsdemands combination and consequences of the flow experience itself. For example, if a skills-demands fit leads to positive mood this could also be due to a feeling of self-efficacy and cannot automatically be attributed to the flow experience that typically emerges under conditions of a skills-demands-compatibility. We will therefore explicitly indicate if the presented evidence can be interpreted as a consequence of flow or as a consequence of skills-demands compatibility.

Affective Consequences of Flow and a Skills-Demands Compatibility It appears intuitively plausible to assume that an experience so enjoyable as the flow experience should lead to positive affect and happiness. Csikszentmihalyi (1999) for example concluded that his studies “have suggested that happiness depends on whether a person is able to derive flow from whatever he or she does” (pp. 824f) and even goes as far as to term flow “the bottom line of existence (because) without it there would be little purpose in living” (Csikszentmihalyi, 1982, p. 13). He states that happiness is derived from personal development and growth—and flow situations (i.e., situations in which we are confronted with demands that can be handled) permit the experience of personal development. The feeling of progress should lead to positive affect after an experience of flow but also in the long run (Csikszentmihalyi, 1990). As Moneta (2004) wrote, additional to the postulated direct effect of flow on happiness, an indirect effect on general subjective wellbeing (see Box 3.3) is assumed: [F]low theory states that flow has an (. . .) indirect effect on subjective well-being by fostering the motivation to face and master increasingly difficult tasks, thus promoting lifelong organismic growth. In particular, flow theory states that the frequency and intensity of flow in everyday life pinpoint the extent to which a person achieves sustained happiness through deliberate striving, and ultimately fulfills his or her growth potential (p. 116).

Box 3.3 Subjective Well-Being Subjective well-being comprises an affective as well as a cognitive component. Whereas pleasant and unpleasant affective states constitute the affective component, the cognitive component is life satisfaction (Pavot & Diener, 1993). Life satisfaction refers to a cognitive judgmental process and can be defined as “a global assessment of a person’s quality of life according to his chosen criteria” (Shin & Johnson, 1978).

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Thus, depending on frequency and intensity of the experience, flow should have a positive impact on affective states as well as life satisfaction, which would also correspond with the fact that a positive influence of intrinsic motivation on wellbeing has been documented (Sheldon, Ryan, Deci, & Kasser, 2004; see also Engeser et al., Chap. 1). However, a confusion of flow and happiness—as reflected in Csikszentmihalyi’s (1999) description of flow as a “dimension of happiness” (p. 821)—in our opinion should rather be avoided since flow and positive affect are conceptually distinct states (cf. Engeser et al., Chap. 1). Surely, flow experiences are enjoyable and therefore positive. However, activity-specific enjoyment (i.e., one enjoys doing something) is not the same as the global state of happiness. In line with this consideration, in a representative sample in Germany only 17% of the respondents agreed with the statement “being completely absorbed by something and forgetting everything around” as their personal interpretation of happiness (Identity Foundation, 2002). Indeed, enjoyment of an activity can make one happy. But then, happiness is a consequence of a flow experience and not a component. More so, individuals may not reflect on their affective state while in flow. As Csikszentmihalyi (1999) stated: “[D]uring the experience people are not necessarily happy because they are too involved in the task (. . .) to reflect on their subjective states” (p. 825). Nonetheless, the enjoyment of the activity as well as the feeling of personal progress may result in a positive affective state. We first take a look at correlational findings examining the proposed relationship between flow and positive affect. As expected, authors measuring the flow experience itself found positive relationships with positive affect (Fullagar & Kelloway, 2009; Rheinberg, Manig, Kliegl, Engeser, & Vollmeyer, 2007), even when former affect was controlled for (Schüler, 2007). Further, Engeser and Baumann (2016) reported flow to be positively related to valence and positive activation. Negative activation showed a negative relation to flow.4 The idea that flow might lead to a more positive valance in a subsequent situation was not supported when controlling for previous valance (ruling out stability effects) and flow at the same time (ruling out intercorrelations of flow and affect). However, this result is not particularly surprising as valance was measured approximately 2 h after a preceding flowepisode and might have been influenced by other factors than flow. In general, a similar pattern emerges when looking at studies that report on correlations between skills-demands-compatibilities and positive affect. With some exceptions (e.g., Nakamura, 1988), studies using the ESM found significant associations between being in the flow quadrant or octant (challenges and skills above average) and experiencing positive affect (Clarke & Haworth, 1994; Csikszentmihalyi & LeFevre, 1989; Ilies et al., 2017; Massimini & Carli, 1988;

4

The following bipolar items were used to measured valance: unhappy—happy and unsatisfied— satisfied, positive activation: shiftless—energetic, tired—wide awake, bored—elated, dull—highly motivated, and negative activation: relaxed—stressed, untroubled—annoyed, calm—nervous, secure—worried (Engeser & Baumann 2016).

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Schallberger & Pfister, 2001; Shernoff, Csikszentmihalyi, Schneider, & Shernoff, 2003). However, results of a study by Csikszentmihalyi and Rathunde (1993) suggest that the positive relationship between flow conditions and affect does not hold for every type of activity. They analyzed 20 types of activities their adolescent participants had reported in an ESM-study and found that only in seven of them happiness was significantly higher in the flow quadrant than in the other quadrants. When doing homework or studying for an exam, participants tended to be happiest when skills were high and challenges low (i.e., when being in the boredom-quadrant). This suggests that at least the relationship between skills-demands-compatibility and positive affect does not hold for every type of activity. The results is also well in line with findings from Engeser and Rheinberg (2008) who show that in actives that are perceived as rather important such as preparing for an exam, a slight dominance of skills over demands seems to be more flow inducing than a straight fit. To our knowledge, there are two studies that tested the causal relationship between a skills-demands-compatibility and affect. While a first study did not find a significant effect of a skills-demands fit manipulation on affect within the game Tetris (Keller, Bless, & Blomann, 2011; experiment 2), a second study which was also using the game Tetris found higher positive affect ratings when skills and demands were in balance compared to two imbalance conditions (Harmat et al., 2015). In both studies, participants in the skills-demands fit condition expectedly reported higher flow-scores than participants in non-fit conditions, indicating that both studies were designed in a way which enabled the experience of flow. Keller, Bless, Blomann, and Kleinböhl (2011) used a between-design while Harmat et al. (2015) applied a within-design for the three experimental conditions (i.e., boredom, fit and overload), resulting in a higher statistical power for the latter study, which might be one reason for the different results. However, to draw final conclusions, further experimental analyses considering different task characteristics and personality factors as potential moderators of the affect-flow/skills-demands fit relation are necessary. Taken together, empirical evidence indicates that both flow conditions and flow experiences coincide with positive affect under many circumstances (cf. Abuhamdeh, Chap. 6; Peifer & Engeser, Chap. 16). When individuals experience flow in a situation, they also tend to be happy afterwards. The same holds for the experience of a fit between high demands and skills. However, these relationships are possibly moderated by situational and personal factors that should be disclosed and analyzed in future research. In consideration of the fact that there seems to be mutual consent with respect to the notion that positive affective states and even life satisfaction are consequences of flow experiences, researchers should put more effort in the examination of the causal relationship to back up their assumption by empirical results. Schüler (2007) did a first step in this direction by using a longitudinal design and controlling for former affect and thus ruling out the possibility that the relationship is driven by a reverse effect (i.e., positive affect makes flow experiences more likely). Another appropriate way would be to test the relationship between a skills-demands-compatibility and positive affect in different

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experimental paradigms, examining a possible mediation of the skills-demandscompatibility effect on affect via experienced flow.

Cognitive Consequences of Flow and a Skills-Demands Compatibility Flow as a specific state of consciousness may also trigger particular cognitive states and mechanisms, that is, it could have an influence on cognitive capacity and processing styles, at least directly after the experience (or even in the long term). Yet, there are only a few studies examining cognitive consequences of flow experiences. We will discuss these studies in the following and present additional theoretical ideas about how flow affects cognitive processes.

Cognitive Capacity Deep concentration is a distinct attribute of the flow experience which may transfer to tasks and situations following a flow experience (e.g., Christandl et al., 2018). An individual who is engaging in a task in a deeply concentrated mode may maintain this working style even when the flow experience is over. Based on the rationale that a repeated activation of a cognitive strategy in situation A should foster its accessibility in a subsequent situation B (cf., Higgins, 1996) frequent flow experiences (and, thus, frequent episodes involving a deep concentration on a task) could enhance the likelihood to adapt this concentrated information processing during an episode following a flow state. However, besides this theoretical reasoning, no empirical evidence is available for this rationale. As one aspect of flow is a reduced self-consciousness, individuals experiencing flow could have more self-regulatory resources available in a successive situation than individuals not in flow. That is, such individuals could be less depleted than individuals in non-flow (cf. Baumeister, Bratslavsky, Muraven, & Tice, 1998). First evidence for this idea comes from a study which found that flow (especially enjoyment) during work was associated with vigor and low exhaustion after work and at the end of the day (Demerouti, Bakker, Sonnentag, & Fullagar, 2012). Another study in this context stresses a possible reciprocal relation between flow and recovery (Debus, Sonnentag, Deutsch, & Nussbeck, 2014). Among software professionals, those who reported to feel recovered in the morning, more flow was reported during the day. More specifically, not being recovered was associated with a low and decreasing level of flow over the course of a working day, while feeling recovered was related to flow experience that followed an U-shape: Relatively high flow scores were reported at the beginning and at the end of a working day, with a minimum of flow after lunch.

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Processing Styles Based on the assumption that flow experiences are characterized by a focusing of attention (that is, a narrowed focus on the details of the current task, rendering other information less important), one may assume that individuals in a flow state—and probably after, as well—adopt a processing orientation characterized by a focus on details, reflecting a “tunnel vision”. Following the idea that processing orientation (i.e., global vs. local processing) could be manipulated and transferred from task to task (e.g., Macrae & Lewis, 2002) one could expect that flow experiences may foster bottom-up processing strategies. However, if flow experiences indeed put individuals into a positive mood state (as described above), they also could foster top-down processing strategies. Note that a substantial amount of research indicates that positive mood states influence cognitive processing styles in a way that heuristic processing strategies (based on general knowledge structures) dominate individuals’ information processing and judgments (e.g., Bless et al., 1996; Chartrand, van Baaren, & Bargh, 2006; Huntsinger, Clore, & Bar-Anan, 2010). Insofar as individuals after the experience of flow are indeed in a happy mood, flow should have an indirect effect on processing styles in such a manner that individuals after the experience of flow should tend to rely on top-down processing strategies. That is, there are contradictory hypotheses regarding the influence of flow experiences on processing styles. There is only one single study analyzing cognitive consequences of flow experiences we know about. This experimental study found a significant relationship between a flow manipulation (i.e., skills-demands-compatibility in a computer task) and degree of clustering in a free recall task which is an indicator for level of processing (Keller, Bless, & Blomann, 2011). After a flow manipulation by means of a computerized knowledge task, participants were asked to learn 16 words (four words selected from each of the categories plants, furniture, animals, and vehicles) and had to recall as many words as possible after a delay of 5 min. The authors assessed the degree to which the recalled information was clustered based on the categories the words were selected from. The degree of clustering served as an indicator of how much participants encoded and recalled the presented pieces of information referring to higher-order categories (versus a lower level of abstraction; e.g., Hamilton, Katz, & Leirer, 1980). Participants in the flow condition showed a significantly lower level of clustering relative to their counterparts in two non-adaptive conditions (boredom and overload). This finding indicates that working under skills-demands-compatibility may trigger narrow, low-level categorization processes (reflecting a “tunnel” vision perspective as proposed by flow theory). As one can see, elaborated analyses of the cognitive consequences of flow experiences could generate interesting findings, and different mechanisms are possible. As there is almost no research available in this field until now, we hope that research on cognitive consequences of flow will be more in the focus of attention in the next generation of flow research.

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Fig. 3.3 Assumed direct and indirect relation of flow and performance

Flow and Performance Since the beginning of flow research, a close relationship between flow experiences and performance has been postulated. This association has two plausible reasons. First of all, flow is characterized by high concentration and a sense of control, which are facilitators of performance (Eklund, 1996). As such, flow is a highly functional state and should result in better performance by itself. Second, flow could be seen as a motivating force for excellence (Engeser & Rheinberg, 2008). As the flow state is experienced as intrinsically rewarding, individuals seek to replicate flow experiences. This introduces a selective mechanism into psychological functioning that fosters personal growth. People develop greater levels of skills whenever they master challenges in an activity. To maintain the level of demands that fosters flow experiences, they must engage progressively in more complex tasks. Therefore, flow experiences imply a growth principle, whereby more complex demands are sought after and more complex abilities are likely to develop (Csikszentmihalyi, 1975; Nakamura & Csikszentmihalyi, 2009; Shernoff et al., 2003). That is, individuals who tend to experience flow in a special set of activities should be motivated to engage in those activities and therefore gain expertise, at least in the long run. Thus, flow should have a direct as well as an indirect effect on performance, which are both depicted in Fig. 3.3. However, a reciprocal relationship has to be assumed between flow and performance. Consequently, in a correlational study, it remains unclear whether flow leads to a better performance or a good performance makes flow experiences more probable. The central precondition of flow experiences is a perceived fit between skills and demands. But such a fit should only be perceived in case the individual has the competence to deal with the demands of the situations. And obviously, an association between competence and performance can be postulated. In other words: In a correlational design perceiving a fit between skills and demands can hardly be detangled from perceiving oneself as competent and thus, increasing the likelihood of performing well on this task. To show positive correlations between skills-demands-compatibility and performance seems even more trivial because the specific skills-demands-constellation has

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a built-in effect on performance—independent of the flow experience. When flow is operationalized as “challenges and skills above average”, the independent and the dependent variables are confounded as high skills (above average) make good performance quite likely. For this reason, only studies measuring components of the flow experience itself (instead of challenges and skills) will be discussed in the following. Reported results were obtained in diverse areas, such as academics, music, sports and computer games.

Academic Performance In the academic context correlational studies indeed found significant associations between flow experiences and performance. Schüler (2007) for example conducted a study with students of a psychology course and found a positive relation between flow experiences in a typical learning situation and final grades. However, as former performance was not measured, one cannot draw conclusions about the direction of the relationship. Engeser and Rheinberg (2008; see also Engeser, Rheinberg, Vollmeyer, & Bischoff, 2005) report on two studies in which they confronted this problem. In a first study, students in a voluntary French course rated their actual flow experiences after 60 min of class time at two points during the semester. These ratings correlated significantly with self-assessed learning progress after class as well as with the final marks which were based on oral participation and results of the final exam. In a second study, more than 250 psychology students reported on their level of flow experiences whilst working on a statistical task 1 week prior to the final statistics exam. Again, a positive relationship between flow and final grades was found. Moreover, the effect of flow on grades (both in the French course and in the statistics course) was small but remained significant when previous knowledge was controlled for. Thus, the authors conclude that “flow can be seen as a predictor of performance rather than just being part of high performance” (Engeser & Rheinberg, 2008, p. 161). Demerouti (2006) investigated the effect of flow experiences on performance in the work context and found first evidence that the association between flow and performance may be moderated by personality characteristics. Employees in ten companies completed the work-related flow scale (WOLF; Bakker, 2008). Their job performance was rated by participants’ colleagues. Whereas flow at work did not significantly correlate with peer-ratings of job performance, an interaction term between flow and conscientiousness did. Participants who had high scores on conscientiousness and flow experiences at work achieved the highest ratings regarding in-role as well as extra-role performance (see Peifer & Wolters, Chap. 11, for a detailed analysis of flow at work and Baumann, Chap. 9 for an analysis of flow and personality). In the field of music, there is first evidence that flow experiences and creativity in group-composition go hand in hand (MacDonald, Byrne, & Carlton, 2006). Students had to meet in groups of three to work on group compositions and were asked to report on flow experiences every time they met. The creativity of their compositions

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was rated by lecturers and postgraduates, and interestingly a significant correlation between group levels of flow and rated creativity emerged, suggesting that skilled music students tend to experience flow and/or flow experiences lead to creative compositions (see Harmat, de Manzano & Ullén, Chap. 14, for a detailed analysis of flow in music and arts). A hint, suggesting that flow is associated with performance in teams, comes from a study investing the experience of students during a project management simulation (Aubé, Brunelle, & Rousseau, 2014). Meeting in teams of four to six, students were instructed to build a scale model of a goods vehicle. After 6½ h, the vehicle had to be able to successfully travel two routes varying in difficulty. Team performance, which was measured on a scale from 1 ¼ the vehicle did not start to 6 ¼ the vehicle completed two routes, was predicted by flow experienced in the team during the construction. Interestingly, members’ commitment to the team goal fully mediated this path supporting the idea that flow has an indirect impact on performance via motivation and engagement (see the indirect path in Fig. 3.3). In the context of flow in teams, the finding that information exchange moderated the relation between flow and team performance is especially interesting: In teams that reported a high (vs. low) information exchange the positive relation between flow and performance was stronger.

Performance in Sports An area where the relationship between flow and performance is frequently assumed is the domain of sport. In this context, the flow experience is often related to the concept of peak performance (cf., Jackson & Roberts, 1992; McInman & Grove, 1991). Most flow studies in the context of sports and performance have limitations as some studies did not assess flow as state, but used a retrospective approach (e.g., Jackson & Roberts, 1992) and others measured perceived success in competition (e.g., Jackson, Kimiecik, Ford, & Marsh, 1998) or satisfaction with performance (e.g., Stein, Kimiecik, Daniels, & Jackson, 1995) instead of capturing performance per se. However, there are some exceptions: Jackson, Thomas, Marsh, and Smethurst (2001) let participants rate their flow experience directly after a competition and found a small but significant relation between flow experiences and finishing position. Further, soccer players’ performance was associated with flow experience during the game (Bakker, Oerlemans, Demerouti, Slot, & Ali, 2011). Interestingly, this relation was found both when performance was rated by the soccer players and by the coach (limiting the possibility that a common-method bias accounts for the relation). In addition, social support from the coach and performance feedback for the players were related with more flow experience, which in turn partially mediated the relation between social support/performance feedback and performance. In marathon races, no relationship between flow experience and performance in the race (i.e., running time) was found by Stoll and Lau (2005) as well as Schüler and Brunner (2009). Yet, the latter showed that flow during the training fostered pre-race

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training behavior which again predicted race performance. This provides further evidence for an indirect effect of flow on performance, mediated by motivation to exercise (see Chap. 14, for a detailed analysis of flow in sports).

Performance in Experimental Studies The few experimental flow studies that involve the measurement of performance, found mixed evidence for a flow and performance relation. Engeser and Rheinberg (2008) who instructed their participants to play “Pacman” at different difficulty levels found that flow experiences at medium difficulty level (flow condition) explained a small amount of the variance of the performance in this playing mode (when controlling for baseline performance). Schiefele and Roussakis (2006; using the game Roboguard) as well as Keller and colleagues (Keller & Bless, 2008; Keller & Blomann, 2008; using the game Tetris) did not find an association between flow experiences and performance when controlling for the different playing modes. The differences in results may be due to different measures of flow experiences. Whilst the Flow Short Scale used by Engeser and Rheinberg (2008) included sense of control and smooth action, which can be presumed to be facilitators of performance, the flow measures applied in the other studies concentrated on other components of the experience (as involvement and enjoyment). Therefore, it would be helpful to clarify which components of the flow experience yield a positive effect on performance and which components do not play an important role in this context. Christandl and colleagues (2018) provided the most recent study to understand the influence of flow on performance by examining spillover effects between two subsequent tasks. As outlined in part 1 of this chapter, the authors examined the influence of subjective time progression on the experience of flow across four studies. Focusing on performance, they found that perceiving time to fly in task was associated with a better performance in a subsequent similar task (Study 2, 3 and 4). In Study 3 and 4, the authors were able to show that recalled flow in the first task partially mediated the effect on performance in the second task, while several alternative mediators were controlled for. Hence, these results provide supporting evidence for a causal positive relation between flow and performance.

Towards a Better Understanding of the Relationship Between Flow and Performance As we have seen, even in correlational studies, evidence regarding better performance in flow situations is mixed. Flow experiences and performance seem to go hand in hand, at least during music composition, in sports and in learning settings, but the association probably is a reciprocal one and studies using a longitudinal design, which also controls for prior performance (Engeser et al., 2005; Engeser & Rheinberg, 2008) suggest that the causal effect of flow experiences on performance is, if existent, of small magnitude. Therefore, when evaluating correlational data in a

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cross-sectional design, one should consider that a positive association may be basically driven by the influence of good performance on flow experiences, and not the other way around. Besides, the association does not hold for every kind of activity. While it has been observed in some activities (academics, music, sports), there was no correlation between flow and performance observable in participants playing different computer games (Keller & Bless, 2008; Keller & Blomann, 2008; Schiefele & Roussakis, 2006). It is possible that the relationship only holds for activities that are perceived as important (cf. Engeser & Rheinberg, 2008). Especially regarding meaningful activities, such as learning statistics as a psychology student, flow should have an indirect effect on performance, mediated by enhanced exercising behavior. This is what Schüler and Brunner (2009) found for marathon runners. Also, other researchers (Delle Fave & Bassi, 2000; Nakamura, 1988; Shernoff et al., 2003) note that flow experiences may influence learning behavior in high school students and Lee (2005) found a substantial negative correlation between flow in learning situations and procrastination. But as all those studies are correlational in nature, the data do not suit for conclusions regarding the direction of the relationship. However, an indirect effect of flow experiences on performance, mediated by motivation to exercise, seems very likely (cf. Aubé et al., 2014). Considering the implications for practice (e.g., organizing learning environments in a way that fosters flow experiences; see Shernoff et al., 2003), further longitudinal studies should examine this proposed mediation to come to a better understanding of the relationship between flow and performance. It has to be noted that even the classic experimental paradigms that have been developed in flow research cannot test for causality regarding performance as flow usually is induced by a manipulation of task difficulty. Therefore, the best strategy to test for a causal relationship between flow and performance seems to be a longitudinal design and the usage of promising variations of experimental flow paradigms (e.g., Christandl et al., 2018).

Summary and Conclusion The first part of the present chapter was devoted to the question of what builds the basis for flow experiences to emerge and what may determine the of flow experiences. First, we addressed the antecedents of flow and highlighted the fact that the emergence of flow is basically dependent on a perceived fit of skills and task demands. By regarding the specific components of flow experience, we then identified additional situational factors beyond a perceived fit of skills and demands that could be relevant for the experience of flow. Then, we critically discussed the “above average” perspective and the related quadrant and octant models of flow. We argue that the “above average” notion is based on assumptions that seem quite problematic. We further addressed determinants of flow intensity that have not been systematically discussed so far. In this context, we propose a revised flow model which

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builds on the original notion of perceived fit of skills and task demands and includes the value attributed to the relevant activity as additional crucial factor. In this regard, we highlighted the concept of regulatory compatibility as important theoretical construct in the analysis of the determinants of flow intensity. In the second part of the chapter, we focused on boundary conditions of the flow experience. Here, we presented empirical evidence suggesting the so called autotelic personality (which is predisposed to experience flow) is characterized by a low level of fear of failure, a strong internal locus of control and a habitual action-orientation. With a side-glance to more distal situational aspects that might qualify the experience of flow (beyond a fit of skills and demands) we reported empirical evidence revealing that the perceived importance of an activity influences flow experience. Further, we presented a promising taxonomy that allows the examination of psychologically meaningful aspects of situations and their impact on the flow experience. In the third part of the chapter, we analyzed the empirical evidence regarding affective, cognitive and performance-related consequences of flow. The current literature suggests that flow and a skills-demands compatibility coincide with increased positive affect. However, most of the evidence arises from cross-sectional correlational studies while longitudinal and experimental results are scarce, which does not put us in a strong position to draw final inferences about the flow-affect relation. Regarding the cognitive consequence of flow, there is much to be done as almost no empirical evidence is available. However, first studies suggest a bidirectional relation between flow and recovery. The relation between flow and performance has been examined in several contexts (e.g., in academics, music and sports). Also between these two constructs a bidirectional relation is likely. Longitudinal studies and recent experimental evidence suggest that the causal effect of flow experiences on performance seems to be small, but present. Hence, associations in cross-sectional correlational studies may basically reflect the influence of good performance on flow experiences, and not the other way around. From these deliberations it becomes apparent that many questions in flow research are still unsolved. With this chapter we aimed to introduce the reader to our view on the current flow literature from which several research intentions can be derived. We hope to encourage others to contribute to the process to close these gaps in research step by step. From our point of view, this process should especially focus on the following aspects: (1) The proposed revised flow model, presented in the first part of the chapter, is based on an elaborated theoretical foundation. However, an empirical test analyzing the critical role an activity’s subjective value for the experience of flow remains to be done. (2) As there is increasing evidence that personality traits function as boundary condition for the experience of flow in terms of an autotelic personality, studies should evaluate further personality concepts that might be critical for the experience of flow. (3) Promising frameworks like the 8 DIAMONDS (Rauthmann et al., 2014) are now available in order to study the influence of psychologically meaningful aspects of situations on experience and behavior. These new approaches should also be used to examine the experience of

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flow with the aim to identify situational boundary conditions beyond a skillsdemands compatibility. As repeatedly mentioned in the chapter, the experimental analysis of flow is rare (an exception is the line of research focusing on the physiological correlates o flow experience; see Chap. 7). Therefore, we strongly encourage researchers to apply experimental designs to analyze flow, which allows drawing causal inferences about this state. However, even an experimental analysis of flow is tricky sometimes, due to the hazard to differentiate between consequences of flow and consequences of a skills-demands compatibility. The application of mediation analysis might provide a remedy, as it can provide information, whether the effect of a skills-demands compatibility on certain criteria (e.g., affective consequences) is caused via the experience of flow or not. In addition, in cases where classic experimental designs are not suitable (e.g., when analyzing the performance-related consequences of flow), appropriate longitudinal designs should be applied.

Study Questions • Describe the antecedents of flow experiences proposed in flow theory and how these factors are conceptually linked to each other. According to flow theory, a state of flow emerges when three antecedents are met: (1) clear goals in the sense of clear task instructions, (2) immediate, unambiguous feedback reflecting diagnostic information regarding one’s progress or success in executing the activity, and (3) a balance of perceived skills and perceived task demands. Antecedents (1) and (2) can be considered to be incorporated in antecedent (3) because individuals can only arrive at a meaningful evaluation of their skills and the task demands to the degree that they (a) understand the nature of the task (based on clear task instructions) and (b) can diagnose whether they are successful in their task execution or not. • Explain the “above average” thesis introduced by the proponents of the quadrant and octant model of flow. Discuss the problematic assumptions that are entailed in the “above average” thesis. The “above average” thesis holds that individuals can only enter a state of flow when the perceived level of skills and task demands is above the average level across various activities the individual is engaging in. This thesis can be considered as problematic for three main reasons: (1) It is questionable whether perceived demands (or “challenges”) and perceived skills can be considered to represent orthogonal (independent) constructs. It is evident that individuals have to take the demands of the task into account to arrive at an evaluation of their skills in the task (and vice versa) and accordingly measuring perceived skills and demands separately and considering the constrictions as orthogonal in nature seems not particularly meaningful. (2) Comparing the evaluations of skills and task demands involved in different activities (e.g. washing the dishes and playing chess) would only be meaningful if respondents had in mind an absolute

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comparison standard when editing their responses (such as measuring the length of a table and comparing the resulting value with the value obtained when measuring the length of a bed is only meaningful when both measurements refer to the same measurement standard). Such a standard is typically not available when individuals evaluate the skills and task demands of different activities they are engaging in. Individuals construe their evaluations of skills and task demands on the spot and it is highly likely that evaluations of skills and task demands involved in one and the same activity vary substantially depending on contextual factors. (3) If the “above average” thesis was correct, individuals should not be able to experience flow when they engage in activities that are not particularly demanding (such as playing a trivial board game such as Ludo) where skills and demands are definitely not “above average.” Empirical studies based on fairly trivial activities are not consistent with this perspective because individuals were found to enter a state of flow even under conditions where skills and demands were most likely clearly “below average.” • Specify the revised flow model and exemplify the reasoning concerning the intensity of flow experiences contained in the theoretical perspective underlying the revised flow model. The revised flow model builds on the original notion of perceived fit of skills and task demands and refers to subjective value of the activity as a crucial second factor. That is, the model rests on the “classic” notion that flow can emerge under conditions where individuals perceive a balance between skills and task demands in an activity. Moreover, the intensity of flow experienced under such conditions is conceptualized as a function of the subjective value the individual assigns to the relevant activity. Subjective value is defined with reference to the perspective outlined by Higgins (2006) who noted that value is resulting from two basic ingredients: (a) hedonic experience (pleasure/pain properties of the value target) and (b) engagement strength, which can be based on regulatory fit or the use of proper means (among other factors). It can be assumed that regulatory compatibility—a phenomenological experience that arises when individuals experience a compatibility of (personal and situational) factors that are involved in performing a task or activity – builds one important basis for the subjective value individuals assign to activities and hence serves as a basis for the intensity of flow experiences. • Specify the factors that can qualify the relation between a fit of skills and demands and the experience of flow The factors qualifying this relation can be divided into two categories. (1) The idea that certain personality characteristics influence the easiness with which individuals experience this state was formulated in the autotelic personality hypothesis. Empirically, a low level of fear of failure, a strong internal locus of control and a habitual action-orientation were identified as fostering personality characteristics of flow. (2) Less intensively examined is the idea that situational characteristics have an impact on the experience of flow. First evidence suggests that there is a stronger relation from a skills-demands compatibility to flow when the consequences of the performed activity is perceived as rather unimportant.

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Further, activities that entail intellectually demanding features seem to enhance the chance to experience flow. • Which problem is there in analyzing consequences of flow experiences? The main part of flow research is correlational in nature and does not allow for causal inferences. In experimental studies, flow usually is manipulated by establishing a skills-demands-compatibility, which is the central precondition of flow. Hence, it seems possible to draw causal conclusions about the consequences of a skills-demands-fit. It seems much more complex to analyze causal consequences of the flow experience itself because skills-demands-compatibility could have consequences independent of flow. One possibility to overcome this problem is to measure the experiential components of flow and to check whether the effect of skills and demands on supposed consequences is mediated by the experience of flow. • Does flow experience/skills-demands-compatibility lead to positive affect? A definite conclusion regarding the relationship between skills-demands-compatibility and positive affect is currently not possible. So far, we only can conclude that there is a positive association between flow preconditions as well as flow experiences and positive affect that is also supported by qualitative results but we cannot draw causal inferences. Even though a causal relationship between flow and affect would make sense, it may be suggested that neither the relationship between skills-demands-compatibility and positive affect nor the relationship between flow experience and positive affect is a deterministic one but qualified by characteristics of the individual, the situation, and the task. • Describe the proposed relationship between flow and performance. A positive relationship between flow experiences and performance is postulated because of two reasons. First, flow is characterized by high concentration and a sense of control, which were found to be facilitators of performance. Second, flow could be seen as a motivating force for excellence which fosters personal growth. Individuals who tend to experience flow in a special set of activities should be motivated to engage in those activities and therefore gain expertise, at least in the long run. Therefore, flow experiences imply a growth principle, whereby more complex challenges are sought after and more complex abilities are likely to develop. Thus, flow should have a direct as well as an indirect positive effect on performance. However, one has to keep in mind that the relationship between flow and performance is a reciprocal one in all likelihood.

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Moneta, G. B. (2004). The flow experience across cultures. Journal of Happiness Studies, 5, 115–121. https://doi.org/10.1023/B:JOHS.0000035913.65762.b5. Moneta, G. B., & Csikszentmihalyi, M. (1996). The effect of perceived challenges and skills on the quality of subjective experience. Journal of Personality, 64, 275–310. https://doi.org/10.1111/j. 1467-6494.1996.tb00512.x. Murray, H. A. (1943). Thematic apperceptive test manual. Cambridge: Harvard University Press. Nakamura, Y. (1988). Optimal experience and the uses of talent. In M. Csikszentmihalyi & I. S. Csikszentmihalyi (Eds.), Optimal experience: Psychological studies of flow in consciousness (pp. 319–326). Cambridge: Cambridge University Press. Nakamura, J., & Csikszentmihalyi, M. (2009). Flow theory and research. In S. J. Lopez & C. R. Snyder (Eds.), Handbook of positive psychology (pp. 195–206). New York: Oxford University Press. Nolen-Hoeksema, S. (1991). Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology, 100, 569–582. https://doi.org/10.1037/0021-843X. 100.4.569. Olčar, D., Rijavec, M., & Golub, T. L. (2017). Primary school teachers’ life satisfaction: The role of life goals, basic psychological needs and flow at work. Current Psychology, 1–10. https://doi. org/10.1007/s12144-017-9611-y. Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological Assessment, 5, 164–172. https://doi.org/10.1037/1040-3590.5.2.164. Percival, G., Crous, F., & Schepers, J. M. (2003). Cognitive potential and job complexity as predictors of flow. The Journal of Individual Psychology, 29, 60–71. https://doi.org/10.4102/ sajip.v29i2.96. Pfister, R. (2002). Flow im Alltag [Flow in everyday life]. Bern: Peter Lang. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. https://doi.org/10.1037/0021-9010.88.5.879. Privette, G. (1983). Peak experience, peak performance, and flow: A comparative analysis of positive human experiences. Journal of Personality and Social Psychology, 45, 1361–1368. https://doi.org/10.1037/0022-3514.45.6.1361. Rauthmann, J. F., Gallardo-Pujol, D., Guillaume, E. M., Todd, E., Nave, C. S., Sherman, R. A., et al. (2014). The Situational Eight DIAMONDS: A taxonomy of major dimensions of situation characteristics. Journal of Personality and Social Psychology, 107, 677–718. https://doi.org/10. 1037/a0037250. Rauthmann, J. F., Sherman, R. A., & Funder, D. C. (2015). Principles of situation research: Towards a better understanding of psychological situations. European Journal of Personality, 29, 363–381. https://doi.org/10.1002/per.1994. Rheinberg, F., & Engeser, S. (2018). Intrinsic motivation and flow. In J. Heckhausen & H. Heckhausen (Eds.), Motivation and action (3rd ed., pp. 579–622). New York: Springer. Rheinberg, F., Manig, Y., Kliegl, R., Engeser, S., & Vollmeyer, R. (2007). Flow bei der Arbeit, doch Glück in der Freizeit. Zielausrichtung, Flow und Glücksgefühle [Flow during work but happiness during leisure time: Goals, flow-experience, and happiness]. Zeitschrift für Arbeitsund Organisationspsychologie, 51, 105–115. https://doi.org/10.1026/0932-4089.51.3.105. Rijavec, M., Golub, T. L., & Olčar, D. (2016). Can learning for exams make students happy? Faculty related and faculty unrelated flow experiences and well-being. Croatian Journal of Education, 18, 153–164. https://doi.org/10.15516/cje.v18i0.2223. Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80, 1–28. https://doi.org/10.1037/h0092976. Schallberger, U., & Pfister, R. (2001). Flow-Erleben in Arbeit und Freizeit. Eine Untersuchung zum “Paradox der Arbeit” mit der Experience Sampling Method (ESM) [Flow experiences in work and leisure. An experience sampling study about the Paradox of Work]. Zeitschrift für Arbeitsund Organisationspsychologie, 45, 176–187. https://doi.org/10.1026//0932-4089.45.4.176.

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Schiefele, U., & Roussakis, E. (2006). Die Bedingungen des Flow-Erlebens in einer experimentellen Spielsituation. Zeitschrift für Psychologie, 214, 207–219. https://doi.org/10. 1026/0044-3409.214.4.207. Schüler, J. (2007). Arousal of flow experience in a learning setting and its effects on exam performance and affect. Zeitschrift für Pädagogische Psychologie, 21, 217–227. https://doi. org/10.1024/1010-0652.21.3.217. Schüler, J., Brandstätter, V., Wegner, M., & Baumann, N. (2015). Testing the convergent and discriminant validity of three implicit motive measures: PSE, OMT, and MMG. Motivation and Emotion, 39, 839–857. https://doi.org/10.1007/s11031-015-9502-1. Schüler, J., & Brunner, S. (2009). The rewarding effect of flow experience on performance in a marathon race. Psychology of Sport and Exercise, 10, 168–174. https://doi.org/10.1016/j. psychsport.2008.07.001. Schultheiss, O. C., & Pang, J. S. (2007). Measuring implicit motives. In R. W. Robins, R. C. Farley, & R. F. Krueger (Eds.), Handbook of research methods in personality psychology (pp. 322–344). London: The Guilford Press. Sheldon, K. M., Ryan, R. M., Deci, E. L., & Kasser, T. (2004). The independent effects of goal contents and motives on well-being: It’s both what you pursue and why you pursue it. Personality and Social Psychology Bulletin, 30, 475–486. https://doi.org/10.1177/ 0146167203261883. Shernoff, D. J., Csikszentmihalyi, M., Schneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18, 158–176. https://doi.org/10.1007/978-94-017-9094-9_24. Shin, D. C., & Johnson, D. M. (1978). Avowed happiness as an overall assessment of the quality of life. Social Indicators Research, 5(475), 492. https://doi.org/10.1007/BF00352944. Stein, G. L., Kimiecik, J. C., Daniels, J., & Jackson, S. A. (1995). Psychological antecedents of flow in recreational sport. Personality and Social Psychology Bulletin, 21, 125–135. https://doi.org/ 10.1177/0146167295212003. Stoll, O., & Lau, A. (2005). Flow-Erleben beim Marathonlauf – Zusammenhänge mit Anforderungspassung und Leistung [Experiencing “flow” during a marathon. Associations with the fit between demand and ability]. Zeitschrift für Sportpsychologie, 12, 75–82. https:// doi.org/10.1026/1612-5010.12.3.75. Sudman, S., Bradburn, N. M., & Schwarz, N. (1996). Thinking about answers: The application of cognitive processes to survey methodology. San Francisco: Jossey Bass. Tozman, T., & Peifer, C. (2016). Experimental paradigms to investigate flow-experience and its psychophysiology: Inspired from stress theory and research. In L. Harmat, F. Ø. Andersen, F. Ullén, J. Wright, & G. Sadlo (Eds.), Flow experience (pp. 329–350). Springer International Publishing. https://doi.org/10.1007/978-3-319-28634-1_20. Wells, A. J. (1988). Self-esteem and optimal experience. In M. Csikszentmihalyi (Ed.), Flow: The psychology of optimal experience (pp. 327–341). New York: Harper and Row. Wicklund, R. A., & Duval, T. S. (1971). Opinion change and performance facilitation as a result of objective self-awareness. Journal of Experimental Social Psychology, 7, 319–342. https://doi. org/10.1016/0022-1031(71)90032-1. Yoshida, K., Sawamura, D., Inagaki, Y., Ogawa, K., Ikoma, K., & Sakai, S. (2014). Brain activity during the flow experience: A functional near-infrared spectroscopy study. Neuroscience Letters, 573, 30–34. https://doi.org/10.1016/j.neulet.2014.05.011.

Chapter 4

Flow in Nonachievement Situations Anja Schiepe-Tiska

and Stefan Engeser

Abstract Flow research began with the study of activities, which often occurred in achievement situations. To this day, most flow research still deals with achievement in the areas of sports, academia, and work where the balance of challenge and skill is important to foster flow. This chapter extends traditional flow theory by introducing the concept of implicit and explicit motives as personal needs that explain how individuals can experience flow not only in achievement situations but also in social situations like affiliative or power situations. We propose that flow emerges from the interaction of motive-specific incentives in a situation, such as challenge and skill balance, and a person’s motives. These motives are conducive to structuring situations, which in turn foster flow. In this context, we also present studies dealing with flow in groups. We end this chapter by revealing some perspectives on future research on flow in nonachievement situations.

Introduction The concept of flow was first described by Csikszentmihalyi (1975) in autotelic activities with strong achievement content, such as chess, rock climbing, and surgery. All of these activities have something in common: They provide clear standards of excellence, with unambiguous feedback about success and failure (for additional aspects of achievement situations, see Box 4.1). A chess player gets an immediate impression about his current performance and his own skills. For climbing, standards are clear, and skills can be obtained immediately. Likewise, A. Schiepe-Tiska (*) Center for International Student Assessment (ZIB), TUM School of Education, Munich, Germany Universität München, Munich, Germany e-mail: [email protected] S. Engeser Institute of Psychology, University of Trier, Trier, Germany e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_4

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surgeons have clear standards for success and we all hope that they have the skills to succeed in their (difficult) tasks. Looking at the topics of current research on flow, achievement still plays a major role (cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1). Research is dealing with sports performance (e.g., Schüler, 2010; Schüler & Brandstätter, 2013), academic learning (e.g., Bassi, Steca, Delle Fave, & Caprara, 2007; Engeser & Rheinberg, 2008; Hsieh, Lin, & Hou, 2013), human-computer interaction (e.g., Kim & Ko, 2019) and innovations in work settings (e.g., Steiner, Diehl, Engeser, & Kehr, 2011). However, since the first study of daily experiences using the Experience Sampling Method was conducted (cf. Moneta, Chap. 2), it has been found that flow can also occur in activities, which usually have no obvious achievement aspects like watching TV or meeting friends (Csikszentmihalyi, Larson, & Prescott, 1977; Csikszentmihalyi & LeFevre, 1989). For example, Csikszentmihalyi (1975) discovered that some individuals mainly experienced flow in interactions with others, some in physical movements like walking, some in reading books or watching TV, and some experienced flow while watching people walking down the street. Also, a mother reading alternately with her daughter “loses touch with the rest of the world and is totally absorbed in what she is doing” (Csikszentmihalyi, 2004, p. 41). Aside from that, Csikszentmihalyi (1975) observed very early in his research that chess players perceived different incentives in the same situation while experiencing flow. Incentives are situational cues provided by a situation that are inherently affective rewarding for a person (task-intrinsic incentives; Stanton, Hall, & Schultheiss, 2010). Besides reaching flow “through self-imposed challenges” (Csikszentmihalyi, 1975, p. 62) some chess players attained flow “through beating strong opponents and advancing in the hierarchy of ratings, others achieved flow through interacting with friends, [or] through playing when they feel like it. . .” (Csikszentmihalyi, 1975, p. 62). In fact, on the one hand, individuals differ in the situations in which they experience flow, and on the other hand, they can get an affective reward from different incentives in the same situation. In the present chapter, we will briefly review aspects of traditional flow theory that are important for the understanding of flow in nonachievement situations. Next, we aim to extend this theory by introducing the concept of implicit and explicit motives as personal needs that explain how individuals can experience flow in different situations and why they perceive different incentives in the same flow occurring situation. In this context, we will present studies on flow in social situations like affiliative or power situations as well as research on flow in groups. We will end this chapter by revealing some perspectives on future research on flow in nonachievement situations. Box 4.1 Key characteristics of achievement situations (cf. Brunstein & Heckhausen, 2018) 1. In the situation, a standard of excellence is salient. (continued)

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Box 4.1 (continued) 2. The standard of excellence can be the performance of the individual in the past, the performance of others, or a standard inherent in the task. 3. A person can reach (succeed) or not reach (fail) the standard of excellence. 4. The outcome (success and failure) is controllable by the individual and can be attributed to the individual’s effort and skill and not (only) to luck or other external or uncontrollable causes. 5. The outcome provides sufficiently clear feedback about success and failure.

Challenge and Skills in Nonachievement Situations As outlined in other chapters (Engeser et al., Chap. 1; Moneta, Chap. 2; Barthelmäs & Keller, Chap. 3), challenge and skills have played a prominent role in flow research. In numerous ESM studies, individuals were asked to rate challenge and skills from low to high. If individuals indicate high challenge and high skills at the same time, they are expected to experience flow according to the quadrant model (cf. Moneta, Chap. 2; Barthelmäs & Keller, Chap. 3). This operationalization of flow is problematic, because it only gathers one component of flow (cf. Pfister, 2002; Rheinberg & Engeser, 2018; and Moneta, Chap. 2 for an extended discussion about this operationalization of flow). Furthermore, the challenge and skill balance seems to make sense only in achievement situations. Here, people can readily indicate how challenging an activity is and how skilled they think they are. In nonachievement situations, the question is less straightforward. Meeting friends, reasoning with colleagues, or watching TV lie within a range of normal abilities that do not necessarily have a challenging character in the usual sense of the word challenge. But when defining challenge more broadly as “opportunities for actions”, as Csikszentmihalyi (1975, p. 49) did in the beginning of his research, one comes closer to an explanation of why flow can be experienced in situations without an obviously challenging character as well. Csikszentmihalyi stated that there are generally many opportunities for actions in a situation, but a person cannot act upon all of them. “The question becomes one of a choice: which of these possible actions will I attempt to turn into my action?” (Csikszentmihalyi & Bennett, 1971, p. 45). According to Csikszentmihalyi, the person would respond to an opportunity that fits one’s perceived action capabilities (Csikszentmihalyi & Bennett, 1971). This had been later identified as challenge and skills balance. These action opportunities provide clear goals in the sense that the person knows exactly what to do next, which action opportunity to choose next. Consequently, the situation becomes well structured which in turn foster becoming completely immersed in an action. As a result, the individual is highly engaged in the activity without consciously thinking about what to do next and flow occurs.

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The Importance of Motives for Turning the Spotlight on an Action Opportunity Besides challenge and skills, we argue that personal needs play an important role in whether or not a person recognizes and responds to an action opportunity. For this purpose, we want to introduce the concept of motives, which draw a person’s attention to different motive-specific incentives of an action opportunity. Those motive-specific incentives are inherently rewarding for individuals high in a given motive. They arouse the individual’s implicit motives and accordingly affect one’s behavior. Implicit motives are unconscious motivational needs that orient attention, and select and energize behavior towards specific classes of rewarding task-intrinsic incentives (McClelland, 1987; Schultheiss & Brunstein, 2010). They are shaped by ontogenetically early, prelinguistic, affectively toned learning experiences (McClelland, Koestner, & Weinberger, 1989). Because people have no insight into their implicit motives, they are assessed using projective or semi-projective measures (see Box 4.2). Box 4.2 Assessment of Implicit Motives Implicit motives are assessed using projective measures such as the Picture Story Exercise (PSE; Pang & Schultheiss, 2005) or the Operant Motive Test (OMT; see Baumann, Chap. 9) as well as semi-projective measures like the Multi-Motive Grid (MMG, Sokolowski, Schmalt, Langens, & Puca, 2000). These two types of measure have in common that people are shown different picture cues. For the PSE or the OMT, people are instructed to write imaginative stories (see Pang, 2010 to learn more about how to use the PSE) or short statements in response to the pictures. Afterwards, these stories or statements are coded for the different motives (Winter, Unpublished manuscript, 1994; Kuhl & Scheffer, Auswertungsmanual für den Operativen Motiv Test (OMT) [Scoring system for the Operant Motive Test]. Unpublished manuscript, University of Osnabrück, 1999). For the MMG, people are instructed to indicate whether or not a series of descriptions (e.g. “Trying to influence other people”, “Feeling confident to succeed at this task”) following a picture (e.g. a robe climber) describes the way they would think or feel in the situation shown in the picture. Current research also aims to develop a version of the Implicit Association Test (IAT), which was originally developed to measure racial or other attitudes that may be strongly distorted by social desirability, to assess implicit motives (Brunstein & Schmitt 2004, 2010). The research on motives has focused on the so-called “big three” motives: achievement, affiliation-intimacy, and power (McClelland, 1987; cf. Heckhausen

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Table 4.1 Motive-specific incentives Motive Achievement motive Affiliation-intimacy motive Power motive

Task-intrinsic incentive Doing better for its own sake Experiencing friendly, warmhearted social contacts Feeling important, strong, dominant and influential

Example Performing difficult task, getting performance feedback Chatting with friends, consoling a friend Dominating others in competitions, teaching

& Heckhausen, 2018). The achievement motive is an enduring concern with maintaining or surpassing standards of excellence (McClelland, Atkinson, Clark, & Lowell, 1953), whereas the affiliation motive is an enduring concern about establishing and maintaining or restoring positive relationships with others (Atkinson, Heyns, & Veroff, 1958). The power motive is a recurrent concern for having an impact on others (Winter, 1973). Each motive is aroused by different motive-specific incentives (see Table 4.1). Individuals differ in the strength of their motives. Depending on the strength, motives elicit different responses to the same situational incentive. For example, in a task of moderate difficulty, people with a high achievement motive increase their efforts more than people with a low achievement motive (Brunstein & Heckhausen, 2018). It has also been shown that the implicit achievement motive selectively elicits performance on challenging tasks, but not on easy or difficult tasks (McClelland et al., 1953). Applying these findings to flow research, the balance of challenge and skills would be expected to be a motive-specific incentive for the achievement motive. Indeed, the achievement motive was found to moderate the relationship between challenge-skill balance and flow experience (Engeser & Rheinberg, 2008; Schüler, 2007). The challenge-skill balance predicted flow only for individuals high in the achievement motive but not for individuals low in achievement motive. However, for the affiliation and the power motive other incentives would be more relevant. For these motives, skills are also notable, but they do not need to be in balance with the challenge in order to foster flow. Presumably, the skills have to be perceived as sufficient to act upon the chosen action opportunity (cf. Kehr, Strasser, & Paulus, 2018). To summarize, a person has many action opportunities in a situation but cannot attempt all of them; rather, he or she has to choose one. Whether or not a person responds to an opportunity depends in part on his or her skills, but also on his or her motives. The more motive-specific incentives an opportunity contains for the person, the more he or she orients his or her attention to it. Hence, that opportunity becomes more salient to the person. We illustrate this in Fig. 4.1. Circles represent action opportunities and dots, triangles, and quadrates represent different motive-specific incentives inherent in this opportunity. The bigger the size of the circle, the more incentives are inherent in the respective opportunity. Therefore, action opportunity

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AO 1 AO 2

AO 6

AO 3

AO 7

AO 4

AO 5

Situation Action opportunity (AO) power incentives achievement incentives affiliation-intimacy incentives Fig. 4.1 Action opportunities and motive-specific incentives

7 contains the most motive-specific incentives; hence, it becomes more salient and accordingly will be chosen to act upon. For instance, let us imagine that a person is to develop a display for smartphones that prevents not just scratches but also fingerprints. He or she can decide to carry out the task alone (this would be a choice for people high in the achievement motive), to do it in a team and become an equal team member (choice for people high in the affiliation motive), or to do it also in a team but as the leader of the team (choice for people high in the power motive). Depending on the person’s motive pattern, each of those opportunities has the chance to foster flow. If the person chooses the action opportunity that is in line with his or her motives, and the perceived skills are sufficient, the situation becomes structured and he or she is more likely to experience flow.

The Flow Hypothesis of Motivational Competence Besides implicit motives, there are also explicit motives influencing the decision for or against an action opportunity (McClelland et al., 1989). Explicit motives (also called self-attributed motives) are consciously accessible evaluations of a person’s

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self-concept (McClelland et al., 1989). They are cognitively based on verbal learning of rules, demands, and expectations and reflect people’s self-attributed view of their own implicit motives (McClelland, 1995; McClelland et al., 1989). Because a person is conscious of his or her explicit motives, they are measured via self-report questionnaires (see Box 4.3). Explicit motives respond to social-extrinsic incentives (e.g. values, beliefs) and influence the conscious decision for choosing an action opportunity. They attempt to channel implicit motives in line with conscious purposes, values and beliefs (McClelland, 1987). Research also distinguishes between explicit affiliation-intimacy motive, explicit power motive, and explicit achievement motive analogous to implicit motives (McClelland et al., 1989). Box 4.3 Assessment of Explicit Motives Explicit motives are measured via self-report questionnaires like the Personality Research Form (PRF, Jackson, 1984). People usually complete the scales for achievement, affiliation and dominance. Sometimes, the scale for aggression is also used as another key component of the power motive (e.g. Schultheiss, Yankova, Dirlikov, & Schad, 2009). People are instructed to indicate how they would behave in general, but not in response to specific situational contexts. They decide whether or not the statements apply to themselves. For example, an item measuring power (dominance) is “I feel confident when directing the activities of others”, an item measuring affiliation is “I try to be in the company of friends as much as possible” and an item measuring achievement is “I will not be satisfied until I am the best in my field of work”. The implicit and explicit motive systems coexist within a person but are widely independent of each other (Köllner & Schultheiss, 2014; McClelland et al., 1989; Weinberger & McClelland, 1990). They are triggered by different stimuli and influence different behavioral responses (McClelland et al., 1989; Schultheiss & Brunstein, 1999). Despite the independence and differences, the two systems interact with one another in channeling a person’s behavior over the lifetime (Winter, John, Stewart, Klohnen, & Duncan, 1998). As long as both motive systems are independent of each other, there are individuals with congruent implicit and explicit motives (high implicit/high explicit motives or low implicit/low explicit motives) and individuals with incongruent implicit and explicit motives (high implicit/low explicit motives or low implicit/high explicit motives). Individuals with high congruence are more likely to choose explicitly opportunities in line with their implicit motives, which energize their behavior and lead almost effortlessly to a positive, enjoyable experience. However, for individuals with incongruent implicit and explicit motives, McClelland et al. (1989) stated “whatever the reasons for discordance [incongruence] between implicit and explicit motives, it can certainly lead to trouble” (p. 700). For example, motive incongruence increases negative affect (Schüler, Job, Fröhlich,

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& Brandstätter, 2008) and unhealthy eating behavior (Job, Oertig, Brandstätter, & Allemand, 2010) as well as decreases emotional (Brunstein, Schultheiss, & Grässmann, 1998; Brunstein, Schultheiss, & Maier, 1999) and physiological wellbeing (Baumann, Kaschel, & Kuhl, 2005; cf. Brunstein, 2018). Therefore, when individuals choose opportunities that are only in line with their explicit, but not with their implicit motives, they do choose things that seem suitable for themselves and seem to be important and valuable. The problem is that attempting to act on opportunities recurrently without supporting implicit motives needs conscious effort and brings little or no enjoyable experience. Thus, flow will be prevented. For example, a manager with a high explicit but a low implicit power motive, works in a leading position without enjoying the opportunity of influencing others. This could result in physiological and emotional stress like getting migraine right up to stomach ulcer or burnout. Another example would be a student with a high implicit but a low explicit affiliation-intimacy motive. She prepares for an exam alone, but soon becomes bored and starts to distract herself by cleaning up her apartment, because she would much rather be with her classmates in a study group, where they could talk about the things they are learning. The ability to select motive-corresponding action opportunities is termed motivational competence (Rheinberg, 2002). This means “a person’s ability to reconcile current and future situations with his or her activity preferences such that he or she can function effectively, without the need for permanent volitional control” (Rheinberg & Engeser, 2010, p. 532; cf. Bruya, 2010). Motivational competence has five components (Box 4.4). Box 4.4 Components of Motivational Competence (Rheinberg & Engeser, 2010) 1. Congruence between one’s implicit and explicit motives (indicating an accurate motivational self-concept). 2. The ability to evaluate different incentives in a situation. 3. If there are no incentives in line with one’s implicit motives, the ability to endow the situation with motive-corresponding incentives. 4. In long-term projects, focusing not just on expected benefits but on taking pleasure in the activities themselves. 5. Knowledge of internal and external conditions influencing one’s motivational processes (metamotivational knowledge).

The flow hypothesis of motivational competence states that individuals high in motivational competence are more likely to experience flow (Rheinberg & Engeser, 2010). When a person’s life is mostly self-determined and the motivational selfconcept is accurate, one can select action opportunities in line with one’s implicit motives. Implicit motives support and energize motive-corresponding action opportunities. The person knows exactly what to do next, the situation becomes

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well-structured and flow can arise. In the case that no motive-specific incentives are present, the person would endow the action opportunities with incentives that correspond to his or her motives in order to elicit flow. First evidence for the hypothesis was found by Clavadetscher (2003, reported in Rheinberg & Engeser, 2010), who investigated members volunteering for an organization to help organize cultural events like concerts. The members were allowed to choose the helping activity by themselves (e.g. inviting well-known artists, administration, or running the bar in the concert break). Clavedetscher found that the better the correspondence between the implicit and explicit motives, the more flow was experienced while performing their chosen activity. Kehr (2004) had a similar idea to Rheinberg in his compensatory model of work motivation and volition. The model proposes that the “congruence of implicit motives, explicit motives, and perceived abilities is associated with flow experience” (p. 489). In doing so, the model also allows for partial congruence. This means that the motive-specific incentives of an action opportunity arouse the implicit motives of a person and may or may not activate the congruent explicit motives. However, when explicit motives are activated, which compete with the aroused implicit motives, attention is distracted from the activity and flow will be prevented. For example, someone with a high implicit affiliation-intimacy motive goes to a party and meets a very good friend there. Both start chatting extensively about their past experiences, about their boyfriends etc. The person can experience flow while talking to her friend with or without knowing about her own motives (a high or a low explicit affiliation-intimacy motive). However, if the person believes herself to have a high implicit power motive, the party could also arouse her explicit power motive. Accordingly, she would prefer to walk around and talk to different people in order to extend her network rather than to be in a deep conversation with her very good friend. Hence, she cannot experience flow while talking to her friend because a conflicting explicit power motive is aroused. The person is distracted by taskirrelevant incentives that prevent flow experience. At the same time, even if she were to walk around and try to extend her network, she would probably not experience flow because the support of an implicit power motive is missing. The model also differs between distal levels of motivational processes that are related to proximal levels (cf. Kanfer & Heggestad, 1997). The arousal of implicit motives at the distal level would lead to implicit behavioral tendencies at the proximal level, which are expressed as affective preferences. Affective preferences indicate whether or not the current task is pleasant for the person, and thus the arousal of implicit motives by a certain task will lead to affective preferences for that task (Kehr, 2004; Kehr et al., 2018). The arousal of explicit motives leads on the proximal level to cognitive preferences. They indicate whether or not the current task is important to the person. High cognitive preferences for a task will ensure that a person will limit his or her stimulus field in order to concentrate on the task. By doing so, no other task will be able to distract the attention of the person from the task at hand. Engeser and Rheinberg (2008) showed that high cognitive preferences moderated the relation between the challenge-skill balance and flow. For highly important tasks, flow was still high when the perceived challenge were low. For less

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important tasks, flow was high only when people perceived a challenge-skill balance. When a person chooses an action opportunity and acts upon it, the situation alters and other opportunities emerge. Again, the person responds to one opportunity and therefore changes the situation and so on (see Fig. 4.2). The implicit motives orient the attention and help to select the next motive-corresponding opportunity. Hence, the individual knows exactly which opportunity to attempt next and loses him or herself in the series of actions, which in turn elicit flow. Competing explicit motives activated through task-irrelevant incentives would interrupt the series of actions and therefore disturb flow experience. Kehr (2004) also integrated the skills back into a joint model with implicit and explicit motives. In contrast to Csikszentmihalyi (1975), the compensatory model proposes “Perceived abilities surpassing task demands do not necessarily lead to boredom, or otherwise counteract flow.. . . Low, compared to high, task demands only counteract flow if they prevent arousal of flow-concordant implicit motives or activate conflicting explicit motives” (Kehr, 2004, p. 489). To summarize, flow is a motivational state that emerges from a person x situation interaction. On the person’s side, implicit and explicit motives as well as perceived abilities help to orient attention in situations und structure them. If one chooses an action opportunity in line with one’s motives and the perceived skills are sufficient, one is more likely to experience flow because one knows exactly what to do next. Then, flow itself affects experience and behavior like wellbeing and performance (Fig. 4.3).

Factors Contributing to Flow in Social Situations Affiliation: Intimacy and Its Incentives As outlined above, implicit motives are primarily aroused by factors intrinsic to the process of performing an activity. Hence, the affiliation-intimacy motive can be aroused through incentives in social situations and therefore foster flow experience. The affiliation-intimacy motive is scored in the PSE stories whenever a character expresses concern for establishing, maintaining, or restoring friendly relations with others or expresses sadness or other negative emotions about separation or disruption of a relationship (Pang & Schultheiss, 2005). Individuals with a high affiliationintimacy motive prefer situations that allow them to feel closely related to other people and to enjoy the presence of others (McClelland, 1985). Box 4.5 provides key characteristics of affiliative situations.

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Fig. 4.3 Adapted basic model of “classical” motivational psychology (following Rheinberg, 2006)

Box 4.5 Key Characteristics of Affiliative Situations 1. Affiliative situations offer action opportunities to establish, maintain, intensify or restore a relationship (e.g. spending friendly time together, parties or just friendly small talk). 2. Affiliative situations provide social interactions with the expression of warm, friendly, or intimate feelings. 3. The outcome can be attributed to mutual affection of the interactants instead of instrumental gestures of kindness to achieve a different goal (e.g. sales situation). 4. The outcome of an interaction provides (sufficiently) clear feedback about the quality of the social relationship.

Research on the affiliation-intimacy motive distinguishes between affiliation and intimacy. The two are highly correlated but can be differentiated regarding the person with whom one has a relationship. Intimacy refers to close dyadic interactions like romantic relationships, whereas affiliation refers to any friendly and warm social contact. Therefore, the affiliation-intimacy motive is a “recurrent preference or readiness for experiences of close, warm, and communicative exchange with others—interpersonal interaction that is seen by the interactants as an end in itself, rather than a means to another end” (McAdams, 1984, p. 45). Individuals high in the affiliation-intimacy motive seek opportunities to enjoy interpersonal relationships. Once again, the incentives for capturing the implicit or explicit motive are somewhat different. Previous research has found that people high in the implicit affiliation-intimacy motive showed more listening, smiling, eye contact, and laughter, which are likely to promote pleasant interpersonal interactions. In contrast, people high in the explicit affiliation-intimacy motive want to reinforce their self-view as sociable, competent in interactions with others and therefore place greater value on establishing positive interactions (McAdams, Healy, & Krause, 1984; McAdams, Jackson, & Kirshnit, 1984).

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Craig, Koestner, and Zuroff (1994) used the Rochester Interaction Record (Wheeler & Nezlek, 1977) to examine differences in the implicit and explicit intimacy motive, which is a similar method to the ESM technique but without giving a signal to indicate the completion of the questionnaires. Participants are asked to complete an interaction record for every interaction that lasted 10 min or longer for 1 week. The authors showed that the explicit intimacy motive predicted the total number of interactions in a week, and the implicit intimacy motive the percentage of dyadic interactions. Furthermore, the implicit intimacy motive was associated with displaying higher levels of self-disclosure when interacting with close friends (because this is an interaction-intrinsic incentive). On the other hand, the explicit intimacy motive was only associated with greater self-disclosure in men and when the situation explicitly called for such behavior because it was the nature of the interaction, such as being on a date. To sum up, individuals high in the affiliation-intimacy motive seek opportunities for reciprocal dialogue and interpersonal closeness. Likewise, Csikszentmihalyi (1975) discovered that in playing chess, an activity in which individuals often experience flow, players who found friendship and companionship important— which are opportunities containing affiliation-intimacy motive-specific incentives—belonged to more chess clubs. The camaraderie of other players was important to them, and playing chess provided a situation of social bonding. In Csikszentmihalyi’s interviews, one player described that “Most of my social life is centered around chess players” and “I enjoy other chess players and the social life around chess which is unique” (1975, p. 68). Likewise, the main reasons for enjoying rock dancing, besides body movement and involvement with the music, were involvement with the partner and a feeling of togetherness (Csikszentmihalyi, 1975). Wong and Csikszentmihalyi (1991) examined the relationship of the explicit affiliation motive to related behaviors and quality of experiences in an ESM study. The outcome variables were derived from the Experience Sampling Form and included level of concentration, self-consciousness, control, feeling good about oneself, and wishing to be doing the activity. Results showed that women high in the explicit affiliation motive felt happier, better about themselves, more involved, and more in control when in interaction with a friend compared to women low in the explicit affiliation motive. The experiences of men did not differ a great deal depending on whether they were high or low in the affiliation motive. Although all other variables from the Experience Sampling Form were reported, challenge and skills were not reported in the study, or were possibly not assessed. Hence, the balance of challenge and skills does not seem to be primarily relevant in affiliative situations, and therefore underlines our theoretical framework presented in this chapter. The results of the aforementioned study also give rise to the assumption that whether or not a person reaches flow experience in social interactions may also depend on gender. Studies showed that women have higher levels of the implicit affiliation motive than men (see Drescher & Schultheiss, 2016 for a meta-analysis), and also tend to describe themselves as more affiliative (Feingold, 1994). This

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gender difference may reflect a possibly different socialization of women and men. Women are often expected to behave in a more communal and affiliative way than men (Bem, 1974; Deaux & Lewis, 1983). Hence, they have more opportunities to acquire such experiences. One could conclude that, in general, women may experience more flow in social situations than men. However, this would be an interesting aspect to examine in future studies. Additionally, developing interpersonal relationships also requires skills. To get the intrinsic reward of flow experience from a social situation, sufficient social skills are required. Thus, women may have an advantage in experiencing flow in social situations because they have developed a stronger affiliation-intimacy motive and have better social skills to enjoy the situation and interact adequately with other people. After being in a successful interaction with another person that has rewarded one with flow experience, an individual with a high affiliation-intimacy motive will seek such a social situation again because he or she wants to attain this state again. Therefore, he or she becomes more familiar with such situations and thus develops more social skills, resulting in the ability to actively structure the social interaction. Hence, the person knows how to respond without volitionally thinking about what to do next and can reach flow experience more easily.

Studies on Flow in Social Situations Flow research has not focused on social situations exclusively, but has gathered data from such situations by chance in ESM studies (e.g. Csikszentmihalyi & LeFevre, 1989). Good initial impetus for systematically analyzing flow in social situations was provided by Graham (2008), who examined couples in their daily lives using the ESM technique. Among other things, he investigated whether the level of flow experienced during an activity was positively associated with relationship quality. Therefore, he determined the time partners spend together and categorized their activities in non-free time, including paid work, household work, child care, obtaining goods (e.g. shopping, doctor visits), personal needs (e.g. washing, eating, drinking) and education (e.g. homework, attending classes), in contrast to free time activities like volunteer work, movies, museums, sports, hobbies, reading, watching TV, conversation, cuddling, kissing, and sex. Results indicated that regardless of whether or not the activity included free-time components, flow during these activities was positively associated with relationship quality and how close a person felt to his or her partner. When both partners in a relationship focus their attention on the interaction and feel safe in the interaction, distraction is minimized and flow is a very likely experience. Moreover, in social situations, flow can cross over from one person to another. Bakker (2005) showed that the higher the flow of music teachers had been, the higher the flow the students experienced. This effect of emotional contagion is explained as “The tendency to automatically mimic and synchronize facial

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expressions, vocalizations, postures and movements with those of another person and, consequently, to converge emotionally” (Hatfield, Cacioppo, & Rapson, 1994, p. 5). The crossover effect may become even more important in situations where any more people are involved.

Flow in Groups The question “Is doing it together better than doing it alone?” (Walker, 2010, p. 1) guides flow research in a new direction. Walker (2010) determined whether flow in social companionships is more enjoyable than flow in situations without the presence of others. Students were asked to write down two situations in which they had recently experienced flow, one in which they had been alone and one in which they had been with others. Afterwards, they rated how joyful the experience was for them. As a result, students reported more joy in interactive flow situations than if they were alone while experiencing flow. However, the study did not aim to examine whether people experienced more or less flow when they were alone or with others, nor it took possible moderators like the affiliation-intimacy motive into account, so the empirical question remains open. Flow in groups is a very complex phenomenon because there are many interactions between numerous people. Besides factors of the person and the situation, other factors on the group level may become important to foster flow experience, for example size and structure of the group, relationship between group members, or trust (cf. Walker, Chap. 10). In sports settings, there is some evidence on flow in team sports (Jackson, 1995; Russel, 2001; cf. Walker, Chap. 10). Here, the balance of challenge and skills is, as an achievement-motive specific incentive, of course, important to experience flow. However, team sports also provide opportunities to be with friends, colleagues and teammates and can therefore capture the affiliation motive. For individuals high in the affiliation motive it is important to get to know the teammates very well in order to establish and maintain positive relationships. This in turn, may provide additional support to structure the sport situation, in order to elicit flow. For example, playing soccer is a complex situation for a team. It is not sufficient to have 11 great solo players; they also need to interact well with each other. The better a player knows the other team members, the better he is aware of how the others will react in specific situations, and what action opportunity they will choose. Therefore, he always knows what will happen next and which action opportunity to choose next depending on his or her teammates. As a result, flow can possibly occur. Jackson (1995) interviewed elite level athletes who competed as individual participants or as part of a team in international competitions. Athletes were asked about factors that help them to get into flow, factors preventing flow, and factors disrupting flow (cf. Walker, Chap. 10). For athletes in team sports, a positive team play and interaction, indicated through trust between players, a positive feeling on the team, unison movements, and focus among interacting teammates, were

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important to experience flow. In contrast, negative team interactions like negative talk or negative feelings within the team, as well as not feeling part of the team, not being trusted by the team, or not focused partner prevented and also disrupted flow. Also, the results of a study among college athletes showed that they appear to have similar experiences of flow states, regardless of whether they participate in individual (e.g. swimming, track, wrestling, triathlon) or team sports (e.g. football, baseball, volleyball, softball) (Russel, 2001). Again, individual and team sports may provide different action opportunities with different motive-specific incentives. Therefore, both can elicit flow but because of the arousal of different motives. Recapitulating, action opportunities which contain affiliation-intimacy motivespecific incentives can arouse the affiliation-intimacy motive. Individuals high in this motive orient their attention to those opportunities that in turn become more salient to the person. When such an action opportunity is chosen to act upon, flow is very likely to occur. Therefore, individuals high in the affiliation-intimacy motive should experience flow especially in situations that provide affiliation-intimacy motive-specific incentives.

Factors Contributing to Flow in Power Situations Power and Its Incentives The power motive is a recurrent concern for having impact on others (Winter, 1973) in order to feel important and strong. Individuals high in the power motive have the desire to influence, control, or impress others and to be recognized for related behavior. Therefore, similar to individuals high in the affiliation-intimacy motive, they often need the (at least virtual) presence of another person upon whom they can have an impact. However, in contrast to individuals with a high affiliation-intimacy motive, who like social situations due to the opportunities to spend harmonious times with others, individuals with a high power motive prefer social situations because they can influence other people. For them, it is more about networking and knowing many people. Box 4.6 provides key characteristics of power situations. Box 4.6 Key Characteristics of Power Situations 1. Power situations offer action opportunities in which another person/the world at large is present upon whom/which one can have an impact or whom one can impress. 2. Power situations provide interactions in which a person can feel important, strong, and influential. (continued)

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Box 4.6 (continued) 3. The outcome (successful influence) can be attributed to the individual’s charisma and strength (e.g. rhetoric, influence strategies, forming alliances). 4. The outcome provides (sufficiently clear) feedback about being successful in influencing and/or impressing others.

Power situations provide a broad range of action opportunities with different power motive-specific incentives (Winter, Unpublished manuscript, 1994; Busch, 2018). The first opportunities to exercise power are strong, forceful actions, which have an impact on other people or the world at large, such as possibilities to accuse, attack, demand, chase, or threaten someone. The opportunity to resist the impact another person has on oneself also elicits the power motive. Every competitive situation—on a personal or on a team level—belongs to this category as well. Second, situations in which a person can control, regulate, or check upon other people provide action opportunities that arouse the power motive. Likewise, opportunities in which someone can persuade, convince, prove a point, or argue with others can activate the power motive. Fourth, each opportunity in which someone tries to impress others and shows concerns with fame, prestige, or reputation contains power motive-specific incentives. The final two power situations have a somewhat different connotation than the previous ones. They provide action opportunities to help, advise, or give support that is not explicitly solicited, for example, when a manager gives advice to one of her employees. Finally, any situation that provides an opportunity for a person to elicit a strong (positive or negative) emotional reaction to the action of another person intentionally, is an incentive for the power motive. Eliciting emotions in others is a powerful socialized way of influencing other people (cf. McClelland, 1975; Winter, 1973). The personalized and socialized power motive. The different power motive-specific incentives already point to two different aspects of the power motive: the personalized and the socialized power motive (Busch, 2018; McClelland, 1970, 1975, 1985; McClelland & Wilsnack, 1972; Winter, 1973 ; Winter & Stewart, 1978). Both motives aim to influence others, but can be differentiated according to the outcomes with regard to the welfare of others. Individuals high in the personalized power motive experience positive affect from making a purely self-interested impact on others. A high personalized power motive is positively associated with escalating a conflict to serve one’s own interests at the expense of consequences for others (Magee & Langner, 2008), extreme risk taking (McClelland & Watson, 1973), sexual aggression (Winter, 1973; Zurbriggen, 2000), the acquisition of prestigious possessions (Winter, 1973), assertiveness in friendships (McAdams, Healy, & Krause, 1984), increasing testosterone after dominating an opponent in a competitive game (Schultheiss, Campbell, & McClelland, 1999),

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and conflict escalating decision making (Magee & Langner, 2008), while it is negatively associated with making confessions during conflict resolution (Langner & Winter, 2001). Individuals high in the socialized power motive experience the same positive affect from having an impact that benefits other people. In contrast to the personalized power motive, a high socialized power motive is positively associated with carrying for others well-being (Magee & Langner, 2008) and ratings of oneself as a responsible person (Winter & Stewart, 1978). Thus, individuals high in the socialized power motive may choose action opportunities like teaching or parenting in order to experience flow. Looking at current research on flow, there is some evidence for both forms of power motive and their influence on flow experience.

Studies on Flow in Power Situations Flow in Competitions In his group of chess players, Csikszentmihalyi (1975) found different perceived reward structures based on the competitive level at which chess was played. He stated that players involved at a low competitive level enjoyed more autotelic elements of the game (the experience) whereas players involved at a high competitive level seemed to enjoy “exotelic aspects” (p. 60), like the competition itself and prestige. In this case, it may was premature to assume these to be exotelic aspects. In fact, competitions, which are about prestige and beating someone in order to feel strong after a victory, are task-inherent incentives that can elicit the implicit personalized power motive. In the first study that examined directly the relationship between the implicit and explicit power motive and flow, Schiepe-Tiska (2013) invited male students to the laboratory for a competition in which the contest outcome—victory or defeat—was experimentally varied. The competition contained power motive-specific incentives and was thus expected to arouse the power motive. Results showed that men with a high implicit and explicit power motive experienced more flow after winning the competition than men with a high implicit and explicit power motive after losing a competition. In addition, winners with a high implicit and explicit power motive experienced more flow than winners with a low implicit, but high explicit power motive. Therefore, the congruence between implicit and explicit power motive indicating an accurate motivational self-concept led to a high flow experience. Neither the achievement motive congruence, nor the affiliation motive congruence predicted flow. In sum, the results support the proposed theory of this chapter explaining how flow can be experienced in nonachievement situations. The competition aroused the implicit and explicit power motive. Hence, the motives were conducive to structuring the situation, which in turn fostered flow. Additionally, one study analyzed the effect of playing against computervs. human-controlled opponents in an online game (Weibel, Wissmath, Habegger,

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Steiner, & Groner, 2008). It was found that participants who believed that they were playing against a human user experienced more flow than participants who believed that they were playing against the computer. Here, the presence of another person may had been a stronger action opportunity to elicit the implicit power motive than merely playing against the computer, resulting in higher flow experience for individuals playing against a human user.

Flow and Leadership Early research on the implicit power motive indicated that a high implicit power motive is one helpful prerequisite for being a successful leader (Busch, 2018; McClelland & Boyatzis, 1982; Stewart & Chester, 1982; Winter, 1988; Winter & Stewart, 1977, 1978). The power motive structures situations in such a way that it makes action opportunities to have an impact on others salient. Therefore, leading others is a predestined job for individuals high in the power motive. However, a leader does not only structure situations for him or herself; he or she also clarifies the rules and structures of the situation for his or her followers. Especially a leader who is high in the socialized power motive also provides his or her followers with action opportunities that meet their motives. Hence, they can act upon these opportunities successfully without being persuaded by the leader, and flow at the follower level is more likely to occur. Current research particularly determines the effects of transformational leadership, which was found to have positive effects on the experiences and behaviors of followers (e.g. Bass, 1997; Dubinsky, Yammarino, Jolson, & Spangler, 1995; Tsai, Chen, & Cheng, 2009). Transformational leaders have a vision and inspire their followers to perform beyond their expectations by stimulating and transforming the followers’ attitudes, beliefs, values, and needs (Bass, 1985). It is described by four dimensions: individualized consideration, inspirational motivation, idealized influence, and intellectual stimulation. Therefore, transformational leadship shares some common characteristics with the socialized power motive. Amann (2014) found that people high in implicit power motivation indeed preferred leaders that show aspects of inspirational motivation such as having an attractive vision, inspiring followers, and who show a bigger meaning for daily work tasks. In addition, people high in implicit affiliation motivation preferred leaders who showed high levels of individualized consideration such as leaders having the attitude of a coach or mentor and having an intense, collaborative communication with their followers. Linsner (2009) examined the relationship between transformational leadership and work-related flow of the followers. The study indicated that transformational leaders who created a climate of contribution, recognition, and challenge had a positive influence on flow experience of the followers. Moreover, transformational leadership and flow experience together had a positive effect on work climate. Boerner and von Streit (2006) showed that in an orchestra, the transformational leadership style of a maestro in interaction with flow experience of the musicians of the orchestra increased the cooperative climate of the orchestra (e.g. sticking

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together, no tensions between the instrument groups). When the maestro was high in transformational leadership and the orchestra players were high in flow, the cooperative climate was also high. If the maestro was high in transformational leadership but the orchestra players were low in flow, there was no increase in the cooperative climate. Individuals high in the socialized power motive also tend to select a teaching profession for their career (Winter, 1973; Winter & Stewart, 1978). Hence, teaching provides action opportunities eliciting the power motive and can thus foster flow experience. Froh, Menges, and Walker (1993) examined award-winning teachers and their most intrinsic rewarding situations. These teachers recalled their most joyful flow experiences in situations where they were vigorously engaging in classroom discussions and became completely absorbed in the discussion. This was the result of structuring the discussion for themselves and for their students through their socialized power motive. Applying and testing the flow hypothesis of motivational competence, Schiepe-Tiska (2013) showed that teachers high in implicit and explicit power motive congruence experienced indeed more flow while teaching classes as compared to teachers with incongruent implicit and explicit power motives. Summarizing, action opportunities in social situations can not only contain affiliation-intimacy motive-specific incentives but also personalized or socialized power motive-specific incentives. Thus, individuals high in personalized or socialized power motive can also experience flow in social situation when they choose action opportunities to act upon that arouse their power motive.

General Conclusion and Perspectives The conclusion drawn from this chapter is not to leave the key component of Csikszentmihalyi’s flow theory—balance of challenge and skills—aside but to consider it as one possible motive-specific incentive that particularly arouses the achievement motive (cf. Baumann, Chap. 9 of this volume). Besides challenge and skills, there are other motive-specific incentives that can arouse motives, which in turn foster flow because they are conducive to structuring the situation. Luckily, a situation can contain many action opportunities with different motive-specific incentives and an individual can choose between them (according to their motives). Moreover, a person high in motivational competence is able to endow the situation with motive-specific incentives, which helps the person to frame the situation in such a way that it fits the person’s own motives. We hope that the broader theoretical framework offered by this chapter will make it easier to examine flow in situations where the balance of challenge and skills does not have priority. Hence, researchers do not have to try to fit their hypotheses and explain their findings in the light of balance and skills, but can determine flow in nonachievement situations, too. Very little research has been conducted in this area,

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possibly also due to the lack of a corresponding theory. The good thing is that many research questions still remain open. First of all, the assumed connections between implicit motives and flow need to be confirmed empirically. First studies showed the proposed relationship between the implicit achievement motive (Engeser & Rheinberg, 2008; Schüler, 2007), implicit and explicit achievement motive congruence (Schattke, Brandstätter, Taylor, & Kehr, 2015), as well as power motive congruence (Schiepe-Tiska, 2013) and flow experience. Even a relationship between the explicit affiliation motive and flow experience has been pointed out (Wong & Csikszentmihalyi, 1991). Nevertheless, more studies need to be conducted to confirm the presented theory. A further aspect to consider is that individuals can be high not just in one motive but also in two or more motives. Hence, the question arises of whether a clearly high characteristic in one motive or a special motive pattern is more conducive to fostering flow in the respective situation. For example, in the leadership context, besides the implicit power motive, the interplay with a high implicit affiliation motive seem to boost the relation to followers’ satisfaction and leaders’ career success (Steinmann, Ötting, & Maier, 2016). It might also additionally foster their flow experience. Additional research questions could deal with the processes of flow in groups in order to better understand the factors that foster and maintain flow in social situations. One main question that remains to be answered is whether flow in groups is merely the sum of flow experiences of the group members, because every member has aroused motives and individual flow experience crosses over from one to the other, or whether it is an collective phenomenon that is even greater than the sum of flow experiences, and which has different required structures for the situation itself (e.g. Sawyer, 2003). The flow of a group may also explain the great moments of teams when all members reach their highest potential and even unexpectedly beat a much stronger opposing team (cf. Walker, Chap. 10). Moreover, such a moment is a possible opportunity for the spectators to lose track of time and become totally absorbed while watching the game (cf. Walker, Chap. 10). Furthermore, research on leadership has just begun to recognize flow as a possibility to motivate employees. In addition, the aspect that in flow, the self and the action are merged, might be described as a feeling of oneness, which seems to be important in nonachievement situations as well, but needs more theoretical and empirical research in order to be better understood (Schmid, 2007; Siegel & Weinberger, 1998; Weinberger, Cotler, & Fishman, 2010).

Study Questions 1. How did Csikszentmihalyi understand challenge and skills at the beginning of his research?

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In the beginning of Cskiszentmihalyi’s flow research, he depicted challenge more broadly than the usual sense of the word challenge, as opportunities for actions and skills, as a person’s perceived action capabilities. How can the incentive “balance of challenge and skills” be integrated into the theoretical framework presented here? The balance of challenge and skills is one possible motive-specific incentive which particularly elicits the achievement motive, which is conducive to reaching flow experience. Besides motive-specific incentives arousing the achievement motive, there are other incentives that activate the affiliationintimacy and power motive, which can also help to structure the situation. Therefore, the person knows exactly what to do next and thus, flow experience is more likely to occur. What are implicit and explicit motives? Are they dependent on each other? Implicit motives are unconscious motivational needs that orient attention, and select and energize behavior towards specific classes of rewarding taskintrinsic incentives. Explicit motives are consciously accessible evaluations of a person’s self-concept and reflect people’s self-attributed view of their own implicit motives. Both motive systems coexist within an individual but are widely independent of each other. Research almost always points to a correlation close to zero between implicit and explicit motives. How are motives conducive to fostering the emergence of flow experience? In a given situation, individuals have many action opportunities but cannot attempt all of them. They have to choose one. The more motive-specific incentives an opportunity contains, the more a person orients his or her attention to the opportunity. Therefore, the opportunity becomes more salient to the person. Hence, the motives are conducive to structuring the situation by making suitable action opportunities salient. What does the Flow Hypothesis of Motivational Competence state? The flow hypothesis of motivational competence states that individuals high in motivational competence are more likely to experience flow. When a person’s life is mostly self-determined and, moreover, the implicit and explicit motive are congruent, one can select action opportunities in line with one’s implicit motives. This will likely result in flow experience because implicit motives support and energize motive-specific action opportunities and therefore, help to structure the situation. Do implicit and explicit motives have to be congruent all of the time when one is experiencing flow? Kehr et al. (2018) assume that implicit and explicit motives do not necessarily need to be congruent. The motive-specific incentives of an action opportunity arouse a person’s implicit motives and may or may not activate the congruent explicit motives. However, only when explicit motives are activated, which compete with the implicit motives, flow will be prevented. Then attention is distracted, and this hinders the experience of flow. What is the affiliation-intimacy motive? How does affiliation and intimacy differ from each other?

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The affiliation-intimacy motive is a recurrent concern for establishing, maintaining, or restoring friendly relations with others. Affiliation refers to any friendly and warm social contact, whereas intimacy refers especially to close dyadic interactions like romantic relationships. 8. What are the key characteristics of affiliative situations? Affiliative situations offer action opportunities, where another person is present with whom one can establish, maintain, intensify or restore a relationship (e.g. spending friendly time together, parties or just friendly small talk). They provide social interactions with the expression of warm, friendly, or intimate feelings and their outcome (positive interaction) can be attributed to the individual’s personality, effort, and skills (social competencies e.g. empathy, active listening). This outcome provides (sufficiently) clear feedback about the quality of the social relationship. 9. What are possible motive-specific incentives that arouse the power motive? Motive-specific incentives for the power motive are inherent in each action opportunity that entails an impact on others or the world at large, for example accusing, attacking, demanding, chasing, or threatening someone. Moreover, competitive action opportunities, concerns about fame, prestige, or reputation as well as opportunities to help, advise, or give support that is not explicitly solicited, and opportunities to intentionally elicit a strong (positive or negative) emotional reaction in others arouse the power motive. 10. Can social situations offer action opportunities with power motive-specific incentives, too? If so, please explain the relationship between the power motive and social situations. Yes, action opportunities in social situations can also offer power motivespecific incentives. Both, the affiliation-intimacy and the power motive, are mostly dependent on the presence of another person. The presence of others is a key characteristic of social situations regardless of affiliative or power situation. Hence, social situation can be seen as a broader term for both types of situations, but the similar situation can be structured by motives differently. Additionally, a situation can contain different motive-specific incentives activating different motives, and a person high in motivational competence is also able to endow the situation with motive-specific incentives and to reframe the situation in such a way that it fits one’s own motives.

References Amann, D. (2014). Transformational leadership meets follower motives: Compatibility of dimensions of transformational leadership and follower motives determines work-related outcomes (Dissertation). Technical University of Munich, Munich. Retrieved from https://mediatum.ub. tum.de/?id¼1227177 Atkinson, J. W., Heyns, R. W., & Veroff, J. (1958). The effect of experimental arousal of the affiliation motive on thematic apperception. In J. W. Atkinson (Ed.), Motives in fantasy, action, and society: A method of assessment and study (pp. 95–104). Princeton, NJ: Van Nostrand.

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Chapter 5

Flow Theory and Cognitive Evaluation Theory: Two Sides of the Same Coin? Sami Abuhamdeh

Abstract Flow theory (Csikszentmihalyi, Beyond boredom and anxiety: Experiencing flow in work and play. Jossey-Bass, San Francisco, 1975) and cognitive evaluation theory (Deci and Ryan, Intrinsic motivation and self-determination in human behaviour. Plenum, New York, 1985) have each inspired a large body of research dedicated to understanding why we enjoy doing what we enjoy doing. Although both theories ostensibly address the same category of behavior—namely, intrinsically motivated behavior—there have been few serious efforts to reconcile these two theories. This is the purpose of the current chapter. After a review and assessment of relevant empirical findings, I suggest that the two theories are most applicable to different types of behavior, distinguished by their state-level motivational orientations. Furthermore, whereas CET appears to be more applicable to understanding the process of developing intrinsic motivation, flow theory appears the more useful framework for understanding variations in enjoyment once intrinsic motivation for an activity has been firmly established.

Within the intrinsic motivation literature, a large chunk of the research has been guided by cognitive evaluation theory and, perhaps to a lesser extent, flow theory. These two areas of research have proceeded largely independently, with little crossfertilization. Perhaps one reason for this is that it remains unclear how the two theories are related. On the one hand, both theories emphasize what may be loosely termed “competence-related processes”: flow theory proposes that intrinsic motivation is dependent on a balance of perceived challenges and skills, and CET proposes intrinsic motivation depends on perceiving oneself as competent at the given activity. Yet despite this shared emphasis on competence-related processes, there are conspicuous differences between the two theories that appear, at least on the surface, This chapter is a revised and updated version of the chapter “A Conceptual Framework for the Integration of Flow Theory and Cognitive Evaluation Theory” (Abuhamdeh, 2012), published in the first edition of this book (Engeser, 2012). S. Abuhamdeh (*) Department of Psychology, Marmara University, Istanbul, Turkey © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_5

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to be incompatible. Within the framework of flow theory, optimal challenges promote enjoyment primarily because they heighten engagement in the task at hand, whereas according to CET, optimal challenges promote enjoyment because they maximize perceived competence. Furthermore, whereas CET stresses the importance of perceived autonomy (along with perceived competence), the concept of autonomy is altogether absent from flow theory. Given the two theories are both intended to explain the same category of behavior, how can such stark differences be explained? In this chapter, I attempt to bring some clarity to this issue. I begin by summarizing and assessing relevant findings relating to the aforementioned competence-related processes. I highlight findings that are inconsistent with what the two theories propose, as these prove especially useful in delineating the boundaries of each theory’s explanatory potential. Towards the end of the chapter, I offer an explanation for why CET emphasizes the concept of autonomy, whereas flow theory does not. An assumption I make throughout this chapter is that flow theory is relevant for predicting not only flow but also enjoyment. Indeed, in most of the studies I refer to, the outcome variable was enjoyment rather than flow. This assumption is fully consistent with how the model was intended to be used since its inception (Csikszentmihalyi, 1975). That is, although the model was developed by examining the conditions associated with optimal experience (flow), the intention was to create a model that could be applied toward a broader range of intrinsically motivated behavior.

Flow Theory The Optimal Challenge Proposition Flow theory was developed based on extensive interviews with rock climbers, chess players, athletes, and artists (Csikszentmihalyi, 1975; cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1). These individuals described their most rewarding experiences while engaged in the activities they most enjoyed and the situational conditions associated with these experiences. Particularly prevalent among these conditions was the presence of significant challenge—challenge that pushed one’s skills to their limit, but that were nevertheless not beyond one’s perceived capacities. Csikszentmihalyi referred to such challenges as “optimal challenges.” When such challenges were present, the rock climbers, chess players, and artists sometimes experienced a deeply rewarding state of mind, which Csikszentmihalyi termed “flow.” Later work on optimal challenges departed from this early focus on intrinsically motivated activities. Using the experience sampling method, researchers typically sampled a wide range of activities individuals engaged in during their day-to-day lives, including school-related activities and work-related activities. Results from these studies indicated that, whereas high perceived skills were consistently linked to enjoyment in these studies, high perceived challenges were not (e.g., Adlai-Gail,

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1994; Carli, Fave, & Massimini, 1988; Clarke & Haworth, 1994; Haworth & Evans, 1995). Indeed, zero-order correlations between challenge and enjoyment, from those studies that reported them, were either absent or negative (Chen, Wigand, & Nilan, 2000; Hektner, 1997; Moneta & Csikszentmihalyi, 1996; Shernoff, Csikszentmihalyi, Shneider, & Shernoff, 2003).

State-Level Moderators of the Link Between Challenge and Enjoyment In assessing the implications of these findings, it is important to keep in mind that the studies they are derived from typically did not restrict their focus to goal-directed, intrinsically motivated activities (as the original 1975 study had), but instead sampled a wide range of the everyday activities participants engaged in. This is important to consider for at least two reasons. First, many of these everyday activities, such as school-related activities and work-related activities, are not activities typically engaged in voluntarily for fun (i.e., intrinsically motivated), but out of obligation or necessity (e.g., Graef, Csikszentmihalyi, & Gianinno, 1983). In such contexts, individuals appear to most enjoy relatively low levels of challenge (Abuhamdeh and Csikszentmihalyi, 2012a, 2012b; Harter, 1978; Koestner, Zuckerman, & Koestner, 1987). Second, many of the activities we engage in during our day-today lives are not goal-directed, even those that are intrinsically motivated. Perceived challenge—a construct which implies the active pursuit of goals—would therefore seem to have less relevance for the enjoyment of such non-goal-directed activities. In an ESM-based study of US college students, both state-level motivational orientation (intrinsic, non-intrinsic) and activity type (goal-directed, non-goaldirected) were assessed as potential moderators of the within-person relationship between challenge and enjoyment (Abuhamdeh and Csikszentmihalyi, 2012b). As expected, the relationship was considerably stronger for activities that were intrinsically motivated than for those that were not. Furthermore, among these intrinsically motivated activities, challenge predicted enjoyment significantly more in the context of goal-directed than in the context of non-goal-directed activities. A second study examined the challenge-enjoyment relationship in the context of a single intrinsically motivated, goal-directed activity—internet chess. (It is worth keeping in mind that chess was one of the “autotelic activities” that were examined in Csikszentmihalyi’s initial work on optimal experience (1975) that served to inform flow theory.) Within this context, perceptions of challenge were strongly related to enjoyment (r ¼ 0.69), even more so than state-level perceptions of competence. Furthermore, games against opponents with higher skill ratings were more enjoyable than games against opponents with lower skill ratings, even though the latter were associated with higher state-level perceptions of competence than the former.

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Another potential moderator of the degree to which challenge is enjoyable— perceived outcome importance—was examined by Engeser and Rheinberg (2008). [Although the variable was referred to as “perceived [activity] importance” in the paper, I refer to it here as perceived outcome importance as this seems closer to how the authors conceptualized the construct, as is evident in the three items that were used in its operationalization (e.g., “I am worried about failing”)]. Three different activities—playing Pac Man, studying statistics, and learning a foreign language— were examined. Flow-related experiences were most associated with a balance of challenges and skills only for the Pac Man game (which presumably was associated with lower outcome importance than the other two activities). For the other two activities, flow-related experiences were greatest when participants perceived their skills to be significantly greater than the challenges they faced. Furthermore, for each of the three activities, participants who attached relatively low importance to the outcome of what they were doing reported more flow-related experiences when there was a balance of challenges and skills than participants who attached more importance to outcome.

Conclusion The optimal challenge proposition appears to best address the enjoyment of goaldirected activities characterized by a predominantly intrinsic motivational orientation. For this reason, the concept of optimal challenge appears most useful for predicting enjoyment in the context of goal-directed leisure activities such as sports and games. When concerns regarding performance outcomes are high (i.e., high extrinsic motivation), however, lower levels of challenge are typically more enjoyable (or perhaps less aversive), presumably because this implies a higher likelihood of attaining the extrinsic rewards that are sought (or avoiding the negative consequences of failure).

Cognitive Evaluation Theory CET is a subtheory within self-determination theory (Deci & Ryan, 1985) that aims to explain the conditions that elicit and sustain intrinsic motivation. According to CET, we enjoy activities to the extent that they satisfy the fundamental human needs of competence and autonomy. That is, activities that increase our sense of competence and/or autonomy are expected to be enjoyable (because they satisfy our “basic” needs of competence and autonomy), and thus intrinsically motivating.

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The Perceived Competence Proposition Perceived competence represents the degree to which an individual perceives him/herself to be competent at a given activity. Thus, the Perceived Competence Scale, “one of the most face valid of the instruments designed to assess constructs from Self Determination Theory” (Perceived Competence Scales, n.d., para. 2), assesses respondents perceived competence for an activity or domain. For example, a sample question from the Perceived Competence for Learning Scale (Williams and Deci, 1996) reads: “I am capable of learning the material in this course.” Perceived competence represents a domain-level cognitive assessment based on socialcontextual information and therefore must be distinguished from state-level “feelings of efficacy” (White, 1959) or “task-referential competence” (Elliot, McGregor, & Thrash, 2002) that may accompany moment-to-moment behavior. The “perceived competence proposition” is described as such: “Simply stated we would expect a close relationship between perceived competence and intrinsic motivation such that the more competent a person perceives himself to be at some activity, the more intrinsically motivated he will be at that activity” (Deci & Ryan, 1985, p. 58). Thus, if Ahmet believes he is a highly skilled tennis player, he should be more intrinsically motivated to play tennis than Mehmet, who believes he is a moderately skilled player. Support for the perceived competence proposition comes from almost four decades of empirical research. In laboratory-based studies, perceived competence has typically been manipulated by varying the type of performance feedback participants receive while controlling for actual performance. Participants assigned positive verbal feedback (e.g., “you performed very well”), which increases participants’ perceived competence, show more subsequent enjoyment of the task than participants assigned negative feedback or no feedback (e.g., Blanck, Reis, & Jackson, 1984; Deci, 1971; Koestner et al., 1987). Additionally, in the context of competitive activities, participants who were told that they won the competitive activity they had just engaged in (and there-fore received implicit positive performance feedback) subsequently enjoyed the activity significantly more the next time they engaged in it than participants who were told that they lost (e.g. Reeve & Deci, 1996; Reeve, Olson, & Cole, 1987; Tauer & Harackiewicz, 1999; Vallerand & Reid, 1984; Vansteenkiste & Deci, 2003). The intervening period between competitive outcome and subsequent task engagement was as long as 3 weeks (Vallerand & Reid, 1984). Several studies found that perceived competence mediates this carryover effect between outcome feedback and subsequent enjoyment (Fig. 5.1) (Reeve & Deci, 1996; Vallerand & Reid, 1984; Vansteenkiste & Deci, 2003).1

1 However, one study found that positive affect, rather than perceived competence, fully mediated the relationship (Tauer & Harackiewicz, 1999).

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Fig. 5.1 The carryover effect between positive outcome feedback and subsequent enjoyment

State-Level Moderators of the Link Between Perceived Competence and Enjoyment Results from other studies, however, suggest perceived competence does not always promote enjoyment. Elliot and Harackiewicz (1994) examined factors associated with the enjoyment of playing pinball. Participants were screened so that only experienced pinball players were included in the sample. Neither anticipated performance, nor midgame perceived competence, nor postgame perceived competence predicted enjoyment (in this study, like most studies which examine perceived competence as a predictor of enjoyment, actual performance was experimentally controlled). The authors suggested that while perceived competence may promote the development of intrinsic motivation for an activity, maintaining intrinsic motivation for that activity is largely dependent on other factors. This null finding was replicated in a subsequent study which used a similar study design (Cury, Elliot, Sarrazin, Da Fonseca, & Rufo, 2002). The condition under which perceived competence can account for intrinsic motivation was the specific focus of a pair of laboratory-based studies by Carol Sansone (1986). In study 1, task feedback (being provided answers to previously unknown questions) influenced enjoyment independent of perceived competence (perhaps by satisfying curiosity). In study 2, the positive relationship between perceived competence and enjoyment only held when competence had first been emphasized through the use of an ego-involvement manipulation: participants were told that doing well at the task was associated with greater intelligence and creativity. For participants who did not receive this ego-involvement manipulation, enjoyment was unrelated to perceived competence. A follow-up study which employed a similar design again found no relationship between perceived competence and enjoyment (Sansone, 1989).

Conclusion Although perceiving oneself as competent at a given activity appears to promote enjoyment when performance outcomes are of relatively high importance (i.e., high

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extrinsic motivation), when performance-related concerns are minimal or absent, perceived competence may have little or no effect on enjoyment.

Reconciling the Perceived Competence Proposition with the Optimal Challenge Proposition What may be apparent at the point is the contrasting nature of the motivational orientations for which the two constructs appear to have greatest relevance. Whereas optimal challenge appears to be most useful in understanding the enjoyment of intrinsically motivated activities (such as hobbies), in which performance outcomes are typically of relatively low concern, perceived competence appears most relevant to the enjoyment of activities in which performance outcomes are of relatively high importance. This makes sense when we consider that higher challenge decreases the likelihood of attaining a positive performance outcome, and higher perceived competence increases it. Thus, if a student desires a good grade on a math exam, then immediate experience will be greatest when perceived competence is high and challenges are low because of the implications for performance outcomes. In contrast, when the prime motivation is to simply have fun, optimal challenge becomes more relevant, and perceived competence less. A tennis hobbyist who considers himself to be an “average” tennis player (i.e., medium perceived competence) should have just as much fun playing a game of tennis as a tennis hobbyist who considers himself to be a “very good” tennis player (i.e., high perceived competence) if, in both cases (and controlling for other factors), optimal challenges are present. Because outcome-related concerns for a given activity often decrease over time as an individual gains experience and acquires skills in an activity, the explanatory potential of the two constructs is likely to vary accordingly. Consider the following example. Kedi is a student in a geometry class. On the first day of class, she is asked by the teacher to solve a relatively easy problem on the board. She nervously writes what she thinks may be the correct solution (due to her inexperience, she receives very little feedback from the task itself). At this point, how useful are the two constructs in predicting the enjoyment Kedi experiences while attempting the solution on the board? The optimal challenge construct would not appear to be of much help here. Both laboratory-based and field-based studies have shown that when extrinsic motivation for an activity is high, “optimal challenges” do not tend to optimize experience (Abuhamdeh, 2008; Abuhamdeh & Csikszentmihalyi, 2012b; Engeser & Rheinberg, 2008; Harter, 1978). Rather, in these situations, we tend to prefer relatively easy tasks. In contrast, the perceived competence construct is more relevant—the more competent Kedi believes she is at geometry, the more confidence she will have that her solution is correct, and the more enjoyable (or less aversive) the process will be. Furthermore, we should expect the performance outcome— whether her solution is right or wrong—to have a significant impact on her subsequent motivation for and enjoyment of geometry. If her solution is correct, the

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teacher will congratulate her, providing her with positive performance feedback. This positive performance feedback should increase her perceived competence in geometry, and this increase in perceived competence should make her more likely to enjoy herself the next time she attempts to solve a geometry problem (i.e., a carryover effect). Let us assume that Kedi’s solution was correct and that she goes on to develop a keen interest in geometry—she now freely chooses to work on geometry problems outside of class, during her free time, purely for the fun of it. In other words, Kedi is now intrinsically motivated to engage in geometry (interest researchers may say she has established individual interest for geometry) (e.g., Krapp, Hidi, & Renninger, 1992). How useful are the two constructs now for predicting the variations in enjoyment she experiences from one episode of engagement to another? Let us first consider perceived competence. At this point, would it be possible to predict the enjoyment Kedi experiences while engaged in a given geometry problem based on the performance outcome of her previous episode of engagement (i.e., a perceived competence carryover effect)? This would seem unlikely. At this point, she would be well acquainted with geometry and would have already established a sense of her own competence at it. Any single outcome would be unlikely to have much of an impact on this assessment. Furthermore, intrinsically motivated, enjoyable activities provide participants with a stream of real, meaningful performance feedback during the course of engagement. Kedi would now have the experience needed to accurately interpret this feedback, and it is this feedback that would guide her behavior and experience, not feedback from a previous problem she attempted to solve.2 In contrast to perceived competence, optimal challenge does appear useful in predicting variations in enjoyment from one episode of engagement to the next once intrinsic motivation for an activity has been established. If an activity is too easy, boredom will ensue. If an activity is too difficult, boredom again (unless there is significant performance pressure, in which case anxiety will be more a more likely response). A balance between activity demands and perceived capacities offers the best potential for enjoyment and may even lead to flow. Of course, to speak of intrinsic motivation as having been “established” or “not established” implies a binary that is unlikely to exist. More realistic is to conceive of a continuum, with one end representing high extrinsic motivation and low intrinsic motivation (e.g., taking a college placement exam) and the other end representing high intrinsic motivation and low extrinsic motivation (e.g., playing a familiar video game against a computer opponent). Across this continuum, the importance of perceived competence for enjoyment and the importance of challenge for enjoyment would be inversely related, as shown in Fig. 5.2.

2 This is not to suggest that if Kedi’s perceived competence for geometry steadily decreased over time, this would not have negative repercussions for her intrinsic motivation. Rather, it is to say that perceived competence loses its ability to predict the enjoyment of single episodes of engagement— a math problem, a tennis match, etc.—when a motivational orientation is primarily intrinsic.

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Fig. 5.2 An idealized representation of the contribution of perceived challenge and perceived competence to enjoyment as a function of motivational orientation

CET and Optimal Challenge For those who are unfamiliar with CET, it may come as a surprise that CET, like flow theory, emphasizes optimal challenge as an important condition for enjoyment (and therefore intrinsic motivation). This does not contradict CET’s assertion that intrinsic motivation is rooted in the needs for competence and autonomy because, according to CET, “it is success at optimally challenging tasks that allows people to feel a true sense of competence” (Deci & Ryan, 2000, p. 260). “When children are working with optimally challenging activities, perceived competence will tend to come naturally, for they will be having the experiences of success following concerted effort that lead to the perceptions of competence” (Deci & Ryan, 1985, p. 124). This conceptualization of optimal challenge is difficult to evaluate, as there seems to be an assumption that success experiences always accompany optimally challenging activities. Optimally challenging activities, however, are relatively difficult activities, and relatively difficult activities sometimes end in failure. A chess player involved in a very close game of chess, for example, faces the significant possibility of defeat, and thereby receiving negative outcome feedback. However, it is possible to consider a different though related possibility—that the enjoyment of optimal challenge can be accounted for by state-level perceptions of competence that accompany the process of engagement itself. There is some evidence to suggest that the pursuit of optimal challenge is associated with heightened performance (e.g., Jackson et al., 2001; Schüler, 2007), and feeling good about one’s performance while engaged in an intrinsically motivated activity is positively related to enjoying that episode of engagement (e.g., Fave, Bassi, & Massimini, 2003; Jones, Hollenhurst, & Perna, 2003). Furthermore, interview-based findings indicate that the pursuit of optimal challenge is typically associated with feelings of control (Csikszentmihalyi, 1975).

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Still, the general proposition that the enjoyment of optimal challenge can be reduced to information-based perceptions of competence seems unlikely. In a study of the enjoyment of internet chess games (Abuhamdeh & Csikszentmihalyi, 2012b), experienced chess players most enjoyed games in which they performed only slightly better than equally rated opponents—outperforming these opponents by a wider margin was not as enjoyable. It seems unlikely that outperforming an equally rated opponent by a small margin would provide more positive competence information (both during and at the conclusion of the game) than outperforming the same opponent by a wider margin, so it is unclear how CET would account for this difference in enjoyment. Below, I suggest a couple alternative possibilities.

Other Reasons Why Optimal Challenges Are Enjoyable Optimal Challenges Maximize Attentional Involvement From its inception, the flow model (1975) stressed the role of attentional processes. According to the model, when challenges are balanced by skills, attention is channeled from stimuli unrelated to the task at hand (e.g., self-focus, monitoring time, etc.) to the task itself. This heightened attentional involvement allows the person to enjoy the experience of being fully engaged in an intrinsically rewarding activity. Interviews and case studies of artists, surgeons, athletes, and others have found that when these individuals describe their most enjoyable moments, they frequently mention high concentration and an intense involvement in whatever they happen to be doing (Csikszentmihalyi, 1975; Jackson & Csikszentmihalyi, 1999). Several laboratory-based studies have provided empirical support for a positive relationship between attentional involvement (referred to as “task involvement” or “task absorption” in these studies1) and enjoyment (Deppe & Harackiewicz, 1996; Elliot & Harackiewicz, 1994; Harackiewicz & Elliot, 1998). The possibility that attentional involvement mediates the relationship between a balance of challenges and skills and enjoyment was examined in an experience sampling study of US college students (Abuhamdeh and Csikszentmihalyi, 2012a). Multilevel, within-person analyses indicated that (1) as expected, there was a greater balance of challenges and skills associated with greater enjoyment, and (2) this positive relationship was fully mediated by attentional involvement. That is, when attentional involvement was statistically controlled, the relationship between challenge/skill balance and enjoyment was no longer significant. Due to the observational nature of the data, however, it was not possible to rule out spurious relationships, nor could conclusions regarding causality be made. Indeed, it seems possible, even probable, that reciprocal causality exists in the relationship between attentional involvement and enjoyment, with attentional involvement being both a cause and consequence of enjoyment.

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Optimal Challenges Maximize Suspense The impact of suspense on enjoyment has primarily been the purview of film theorists, literary critics, and media analysts (Guidry, 2005). The focus here has been on non-goal directed, observer/spectator activities, primarily novel reading, film-watching, and spectator sports. In the context of such activities, outcome uncertainty has been shown to lead to feelings of suspense, and, in turn, enjoyment. For instance, participants who read short stories rated them as significantly more suspenseful when their outcomes were not revealed beforehand ( p < .001) (Brewer & Lichtenstein, 1981). The notion that outcome uncertainty (and the resulting feelings of suspense) may promote enjoyment may extend beyond observer/spectator activities to first-person, goal-directed activities as well. Specifically, part of the enjoyment of optimal challenges may come from the suspense of not knowing what the ultimate outcome will be. Optimal challenges maximize outcome uncertainty, and outcome uncertainty adds significance and drama to one’s immediate actions and promotes further involvement in the activity. When outcome uncertainty is low—as when a chess player outperforms his or her opponent by a wide margin—so too is the degree of suspense. The possibility that optimal challenge may be linked to suspense in the context of a goal-directed activity, and that this heightened suspense may, in turn, increase enjoyment, was examined in a study which had participants play four games of “Speed Slice” on the Wii video game console (Abuhamdeh, Csikszentmihalyi, & Jalal, 2015, Study 1). In order to minimize performance-related concerns, participants were told they were playing against the computer (although in reality their opponent was an out-of-view research assistant) and were left to play alone in a private area, unobserved. Additionally, participants were told previous research indicated that game performance was unrelated to both mental and physical ability. During each game, play was paused, and participants completed a short survey which measured various subjective states including suspense and enjoyment. Additionally, the game score at that time was recorded. Multilevel, within-person results indicated that, as expected, very close, “optimally challenging” games were associated with greater enjoyment than lopsided games (even those in which participants outperformed their opponents by a wide margin). Furthermore, suspense mediated this positive relationship, even after controlling for perceived competence, accounting for 36% of the variance in enjoyment. A second study incorporated a behavioral measure of intrinsic motivation (Abuhamdeh et al., 2015, Study 2). Participants first played two different games, one in which they won by a slim margin (i.e. high outcome uncertainty), and a different game in which they won by a large margin (i.e. low outcome uncertainty). Afterwards, participants were asked which game they would like to play one more time. The majority of participants chose to play the game they won by a slim margin over the game they won by a large margin, despite the fact that the latter was associated with greater perceptions of competence than the former. Performance

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concern moderated this preference, with participants low in performance concern showing a stronger preference for the game they won by a slim margin than participants high in performance concern. Results from these two studies lend support to the notion that suspense can contribute significantly to enjoyment (and by implication intrinsic motivation), even in the context of first-person, goal-directed activities.

CET’s Perceived Autonomy Proposition In addition to a need for competence, CET proposes humans also have a need for autonomy—a need to experience the initiation and regulation of behavior as selfdetermined (Ryan & Deci, 2000). CET’s perceived autonomy proposition states that events that increase a person’s perceived autonomy while performing a certain behavior will increase intrinsic motivation for that behavior, whereas events that decrease perceived autonomy will decrease intrinsic motivation (Deci & Ryan, 1985). This proposition has been well supported empirically. Participants given a choice about which puzzles to work on, for example, subsequently enjoyed the puzzles to a greater extent than participants who were not given this choice (Zuckerman, Porac, Lathin, & Deci, 1978)—in other words, a perceived autonomy carryover effect. Other studies have shown that extrinsic rewards can undermine subsequent enjoyment (e.g., Deci, 1971), presumably because such rewards are experienced as controlling and therefore reduce one’s sense of autonomy and freedom. The concept of autonomy is not explicitly represented in flow theory, as Deci and Ryan (2000) have pointed out: Perhaps the most important [difference between CET and flow theory] is that flow theory does not have a formal concept of autonomy, instead basing intrinsic motivation only in optimal challenge (which, as a concept, is relevant primarily to competence rather than autonomy). SDT, on the other hand, has always maintained that even optimal challenges will not engender intrinsic motivation or flow unless people experience themselves as autonomous in carrying them out—that is, unless the behaviors have an I-PLOC [internal perceived locus of causality]. Although Csikszentmihalyi has at times referred to the idea of autonomy, it has not been represented as a formal element in the theory (p. 261).

Deci and Ryan go on to suggest that expanding flow theory so that it accounts for the need for autonomy (as well as the need for competence and the need for relatedness) would significantly increase the range of behaviors capable of being addressed by the theory. Given the huge number of positive motivational outcomes the concept of autonomy has been linked to within the STD literature (for a review, see Ryan & Deci, 2006), it is worth considering possible reasons for the absence of a concept of autonomy in flow theory. The original intention of the theory was to account for enjoyment in the context of “autotelic” (i.e., intrinsically motivated) activities (Csikszentmihalyi, 1975). When the chess players, athletes, etc., who were

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interviewed were asked to describe their “optimal experiences,” heightened perceptions of autonomy or freedom were not commonly reported as distinguishing features of these experiences. That is, they did not distinguish optimal experiences from more mundane (but still relatively enjoyable) experiences while engaged in these activities. This makes sense when one considers the nature of autotelic activities. These activities are associated with preponderantly intrinsic motivational orientations (i.e., located on the far right of the x-axis in Fig. 5.2). In the context of such activities, variations in enjoyment would seem to have little to do with perceptions of autonomy because perceptions of autonomy are no longer a significant issue. If a chess enthusiast plays two consecutive games of internet chess within a single sitting and enjoys the second game significantly more than the first, how likely is it that perceptions of autonomy would have much to do with the difference in enjoyment? In the context of intrinsically motivated activities, fluctuations in enjoyment appear to be influenced primarily by factors other than perceived autonomy and perceived competence, such as perceptions of challenge and feelings of efficacy (White, 1959). The question of whether flow theory may profit by incorporating the concept of autonomy, therefore, depends on the intended scope of the theory.

Conclusion Albert Bandura (1986) observed that while moderate levels of self-efficacy (i.e., perceived competence) seems necessary for interest in an activity to develop, additional self-efficacy does not appear to promote additional interest. He therefore proposed the existence of a “threshold” beyond which additional self-efficacy has no effect on interest.3 Building on this, Elliot and Harackiewicz (1994) distinguished between the development of intrinsic motivation for an activity for which intrinsic motivation has yet to be established, and the maintenance of intrinsic motivation for an activity once intrinsic motivation for an activity has been established. Although this distinction was originally made an account for perceived competence null findings (perceived competence did not predict enjoyment for an intrinsically motivated activity, Elliot & Harackiewicz, 1994), the general distinction also appears useful for identifying the motivational contexts in which optimal challenges promote enjoyment. Whereas both perceived competence and perceived autonomy appear to have greatest explanatory potential when extrinsic motivation is high and intrinsic motivation is low, optimal challenge appears most relevant for the enjoyment of activities high in intrinsic motivation and low in extrinsic motivation, activities which Csikszentmihalyi referred to as “autotelic” (Csikszentmihalyi, 1975). Referring back to the example of Kedi the Geometry 1 student, CET may aid in understanding why Kedi develops intrinsic motivation for geometry, whereas flow theory appears more useful for understanding why Kedi continues to enjoy geometry once she has established intrinsic motivation for it.

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When considering the often mundane activities we engage in on a daily basis, CET may have broader applicability than flow theory. This is because most of the activities we engage in are not engaged in purely for the sake of interest or enjoyment, but for largely extrinsic reasons (cite). Indeed, much of the support for the importance of perceptions of competence and autonomy come from studies which examined extrinsically-motivated activities, including diabetes control (cite), and smoking cessation (cite), Yet when the goal is to understand what makes our most rewarding experiences so enjoyable, flow theory offers unique insights. Clearly, any attempt to understand the full range of intrinsically motivated behaviors must take both theories into account.

Study Questions 1. In Csikszentmihalyi’s initial work on optimal experience (1975), challenge was a key condition for the enjoyment of autotelic activities such as chess, rock climbing, and dance. Later findings from studies which used the experience sampling method to examine the enjoyment of challenge across a wide range of everyday activities suggest that in many of these studies, challenge was unrelated to enjoyment. What is one possible reason for this discrepancy in findings, according to the author? • One possible reason is that many of the activities sampled in studies which used the experience sampling method were often not intrinsically motivated. A second possible reason is that many of the activities were not goal-directed. 2. According to CET, what two fundamental needs underlie intrinsically motivated behavior? • The need for competence and the need for autonomy. 3. Describe a real-life scenario in which, according to the author, a perceived competence carryover effect is likely to occur. • A perceived competence carryover effect is likely to occur in the context of an unfamiliar activity associated with significant outcome importance, especially if the activity provides minimal performance feedback to the participant during the process of engagement. Imagine, for example, a blindfolded boy playing the children’s game Pin the Tail on the Donkey for the first time, in front of his peers. If he receives positive outcome feedback, his perceived competence at the game should increase, and this should have a positive effect on enjoyment the next time he plays the game. 4. Zach is playing his favorite video game for the 896th time. According to the author, which of the two theories (flow theory or CET) is more likely to be useful in this situation for predicting how much Zach will enjoy the game? • Flow theory.

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4.1. According to CET, why are optimal challenges enjoyable? • According to CET, optimal challenges are enjoyable because they maximize perceptions of competence. 4.2. Describe another possible reason why optimal challenges are enjoyable. • Optimal challenges may promote attentional involvement, and this would promote enjoyment. Optimal challenges may also heighten suspense, which has been linked to enjoyment.

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Reeve, J., Olson, B. C., & Cole, S. G. (1987). Intrinsic motivation in competition: The intervening role of four individual differences following objective competence information. Journal of Research in Personality, 21(2), 148–170. https://doi.org/10.1016/0092-6566(87)90004-3. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps. 1999.1020. Ryan, R. M., & Deci, E. L. (2006). Self-regulation and the problem of human autonomy: Does psychology need choice, self-determination, and will? Journal of Personality, 74(6), 1557–1585. https://doi.org/10.1111/j.1467-6494.2006.00420.x. Sansone, C. (1986). A question of competence: The effects of competence and task feedback on intrinsic interest. Journal of Personality and Social Psychology, 51(5), 918–931. https://doi.org/ 10.1037/0022-3514.51.5.918. Sansone, C. (1989). Competence feedback, task feedback, and intrinsic interest: An examination of process and context. Journal of Experimental Social Psychology, 25, 343–361. Schüler, J. (2007). Arousal of flow experience in a learning setting and its effects on exam performance and affect. Zeitschrift Für Pädagogische Psychologie, 21(3/4), 217–227. https:// doi.org/10.1024/1010-0652.21.3.217. Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18 (2), 158–176. https://doi.org/10.1521/scpq.18.2.158.21860. Tauer, J. M., & Harackiewicz, J. M. (1999). Winning isn’t everything: Competition, achievement orientation, and intrinsic motivation. Journal of Experimental Social Psychology, 35(3), 209–238. https://doi.org/10.1006/jesp.1999.1383. Vallerand, R. J., & Reid, G. (1984). On the causal effects of perceived competence on intrinsic motivation: A test of cognitive evaluation theory. Journal of Sport Psychology, 6(1), 94–102. Vansteenkiste, M., & Deci, E. L. (2003). Competitively contingent rewards and intrinsic motivation: Can losers remain motivated? Motivation and Emotion, 27(4), 273–299. https://doi.org/10. 1023/A:1026259005264. White, R. W. (1959). Motivation reconsidered: The concept of competence. Psychological Review, 66(5), 297–333. https://doi.org/10.1037/h0040934. Williams, G. C., & Deci, E. L. (1996). Internalization of biopsychosocial values by medical students: A test of self-determination theory. Journal of Personality and Social Psychology, 70, 767–779. Zuckerman, M., Porac, J., Lathin, D., & Deci, E. L. (1978). On the importance of self-determination for intrinsically-motivated behavior. Personality and Social Psychology Bulletin, 4(3), 443–446. https://doi.org/10.1177/0146167278004003.

Chapter 6

On the Relationship Between Flow and Enjoyment Sami Abuhamdeh

Abstract Ever since Csikszentmihalyi’s earliest work on flow, he has conceived of flow as a form of enjoyment. Nevertheless, alternative views have arisen, most influentially Martin Seligman’s view of flow as devoid of emotions. In the first part of this chapter, these two contrasting views are clarified and then evaluated. While Csikszentmihalyi’s view, with some adjustment, may be reconciled with current scientific understanding of emotions, Seligman’s is based on a premise which conflicts with appraisal theorists view that the elicitation of emotions is often automatic (especially within the context of well-rehearsed activities) and need not consume significant attentional resources. The common misconception of flow as devoid of emotions is then traced to three sources: (1) a failure to differentiate between experiencing an emotion and the awareness of experiencing it, (2) incorrectly assuming that the enjoyment experienced during flow is of the “happy-smiley” type, and (3) Csikszentmihalyi’s unconventional usage of the term “pleasure” in his writings. Potential explanations for the enjoyable, intrinsically-motivating nature of flow are then suggested. The nature of the relationship between flow and enjoyment is a point of some contention among flow researchers. This first hit home when, several years ago, as part of a symposium on flow, I presented findings from my work on the enjoyment of challenge. Following the panel’s question and answer session, a fellow panel member turned to me abruptly, clearly agitated, and warned, “Flow and enjoyment are not the same thing! You should be more careful!!” Since that time, through my interactions with other flow researchers, it has become apparent that there is little consensus on how flow and enjoyment are related to one another. I hope this chapter will provide some needed clarification.

S. Abuhamdeh (*) Department of Psychology, Marmara University, Istanbul, Turkey © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_6

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Csikszentmihalyi’s View of the Relationship As the concept of flow was conceived by Csikszentmihalyi, any attempt to understand how flow and enjoyment are related would do well by beginning with his corpus. As it turns out, one need not dig very deep. In Beyond Boredom and Anxiety (1975), Csikszentmihalyi’s seminal work in which the concept of flow was first introduced, one only has to go as far as the second paragraph on the dust flap to read the following: In order to understand the nature of enjoyment, Mihaly Csikszentmihalyi studied a large number of people who were deeply engaged in activities where conventional rewards are not important. He examined chess masters, composers, rock climbers, dancers, basketball players, and many others—and found that enjoyable activities, no matter how different from each other, provide a common experience: a satisfying, often exhilarating, feeling of creative accomplishment and heightened functioning. Csikszentmihalyi calls this experience flow.

Then, just a few paragraphs into the preface: The goal was to focus on people who were having peak experiences, who were intrinsically motivated, and who were involved in play as well as reallife activities, in order to find out whether I could detect similarities in their experiences, their motivation, and the situations that produce enjoyment (emphasis added) (pp. xiii).

What is clear when we examine these and other passages from the book is that Csikszentmihalyi conceived of flow as a form of enjoyment. Indeed, his primary research aim was to uncover the common characteristics of our most enjoyable experiences—so-called “optimal” experiences—which he termed flow. In other words, flow, as conceptualized by Csikszentmihalyi, is inherently enjoyable. Csikszentmihalyi stressed that the enjoyment associated with flow experiences is experienced during the process of engagement, rather than afterwards. It is this process-based enjoyment, he asserted, that motivates people to engage in “autotelic” activities: The process of making their products was so enjoyable that they were ready to sacrifice a great deal for the chance of continuing to do so (pp. xii).

Beyond Boredom and Anxiety was written over 40 years ago, and sometimes an individual’s views on a topic change. Csikszentmihalyi’s view of flow as a form of enjoyment, however, has not. In more recent work by Csikszentmihalyi and his colleagues, the enjoyable, “autotelic” (i.e. intrinsically rewarding) nature of flow has been consistently emphasized (e.g. Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014; Nakamura & Csikszentmihalyi, 2009). As Csikszentmihalyi himself has put it, “From the start I described flow as a special form of enjoyment, and so I do to this day” (personal communication, November 15, 2015).

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Fig. 6.1 Some of the different types of enjoyment proposed by Ekman and Ekman, along with their associated range in intensity

So What Is Enjoyment? A common definition of enjoyment is “the state or process of taking pleasure in something” (Oxford Dictionary, 2018). This obviously covers a lot of emotional territory. Correspondingly, some emotion theorists have pegged enjoyment as a “basic” emotion (Ekman, 1992; Izard, 1977; Tomkins, 1962). To appreciate just how much emotional ground enjoyment covers, Paul and Eve Ekman’s conceptualization of the “continent of enjoyment,” from their Atlas of Emotions project (http:// www.atlasofemotion.org), is useful. In their view, enjoyment comes in many forms, including those listed in Fig. 6.1.1 As can be seen, many forms of enjoyment may be differentiated—wonder, pride, joy, etc. Within this classification scheme, an important distinguishing feature of each type of enjoyment is its intensity. For example, whereas “relief” may range in intensity from very low to very high, ecstasy is, by definition, a very intense experience. While some may argue whether some of the emotions in Ekman and Ekman’s scheme really deserve their own label (e.g. “naches,” or pride in the achievement of offspring), the breadth of the emotional terrain covered by enjoyment is well-demonstrated. Ekman and Ekman did not

1

The Atlas of Emotions is guided by data gathered from a survey of 248 emotion researchers (see “What scientists who study emotion agree about,” Ekman 2016). Nevertheless, a good deal of guesswork is involved as well, and some of the guesses don’t seem particularly convincing. For example, with respect to their “continent of enjoyment” (see http://atlasofemotions.org/#states/ enjoyment), it is unclear why sensory pleasure should be restricted to lower levels of intensity, given humans are wired to experience intense pleasure from orgasm, among other sensory pleasures. Interpretation of their “continent of enjoyment” graphic is further complicated by the fact that intensity appears to be represented on both the x and y axes simultaneously. This is to say that their Atlas of Emotions is probably best taken as suggestive rather than definitive.

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incorporate flow into their scheme (their list is not intended to be exhaustive), but the natural location for flow would be seem to be at the high end of the intensity scale, coupled with a relatively restricted range in intensity. Is flow, then, best thought of as a specific form of enjoyment? While such a characterization may work informally, it is probably best not taken too literally. For one, a characteristic quality of flow is a deep interest in the task at hand, and most emotion theorists do not consider interest to be a type of enjoyment, but a separate, distinct positive emotion (Izard, 1977; Panksepp, 2005; Silvia, 2005; Tomkins, 1962). This view is backed by empirical findings which indicate that interest and enjoyment, in at least some contexts, have different antecedents, as well as different trajectories in response to performance feedback (Egloff, Schmukle, Burns, Kohlmann, & Hock, 2003; Reeve, 1989). In a laboratory-based study by Reeve (1989), for instance, enjoyment was predicted by progress towards a goal, whereas interest was more strongly tied to the “collative properties” (Berlyne, 1960) of the task stimuli (even though enjoyment and interest were strongly correlated, as is usually the case in the context of goal-directed activities). Enjoyment and interest are thought to work in tandem, in a reciprocal fashion, to maximize intrinsic motivation (Ainley & Hidi, 2014; Izard, 1977; Reeve, 1989; Tomkins, 1962). Thus, to account for the emotional quality of flow, both enjoyment and interest should be accounted for. A second issue with conceiving of flow as a form of enjoyment is that doing so classifies flow based on an emotional aspect (enjoyment) to the exclusion of its cognitive and motivational components. Perhaps the most distinctive characteristic of flow is deep attentional involvement in moment-to-moment activity (i.e. a cognitive aspect), so it is debatable whether enjoyment should be given primacy in classifying flow.

Flow and Enjoyment: Empirical Findings Box 6.1 Enjoyment vs. “Autotelic” Experience Flow is often described by Csikszentmihalyi and his colleagues as consisting of six experiential components (Nakamura & Csikszentmihalyi, 2002): (1) intense and focused concentration on the task at hand, (2) a merging of action and awareness, (3) a loss of self-consciousness, (4) a sense of control, (5) the distortion of temporal experience, and (6) intrinsically-rewarding (i.e. “autotelic”) experience. Although the term “enjoyment” is not included in this list, it is implied by the sixth component (intrinsically-rewarding experience). That is, within the context of goal-directed activities, an experience is intrinsically rewarding because it is enjoyable and interesting. Indeed, within the field of intrinsic motivation, enjoyment and interest have (continued)

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Box 6.1 (continued) traditionally been used as the two primary experiential indicators of intrinsic motivation. Given Csikszentmihalyi’s conceptualization of flow as a type of enjoyment—specifically, as an “optimal experience”—it may be argued (as I would) that flow, by its very nature, is inherently enjoyable; that if a state of consciousness is not enjoyable, it isn’t flow. Nevertheless in past research flow has sometimes been operationalized without the enjoyment component, and/or without the “autotelic,” intrinsically motivating component (which in effect excludes enjoyment, see Box 6.1). And it is worthwhile to note that the “flow” measured in these studies has usually been associated with enjoyment. For example, several studies have operationalized flow using challenge-skill quadrants (cf. Moneta, Chap. 2). In these studies, “flow” has typically been positively correlated with enjoyment, and the “flow” quadrant, compared to the other three quadrants, has often been associated with highest enjoyment (e.g. Asakawa, 2004; Ceja & Navarro, 2012; Clarke & Haworth, 1994; Fink & Drake, 2016; Haworth & Evans, 1995; Sartika & Husna, 2014). (Footnote: Not all studies that used challenge-skill quadrants to operationalize flow, however, found that the flow quadrant was associated with highest enjoyment. The inconsistency can be explained by the fact that not all of the studies measured “flow” in the context of goal-directed, intrinsically-motivated activities (Abuhamdeh & Csikszentmihalyi, 2012b). Additionally, when flow has been operationalized using the six experiential dimensions of flow frequently mentioned by Csikszentmihalyi and his colleagues (i.e. intense concentration on the task at hand, a merging of action and awareness, etc.), but without the “autotelic,” intrinsically motivating component, this version of flow has been consistently associated with enjoyment (e.g. Baumann, Lürig, & Engeser, 2016; Brom et al., 2017; Rodríguez-Sánchez, Schaufeli, Salanova, Cifre, & Sonnenschein, 2011; Weibel & Wissmath, 2011; Weibel, Wissmath, Habegger, Steiner, & Groner, 2008; Wissmath, Weibel, & Groner, 2009). An important, emerging area of flow research examines the physiological correlates of flow (e.g. Bian et al., 2016; de Manzano, Theorell, Harmat, & Ullén, 2010; Gaggioli, Cipresso, Serino, & Riva, 2013; Harmat et al., 2015; Keller, Bless, Blomann, & Kleinböhl, 2011; Knierim, Rissler, Hariharan, Nadj, & Weinhardt, 2018; Mauri, Cipresso, Balgera, Villamira, & Riva, 2011; Peifer, Schulz, Schächinger, Baumann, & Antoni, 2014; Tian et al., 2017; Tozman, Magdas, MacDougall, & Vollmeyer, 2015; Tozman, Zhang, & Vollmeyer, 2017). Recent findings of a link between “flow proneness” and dopamine receptor availability (de Manzano et al., 2013; Gyurkovics et al., 2016) provide compelling support for the notion that flow is an enjoyable, intrinsically-motivating state, given the link between dopamine and both pleasure and motivation.

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Seligman’s Contrasting View of Flow Despite Csikszentmihalyi’s conceptualization of flow as a very enjoyable experience, it is not uncommon for those working both within and outside the field of flow research to assert that flow, as a phenomenological state, excludes all emotions, including enjoyment (e.g. Boniwell, 2012; Hetland et al., 2018; Kyriazos et al., 2018; Peterson, Park, & Seligman, 2005). This view seems to have its origins in the work of Martin Seligman, who, in his best-selling book Authentic Happiness (2002), wrote: In fact, it is the absence of emotion, of any kind of consciousness, that is at the heart of flow (pp. 115).

And: It is the total absorption, the suspension of consciousness, and the flow that the gratifications produce that defines liking (autotelic) activities—not the presence of pleasure. Total immersion, in fact, blocks consciousness, and emotions are completely absent (pp. 111).

Thus, Seligman conceives of flow as an emotionless state. He seems to imply that the intensive allocation of attentional resources towards moment-to-moment activity prevents emotions from being elicited and experienced. This proposition is expressed more clearly in his modestly titled follow-up book, Flourish: A Visionary New Understanding of Happiness and Well-being (2011): I believe that the concentrated attention that flow requires uses up all the cognitive and emotional resources that make up thought and feeling (pp. 11).

Box 6.2 Flow as an Emotionless and Thoughtless State? In asserting that there are not only no feelings during flow, but also no thoughts, Seligman implies a coma-like state. The misconception that there is no thought during flow recalls a common misconception about states of concentrative meditation—that during these states the mind is blank, empty, thinking of nothing. This is not the case. The mind is intensely focused on the subtle moment-to-moment changes occurring in the object of attention— whether this be one’s breath, a mantra, or any other constantly changing stimulus (Sekida, 1985). Indeed, one of the main goals of concentrative mediation is to condition one’s thoughts to focus on the task at hand (as in during flow), not to obliterate them altogether. To summarize, Seligman argues that flow is an emotionless state, and that the reason for this is that flow usurps the resources needed for the experience of emotions (and apparently thought too). Seligman’s argument rests on the assumption that the experience of emotion during flow consumes a significant amount of attentional resources. But is this assumption justified? Among contemporary theories of emotion, perhaps the most complete account of how emotions are elicited is provided by appraisal theories of

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emotion (Arnold, 1960; Frijda, 1986; Lazarus, 1966; Moors, Ellsworth, Scherer, & Frijda, 2013; Oatley & Johnson-Laird, 1987; Scherer, 1984; Smith & Ellsworth, 1985). Appraisal theories have been criticized by some for “over-cognitivizing” the process which leads to the elicitation of emotions (Ellsworth & Scherer, 2003), largely due to work by Robert Zajonc and others which suggests that affect and cognition are driven by separate and largely independent systems (Ledoux, 1996; Murphy & Zajonc, 1993; Rachman, 1981; Zajonc, 1980, 1984). Nevertheless, even among appraisal theorists, there is consensus that appraisals often do not require conscious intervention (Moors, 2010; Scherer, 2005). In fact it is generally presumed that appraisal processes usually occur automatically (Moors, 2010; Smith & Kirby, 2001). Appraisals must be fast and efficient given that changes in the environment can occur very quickly (Lazarus, 2001). Thus, as automatic processes, they need not consume significant attentional resources. Appraisal theorists also agree that with increasing practice there is greater automatization of appraisal processes (Moors et al., 2013). This has particular relevance for the topic at hand because flow is usually experienced by individuals who have developed a fair degree of skill in the respective activities (Csikszentmihalyi, 1975; Marin & Bhattacharya, 2013). Therefore it is likely that any appraisal processes that may occur during flow are mostly or fully automatic.2 A further reason to assume that the appraisal processes associated with flow states do not usurp attentional resources that would otherwise be devoted to the task at hand has to do with the nature of the appraisals themselves. While there is ongoing debate among appraisal theorists regarding the full list of appraisal criteria/dimensions that are responsible for the elicitation of emotions, there is general consensus that “coping potential” (or control/power) is one of them (Moors et al., 2013). The appraisal of coping potential involves assessing the degree to which one’s skills are capable of coping with the demands of the activity. This appraisal process appears to be built into the process of engagement in autotelic activities. During autotelic activities, one is constantly receiving very specific performance feedback, and to enter and maintain flow one must efficiently interpret this moment-to-moment feedback. Indeed, the sense of control that is characteristic of flow may be a consequence of positive appraisals of coping potential. In sum, although flow experiences are characterized by the intense allocation of attentional resources to the task at hand, this does not preclude the experience of enjoyment (or interest) during the process of engagement. Indeed, if this were not the case, what could possibly motivate us so strongly to seek such experiences again and again?

2 For an extended account of how interest may be elicited through automatic appraisal processes, see Silvia (2005).

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Three Possible Sources of Confusion From the earliest interviews of artists, surgeons, basketball players, etc., flow has consistently been described by those who experience it as extremely enjoyable (Csikszentmihalyi, 1975). Given the clear link between flow and enjoyment, it is surprising to find that views to the contrary persist. I believe there are several reasons for this continued misinterpretation of flow. One is a failure to differentiate between experiencing emotions and one’s awareness and labeling of them. For example, in a recent study of flow in cross—country skiers who engaged in a long backcountry trek (Hetland et al., 2018), the authors concluded (incidentally, citing Seligman): There are no moment-to-moment experiences during the flow event, the feelings appear only retrospectively, after the flow state has ended (pp. 11).

And from another paper on flow published this year (Kyriazos et al., 2018): Flow-ers seem to be almost beyond experiencing emotions, probably due to the absence of self-awareness (pp. 1358).

But the fact that one is not aware of one’s emotions during an event in no way precludes one from experiencing them during the event. If this were not the case, self-awareness would be a prerequisite for the experience of emotions. Clearly this is not so, as is evident by the capacity for non-human mammals who lack a sense of self to experience emotions (Panksepp, 2005). Indeed, among humans, those younger than 7 months (and who therefore have not yet developed a sense of self) are nevertheless able to experience a wide range of emotions (Izard et al., 1995). The only emotions not in the repertoire of these children appear to be the so-called “selfconscious emotions” (e.g. pride, shame, guilt), which young children first appear capable of experiencing between the ages of 2½ and 3 years (Lewis, 1996). In fact even children who lack a cerebral cortex have been shown to be capable of experiencing emotions (Merker, 2007). A second possible cause of the continued misinterpretation of flow as devoid of emotion is the error of assuming that the pleasure experienced during flow is of the “happy-smiley” variety. As an example, consider again the aforementioned study of cross-country skiers (Hetland et al., 2018). The facial expressions of the skiers were captured via helmet-mounted video cameras while they were engaged in their strenuous treks. The researchers found that during their treks, while the skiers were skiing, there were fewer facial expressions of happiness than during the breaks that the skiers took. On the basis of this, the researchers concluded that “difficult activities are not pleasant,” and that the flow that is experienced while pursuing difficult activities is not enjoyable. However the enjoyment experienced during flow is not of the smiley-happy type, so you would not expect individuals in flow to be smiling. Indeed, it would probably strike one as very odd to see Serena Williams or Lionel Messi or any other athlete smiling while they are in the zone and performing at their peak (with the exception of synchronized swimmers). This does not mean that they are not experiencing enjoyment. If there is a facial expression that is

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characteristic of flow, it would probably be that of interest (Reeve, 1993), not of happiness. A third possible reason for the misinterpretation of flow as devoid of emotion is Csikszentmihalyi’s nonstandard usage of the word “pleasure” in his writings. He has written that certain presumed flow experiences may not necessarily be “pleasurable” (Csikszentmihalyi, 1990). This may lead some readers to assume that flow is not necessarily an enjoyable experience. However to properly interpret Csikszentmihalyi’s intended meaning it is crucial to note the sharp distinction he draws between pleasure and enjoyment. This distinction is perhaps most apparent in a section of his book Flow (1990) specifically devoted to the topic (pgs. 45–47). He defines pleasure as “a feeling of contentment that one achieves whenever information in consciousness says that expectations set by biological programs or social conditioning have been met.” (p. 45), In other words, pleasure, in Csikszentmihalyi’s sense, results from satisfying biological needs (e.g. food, rest, sex) and/or socially conditioned desires. It is easy to see how enjoyable experiences may not necessarily be pleasurable in this sense. For example, an artist who stays up all night, feverishly working on a painting, foregoing both food and rest, is not having a “pleasurable” experience in Csikszentmihalyi’s sense. But this should not be interpreted as implying that the experience isn’t enjoyable. Quite the opposite, the enjoyment experienced by the artist is so strong that it overrides the artist’s biologically-based needs such as food and rest.

So Why Is Flow So Enjoyable? This is a question that has yet to receive adequate empirical attention. Nevertheless, it seems likely that several factors contribute to the intrinsically-rewarding nature of flow (cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1). For one, “feelings of efficacy” (in Robert White’s (1959) sense, see Box 6.3) may play a significant role, especially in the context of physical activities. White proposed that humans and other animals derive inherent pleasure from exercising their skills on the environment. During flow, opportunities to exercise one’s skills are especially high, given the characteristic “optimal” levels of challenge associated with flow activities. Feelings of efficacy are therefore also likely to be high during flow, which may account for the “sense of control” that individuals report during flow. Box 6.3 Feelings of Efficacy vs. Perceptions of Competence It is important to differentiate feelings of efficacy with self-determination theory’s “perceived competence” construct (Deci & Ryan, 1985), even though these two terms are sometimes used interchangeably within self-determination theory (Elliot, McGregor, & Thrash, 2002). Whereas the latter is (continued)

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Box 6.3 (continued) conceptualized as a self-evaluation (e.g. “I am good at this game”), the former is not and can therefore be experienced by organisms lacking a sense of self (see White, 1959). White used the concept of feelings of efficacy (rooted in what he termed “effectance motivation”) to help explain a wide range of otherwise perplexing behaviors exhibited by both human and non-human animals alike, from the enthusiasm rats show when running in their running wheels to the joy infants experience when they shake their rattlers. Because perceptions of competence are based on self-evaluations, it does not lend itself very well as a primary explanation for why flow is enjoyable, given that during flow self-consciousness is low or absent. Self-evaluations are therefore unlikely to occur (or at least be at the forefront of consciousness) during flow. Another feature of flow which is likely to contribute to its enjoyable, intrinsicallyrewarding nature is the deep attentional involvement which is so characteristic of it. The intensive allocation of attentional resources towards the task at hand allows the features of the activity that evoke interest (i.e. the “collative properties” of stimuli, Berlyne, 1960) to be experienced more fully and appreciated to a greater degree than they otherwise would be, whether these are the aesthetic properties of a painting or the intellectual properties of a chess problem (Abuhamdeh & Csikszentmihalyi, 2012a). The intense attentional involvement that is a central feature of flow may reward in another way, besides bringing certain properties of the activity into greater focus— by channeling attention away from the self. Some theorists have suggested, attention directed at the self is typically associated with aversive experience (e.g. Baumeister, 1991; Csikszentmihalyi, 1975), and this has largely been borne out by empirical findings (Fejfar & Hoyle, 2000). Thus channeling attention away from the self towards a task should be associated with comparatively positive experience, momentarily freeing oneself from the “burden of selfhood” (Baumeister, 1991). Finally, it is worth noting that the deep attentional involvement of flow itself may be enjoyable independent of the objects that are brought inside or outside of awareness. From an evolutionary perspective, the enjoyment of being immersed in an activity would seem to offer survival advantages, as it seems to be associated with both enhanced performance and skill development (e.g. Engeser & Rheinberg, 2008; Schüler, 2007). It may be the case, therefore, that the experience of high attentional involvement in itself is intrinsically rewarding, independent of the features of engagement that are brought in or out of attentional focus. It has been suggested that focused attention is intrinsically rewarding because it avoids the neurological burden of vigilance and the weighing of alternative action patterns (Bruya, 2010).

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Study Questions • The author asserts that flow is inherently enjoyable. Why does he say this? Because (he argues) this is how Csikszentmihalyi has always conceptualized flow. • What empirical evidence does the author present to support the notion that selfconsciousness is not needed for the experience of emotions? 1. Non-human mammals who lack a sense of self are nevertheless capable of experiencing emotions (Panksepp, 2005). 2. Human infants younger than 7 months (and who therefore have not yet developed a sense of self) are nevertheless able to experience a wide range of emotions (Izard et al., 1995). 3. Children who lack a cerebral cortex have been shown to be capable of experiencing emotions (Merker, 2007). • Despite plenty of evidence in support of the notion that flow is enjoyable, views to the contrary persist. What are three reasons for this? 1. A failure to differentiate between experiencing emotions and one’s awareness and labeling of them. 2. Incorrectly assuming that the enjoyment experienced during flow is of the “happy-smiley” type. 3. Csikszentmihalyi’s unconventional usage of the word “pleasure” in his writings.

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Chapter 7

The Dark Side of the Moon Zsuzsanna Zimanyi and Julia Schüler

Abstract When talking about flow, most people probably think of a highly desirable state associated with a broad variety of positive outcomes in terms of positive motivation, well-being and performance. In contrast, this chapter suggests that the characteristics of flow also have the potential to be evil. First, we will explain how flow can lead to addiction when exercising, playing games and using the internet. Then we will consider how flow is linked to impaired risk perception and risky behavior. As a third negative facet of flow, we will outline how it can also be experienced in antisocial contexts and during combat. The chapter ends with some broader comments on the dark and bright sides of flow, including flow as a universal experience, the implications for practical interventions, ethical questions related to flow, and future research questions.

The Dark Side of the Moon “The Dark Side of the Moon” is the title of an album by the progressive rock group Pink Floyd, which is frequently ranked as one of the greatest rock albums of all time, and also the title of a book by the Swiss author Martin Suter. At their heart, both these masterpieces say that everything has a dark side which is often not visible at first sight. In this chapter we will discuss to what extent this might be true of flow.

The Hitherto Neglected Dark Side of Flow In the literature, flow is conceptualized as an optimal motivational state characterized by a positive quality of experience and associated with high performance (Csikszentmihalyi, 1990; cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1 and Z. Zimanyi (*) · J. Schüler Department of Sports Science, Sport Psychology, University of Konstanz, Konstanz, Germany e-mail: [email protected]; [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_7

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Barthelmäs & Keller, Chap. 3). So far, empirical analyses of flow have mainly focused on the bright sides of flow, revealing flow to be a predictor of performance in the workplace (Csikszentmihalyi, Khosla, & Nakamura, 2017; Eisenberger, Jones, Stinglhamber, Shanock, & Randall, 2005; Ilies et al., 2017) in academic learning settings (Engeser, Rheinberg, Vollmeyer, & Bischoff, 2005) and in sports (Bakker, Oerlemans, Demerouti, Slot, & Ali, 2011). Flow has also been shown to predict persistence in activities (Csikszentmihalyi, Rathunde, & Whalen, 1993) and creativity (Perry, 1999). In addition, the repeated experience of flow has positive effects on mood, for example during one’s working day (Csikszentmihalyi & LeFevre, 1989), energy after work (Demerouti, Bakker, Sonnentag, & Fullagar, 2012) and in an academic learning setting (Schüler, 2007). Analyzing a large sample of 2530 students, Rodríguez-Ardura and Meseguer-Artola confirmed the positive effects of flow on learning in e-learning settings (2017). To summarize, in the literature flow comes across as being a highly desirable state that is worth promoting by creating flow-facilitating environments in a broad variety of life domains. Interestingly, hardly any studies have addressed Csikszentmihalyi’s claim that “Flow experience, like everything else, is not good in an absolute sense” (Csikszentmihalyi, 1990, p. 70). Csikszentmihalyi and Rathunde (1993, p. 91) answer their rhetorical question whether flow is always a good thing with the clear statement that “Like other forms of energy, from fire to nuclear fission, it [flow] can be used for both positive and destructive ends”. Csikszentmihalyi is thereby suggesting that the bright side of flow is accompanied by a dark side. This can already be concluded from a critical look at the extended definition of flow as a state “in which people are so involved in an activity that nothing else seems to matter; the experience itself is so enjoyable that people will do it even at great cost, for the sheer sake of doing it” (Csikszentmihalyi, 1990, p. 4). Is flow associated with a reckless disregard for other important interests and needs of oneself and of others? Does flow have costs? This chapter will to some extent give an affirmative answer to these questions. Under certain circumstances flow is associated with neglecting other domains of one’s life and the interests of other persons. Costs can be time costs, for example spending days and nights working on an interesting project; financial costs such as financing expensive leisure time activities; costs to one’s physical health, and even costs to one’s psychological growth, when important other goals (that do not produce flow and that require self-control) are disregarded. Before elaborating on these possible costs in more detail, the dark and bright sides of each flow component will be described.

The Characteristics of Flow and Their Potential to Be Good or Bad Flow is a multifaceted phenomenon mainly characterized by “[. . .] a subjective state that people report when they are completely involved in something to the point of

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forgetting time, fatigue, and everything else but the activity itself” (Csikszentmihalyi & Rathunde, 1993, p. 59). The deep involvement in a task is usually described as a positive feeling. However, it is also associated with a loss of self-awareness, which means not thinking about the compatibility between one’s current activity and one’s future goals and personal values. For example, spending hour after hour playing computer games might cause time conflicts with one’s future educational goals (revising in order to pass an exam) and one’s personal goals (fostering social relationships), and might be incompatible with one’s high regard for engaging in cultural activities and being an active person. Thus, flow might produce short-term and long-term goal and psychological conflicts. Other characteristics of flow can also be interpreted in a less “bright” way. Thus, the strong concentration on the task at hand means a narrowed focus of attention that makes it impossible to process information which is unrelated to the task, but nevertheless important. For example, individuals who spend night after night playing computer games might neglect social cues about the inappropriateness of their behavior. Individuals experiencing flow feel a sense of high control over their actions. This feeling of control, which is accompanied by an absence of anxiety, might be unrealistic and lead to an underestimation of one’s psychological and physical vulnerability. During flow, individuals often report a distortion of time. Time often seems to pass quickly. This can be interpreted as the absence of boredom (in which time seems to stand still), but could also have negative effects on activities which require a precise sense of time. In addition, flow can wreak havoc on useful time schedules (being home from work at 6; finishing a project on time in order to allocate resources to the following project; stopping playing computer games at midnight). Table 7.1 sums up the potentially dark sides of flow characteristics. In the following, the few existing studies about flow’s dark sides which have been published in the scientific psychological literature so far will be summed up (addiction, risk-taking and fighting in combat). Please note that the empirical basis for flow as a predictor of negative outcomes is still weak and that theoretical and practical implications must therefore be interpreted with caution.

Table 7.1 The dark sides of flow characteristics Flow characteristics Loss of self-reflection Exclusive concentration on the task at hand High control, absence of anxiety Distortion of time

The dark sides of flow characteristics Neglecting further goals and values (of oneself and others) Narrowed focus of attention excluding additional information Overestimation of one’s abilities, unrealistic optimism Neglecting temporal information although it is relevant

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Flow and Addiction The International Classification of Diseases (ICD 10, World Health Organization, 1994) defines addiction (F1x.2 dependence syndrome) as a cluster of phenomena in which “the use of a substance takes on a much higher priority for a given individual than other behaviors that once had greater value.” Box 7.1 Defining Features of Dependence • Strong desire or sense of compulsion to take the substance • Evidence of tolerance • Persisting with substance use despite clear evidence of overtly harmful consequences. Although these criteria (see Box 7.1) were developed for psychoactive substance use and describe the symptoms of mentally ill persons, some criteria are also applicable to the experience of flow (cf. Grant, Potenza, Weinstein, & Gorelick, 2010). Individuals report a strong desire to experience flow again (Csikszentmihalyi & Rathunde, 1993) and prioritize it at the cost of other behaviors. Because the experience of flow is more likely at a balance between the challenge of a task and the skills of a person (Moneta, 2012; Nakamura & Csikszentmihalyi, 2002), the situational challenges have to be continuously adapted to the improving skills of a person. Thus, individuals show evidence of tolerance, such that increased “doses” of the flow-producing behavior are required in order to achieve effects originally produced by lower “doses” of behavior. For example, higher mountains have to be climbed due to increased physical fitness and more ambitious job projects have to be generated due to increased knowledge and competence. Finally, individuals persist in activities although they know the possible harmful consequences (even for their lives) (see below).

Reward as a Mechanism by Which Flow Leads to Addiction As in addictive behavior, individuals desire to experience flow over and over again. According to the principle of operant conditioning, the positive quality of the flow experience functions as a reward which enhances the probability that the activity will be performed again. The bright side of this rewarding process is that individuals enhance their skills and competences and achieve higher performance over time because they are constantly adapting the difficulty of the task to their skills. The dark side is that being rewarded is sometimes a powerful motivating force at the expense of conscious control. In Csikszentmihalyi’s (1990, p. 62) words, “When a person becomes so dependent on the ability to control an enjoyable activity that he

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cannot pay attention to anything else, then he loses the ultimate control: the freedom to determine the contents of consciousness. Thus, enjoyable activities that produce flow have a potentially negative aspect”. Examples of such enjoyable, flowproducing activities which have attracted interest in previous research are summed up in the following paragraphs: exercising, playing computer games and using the Word Wide Web.

Flow and Exercise Addiction The rewarding quality of flow has been described by teenage skateboarders as an intense subjective experience accompanied by heightened concentration, peak performance and transcendence (Seifert & Hedderson, 2010). A study by Partington, Partington and Olivier (2009, p. 176) confirms the rewarding quality of flow in a sample of big wave surfers and in addition analyzes its positive and negative consequences. The authors interviewed the world’s top big wave surfers and found that most of them experienced flow. They report, for example, the exclusive focus on the activity itself (“There is nothing else in your mind. There is nothing else that matters”), the distortion of time (“For a moment in time, time stands still”) and a high sense of control (“You are able to control the most uncontrollable because everything becomes slow motion and that’s when you know you are surfing the best”) (citations from Partington et al., 2009, p. 176). The surfers describe the rewarding quality of flow as a peak experience, comparable to euphoria, as a great joy in performing perfectly which enhances self-esteem, which is accompanied by feelings of personal fulfillment. However, simultaneously they actually use the term “addiction” when describing their experiences. Additional features are reported that would qualify as a diagnosis of exercise dependence according to Hausenblaus and Downs (2002), such as tolerance (increase the speed of surfing to achieve the positive feeling of flow), withdrawal symptoms (depression, feeling depleted when not able to surf), conflicts with social life (surfing conflicts with the interests of the life partner) and continuation despite injuries (e.g., prolonging healing times) (see Box 7.2 for examples from Partington et al., 2009). Box 7.2 Citations Supporting Exercise Dependence of Big Wave Surfers Addiction: “Once you get familiar with that feeling, it’s an addiction” (p. 176). Tolerance: “Nothing is ever enough”, “After each turn, you want to accelerate faster in to the next turn” (p. 176) (continued)

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Box 7.2 (continued) Withdrawal: “There is psychologically after all that is done, there is a depression almost.” (p. 179) Social conflicts: “My husband wants to have babies. I kinda don’t cause I want to keep surfing you know?” (p. 179) Continuation: “I have heard that a separated rib is more annoying than a broken rib . . . It went on for over six months. I tried to pad it, put on wetsuits.” (p. 179) Similar to Partington et al. (2009) flow experience was not only reported by big wave surfers, but also by rock climbers (e. g. Delle Fave, Bassi, & Massimini, 2003). Furthermore, Heirene, Shearer, Roderique-Davies, and Mellalieu (2016) confirm the results from Partington et al. (2009) in relation to addictive behavior in their study with climbers. In semi-structured interviews rock climbers reported withdrawal effects (craving, anhedonia and negative effect) in periods of abstinence independent from their skill level, too. Moreover, the authors showed that high-ability rock climbers experience craving and negative effects in periods of abstinence more frequently and intense than those with low-ability in climbing. These withdrawal symptoms in periods of abstinence are comparable to those shown by individuals with substance or behavioral addiction. (Price & Bundesen, 2005; Willig, 2008). In the literature especially, endurance or high-risk sport athletes, like climbers, surfers and skydivers seem to be more at risk to addictive behavior than low-risk athletes (e.g. Franken, Zijlstra, & Muris, 2006). Among other issues the potentially high frequency of flow experiences during their activities (this “kick” is what differentiates extreme sports from other sports) seem to trigger their obsession (Price & Bundesen, 2005; Willig, 2008). According Jacobs (1989) addiction goes along with dissociative symptoms, such as blurred reality, trance-like state, out-of body feeling and positively altered selfperception. Exploring the potential link between flow and addiction, Wanner, Ladouceur, Auclair, and Vitaro (2006)) investigated the relationship between flow and dissociation in exercisers and in recreational and pathological gamblers. They argue that the pathological phenomenon of dissociation and flow have similar characteristics and thus might conceptually overlap. For example, the distorted sense of time, the merging of action and awareness, and the loss of self-reflection characterize both flow and dissociation. The authors assessed dissociative symptoms, flow experience, exercise and gambling behavior and emotional well-being of their participants. The results revealed that exercisers, addicted and non-addicted gamblers experience flow, which in turn was related to emotional well-being. And as expected, some components of flow—self-consciousness, the merging of action and awareness, and the distorted sense of time—showed communalities with dissociation. Thus, the study further supports an overlap between flow and addiction. Trivedi and Teichert (2017) examined the facets of flow in detail in order to identify the exact flow dimensions that account for the relationship between flow and

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addiction. They focused on six major sub-dimensions (loss of self-consciousness, transformation of time, autotelic experience, clear goals, concentration on the task at hand, sense of control) of the flow state scale adopted from Jackson and Marsh (1996) when assessing the connection between flow and addiction in online gambling. Their findings assume that transformation of time and autotelic experience support addiction in online gambling whereas it is inhibited by the sense of control and concentration on the task. Moreover, Dixon et al. (2017) take up the term “dark (side of) flow” from Partington et al. (2009) relating it to gambling problem, depression and gambling expectancies playing at multiline slot machines. Specifically, the authors used dark flow to outline the “potential negative consequences of becoming absorbed in slot machine games (e.g., mounting losses as time imperceptibly passes and the seeking of this state as a form of escape)” (Dixon et al., 2017, p. 76). These multiline machines produce a unique outcome type, characterized as loss disguised as a win (this means players gain credits at a spin, but less than their former wager/stake/bet, but the machine celebrates this as a win of money). Former research (Dixon, Harrigan, Sandhu, Collins, & Fugelsang, 2010) showed that players physiologically react to these outcomes as if they were wins and not losses with increased arousal, determined by elevated skin conductance levels compared to normal losses. These outcomes appear to enhance a flow-like state because of a smoother playing experience (Dixon et al., 2017). As a final result the authors found relationships between dark flow and problem gambling severity index score and a strong correlation between dark flow and depression symptoms when playing at multiline slot machines.

Flow and Online Game Addiction and Internet Addiction Recent studies show that flow can be experienced not only in natural environments but also in online environments (e.g., Chen, 2006). The World Wide Web is characterized by features such as controllability, immediate feedback and ease of use, which make the experience of flow highly probable. Furthermore, flow has been found to be related to positive affect, exploratory behavior and attitudes toward websites and has been successfully used to explain online shopping behavior and web use (Webster, Trevino, & Ryan, 1993). Again, besides the positive consequences of flow for internet use, there are also negative consequences. The association between flow and problematic or even addictive behavior has been analyzed empirically for the domains of online gaming and internet use. For example, Thatcher, Wretschko, and Fridjhon (2008) assessed, among other things, flow experience and problematic internet use of more than one thousand internet users. Problematic internet use was defined as the “use of the Internet that creates psychological, social, school, and/or work difficulties in a person’s life” (Beard & Wolf, 2001, p. 378) and includes symptoms such as needing to spend more and more time online, loss of control regarding the time spent online

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and withdrawal symptoms. Typical internet activities are general web browsing, e-mailing, news websites, telnet and blogging. Thatcher et al. (2008) reported results which support the flow–addiction link: the stronger the participants experience of flow, the higher their problematic internet use. This study result is supported by Kim and Davis (2009) who also found a positive association between flow, as measured using Webster et al.’s (1993) flow in humancomputer interaction scale (e.g., “When using the Internet, I am totally absorbed in what I am doing”), and problematic internet use, assessed using items such as “I’ve tried and failed to cut down the amount of time spent online” and “My job performance and/or productivity suffers because of the Internet” (Caplan, 2002). In addition, Kim and Davis (2009) identified the participants’ perceived importance of internet activity as being a mediator of this relationship. Thus flow predicted the importance of seven positive internet activities (“shopping/auctioning”; “joining the same interest group”) which in turn led to high scores on the problematic internet use scale. Furthermore, Khang, Kim, and Kim (2013) analyzed predictors of flow and addiction in digital media, especially when individuals use the internet and mobile phones or playing video games. They detected that low self-control and more time spent using media increased media flow and addiction among college students. In addition, two dimensions of dispositional media use motives, more precisely pastime (to avoid boredom, or killing time) and self-presence (to express oneself to others) seem to be predictors for media flow and further addictive behavior. The concept of self-presence was taken up by Sun, Zhao, Jia, and Zheng (2015) labeled as perceived visibility. This means social interactions like sharing scores, social media login, leaderboards to compare with other players or friends and chatting in mobile games. They reported a positive impact of perceived visibility on flow and addiction. Researchers into the phenomenon of online and cyber-game addiction argue that cyberspace behavior is associated with flow because while in the flow state the consumer experiences a sense of happiness, an exploratory desire, and the absence of time pressure. In accordance with the above-mentioned mechanisms which link flow to addiction, Chou and Ting (2003) found that the positive quality of flow promotes the tendency to repeat the activity of cyber-gaming, which in turn can lead to addictive behavior. These findings were underpinned by a review of 58 studies about internet gaming addiction from Kuss and Griffiths (2012). The authors reported occurring withdrawal symptoms, craving, conflicts and mood modifications for online game addicts. Thus, one part of the dark side of flow is that it includes the possibility of making individuals addicted to certain activities. In dealing with addiction, clinical psychologists have established different principles and therapeutic techniques to deal with the problem, such as behavior therapy (operant conditioning) and cognitive behavior therapy (self-control techniques), which might also work in the treatment of flow addiction. However, in contrast to other forms of addiction, which need a specific substance (alcohol, cocaine), flow can be experienced through nearly any activity (Csikszentmihalyi, 1990). In order to prevent individuals from becoming addicted to a certain flow-producing and health-endangering activity (big-wave surfing despite

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injuries), the experience of flow can be spread over a broader range of more moderate sporting activities (starting a new sport) and/or intellectual activities (learning a foreign language). However, the question remains whether the sum of several low-intensity flow activities will be able to balance an extraordinary, highlevel flow experience.

Flow and Risk-Taking Csikszentmihalyi’s (1975) work on flow started with the question what motivates individuals to perform activities such as playing musical instruments, climbing rocks or playing chess without receiving a visible reward in the form of money or recognition, and even expending high costs in terms of effort and time. The following question goes one step further and asks: What are the reasons for individuals deliberately seeking risks to their health and even their life? Why does one and the same individual wear a seat belt when driving a car and take safety rules at the workplace very seriously, while engaging in skydiving, rock climbing or whitewater kayaking during his or her leisure hours? The answer is the same as the answer to Csikszentmihalyi’s initial question: Individuals perform these activities for their own sake, simply for the pleasure associated with the activity itself and regardless of the (negative) consequences. In the following we will provide support for the assumption that one of these negative consequences can be risky behavior.

The Mechanisms that Link Flow with Low Risk Perception and High Risk-Taking The mechanisms which link flow with risk lie on the one hand in the conditions for flow, on the other hand in the features of flow, and lastly in the consequences of flow. An important condition for flow is the balance between the skills of a person and the challenges of a situation (Csikszentmihalyi, 1990; cf. Moneta, Chap. 2). Thus individuals seek challenging situations in which the probabilities of succeeding or failing are approximately equal. This can be dangerous in terms of injuries in highrisk sports or in terms of financial loss in gambling. In addition, some features of flow, such as the loss of self-reflection, which prevents worrying about danger, and the restriction of perception to a limited field of activity, which prevents the perception of signs of danger, combined with a high sense of control can be highly problematic when performing high-risk sports such as riding a motorcycle, rock-climbing or kayaking. Another explanation for the link between flow and risk lies in the consequences of flow. The experience of flow can be so rewarding that individuals are willing to

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hazard the negative consequences of flow and even risk endangering their life as expressed by the following citation: The pleasure of the flow experience “outweighs the risk you know. And maybe that’s not saying much for my regard for, for my life, for my health, but, but I guess I am willing to take the chance of, you know, of any pain or suffering it might cause over the benefits of the rush” (citation from Partington et al., 2009, p. 179).

Studies Dealing with Flow and Risk Perception and Risk-Taking Only a few quantitative and qualitative studies have analyzed the link between flow experience and risk perception and risk behavior. For example, Sato (1988) used direct observations, semi-structured interviews and questionnaires to explain the behavior of Japanese juvenile motorcycle gangs. These “bosozoku” groups consist of mainly young men who engage in illegal high-risk races. The speed of these races is up to twice the speed limit on city roads (up to 100 km/h). The translation of “bosozoku” is “violent-driving tribe” and thus describes the risk of hurting or killing oneself or others in an accident very well. The readiness to expose oneself to physical danger is underlined by the fact that the riders rarely use protective gear such as helmets and boots. Interestingly, the author did not identify negative reasons (e.g., overcoming frustration or feelings of inferiority) for participating in such races, but enjoyable, flow-like experiences. For example, participants describe the centering of attention on a limited range of stimuli (the race, the noise and the atmosphere), the feeling of competence and control, and the merging of their awareness with the activity of driving. The goal of these races is clear (winning, being faster than others) and they provide immediate and clear feedback (noise of the motoring, exact time). It could be argued that “bosozoku” motorcycle gangs represent a lifestyle or youth movement of juveniles rather than being the consequence of the flow experience. However, Rheinberg’s (1991) study with adults shows that there is no fool like an old fool. He interviewed motorcyclists and asked them to describe their experiences when riding their bikes. They often report flow components such as a feeling of control (“In this condition I feel absolutely safe”, p. 358) and the loss of self-reflection (“I am so gone that I feel that I don’t exist”, p. 357). Interestingly, most motorcyclists mentioned the positive aspects of flow, but scarcely anybody realized that this joyful merging with the activity can be dangerous. Consequently, participants “act at times completely contrary to their commendable safety standards” (Rheinberg, 1991, p. 361). In accordance with these findings, Rheinberg (1991) revealed that the more intense the motorcyclists experience of flow, the less afraid they are when riding a motorcycle. In addition, flow was directly related to risk-taking. The greater the experience of flow, the more the motorcyclists

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agreed with items representing a dangerous driving style, such as a preference for high speed on highways and a higher number of accidents. Schüler and Pfenninger (2011) have examined the relationship between flow and risk in white-water kayaking, which it is a risky sport that involves the danger of being seriously hurt or even losing one’s life through drowning. They hypothesize that flow leads to an underestimation of risk in kayaking, which they operationalized by comparing an objective risk rating by experts with the participants’ perceived subjective risk when kayaking. As expected, the experience of flow, which was assessed while kayakers were still sitting in their kayaks, led to an underestimation of the risk of capsizing or hurting oneself while kayaking. The results remained stable even when the authors controlled for sensation seeking, which shares some of the characteristics of the flow experience (e.g., quest for exceptional experiences). A study by Schüler and Nakamura (2013) finds that the flow experience of climbers is related to excessive feelings of self-efficacy which in turn leads to distorted risk perception and risky behavior in climbers. To summarize, a few studies have already confirmed the association between flow experience and risk-taking (e.g., Rheinberg, 1991; Sato, 1988; Schüler & Pfenninger, 2011). However, the above-mentioned mechanisms which link flow to risk are theoretical assumptions rather than empirically tested facts. Future studies should aim to analyze these mechanisms empirically. For example, the degree of self-reflection and the limited field of perception could be measured – or even better: experimentally induced (e.g. enhancing self-reflection using a mirror; giving tasks to focus on environmental characteristics in order to broaden the field of perception). Also, one could try to sever the association between flow and its affective reward, for example by asking participants to think about something sad when they feel that they are about to achieve a state of flow.

Combat Flow Csikszentmihalyi and Rathunde (1993) mention that one danger inherent in flow is that it can be experienced in antisocial contexts. When people lack the experience of flow in other life domains they will seek flow in destructive activities such as aggressive behavior, violence and crime. An extreme form of antipersonnel flow is losing oneself in the action of killing. Harari (2008) has summarized the positive experiences while killing other persons in war. Soldiers report losing reflective awareness and thereby any worries and thoughts about morality and human values. They report full concentration on the task at hand (which in this case is killing) and a distortion of time in which only the present counts, without wasting much thought on the past and the future. In addition, the soldiers report positive experiences during combat, such as a heightened sense of being alive. As a result of the above-mentioned characteristics they disregard the danger to their life and maximize their mental and physical abilities (Harari, 2008, p. 255). Harari (2008, p. 256) cites Leo Tolstoy’s description

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of an artillery battery commander as follows. The commander “did not experience the slightest qualm of fear, and the idea that he might be killed or badly wounded never entered his head. On the contrary, he grew more and more elated. Though he forgot nothing, thought of everything, did everything the best of officers could have done in his position, he was in a state akin to feverish delirium or intoxication”.

The Mechanism that Makes People Enjoy Killing One mechanism which links flow to combat is again its rewarding quality. Like other activities which are rewarded by flow (see above), combat fighting too can be experienced as joyful and can be addictive. In Box 7.3 an interviewed veteran is cited (Caputo, 1977; cited in Harari, 2008, p. 255). Box 7.3 Urge to Experience Flow in Combat I felt a drunken elation. I had never experienced anything like it before. [. . .] Within a year I began growing nostalgic for the war. I could protest against the war as loudly as the most convinced activists, but I could not deny the grip the war had on me, nor the fact that it had been an experience as fascinating as it was repulsive, as exhilarating as it was sad [. . .]. It was something like the elevated state of awareness induced by drugs. And it could be just as addictive [. . .].

That flow can also be experienced while fighting in war supports Csikszentmihalyi’s assumption that flow can be experienced in every activity as long as important conditions for flow are met. In the act of killing too, the rewarding quality of flow facilitates the maintenance of the rewarded activity. Like flow in other activities, combat flow also has its positive sides, for example when an individual enters into a life-or-death struggle, he or she is pretty much going to have “no mind” anyway, and flow enables high concentration and efficacy which might save one’s own life. However, the negative sides are that it overrides selfreflection including morality and even humanity (e.g., killing without necessity, just for the sake of killing) and that one can become addicted to combat flow.

Broader Comments on the Dark and Bright Side of the Moon Flow as a Universal Experience The studies reported above suggest that the “optimal experience” of flow does not necessarily mean that the consequences of flow are always positive. While experiencing flow, individuals can become addicted to the euphoric feelings associated with flow, underestimate their personal risk of being injured and be willing to

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hurt or even kill other people. Thus, the term “optimal” refers to the inner state of perfect physical and mental functioning, but not to the desirability of its outcome. Flow experience is not just a hedonic feeling that enhances an individual’s quality of life; it is also an optimal functional state that can lead to peak performance in sports or music and can be a matter of life and death in life-threatening situations. From an evolutionary point of view, flow has a high adaptive value. The assumption of flow as an evolutionary-based experience is supported by its universality, which is expressed in at least two senses. First, flow experience is relatively similar across a broad variety of activities which do not have much in common. Thus the heart of flow is the same for individuals engaging in sport, in arts, in leisure time activities and scientific activities, as well as for soldiers. Second, the flow experience is relatively similar across different individuals. It is the same for individuals across different cultures and classes, for men and women, and for individuals of different ages (Nakamura & Csikszentmihalyi, 2002). This leads Csikszentmihalyi and Csikszentmihalyi (1988) to the conclusion that there might be an evolutionary predisposition to become deeply involved in activities and to do things for their own sake. This would be in agreement with other theoretical considerations, for example with Deci and Ryan’s (1985, 2000) self-determination theory (SDT) (cf. Abuhamdeh, Chap. 5). SDT assumes that intrinsic motivation is evolutionbased and that performing activities just for the pleasure of doing them is associated with subjective well-being and personal growth. However, SDT assumes that intrinsically rewarding activities are in principle not directed against other persons, but are performed in harmony with the interests of other people or even for the benefit of others. Antisocial behavior is the result of restricting environments thwarting important basic psychological needs rather than one form of expression of intrinsic motivation. In contrast, flow theory does not explicitly exclude the possibility that the positive experience of flow can be associated with “negative” (e.g., antisocial) behaviors. According to flow theory, not even basic need-satisfying environments (e.g., environments providing feelings of autonomy) are needed in order to experience flow, as long as some basic conditions (challenges-skill balance, clear goals, immediate feedback) are fulfilled. Flow can even be experienced in dangerous and needfrustrating situations such as in combat (Harari, 2008) and in concentration camps (Csikszentmihalyi, 1990). “Even an experience involving extreme levels of deprivation, discomfort, and danger turns out to become highly attractive once people enter flow” (Harari, 2008, p. 255). To summarize, flow is a universal competenceenhancing and sometimes even life-saving experience with a high adaptive value in evolution.

Implications for Practical Interventions The negative consequences of flow put a new complexion on practical interventions which aim to facilitate flow. On the one hand, flow-enhancing strategies contribute

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to the cognitive and physical efficiency, motivation and happiness of individuals. On the other hand, flow-enhancing strategies can yield negative outcomes, as outlined in this chapter. It is the responsibility of researchers and practitioners to consider this problem by means of theoretical, practical and particularly moral considerations. For example, flow-enhancing interventions could be matched to the characteristics of specific populations or situations. A person who is beginning a desired activity, such as exercising physically, or who is learning a new language, needs an immediate reward in terms of flow experience. According to the reward mechanism outlined above, this should lead to long-term adherence to the desired activity and a progress in performance. In contrast, individuals who are already experiencing flow while performing an activity, such as elite big wave surfers, passionate kayakers and experienced computer programmers, do not need flow-enhancing strategies but might benefit from the knowledge of strategies to reduce the experience of flow which they can apply in situations that endanger their psychological well-being or health. The former group of people needs to lose themselves in an activity, whereas the latter need self-reflection in order to separate themselves from the activity at the right moment.

Ethical Questions Related to Flow The potential negative consequences of flow may have raised ethical questions among some readers. Two ethical questions are briefly addressed in the following. First, the subjective positive experience of flow is a hedonic feeling which, however, often interferes with other (higher-order) human goals and values. To name but a few examples, individuals addicted to flow in computer gaming might experience a psychological goal conflict when their families, their job performance and their friends, and suffer because of their addiction. The soldier cited above reported a conflict between his political anti-war belief and the joy associated with participating in combat. The risky behavior as a consequence of flow even contradicts the human goal of physical integrity. Thus, losing oneself completely in the moment can be a source of serious psychological conflict and might clash with the interests of other individuals. As already remarked by Aristotle (see also Csikszentmihalyi & Rathunde, 1993) the feeling of excellence of action (as a defining characteristic of flow) might be the highest form of happiness, but nevertheless is not the highest human good. Excelling in actions can be harmful to the self or to others. The fact that individuals derive and enjoy intrinsic rewards from flow activities does not justify their action morally. A second ethical problem is that the flow experience of individuals can be abused. Sometimes, institutions are less interested in people reflecting about what they are doing and flow induction can theoretically be used to exploit individuals for military, political or religious purposes. For example, one strategy for enhancing flow is to have many drills for soldiers. Practicing extensively how to react in certain kinds of situations and consolidating behaviors through repetition helps the right movements

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to become automatic. The movements come too quickly to think about them. Automatic behavior which circumvents conscious thoughts in turn promotes the experience of flow (Delle Fave, Massimini, & Bassi, 2011; Dietrich, 2004). Thus, flow enhancement through drill again has two sides, because on the one hand it helps to save the soldier’s own life, but on the other hand supports the killing of other people. Practicing extensively is also the way to access flow in other activities, such as making music or exercising, which have no or less negative consequences for other people however. This means, that it depends on the sort of flow-activity (killing versus making music) whether a flow-enhancing strategy is good or bad in a moral sense.

Future Research Questions As already mentioned, the findings on the negative consequences of flow are based on a very small number of studies. Moreover, these studies are limited in their study designs. Thus, our understanding of the negative consequences of flow is mainly based on qualitative interviews and correlative field studies. Experiments, which are the ideal way to analyze the dependence of consequences on certain conditions, might reveal more precise or even different insights in the relationship between flow and its outcomes. Further research is needed to overcome methodological problems and to answer further research questions. For example, the research question whether some components of flow are associated more with positive rather than with negative consequences, whereas others are connected more strongly with negative rather than positive effects are still unanswered. In some cases, it is hard to determine when the negative cost exceed the positive gain. For example, Smith, Gradisar, King, and Short (2017) showed in their study with adolescents that the relationship between flow during video gaming and delayed bed time is mediated by gaming duration (video games). But the authors do not investigate whether there are any negative consequences of delayed bed time on well-being, performance in school or other factors. Furthermore, it would be interesting to know whether certain personality traits, such as openness to experience (Costa Jr. & McCrae, 1992), action orientation (Kuhl & Beckmann, 1994) and sensation seeking (Zuckerman, 2006), boost the negative effects of flow, whereas other variables such as state orientation (Kuhl & Beckmann, 1994) or self-control competencies can buffer its negative effects. Additionally, with regard to the predictors of flow, it would be interesting to shed light in detail on the relationship between basic need satisfaction, implicit motives and flow (Schüler, Wegner, & Knechtle, 2014). Furthermore, most research into the consequences of flow is based on participant’s self-reports of flow. Identifying and using physiological, for example neuropsychological, correlates of flow could help to objectivize the measurement of flow (which is an important research aim in itself; cf. Peifer & Tan, Chap. 8) and may help us to analyze the positive and negative consequences more precisely. One of the most important aims of future research is to figure out how to

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control flow in terms of evoking it when it is useful and abandoning it when it is harmful. The final conclusion drawn from this chapter is not to demonize the experience of flow but to bear in mind that the dark sides of flow also need theoretical, empirical and practical attention. The recommendations for researchers and practitioners about how to deal with flow are expressed well by Csikszentmihalyi’s (1990, p. 70) statement that the challenge entails “learning to distinguish the useful and the harmful forms of flow, and then making the most of the former while placing limits on the latter.”

Study Questions 1. List the main characteristics of flow. From the perspective of the devil’s advocate: What are their dark sides? Do you see any additional dark sides beyond those already mentioned? Answer: • “dark sides”: loss of self-reflection: ignoring compatibility between flowproducing activity and one’s own goals and values and the goals and interests of others • Strong concentration: narrowed focus of attention which might exclude potentially self-relevant information • High sense of control: overestimation of one’s abilities, unrealistic optimism • Distortion of time: neglecting temporal information although it is relevant; neglecting important time schedules • Please add further “dark sides”: _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ 2. How does the principle of operant conditioning explain persistence in behavior? How can it be related to the experience of flow? Answer: • Principle of operant conditioning: Behavior which is rewarded is more likely to be performed again in the future than behavior that is not rewarded (or even punished). • Operant conditioning and flow: The positive experience quality of flow functions as an intern reward of the flow-producing activity. Thus, the activity is performed again in order to experience flow again. 3. What are the main features of dependence? How can they be applied to flow? Answer:

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• Main features of dependence: strong desire or sense of compulsion to take the substance, evidence of tolerance, persisting with substance use despite clear evidence of overtly harmful consequences. • Application to flow (see also Box 7.1): – Strong desire or sense of compulsion to take the substance: strong desire to experience flow again – Evidence of tolerance: increased “doses” of the flow-producing behavior are required in order to experience flow – Persisting with substance use despite clear evidence of overtly harmful consequences: persisting with flow-producing activities despite harmful consequences (e.g. for health; continuation with big wave-surfer despite broken ribs) 4. What mechanisms link flow to risk? How do they work? Answer: • Challenge-skill balance: Individuals seek challenging situations in which the probabilities of succeeding or failing are approximately equal (dangerous in high-risk sport) • Loss of self-reflection: prevents thinking about danger • Restriction of perception to a limited field of activity: prevents the perception of signs of danger • High sense of control: overestimation of one’s skills; underestimation of the situation kayaking. • Rewarding quality of flow: Individuals are willing to hazard the negative consequences of flow, even high risks 5. What studies on flow and risk exist? Design a future study addressing one of the several research questions that still remain open. Answer: • Studies on flow and risk: Sato (1988): Participants of “bosozoku” groups experience flow when engaging in high-risk races; Rheinberg (1991): Motorcyclists experiencing flow report dangerous driving style; Schüler and Pfenninger (2011): Flow leads to an underestimation of risk in white-water kayaking. • Your study on flow and risk: _____________________________________________________________ _____________________________________________________________ _____________________________________________________________ 6. Give examples of flow in antisocial contexts.

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Answer: aggressive behavior, violence, crime, losing oneself in the action of killing 7. Can you think of any further ethical questions that are related to flow? Please note your answer: __________________________________________________________________ __________________________________________________________________ __________________________________________________________________

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Moneta, G. B. (2012). On the measurement and conceptualization of flow. In S. Engeser (Ed.), Advances in flow research (pp. 23–50). New York: Springer. Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). Oxford: Oxford University Press. Partington, S., Partington, E., & Olivier, S. (2009). The dark side of flow: A qualitative study of dependence in big wave surfing. The Sport Psychologist, 23, 170–186. Perry, S. K. (1999). Writing in flow. Cincinnati: Writer’s Digest Books. Price, I. R., & Bundesen, C. (2005). Emotional changes in skydivers in relation to experience. Personality and Individual Differences, 38, 1203–1211. Rheinberg, F. (1991). Flow-experience when motorcycling: A study of a special human condition. In R. Brendicke (Ed.), Safety, environment, future (pp. 349–362). Bochum: Institut für Zweiradsicherheit (ifZ). Rodríguez-Ardura, I., & Meseguer-Artola, A. (2017). Flow in e-learning: What drives it and why it matters. British Jornal of Educational Technology, 48, 899–915. Sato, I. (1988). Bosozoku: Flow in Japanese motorcycle gangs. In M. Csikszentmihalyi & I. S. Csikszentmihalyi (Eds.), Optimal experience- psychological studies of flow in consciousness (pp. 92–117). Cambridge: University Press. Schüler, J. (2007). Arousal of flow-experience in a learning setting and its effects on examperformance and affect. Zeitschrift für Pädagogische Psychologie, 21, 217–227. Schüler, J., & Nakamura, J. (2013). Does flow experience lead to risk? How and for whom. Applied Psychology: Health and Well-Being, 5(3), 311–331. Schüler, J., & Pfenninger, M. (2011). Flow impairs risk perception in kayakers. In B. D. Geranto (Ed.), Sport psychology (pp. 237–246). New York: Nova Publishers. Schüler, J., Wegner, M., & Knechtle, B. (2014). Implicit motives and basic need satisfaction in extreme endurance sports. Journal of Sport Exercise Psychology, 36(3), 293–302. Seifert, T., & Hedderson, C. (2010). Intrinsic motivation and flow in skateboarding: An ethnographic study. Journal of Happiness Studies, 11(3), 277–292. Smith, L. J., Gradisar, M., King, D. L., & Short, M. (2017). Intrinsic and extrinsic predictors of video-gaming behaviour and adolescent bedtimes: the relationship between flow states, selfperceived risk-taking, device accessibility, parental regulation of media and bedtime. Sleep Medicine, 30, 64–70. Sun, Y., Zhao, Y., Jia, S. Q., & Zheng, D. Y. (2015). Understanding the antecedents of mobile game addiction: The roles of perceived visibility, perceived enjoyment and flow. In Proceedings of the 19th Pacific-Asia Conference on Information Systems (pp. 1–12). Singapore: Marian Bay Sands. Thatcher, A., Wretschko, G., & Fridjhon, P. (2008). Online flow experiences, problematic Internet use and Internet procrastination. Computers in Human Behavior, 24, 2236–2254. Trivedi, R. H., & Teichert, T. (2017). The Janus-faced role of gambling flow in addiction issues. Cyberpsychology, Behavior and Social Networking, 20(3), 180–186. Wanner, B., Ladouceur, R., Auclair, A. V., & Vitaro, F. (2006). Flow and dissociation: Examination of mean levels, cross-links, and links to emotional well-being across sports and recreational and pathological gambling. Journal of Gambling Studies, 22, 289–304. Webster, J., Trevino, L. K., & Ryan, L. (1993). The dimensionality and correlates of flow in humancomputer interactions. Computers in Human Behavior, 9, 411–429. Willig, C. (2008). A phenomenological investigation of the experience of taking part in ‘extreme sports’. Journal of Health Psychology, 13, 690–702. Zuckerman, M. (2006). Sensation seeking and risky behavior. American Psychological Association.

Chapter 8

The Psychophysiology of Flow Experience Corinna Peifer

and Jasmine Tan

Abstract In recent years, flow has been increasingly investigated from a physiological perspective and interest in such studies is growing fast. In order to contribute to this ongoing research, this chapter aims to report and integrate existing theories and findings concerning the physiology of flow experience and to stimulate further investigation. The first part of this chapter will give an overview about existing literature explicitly dealing with the psychophysiology of flow. Secondly, a theoretical psychophysiological framework is developed based on prominent stress theories. The third part discusses physiological correlates of flow, integrating existing literature on flow and related concepts such as stress, attention and cognitive control. The chapter ends with an integrative definition of flow experience, the proposition of a physiological flow pattern, practical implications and an outlook on future research perspectives.

Introduction: Benefits of a Psychophysiological Perspective to Study Flow Traditionally, psychophysiology focuses on the expression of psychological phenomena in bodily processes. Studying flow with psychophysiological methods can eventually help us to better understand the concept: Until today, the most common instruments used to study flow experience are questionnaires and interviews that are retrospective by nature. However, a key characteristic of flow is that it appears during an activity, when a person is fully absorbed in the task and self-referential

This chapter is a revised and updated version of the chapter “Psychophysiological Correlates of Flow Experience” (Peifer, 2012), published in the first edition of this book (Engeser, 2012). C. Peifer (*) Department of Psychology, University of Lübeck, Lübeck, Germany e-mail: [email protected] J. Tan Goldsmith University of London, London, UK e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_8

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thoughts are completely inhibited. But as soon as participants are asked for their experience, they enter into self-reflection and leave the flow-state. Therefore, the most common way to assess flow by self-report instruments is right after the activity. Psychophysiology can provide flow-indicators that can be assessed during the activity without interrupting the participant. However, since flow is a subjective experience, physiological measures cannot substitute self-report measures. The benefit lies in the additional information and the simultaneous measurement that will open new research possibilities. Such possibilities include assessing micro-processes during flow, like how conducive and obstructive conditions and different kinds of distractors affect the length, depth and stability of flow-episodes. Controversial issues such as the experience of happiness during flow (cf. Chap. 6, Abuhamdeh) can also be addressed with corresponding physiological research methods. By finding physiological correlates, psychophysiology can contribute to the debate on which flow-characteristics constitute flow experience and which are antecedents or consequences of flow. Assessing flow physiologically will also allow us to compare it to related concepts such as stress, attention or meditation and to benefit from these branches of research. This could help us to fully understand the functions and consequences of flow and to infer practical implications, e.g. how to reach a state of flow, how to switch from stress to flow, and how to prevent potential negative consequences.

Part 1: Existing Literature on the Psychophysiology of Flow Experience In the early days of flow research, comparatively little literature addressed the psychophysiology of flow experience. But interest has grown exponentially in recent times. The next section aims to provide a chronological overview on the existing literature. The different approaches will be explained in more detail in part 3 of this chapter. After the introduction of the concept of flow in 1975, Hamilton, Haier, and Buchsbaum (1984) were (to the best of our knowledge) the first to experimentally investigate physiological aspects of flow experience. With their Intrinsic Enjoyment Scale they assessed the probability of experiencing flow in daily activities as a personality trait (cf. autotelic personality, Csikszentmihalyi, 1990; cf. Baumann, Chap. 9). In subjects who scored high on the Intrinsic Enjoyment Scale (as opposed to low-scorers), they found that increased attention even led to decreased effort measured via electroencephalography (EEG; Evoked Potentials). They concluded that individuals differ in their ability and effort required to control attention. From the findings of Hamilton et al. (1984), Csikszentmihalyi (1990) drew the conclusion that individuals with an autotelic personality have the ability to shut down mental activity in all information channels that are irrelevant for task fulfillment to fully concentrate on the task at hand.

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Referring to Hamilton et al. (1984), Goleman (1995) described the neurophysiology of flow experience as a subjective state of effortlessness. He suggested that flow is linked to a decrease in cortical activation, where a minimum of mental energy nevertheless leads to maximum efficiency in highly practiced activities. Activation and inhibition of neural circuitry would be fully adapted to moment-to-moment changes in activity demands during flow experience. In 2001, Marr called for a synthetic theory for flow, including cognitive, behavioral and neurophysiological explanations for the flow phenomenon (see Box 8.1). Building on Goleman (1995), Marr suggested that a reduction of brain metabolism was involved in flow experience and further proposed that the neurotransmitter dopamine was a possible neurophysiological correlate of flow. Box 8.1 Call for a Bio-Behavioral Theory of Flow A bio-behavioral theory of flow explains the latency, duration, and intensity of flow, as well as flow’s effect on cognitive efficiency and creativity. In addition, the theory is parsimonious, testable, and integrates the seemingly independent subject matters of phenomenology, learning theory, and cognitive neuro-psychology. Most importantly, a bio-behavioral theory demonstrates that the flow experience cannot be understood through an appeal to phenomenological, cognitive, neurological, or behavioral variables alone, but only through an integration of the respective metaphors that are engaged by these explanatory schemes (Marr, 2001, p. 6).

Again on a theoretical basis, Dietrich (2004) provided a neurophysiological theory of flow experience. In line with above-mentioned approaches (Csikszentmihalyi, 1990; Goleman, 1995; Hamilton et al., 1984; Marr, 2001), he suggested that flow results from a downregulation of prefrontal activity in the brain (Hypofrontality, Dietrich, 2003): The theory further postulates that during flow, well-trained activities are performed without interference of a conscious control system, which makes the process very fast and efficient (see Box 8.2). Box 8.2 The Hypofrontality-Hypothesis of Flow Experience The explicit system is associated with the higher cognitive functions of the frontal lobe and medial temporal lobe structures and has evolved to increase cognitive flexibility. In contrast, the implicit system is associated with the skill-based knowledge supported primarily by the basal ganglia and has the advantage of being more efficient. From the analysis of this flexibility/efficiency trade-off emerges a thesis that identifies the flow state as a period during which a highly practiced skill that is represented in the implicit system’s knowledge base is implemented without interference from the explicit system. It is proposed that a necessary prerequisite to the experience of flow is a state of transient hypofrontality that enables the temporary suppression of the analytical and meta-conscious capacities of the explicit system (Dietrich, 2004, p. 746).

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Two years later, Kivikangas (2006) implemented the second experimental study addressing the psychophysiology of flow experience. Kivikangas concentrated on facial electromyographic (EMG) activity, indicating emotional valence, and electrodermal activity (EDA), indicating sympathetic arousal. A science fiction first-person shooter computer game was used to induce flow experience. Kivikangas found flow to be negatively associated with activity in the Corrugator Supercilii (CS, ‘frowning muscle’; see Fig. 8.4), suggesting that flow is linked to increased positive valence and decreased negative valence. He found no effects of Zygomaticus Major (ZM, ‘smiling muscle’; see Fig. 8.4) and Orbicularis Oculi (OO, ‘eyelid-muscle’; see Fig. 8.4), nor of electrodermal activity. Researchers in the field of Human-Computer-Interaction show increasing interest in a psychophysiological investigation of flow as well (e.g. Nacke & Lindley, 2009; Prinzel, Freeman, Scerbo, Mikulka, & Pope, 2000; Rani, Sarkar, & Liu, 2005; for an overview see Fairclough, 2009). Their common objective is to find physiological indicators to develop real-time adaptive systems that optimize gaming experience and/or efficiency of the human-computer interaction. By measuring physiological parameters of the user, they aim to distinguish task engagement from boredom or frustration. In contrast to Kivikangas (2006), Nacke and Lindley (2009) found increased activity of ZM and OO (indicating positive valence) and an increase of EDA (indicating high arousal) to be associated with an experimental flow-condition. They concluded that a combination of both measures is promising for further research on adaptive systems. According to their finding, flow can be characterized as a positive state of increased arousal. Weber, Tamborini, Westcott-Baker, and Kantor (2009) put forward an operationalisation of flow as a discrete, energetically optimised and gratifying experience that is the result of a synchronisation between attentional and reward networks that occurs when the challenge of a situation is balanced with the skill of the person experiencing the situation (Box 8.3). Box 8.3 Four Key Hypotheses of the Synchronization-Theory of Flow H1: Flow results in a network synchronization process between cognitive control and reward networks. H2: Network synchronization during flow is a discrete state that is separable from other neuropsychological states. H3: Network synchronization corresponds to an energetically-optimized brain state. H4: Network synchronization manifests as an enjoyable experience. (Weber et al., 2009, p. 414) De Manzano, Theorell, Harmat, and Ullén (2010) investigated physiological aspects of flow experience in professional piano players performing a musical piece of their own choice. In line with Nacke and Lindley (2009), they found high flow values being associated with activation of ZM and sympathetic activation, confirming flow as a positive state of increased arousal. Further, they found an

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association with deep breathing—a sign for increased parasympathetic (co-) activation. Contrary to Kivikangas’ (2006) findings, de Manzano et al. (2010) found no relation of CS-activity and flow. They argued that sympathetic activation indicates that mental effort is necessary for high attention (e.g. Berntson, Cacioppo, & Quigley, 1993; Porges & Byrne, 1992) and draw the conclusion that flow is “a state of effortless attention, which arises through an interaction between positive affect and high attention” (p. 301; see also Box 8.4). Gaggioli, Cipresso, Serino, and Riva (2013) replicated de Manzano’s findings on increased sympathetic activation during flow, this time in daily activities. Box 8.4 A Physiological Definition of Flow Experience (. . .) we forward the hypothesis that flow is experienced during task performance as a result of an interaction between emotional and attentional systems, that is, both cognitive and physiological processes, enabled by a certain level of expertise (de Manzano et al., 2010, p. 309).

Approaching flow experience from a cognitive perspective, Bruya (2010a) edited a book discussing existing theories of attention and action and questioned the common assumption that required effort increases with task demands (e.g. Grier et al., 2003; Kahneman, 1973): Here he referred to flow as a state of effortless attention, that allows “a person to meet an increase in demand with a sustained level of efficacy but without an increase in felt effort—even, at the best of times, with a decrease” (Bruya, 2010b, p. 1). Next, he questioned whether the subjective experience of effortlessness comes along with an objective decrease in effort, as measurable with psychophysiological indicators. Contributers to the volume described their theories on effortless attention and related physiological findings mainly from a cognitive perspective. Keller, Bless, Blomann, and Kleinböhl (2011) experimentally investigated flow using the PC-games “Who wants to be a Millionnaire” and “Tetris”. They report that skills-demands-compatibility (¼ the flow-condition) is associated with elevated cortisol levels and reduced heart rate variability, and suggested that this indicates that flow is a stressful state of increased workload. As a result, they questioned the current dominant picture of flow as a purely positive and healthy state. Klasen, Weber, Kircher, Mathiak, and Mathiak (2012) conducted the first functional Magnetic Resonance Imaging (fMRI) research into the flow experience using a first-person shooter game. They found that players reporting flow had increased activity in the neocerebellum, somatosensory cortex, and motor areas, possibly indicating that brainstructures sensitive to reward are synchronized with taskrelevant cortical and cerebellar areas during flow. They proposed that these exploratory findings offer some support for Weber’s Synchronisation theory of flow. Further researchers investigated the role of reward during flow by addressing the dopaminergic system. De Manzano et al. (2013) were the first to test the Marr’s (2001) hypothesized link between dopamine and flow. They used positron emission tomography (PET) to find that flow proneness (the tendency to experience flow,

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associated with personality) correlated with the availability of dopamine D2-receptors in the dorsal striatum. Further research found increased gray matter in the dopaminergic system to be associated with increased experience of flow proneness in everyday life (Kavous, Park, Silpasuwanchai, Wang, & Ren, 2019). Flow has been linked to the hormonal stress-mediator cortisol (Keller et al., 2011; Peifer, Schächinger, Engeser, & Antoni, 2015; Peifer, Schulz, Schächinger, Baumann, & Antoni, 2014). Since Lazarus, Kanner, and Folkman (1980) and Csikszentmihalyi (1990) described flow in the context of stress-coping, it is plausible to investigate how stress and flow are related from a physiological point of view (Peifer, 2012). Participants playing a complex demanding computer game after experiencing stress showed an inverted u-shaped relationship between the stresshormone cortisol and flow experience (Peifer et al., 2014). High absorption was found at moderately elevated levels of cortisol and levels of absorption fell at higher levels of cortisol. In another experiment, orally applied synthetic cortisol in a dose simulating the cortisol reaction to a strong stressor resulted in a decrease in flow experience compared to a placebo condition (Peifer et al., 2015). It was proposed that these findings reflect an inverse u-function of flow and physiological arousal. The inverted u-shaped relationship between cortisol and flow experience found further support in Tozman’s study on chess players (Tozman, Zhang, & Vollmeyer, 2017). Similarly, an inverted u-shaped relationship of flow was found with low frequency heart rate variability (HRV)—often interpreted as a measure of sympathetic activation (Peifer et al., 2014). Furthermore, increased parasympathetic activation (as indexed with high frequency heart rate variability) was associated with increased flow (Peifer et al., 2014). Taken together with the findings of de Manzano et al. (2010), it is hypothesized that flow is characterized by an inverted u-shaped relationship with sympathetic arousal and cortisol together with a parasympathetic co-activation (Peifer, 2012; Peifer et al., 2014; Tozman & Peifer, 2016). However, other findings on HRV revealed inconsistent results. Tozman, Magdas, Macdougall, and Vollmeyer (2015) and Harmat et al. (2015) found an overall negative linear relationship between low frequency HRV and flow-ratings. These findings rather suggest low sympathetic activation to be associated with flow. As an objective measure of mental effort, HRV has proved useful to test the hypothesis put forward by Goleman (1995) and Bruya (2010b) that flow involves effortless attention (Harris, Vine, & Wilson, 2017). The close link between flow and the activity of the autonomic nervous system that is indexed by HRV is further supported by the finding that influencing parasympathetic activation via stimulation of the vagus nerve affects flow experience (Colzato, Wolters, & Peifer, 2018). Taking a cue from the inverted u-shape function found in psychophysiological studies of flow, Ulrich and colleagues undertook a series of fMRI investigations into experimentally induced flow and looked for neural activity that was highest in flow and lowest in boredom and frustration (Ulrich, Keller, & Grön, 2016b; Ulrich, Keller, Hoenig, Waller, & Grön, 2014). Flow in a mental arithmetic task was induced using the skills-demand compatibility model. Relative to boredom and frustration, flow was characterized by greater activation of the ‘multiple-demand

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system’, involved in task-relevant cognitive functions, but reduced activation of the default mode network, which has been linked to self-referential processing (Ulrich et al., 2014, 2016b). Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) have also been used to examine neural correlates of flow. Their findings shed further light on the neural mechanisms behind flow and test predictions made by theories like Dietrich’s (2004) hypofrontality theory. One study found no associations between reported flow scores and frontal activity (Harmat et al., 2015), but another found greater activation of the ventrolateral prefrontal cortex (VLPFC) in flow and suggested that the VLPFC may process reward and emotion (Yoshida et al., 2014). Larger stimulus-preceding negativities (SPNs) in the EEG-signal were found during flow, indicating increased motivation and anticipatory attention (Meng, Pei, Zheng, & Ma, 2016). Expert table tennis players who reported experiencing more flow showed greater right temporal cortical activity when imagining a stroke, possibly reflecting the automaticity of a highly trained skill (Wolf et al., 2015). Researchers are even beginning to experiment with examining the psychophysiology of joint flow (Labonte-Lemoyne, 2016; Noy, Levit-Binun, & Golland, 2015). Advances in techniques of neural analysis allow us to go beyond identifying the brain regions active during flow and understand how flow could arise as a result of complex networked interactions between these brain regions. Multivariate wholebrain analyses implicate a widespread network of visual, default mode and emotional areas in a naturalistic gaming activity (Ju & Wallraven, 2019). Dynamic causal modelling was used to deduce that the dorsal raphe nucleus may be involved in down-regulating the default mode network in flow (Ulrich, Keller, & Grön, 2016a). Graph theory analyses can be used to examine the properties of these neural networks to answer, among others, questions of how brain regions interact during flow and the energetic cost of such interactions, shedding light on the cognitive efficiency hypothesized by Goleman (1995) and Marr (2001) and testing the hypotheses put forward by Weber’s Synchronisation theory (Huskey, Wilcox, & Weber,2018).

Summary of Part 1: Status Quo of the Psychophysiology of Flow Experience A common theoretical basis regarding the neurophysiology of flow is the downregulation of task-irrelevant processes. This approach has, in the meantime, received reasonable empirical support. Also, the link between flow and dopamine has been examined using genetics and neuroimaging. Possibly, a synchronization of brain networks associated with cognitive control and reward is a central pattern during flow. The physiology of flow was further investigated using measures indicating affective valence and with measures indicating arousal, with some evidence pointing to an inverted u-shaped relationship of flow with cortisol and

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sympathetic activation, and a sympathetic-parasympathetic co-activation during flow. The increasing number of published experimental data in the last decade underlines a clear growth of research interest. Technological advances promise deeper insights into the physiology of flow in the upcoming years. Further studies should attempt to resolve contradictory findings to be able to draw a clear picture on the physiology of flow experience and answer newly raised questions.

Part 2: The Psychophysiology of Flow Experience—A Theoretical Framework In the following section, a theoretical approach linking the concepts of stress and flow will be introduced in order to set up a framework on the psychophysiology of flow experience. In contrast to the limited research on the psychophysiology of flow, more is known about the physiological processes during stress. By linking the two concepts, we can derive hypotheses on the psychophysiology of flow. Therefore, two established stress concepts will be introduced and parallels to the concept of flow will be outlined. This will result in an integrative definition of flow experience.

A Comparison of the Flow Channel Model and the Transactional Stress Model In literature on flow-experience, flow is often reported in stress-relevant work situations such as medical surgery (Csikszentmihalyi, 1975) or teaching (Weimar, 2005) and described in the context of risky activities, such as illegal graffiti spraying (Rheinberg & Manig, 2003) or rock-climbing (Csikszentmihalyi, 1975). These examples suggest that some stress or arousal can promote flow experiences. Stress has been theoretically linked with flow experience by referencing Lazarus’ Transactional Stress Model (Csikszentmihalyi, 1990, 1993; Donner & Csikszentmihalyi, 1992; Lazarus et al., 1980; Ohse, 1997; Peifer et al., 2014, 2015; Weimar, 2005), since it has essential theoretical characteristics in common with flow-theory (cf. Engeser, Schiepe-Tiska, & Peifer, Chap. 1). The Flow Channel Model (Csikszentmihalyi, 1975) describes flow as occurring when there is an optimal balance between challenges and skills (Fig. 8.1). When skills exceed challenges, the resulting experience is boredom or relaxation. When challenges of the situation exceed the skills of the person, the resulting experience is anxiety. Here is a critical similarity of the two models: According to Lazarus (Lazarus & Folkman, 1984), stress—in the form of thread or harm—is experienced if the demands of the situation exceed the skills and resources of a person. This means that the definition of anxiety in the Flow Channel Model and the definition of stress in the Transactional Stress

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Fig. 8.1 Stress in the Flow Channel Model [adapted from Csikszentmihalyi (1975)]

Model are equivalent. Therefore, we added stress to the Flow Channel Model next to anxiety (see Fig. 8.1). Flow is experienced below anxiety (or stress respectively) and above boredom, in the small channel of an optimal challenge-skill-balance (Csikszentmihalyi, 1975). Rheinberg (2008) criticizes that Csikszentmihalyi does not distinguish between challenge and demand. He points out that challenge is already the result of a comparison between the demands of a situation and the individual’s coping resources (cf. Barthelmäs & Keller, Chap. 3). An optimal demand-skill-balance (instead of challenge-skill balance, see Fig. 8.1) would therefore lead to a cognitive appraisal of challenge, which supports flow experience. This conceptualization of challenge fits very well with Lazarus’ understanding of challenge: He defines challenge as a result of “difficult demands that we feel confident about overcoming by effectively mobilizing and deploying our coping resources“(Lazarus, 1993, p. 5). According to Lazarus, challenge is a positive form of stress that is experienced as “exhilarating” (p. 5) and is accompanied by increased performance. In situations appraised as challenging, the process of coping itself can be pleasurable (Lazarus et al., 1980). The concept of challenge in the sense of Lazarus therefore closely resembles the concept of challenge-skill-balance as a prerequisite of flow experience in the sense of Csikszentmihalyi (1975). Referring to Lazarus (Lazarus & Folkman, 1984), Csikszentmihalyi (1990, 1993) states that stress can be transformed into flow experience through reappraisal of a negative situation into a pleasant challenge. Based on that, flow has been described as a cognitive strategy to cope with stressors (Weimar, 2005; see Fig. 8.2). Thus, flow can be integrated in the Transactional Stress Model, as an experience resulting when a situation is evaluated as challenging: According to Lazarus et al. (1980), stress is the result of a transactional process between a person and the environment. They outline the important role of cognition in the evaluation of a potential stressor: In a primary appraisal, a person evaluates a certain event regarding its subjective relevance according to personal goals and needs, and further, whether it can be seen as a threat, loss or challenge. During the secondary appraisal the person evaluates available coping resources. These two appraisals can consistently be renewed during a reappraisal. A subjective experience

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Fig. 8.2 The Transactional Model of Stress and Flow

of stress results if an event is appraised as personally relevant and the demands of the situation exceed the coping resources of the person (see Fig. 8.2). In our adapted version of the Transactional Stress Model—the Transactional Model of Stress and Flow—a challenge appraisal will lead to flow. In his more recent work, Lazarus integrated his stress research into a cognitivemotivational-relational theory of emotions (e.g. Lazarus, 1993), where he discusses the different functions of emotions. One function of positive emotions is to act as sustainers in the coping process (Lazarus et al., 1980). Here Lazarus refers to flow experience as an emotion that helps to sustain coping efforts: What has recently been referred to as “flow” (see Csikszentmihalyi, 1975; Furlong, 1976) appears to be an extremely pleasurable, sustaining emotion that arises when one is totally immersed in an activity and is utilizing one’s resources at peak efficiency. Examples of flow are the basketball player who is “hot” and the inspired performance of a musician, actor, or speaker. The person in flow “finds, among other things, his concentration vastly increased and his feedback from the activity greatly enhanced” (Furlong, 1976, p. 35). Although the experience of flow is characterized by a feeling of effortlessness, it occurs at times when great coping effort is usually required and during these times serves as a powerful sustainer of coping (Lazarus et al., 1980, p. 209).

From the theoretical integration of the Flow Channel Model and the Transactional Stress Model, it can be concluded that flow is a positively-valenced state that is relevant for the stress-coping process to sustain the coping effort at peak efficiency in situations that have been appraised as challenging.

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Flow and the General Adaptation Syndrome Selye (e.g. 1983), “father of the stress field”, also described a positive form of stress. He defined stress as an organism’s unspecific reaction to any kind of external demand. In his General Adaptation Syndrome (GAS), he distinguished among three phases of the stress reaction: an alarm reaction, a stage of resistance, and a stage of exhaustion. The function of the alarm reaction is to activate bodily resources in order to adapt to the stressful situation. This is not necessarily uncomfortable or unhealthy. It can even be experienced as pleasant and increase performance. Only a high intensity and/or the sustained endurance of a stressor lead to the harmful stages of resistance and exhaustion. To distinguish healthy from unhealthy forms of stress, Selye (1983) used the terms distress (latin: dis ¼ bad; associated with negative emotions) and eustress (greek: eu ¼ good, associated with positive emotions). Selye (1983) described eustress as “the pleasant stress of fulfillment” (p. 20), a description that already reminds of flow experience like Lazarus described it. Similar to flow, eustress was described as a pleasant and desirable state caused by challenging demands combined with high control and subjective importance, characterized by positive arousal and increased productivity (Csikszentmihalyi, 1975; Edwards & Cooper, 1988; Selye, 1983). As can be seen, flow and eustress share many core characteristics, so one can assume that both even share the same core-concept with just different labels and research traditions. In a review and theoretical framework on “the impacts of positive psychological states on physical health” Edwards and Cooper (1988) draw a detailed picture on the relation of stress, coping and eustress. They proposed three ways to experience eustress, all related to coping activities: “the stress and coping process may generate eustress through direct appraisal of the environment as exceeding desires, engaging in inherently enjoyable coping activities, or successfully executing coping strategies” (p. 1448). In particular the latter two ways to experience eustress again remind of flow as described by Lazarus et al. (1980), as occurring during the coping process. And they remind of Csikszentmihalyi’s definition of flow as a pleasant and intrinsically rewarding experience during an activity. Given the similarities between eustress and flow, we derive that flow experience plays an active and protective role in the stress-coping process. From a physiological perspective, eustress is defined as a highly functional state of physiological arousal (Ganster & Schaubroeck, 1991; Sales, 1969), which leads us to consider that also flow is characterized by a functional state of physiological arousal. From the comparison of flow and eustress according to the GAS, it can be concluded that both concepts show strong similarities and can even be considered equivalent in many respects: both states are positively-valenced, desirable states, characterized by a high sense of control and increased productivity, and both are caused by challenging demands. A highly functional degree of physiological arousal is characteristic for eustress and, presumably, also for flow.

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A Working Definition of Flow Experience The following proposed working definition of flow experience (Box 8.5) of flow aims to integrate theoretical approaches from Csikszentmihalyi, Lazarus and Selye on flow and (eu)stress: Box 8.5 Working Definition of Flow Experience Flow is a positively-valenced state (affective component), resulting from an activity that has been appraised as an optimal challenge (cognitive component), characterized by optimized physiological activation (physiological component) for full concentration on coping with environmental / task demands (behavioral component).

Part 3: What Does ‘Optimized Physiological Activation’ Mean? At first sight, the physiological component of the proposed working definition seems rather unspecific. What is meant by ‘optimized physiological activation’? Since there are plenty of possible flow-activities with different physiological demands (e.g., climbing vs. computer game playing) it becomes clear that the physiology of “optimal functioning” naturally differs between activities. Still, one can identify a common ground for physiological activation at maximum efficiency: the full concentration of all body functions to the given activity and the downregulation of all functions that are irrelevant for task fulfillment. This leads to a highly efficient use of energy during task performance. Self-related thoughts are (in most cases) irrelevant and even hindering task fulfillment. When demands and skills are in a perfect balance (¼ optimal challenge), all self-related thoughts can be switched off to dedicate all resources to task fulfillment. Performance and flow experience increased when attention was given to an external focus compared to an internal focus (Harris, Vine, & Wilson, 2018; Wulf, 2007). Consequently, self-reflection has to be absent during flow, a state of optimal functioning, resulting in the phenomenon known as ‘merging of action and awareness’ (Csikszentmihalyi, 1975). The absence of self-reflection has another advantage: subjective effort can only be experienced, if the person does some kind of introspection. This could result in the often-described feeling of effortlessness (Bruya, 2010b; Csikszentmihalyi, 1975, 1997, 1999) which automatically sustains focused attention. Still, effortless attention does not necessarily imply that there is no effort. It means that a person does not feel the effort. Hence, Bruya (2010b) defines the term effortless as follows: description of attention or action that (1) is not experienced as effortful or (2) involves exertion and, due to the autotelicity of experience, subjective effort is lower than in normal conditions, with effectiveness maintained at a normal or elevated level (p. 5).

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Effortlessness as a subjective experience can be measured with questionnaires. Whether or not this subjective experience is accompanied by a decrease in objective effort can only be measured with physiological indicators. Given that many studies show moderately increased arousal during flow (e.g., de Manzano et al., 2010; Keller et al., 2011; Peifer et al., 2014; Tozman et al., 2017), it is most likely that the subjective experience of effortlessness during flow is not accompanied by objective effortlessness. However, it is probably associated with highly efficient energy use, as described with the term “optimized activation” as used in the proposed definition of flow. This corresponds to findings of Huskey, Wilcox, and colleagues (2018), that a flow condition was associated with low neural metabolic cost compared to low- or high-difficulty conditions. In the following, existing findings for flow-related physiological concepts are reported. Further, physiological research methods that seem promising regarding the investigation of the flow-phenomenon will be introduced. Since physiological processes are highly complex, no claim of completeness is made here.

Optimal Functioning in the Brain What happens in the brain during flow? As introduced in part 1 of this chapter, many existing theories and findings regarding this question agreed on the assumption that flow is accompanied by a decrease in cortical activity (Csikszentmihalyi, 1990; Dietrich, 2004; Goleman, 1995; Hamilton et al., 1984; Marr, 2001). During flow, a minimum of mental energy was assumed to lead to maximum efficiency in highly practiced activities. Activation and inhibition of neural circuitry are fully adapted to moment-to-moment activity demands during flow experience. Early empirical evidence for this theory was provided by Hamilton et al. (1984) who found subjects scoring high on the Intrinsic Enjoyment Scale (assessing autotelic personality) to show decreased effort in an attention condition compared to baseline. The differentiation made by Dietrich (2004) in his Hypofrontality Hypothesis (see above) may also add to the understanding of efficient energy use. He distinguishes between explicit and implicit information-processing systems (Ashby & Casale, 2002; Dienes & Perner, 1999; Schacter & Bruckner, 1998). The explicit system is tied to conscious awareness and contains higher order knowledge representations. This system is rather slow, but flexible. The implicit system on the other hand, is unavailable to consciousness and contains skills and experiences that cannot be verbalized, but can be observed during task performance. Therefore, this system is very fast and highly efficient in its very specific context (Dietrich, 2003, 2004; Dietrich & Stoll, 2010). Dietrich (2004) concluded that the flow state would be “a period during which a highly practiced skill that is represented in the implicit system’s knowledge base is implemented without interference from the explicit system” (p. 746) and further: “Given that the explicit system is subserved by prefrontal regions, it follows from this proposal that a flow experience must occur during a state of transient hypofrontality that can bring about the inhibition of the

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explicit system” (p. 757). However, Dietrich (2004) did not reduce flow to task performance on an implicit level. As a crucial prerequisite for flow, he outlined a balance of personal skills and challenges of the task: if the implicit system is used to its full ability (and not beyond), no resources need to be dedicated to the explicit system. The only process of the explicit system that he explicitly excluded from being downregulated during flow is executive attention, since it is essential to focus on a certain activity and to selectively inhibit other cognitive prefrontal processes. Studies have examined this theoretical aspect with contradictory results. Using fNIRS to examine flow in the context of Tetris, Harmat et al. (2015) did not find any associations between reported flow scores and frontal cortical oxygenation, concluding that there was no support for relating flow to a state of hypofrontality. However, using the same task, Yoshida et al. (2014) found that activation of the right and left ventrolateral prefrontal cortex was greater in flow compared to boredom in the final 30s of the task and suggested that these areas are processing reward and emotion during flow. Studies have also found that an optimal level of difficulty, compared with an easy or hard level of difficulty, led to greater self-reported flow and reduced activity in the medial PFC (De Sampaio Barros, Araújo-Moreira, Trevelin, & Radel, 2018; Ulrich et al., 2016b). These recent findings suggest that frontal activation during flow is more complex than currently proposed by the Hypofrontality Hypothesis. More research precisely targeting the relevant prefrontal regions can clarify conflicting results. A related line of research shedding more light on the physiology of flow experience involves investigating the default mode network in the brain. It has been consistently found that blood flow in regional brain activity, like in the medial prefrontal cortex (mPFC), increased in a passive, relaxing state compared to a task-focused state (Goldberg, Harel, & Malach, 2006; Gusnard, Akbudak, Shulman, & Raichle, 2001; Raichle et al., 2001; Shulman et al., 1997). Shulman et al. (1997) proposed that the increased activity during a passive state reflects processes like monitoring or exploring the external environment, the body, or the subjective emotional state. Gusnard et al. (2001) argued that the mPFC contributes to selfreferential mental activity or introspection. To explain the phenomenon of decreasing prefrontal activity during task engagement, Goldberg et al. (2006) proposed a global resource allocation network, which disengages task-irrelevant cortical processes, such as self-related cortical representations. Since flow is a highly focused state of task engagement, these findings provide additional evidence, that activity in certain brain regions decreases during flow experiences. The field of flow neurophysiology can tap on the relatively larger research area of the physiology of meditation. Meditational practice has often been linked to flow experience (Csikszentmihalyi, 1990; Goleman, 1995; Posner, Rothbart, Rueda, & Tang, 2010), since their common target state is one of full attentional control. Both phenomena are characterized by a deep concentration on a certain focus. During a meditative state, practitioners report feelings of effortless attention similar to flow. However, meditation is usually practiced in a resting position whereas flow arises during an activity that might require a higher degree of physiological activation. Meditation has a longer research tradition with an early interest in its physiology

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from the 1950s onwards (e.g., Bagchi & Wenger, 1958; Das & Gastaut, 1955). Yet, existing studies show inconsistent results. Some studies found increased activity in frontal lobes during meditation (Herzog et al., 1990; Lazar et al., 2000; Newberg et al., 2001), which would speak against a state of hypofrontality during effortless attention. If it is argued that flow and meditation are similar attentional states, how can these conflicting results be explained? As shown by Brefczynski-Lewis and colleagues (Brefczynski-Lewis, Lutz, Schaefer, Levinson, & Davidson, 2007) in an fMRI-study, very experienced meditation practitioners need much less effort to sustain the attention focus during meditation compared to less experienced practitioners. Since successful meditational practice often requires years of practice, the often-found increase in frontal activity could refer to lacking attentional control characterized by drifting thoughts and processes of self-reflection when novices try to meditate. Other common attributes of flow and meditation are the loss of the senses of time, space, and self-awareness (Baijal & Srinivasan, 2010; Csikszentmihalyi, 1975) as opposed to a relaxing (‘default’) state of mind on one side, and stress on the other. Goldberg et al. (2006) found neurophysiological evidence for the phenomenon of losing the self in the act, specifically a suppression of self-related brain areas during a highly demanding sensory processing task compared with an introspective task. Gusnard et al. (2001) found evidence that the dorsomedial PFC is associated with self-related emotional processing. Since self-related emotions are excluded in the definition of flow, the dorsomedial PFC would be expected to be inactive during flow experience. The senses for space and time are neurophysiologically associated with the superior parietal lobe (Joseph, 1996; Lynch, 1980) therefore they are expected to be inhibited during flow. Austin (2010) suggested that the thalamus plays a key role in effortless attention as experienced during meditation or flow. In the thalamic gateway hypothesis, he argued that the thalamus serves as a filter selecting which events become aware and which are shielded from awareness. Further he explained that a deactivation of thalamic nuclei inhibits self-referential pathways in order to enter a state characterized by selflessness, effortlessness, and fearlessness as typical for flow. A recent multivariate analysis on the brain in flow also found increased activity in the insula, known to encode the passage of time (Ju & Wallraven, 2019). Aggregating the above-mentioned theories and findings, it seems that flow results from a downregulation of task-irrelevant processes, which leads to decreased activity in the default mode network due to focused attention (see Fig. 8.3). This has been borne out by recent neuroscientific findings. By balancing the challenge of a mental arithmetic task with participants’ ability, Ulrich et al. (2014) found that flow was characterized by reduced activity in the mPFC and PCC, both part of the default mode network, with the extent of reduced activity correlating with self-reported flow experience. When task difficulty was matched with individual ability in a computer game, it was associated with higher levels of intrinsic reward and decreased activity within the default mode network (Huskey, Craighead, Miller, & Weber, 2018). Stress research suggests that stress, on the other hand, is associated with increased activation of default mode networks (Van Oort et al., 2017).

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Fig. 8.3 The relation of flow-experience and default network activation in the brain

Studies also show that task-relevant neural activity increases during flow. During a mental arithmetic task, Ulrich et al. (2016a) found increased activity in a ‘multiple demand’ network, which has been suggested to function in a wide array of demanding cognitive tasks including mental arithmetic. Klasen et al. (2012) found increased activity in the neocerebellum, left and primary somatosensory cortex, and motor areas during flow in a computer game. Engaging in any activity also involves planning, goal maintenance, performance monitoring, response inhibition and reward processing, all aspects of cognitive control. A balanced difficulty condition elicited robust neural activity in neural structures related to cognitive control, specifically the dorsolateral prefrontal cortex (DLPFC), and attention (Huskey, Craighead et al., 2018). An EEG study also found increased frontal theta activity, a marker of cognitive control, in flow, alongside moderate levels of frontal and central alpha activity, suggesting that the working memory load is not excessive (Katahira et al., 2018). In agreement with the intrinsically rewarding nature of flow, activity in areas related to reward such as the caudate nucleus, nucleus accumbens and putamen, also increase during flow (Huskey, Craighead et al., 2018; Klasen et al., 2012; Yoshida et al., 2014). The nucleus accumbens also becomes more functionally connected with the DLPFC when task difficulty is balanced with individual ability, suggesting a link between reward and cognitive control. Weber and colleagues’ (2009) Synchronisation Theory stated that during flow, neural areas that are task-relevant and involved in cognitive control become synchronised with reward structures in the brain, resulting in an energetically-optimized state. Graph theory analysis has found evidence for this hypothesis. In an experimental flow condition, the fronto-parietal control network, implicated in cognitive control, had the lowest global efficiency value, indicating low metabolic cost, suggesting an energetically optimized configuration of cognitive control and reward regions during flow (Huskey, Wilcox, & Weber, 2018).

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The recent increased research into the neuroscience of flow has deepened our understanding of flow and opened new areas of research. Research consistently shows that the mPFC, associated with self-referential processing, and the default mode network are down-regulated in flow. Advances in neuroscientific analysis also allow more questions about the neuroscience of flow to be asked but more research is needed to clarify contradictory findings and explore flow in other contexts, particularly if optimal activation is context-dependent.

Contributions of the Neurotransmitter Dopamine As suggested by Marr (2001), dopamine and flow experience may be related. Flow is a highly intrinsically rewarding state and dopamine is considered to be an essential element in the brain reward system. The mesolimbic dopamine-system belongs to the so called ‘pleasure centres’ that have first been described by Olds and Milner in 1954. They had implanted an electrode into the septum of a rat’s brain and the rat could stimulate itself by pressing a button. The self-stimulation was so rewarding, that the rat did not stop regardless of hunger or thirst. The mesolimbic dopamine system regulates reward-related motivational, emotional and cognitive processes (Davis et al., 2009). Engagement in rewarding activities creates positive memories and, therefore, these activities even gain salience for a subject. This process can be seen as an upward spiral of positive reinforcement that increases a subject’s motivation towards the rewarding activity. Using PET and 11C-labeled raclopride, Koepp and colleagues (Koepp et al., 1998) investigated human participants playing a rewarding video game. They found an endogenous dopamine increase particularly in the (ventral) striatum that was positively correlated with the performance level. Other indicators supporting a relation of dopamine and flow experience are the effects of dopamine-agonists, such as cocaine, that strongly resemble some attributes of flow experience: a rewarding feeling of high energy and alertness, accompanied by improved concentration (and therefore performance), a carefree trust in one’s own abilities with a feeling of control over the activity, while forgetting about basic human needs such as hunger or sleep. Since the flow state is rewarding and appetitive, humans strive to experience it more often and are more likely to engage in flow-eliciting activities. That is why highly flow-conducive activities carry, at the same time, a risk for addiction. Examples are playing video-games, internet-surfing or social media activities (Brailovskaia, Rohmann, Bierhoff, & Margraf, 2018). Therefore, the relation of flow and addiction recently came into focus of research (for more details see Zimanyi & Schüler, Chap. 7). One risk factor for addiction disorders seems to be hypo-dopaminergic functioning of the brain reward system so that dopamineenhancing substances or activities are used to counterbalance the lack of dopamine in the system (Davis et al., 2009). Given that flow leads to a dopamine-increase, low basal dopaminergic activity could contribute to an autotelic personality (cf. Baumann, Chap. 9), which implies an active seeking and mastering of

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difficulties in order to experience the rewarding state of flow. Such a relationship remains to be tested. Flow research has tapped on the methods of genetics and neuroscience to examine the link between dopamine and flow. Using positron emission tomography, flow proneness was shown to be positively associated with the availability of dopamine D2 receptors in the striatum (De Manzano et al., 2013). Ulrich et al. (2014) also found evidence for an association of flow during a mathematical task with the nigrostriatal dopamine system. However, a genetic study unexpectedly linked higher flow proneness with a genetic polymorphism associated with lower striatal D2 receptor availability (Gyurkovics et al., 2016). The authors suggest that CC homozygotes show higher efficiency in response inhibition compared to T allele carriers and their lower impulsivity could facilitate flow experience by downregulating task-irrelevant processes. Further linking the dopaminergic system to flow experience, Kavous et al. (2019) also found a small positive correlation (0.13) between proneness to experience flow in everyday life and the volume of gray matter in the dopaminergic system, specifically in the right caudate (Kavous et al., 2019). Linking flow to striatal dopamine also opens up the possibility of using yet another physiological measure of flow. Spontaneous eye blink rate has been suggested as an externally measurable index of striatal dopamine (Jongkees & Colzato, 2016; Karson, 1983), though caution is needed as conflicting results exist (see Dang et al., 2017). Spontaneous blink rate during a game was positively related to the rate of learning to play it, particularly when the amount of self-reported flow experienced during the game was high (Cowley et al., 2018). However, a recent study found a negative relationship between flow and spontaneous blink rate during the video game Pacman (Peifer, Butalova, & Antoni, 2019). While this result may, at first, seem to contradict a positive relationship between flow and dopamine, it may be explained by findings linking spontaneous eye blink rate with visual attention (Poulton & Gregory, 1952), which is highly needed in video game playing. Presumably, the effect of visual attention on spontaneous eye blink rate dominated the effect of dopaminergic activity on spontaneous eye blink rate in that study. These findings are in line with results from an earlier study, which also found evidence for a decreased eye blink rate during flow in a video game (Rau, Tseng, Dong, Jiang, & Chen, 2017). Accordingly, spontaneous eyeblink rate can be used in future research in video games as a physiological flow indicator that is negatively related to flow. The link between dopamine and flow has proved to be a fruitful vein of research, revealing more information on flow physiology and turning up more ways to index flow. While spontaneous eye blink rate as an indicator for striatal dopamine needs to be treated with caution, a promising further avenue of research would be to conduct pharmacological studies, for example, administering L-dopa in an experimental setting. Also plausible would be the use of neuroimaging methods, e.g. fMRI or PET scans with C-labelled raclopride RAC to measure changes in extracellular dopamine levels during flow experience (see Koepp et al., 1998). As research in this field has recently increased very quickly, further evidence can be expected soon.

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Electromyography Electromyography measures action potentials discharged during facial muscle activity (Fridlund & Cacioppo, 1986). The ‘frowning muscle’ Corrugator Supercilii (CS), the ‘smiling muscle’ Zygomaticus Mayor (ZM) and Orbicularis Oculi (OO), the muscle that closes the eyelid (involved in genuine smiling), are often used as physiological indicators of emotional valence (see Larsen, Berntson, Poehlmann, Ito, & Cacioppo, 2008; see Fig. 8.4). Whereas positive affect increases activity in ZM and OO, CS activity is increased by negative affect and inhibited by positive affect compared to baseline activity. CS activity has also been linked to mental effort (Van Boxtel & Jessurun, 1993). Since flow experience is considered to be a positively-valenced state, researchers have started to use EMG measures to investigate flow (De Manzano et al., 2010; Kivikangas, 2006; Nacke & Lindley, 2009; on the role of emotions during flow experiences see also Abuhamdeh, Chap. 6). Starting from enjoyment as a key characteristic of flow, Kivikangas (2006) and de Manzano et al. (2010) expected to find increased ZM activity and decreased CS activity during flow experience. Kivikangas (2006) here found partial support as he reported low CS activity being related to high flow, but no association with ZM and OO activity during video game playing. Taking CS activity as an indicator for mental effort, these results could also be interpreted as evidence for an objective decrease of effort during subjective effortlessness in flow. In contrast, Nacke and Lindley (2009) found an association of the experimental flow condition with increased activity of ZM and OO but no association with CS. Also de Manzano et al. (2010) found a positive relation of flow with ZM activity, but no relation with CS activity in their study with professional piano players. Since only few studies exist that investigated facial muscle activity in relation to flow, it is too early to draw conclusions. The conflicting results might be triggered by various differences in the studies: Whereas participants played a first-person shooter game in Kivikangas’ (2006) and Nacke and Lindley’s (2009) experiments, in the experiment of de Manzano et al. (2010), participants played their favourite piece of Fig. 8.4 Positions of EMG electrodes to measure Corrugator Supercilii, Orbicularis Oculi, and Zygomaticus Major

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music on the piano. Whereas Kivikangas used student volunteers, Nacke and Lindley had recruited male hardcore gamers and de Manzano and colleages had professional piano players as participants. In addition, Nacke and Lindley operationalized flow in an experimental flow condition, while de Manzano and colleagues and Kivikangas measured it with a questionnaire. Further studies are necessary to clarify the conflicting results and to identify a flow-typical EMG-pattern if it exists.

Cortisol The close relation of the concepts flow and stress, as introduced in part 2 of this chapter, suggests a link between the stress-hormone cortisol and flow experience. Cortisol is an endogenous hormone belonging to the group of glucocorticoids which is secreted by the adrenal glands as an end-product of the hypothalamus–pituitary– adrenal (HPA) axis. Cortisol can be measured in blood or in saliva. Contrary to the autonomous nervous system, this endocrine system reacts rather slowly so that a cortisol change after a stressful event is only measurable in saliva 10–30 min after stimulus onset, depending on its intensity. Cortisol secretion follows a diurnal rhythm, and peaks within the first 30 min after awakening (cortisol awakening response; Fries, Dettenborn, & Kirschbaum, 2008). Cortisol is involved in general bodily functions like in metabolism and the immune system. While every cell in the organism contains cortisol receptors, some brain areas are particularly rich in these receptors, e.g. the Hippocampus, the Hypothalamus or the PFC. In addition, cortisol is involved in the regulation of stress-related processes. It is increasingly secreted in stressful situations and, therefore, cortisol is often called ‘stress-hormone’ (for an overview on cortisol and its functions see Ulrich-Lai & Herman, 2009). However, existing studies suggest that cortisol is involved in the coping process by mediating the stress response (Oitzl, Champagne, van der Veen, & de Kloet, 2010; Putman & Roelofs, 2011). Through its enhancing effect on bloodglucose levels, additional energy-resources are provided (Sapolski, Romero, & Munck, 2000) preparing the individual for increased energy demands (Benedict et al., 2009). Lovallo and Thomas (2000) explain the physiology of cortisol secretion referring to the Transactional Stress Model of Lazarus and Folkman (1984). According to their explanation, an individual undergoes an appraisal process, evaluating a given stressor by its threat potential (first appraisal) and the person’s resources to cope with it (second appraisal; compare Fig. 8.2). If the appraisal process results in a global threat evaluation, this may in turn elicit a cortisol response in order to aid situational coping mechanisms. This explanation can be linked to the approach of Baumann (Baumann & Scheffer, 2010; cf. Baumann, Chap. 9), who hypothesizes that flow results from seeing difficulty and mastering difficulty. Integrating both approaches, we propose that seeing difficulties results in a cortisol increase preparing the individual for the stressful situation and providing additional

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resources for mastering. In line with this, it was found that cortisol helps to maintain normal cognitive functioning and memory formation during stress (Lovallo & Thomas, 2000). Gailliot et al. (2007) found a lack of blood glucose to be associated with lower attention control in a Stroop task, while the administration of glucose could maintain performance. Since cortisol acts as an energy supplier by providing glucose to the body, it can help to maintain mental effort. Putman and Roelofs (2011) argue that cortisol facilitates approach-related behavior and shields task performance from irrelevant emotions. Cortisol increases selective attention to stress-relevant stimuli, so that individuals use given information more efficiently and focus on important details rather than the complete picture (De Kloet, Oitzl, & Joel, 1999; Schwabe et al., 2007). This shift of attention deep into the task, protected from irrelevant distractors and accompanied by a most efficient way of performing, is a key aspect of flow experience. In sum, cortisol facilitates an attention mode which is characteristic for flow experience and which helps to cope with demanding situations. Studies have shown empirical evidence for this association between cortisol and flow experience. Increased values of cortisol were associated with higher reported flow (Peifer et al., 2014, 2015; Tozman et al., 2017). Keller et al. (2011) found elevated cortisol levels in participants playing an optimally challenging level of the video game Tetris. Rose, Jenkins, Hurst, Herds, and Hall (1982) found high cortisol reactivity in response to workload to be related with an experience of challenge and engagement. This was also accompanied by more job satisfaction, higher competence-ratings from peers, and less illness. However, literature provides evidence for dose-dependent, inverted u-shaped effects of cortisol (e.g. De Kloet et al., 1999) on cognitive functioning such as on autobiographical memory, spatial memory and memory consolidation (e.g. Lundberg, 2005; McEwen & Seeman, 1999; Young, Drevets, Schulkin, & Erickson, 2011). While moderately increased cortisol levels had positive effects on cognitive functioning, high levels had reverse effects. This finding can be explained by the existence of two receptor types for glucocorticoids in the hippocampus: mineralocorticoid receptors (MRs) and glucocorticoid receptors (GRs). As MRs bind to cortisol with a tenfold higher affinity, GRs bind mainly to cortisol when MR receptors are occupied (Reul & de Kloet, 1985). This leads to a biphasic mechanism: When MR receptors are dominantly occuppied (as in a moderately stressful situation and moderately elevated cortisol levels), cognitive functioning will increase, while a shift of the MR/GR ratio towards GR dominance (elicited by a strong or enduring stressor) will decrease cognitive functioning. In line with this, recent studies have found evidence for an inverted u-shaped relationship between cortisol and flow (Fig. 8.5) in the context of a complex video game and chess-playing (Peifer et al., 2014; Tozman et al., 2017). Also, we found that flow decreases after the oral administration of cortisol, simulating a cortisol response to a strong stressor (Peifer et al., 2015). However, results of Keller et al. (2011) were not in line with an inverted u-shaped relationship between cortisol and flow. In their experiment with university students, they designed three conditions of the computer game Tetris to elicit boredom, flow

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Fig. 8.5 Inverted-u function of physiological arousal and flow experience

or overload. In their flow condition, they found increased cortisol levels compared to the boredom condition and with no difference to the overload condition. In a similar experimental design but in the context of playing chess, Tozman et al. (2017) did find moderately elevated cortisol levels in the flow condition and—as proposed by the inverted u-shaped relationship—they found the highest cortisol levels in the overload condition. A potential explanation of the seemingly contradictory findings could be task relevance (or subjective value, compare Barthelmäs & Keller, Chap. 3): Keller and colleagues had used students and a relatively irrelevant activity, i.e. playing Tetris. Being in the overload condition was probably not too stressful for students. This could explain why cortisol levels did not rise further compared to the fit condition in Keller and colleagues’ experiment. Tozman and colleagues’ participants were professional chess players who had to play chess, which is a highly relevant activity for them. Accordingly, an overload condition of this highly relevant activity would, in comparison, rather increase stress and cortisol (compare Tozman & Peifer, 2016). In line with this, task relevance has been identified as a moderator of the relationship between difficulty and flow experience (Engeser & Rheinberg, 2008). It remains unclear whether an increase in cortisol is always involved in flow experience. Does flow cause an increase in cortisol as suggested by results of Keller et al. (2011)? Or do increased cortisol values in flow situations indicate that flow here results from a stress-coping process? Through the described effects of cortisol on selective attention and blood glucose-levels, additional coping capabilities are available and a stressful state can be transformed into flow experience. Is it possible to experience flow without increased cortisol levels? As yet, there are no studies to answer this question. If cortisol is indeed always involved in flow experience, it raises the question of whether flow has exclusively beneficial effects on health (Keller et al., 2011). Enduring high cortisol levels, as in very stressful life phases, lead to an adaptation of the body: the body downregulates the overflowing cortisol, resulting in hypocortisolism with decreased cortisol reactivity. Low cortisol reactivity has been associated with inadequate coping styles, such as passive coping and avoidance

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behavior (Heim, Ehlert, & Hellhammer, 2000). Furthermore, there is evidence for health impairment due to high and enduring cortisol levels, such as cardiovascular disease, type 2 diabetes and reduced immune functions (Lundberg, 2005; McEwen & Seeman, 1999). This leads us to the following conclusion: Despite the pleasant and rewarding nature of flow, literature suggests that it is still a state of heightened physiological arousal that needs to be counterbalanced by phases of relaxation.

Cardiovascular Measures Cardiovascular psychophysiology focuses on the interaction of psychological phenomena and cardiac activity (Brownley, Hurwitz, & Schneiderman, 2000). The heart is controlled by the sinus node to beat 60–80 times per minute, and the number of beats per minute (bpm) is the measure for heart rate (HR). To adapt the cardiac performance to environmental demands, the heart is further influenced by the autonomous nervous system (ANS): the sympathetic nervous system increases heart rate and stroke volume while the parasympathetic nervous system decreases heart rate (Porges, 1995). During a resting state, parasympathetic influence dominates and, therefore, heart rate is low (Uijtdehaage & Thayer, 2000). Low heart rate is usually accompanied by high heart rate variability (HRV), which represents the variability in the length of the inter-beat-intervals. High HRVvalues represent a high ability to adapt to environmental demands (Lehrer, 2003; Porges, 1995). HRV is an important and sensitive cardiovascular measure that allows a differentiation of central sympathetic and parasympathetic influence. The differentiation can be realized by decomposing different frequencies within a time series of inter-beat-intervals (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). Common measures include the lowfrequency-band (LF; 0.14–0.15 Hz), the high-frequency-band (HF; 0.15–0.4 Hz) and the quotient LF/HF as an indicator for sympathovagal balance. For an overview on the described indicators see Table 8.1. Regarding flow, an interesting aspect of HRV is that it can be used as an indicator for mental effort: In general, it is found that higher mental effort is associated with lower HRV (mainly in the HF-band; Beh, 1990; Hansen, Johnsen, & Thayer, 2003; Mulder, Mulder, & Veldman, 1985; Redondo & Del Valle-Inclán, 1992; Thayer, Hansen, Saus-Rose, & Johnsen, 2009). Therefore, HRV is a measure that can contribute to the debate of whether the reported effortlessness during flow is a subjective experience (Bruya, 2010b). If objective effort would also be reduced during flow, one should find an increase in HRV. However, the opposite was found in a study conducted by Keller et al. (2011). They investigated participants performing a knowledge-task under experimentally induced skillsdemands-compatibility condition, which was found to be associated with flowvalues compared to a boredom and an overload condition. In the compatibilitycondition, they observed a lower HRV in this condition compared to the boredomcondition and on a trend-level significance even compared to the overload condition.

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Table 8.1 Overview on physiological indicators concerning cardiovascular measures Physiological term Heart Rate (HR) Heart Period (HP) Stroke Volume (SV) Inter-Beat-Interval (IBI) Heart Rate Variability (HRV) Autonomous Nervous System (ANS) Sympathetic Nervous System Parasympathetic Nervous System Low-Frequency-Band (LF)

High-Frequency-Band (HF) LF/HF-Ratio (LF/HF)

Meaning Number of heart beats per minute Inverse of HR Amount of blood pumped in one heart beat Time interval between two heart beats Variability of the length of inter beat intervals Branch of the nervous system that performs involuntary functions; influences cardiac activity due to environmental demands; consists of a sympathic and a parasympathetic nervous system Increases HR and SV Decreases HR, active during a resting state Spectral component of the HRV, often taken as an indicator for sympathetic activity or sympathetic and vagal activty, depending on the source Spectral component of the HRV, indicator for vagal activity Quotient of LF and HF, reflecting sympathetic modulation or sympatho-vagal balance, depending on the source

Keller et al. (2011) attributed this finding to the high task involvement during flow and interpreted it as a sign of mental strain. In line with this were findings from de Manzano et al. (2010), who investigated piano players performing a favourite piece of music. They found an activation of the sympathetic branch of the ANS, represented through increased LF/HF ratio and increased heart rate, to be associated with flow. Further research also found increased LF/HF ratio and increased heart rates in flow experienced in everyday activities and in a game under experimental conditions (Gaggioli et al., 2013; Harris et al., 2017). These findings support the view that the effortlessness during flow is a subjective experience that dissociates from the actual physiological costs, at least on the level of cardiovascular activity. Presumably, the feeling of the actual effort that accompanies flow is inhibited. It is possible, that processes of the brain (CNS) and the ANS differ in that point and that a downregulation of default network activity might accompany an activation of the ANS, since a certain degree of alertness is necessary to focus on task performance. However, as proposed for cortisol, there is also evidence that the relation of flow and sympathetic activation is inverted u-shaped. Peifer et al. (2014) found under stressful conditions in the laboratory that not only cortisol, but also LF HRV had an inverted u-shaped relationship to flow. At the same time, high parasympathetic activation as indicated by HF HRV was associated with flow, suggesting that a sympathetic-parasympathetic co-activation might be a typical pattern for flow experience. This was also suggested by de Manzano et al. (2010), based on their finding of increased LF/HF ratio combined with deep breathing during flow. Such a sympathetic-parasympathetic co-activation can be described as a relaxed alertness and is linked to many desired outcomes: evidence shows that this pattern is

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associated with better adaptation to demanding situations (Berntson, Cacioppo, & Quigley, 1991) and to active coping during high workload (Backs, Lenneman, & Sicard, 1999). Accordingly, the particular co-activation pattern provides optimal conditions for successfully dealing with demanding situations. The increased parasympathetic activation during flow is further consistent with flow research showing positive associations between flow and wellbeing (e.g. Bassi, Steca, Monzani, Greco, & Fave, 2013; Fullagar & Kelloway, 2009; Rivkin, Diestel, & Schmidt, 2016; cf. Engeser et al., Chap. 1 and Barthelmäs & Keller, Chap. 3): it was shown that parasympathetic activation enhances wellbeing and positive emotions (Kok & Fredrickson, 2010). At the same time, when parasympathetic activation is regarded in an isolated way, it is likely that it also follows an inverse u-function rather than a linear positive one. When parasympathetic activation is high and at the same time sympathetic activation is low, this pattern is associated with sleepiness (Pressman & Fry, 1989). In line with this, Tozman et al. (2015) found an inverted u-shaped relationship between HF HRV and flow in a stress condition, indicating that moderate but not high or low values of HF HRV are associated with flow. One further study explored the effects of the vagus nerve on flow experience by stimulating it using transcutaneous vagus nerve stimulation (tVNS). Stimulation resulted in decreased flow (Colzato et al., 2018). However, there are also many inconsistent findings in the literature. For example some studies have found an overall negative relationship between LF HRV and flow-ratings (Tozman et al., 2015; Harmat et al., 2015), or the lowest value of LF HRV in an experimental flow-condition (Harris et al., 2017). These findings rather suggest low sympathetic activation to be associated with flow. Also, the picture is not so clear in the case of parasympathetic activation. In an experimental flow condition, the HRV measure RMSSD was increased compared to a condition of boredom or overload, suggesting reduced parasympathetic activation during flow (Keller et al., 2011). The conflicting findings for HRV parameters may also be due to the different conditions under which they were obtained, e.g. within vs. between conditions of task difficulty, level of stress, type of task, and context of the study (laboratory vs. field). A possible explanation is that the optimal experience of flow is associated with the optimal physiological activation that is required for the particular circumstances of the situation. Future studies will need to further explore physiological patterns associated with flow, taking the particular context into account.

Electrodermal Activity Electrodermal activity (EDA) is an indicator for general arousal or attention (e.g. Boucsein, 1992) and is a reliable measure for sympathetic activation (Dawson, Schell, & Filion, 2007). It is defined by the skin’s power to conduct (or resist) electricity, which predominantly depends on skin moisture and is increased through

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sweating. EDA is usually measured via two electrodes placed on the hand palm or on foot soles. Other terms used for EDA are galvanic skin response (GSR), skin conductance (SC) or skin resistance, whereas conductance and resistance are two sides of the same coin. One can distinguish between tonic and phasic EDA, whereas tonic refers to a long-term skin conductance level (SCL), indicating vigilance, sustained attention and heightened arousal over time (Kilpatrick, 1972). Phasic EDA reflects an event-related skin conductance response (see Table 8.2). Flow experience cannot be seen as a discrete situation that is triggered by specific stimuli or peak experiences, but it is characterized by an automaticity in action and a smooth activity-flow enduring over a longer time period. Therefore, tonic SCL is a promising measure to investigate flow as chosen by Kivikangas (2006). Phasic SCL might be of interest to observe the reaction towards sudden obstacles appearing during task performance. Again, a decrease in effort during flow as discussed by Bruya (2010b) should be reflected by lower values in SC compared to stress conditions. However, as already discussed for cardiovascular measures, a downregulation of irrelevant brain processes can come along with a general alertness represented by sympathetic activation as reflected by an increase in EDA. Supporting that view, it has consistently been found that EDA is generally elevated during task performance, information processing and cognition (Dawson et al., 2007; Hugdahl, 1995; Lacey, Kagan, Lacey, & Moss, 1963; Siddle, Lipp, & Dall, 1996). Since flow experience takes place during task performance, it is proposed here that SCL is generally elevated in flow situations compared to relaxation states. This is exactly what Nacke and Lindley (2009) found in their study with experienced players in a first-person shooter game: increased EDA in the experimental flow condition. However, Kivikangas (2006) did not find an association between high EDA and high flow experience. This zero-correlation might refer to a curvilinear relationship between flow and EDA: The increase in SCL depends on the demands of the task in relation to the skills of the person. With increasing task demands and resulting effort, SCL should further rise so that SCL is higher in stress compared to flow-situations. Indeed, this is what has been found in flow induced under experimental conditions. Flow was characterized by moderate skin conductance levels, Table 8.2 Overview on physiological indicators concerning EDA Physiological term Electrodermal activity (EDA) Galvanic skin response (GSR) Skin conductance (SC) Skin resistance (SR) Tonic EDA Phasic EDA

Meaning Ability of the skin to conduct or resist electrical current; mainly depending on skin moisture Synonym for EDA Ability of the skin to conduct electrical current—reciprocal of SR Ability of the skin to resist electrical current—reciprocal of SC Long-term skin conductance level (SCL); indicator for vigilance, sustained attention and heightened arousal over time Event-related skin conductance response; measured with skin conductance response (SCR)

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with higher SCL in a stress condition (Tian et al., 2017). However, EDA was also found to be highest during a flow condition where the challenge of the task was balanced with the skill of the participant and lower in boredom and frustration (Ulrich et al., 2016b). This could possibly be due to participants exerting less effort after experiencing a period of extended frustration in a subjectively irrelevant task (compare contradicting findings above). More research, possibly taking into account participants’ responses to increasing task demands, is needed to account for these conflicting findings. Another interesting aspect of the EDA is that relatively stable individual differences in SCR can be identified by their degree of electrodermal lability (Dawson et al., 2007). Whereas stabiles quickly habituate to certain stimuli and show a low rate of spontaneous SCRs in general, labiles habituate slower and show a higher rate of spontaneous SCRs. It was found that labiles have a higher ability to sustain focused and to prevent performance decrements. Katkin (1975) concludes that electrodermal lability, as a personality trait, seems to reflect central processes that are involved in attention and information processing. A higher ability to focus on tasks could possibly be linked to autotelic personality. It would be very interesting to investigate this relation in future studies.

Conclusion As the conclusion of this chapter, the proposed integrative definition of flow experience is further specified (Box 8.6), practical implications are provided, and directions for future research are summed up.

An Integrative Definition of Flow Experience The following proposed definition (Box 8.6) aims to integrate the reported theoretical approaches and empirical findings regarding flow experience and explicitly includes not only the physiological, but also cognitive, affective and behavioral components. Box 8.6 An Integrative Definition of Flow Experience Flow is a positively-valenced state (affective component), resulting from an activity that has been appraised as an optimal challenge (cognitive component), characterized by optimized physiological activation (physiological component) for full concentration on coping with environmental/task demands (behavioral component). (continued)

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Box 8.6 (continued) Optimized physiological activation during flow refers to (see Fig. 8.6) 1. + (6) Moderate levels of sympathetic activation and moderate parasympathetic activation—i.e. a sympathetic-parasympathetic co-activation 2. Moderate levels of cortisol 3. Decreased activation in default networks of the brain 4. High synchronization of brain regions involved in cognitive control and in reward 5. High activation of the brain’s multiple-demand system, which is involved in task-relevant cognitive functions

Practical Implications How can the described physiological mechanisms underlying the experience of flow be translated into action recommendations to reach flow-states more often? 1. Brain processes It has been proposed that brain processes during flow experience are characterized by a decrease of default networks in the cortex. Reaching such a decrease can be practiced through mental training, such as attention training (e.g. Rueda, Posner, & Rothbart, 2005; Rueda, Rothbart, McCandliss, Saccamanno, & Posner, 2005) or meditation (Tang et al., 2007; for an overview see Posner et al., 2010). Posner et al. (2010) suggested that meditation practice can enhance an individual’s ability to reach a flow state and call for further investigation of this possible relation. Recently, mindfulness training was found to increase experience of flow in competitive cyclists (Scott-Hamilton, Schutte, & Brown, 2016).

Fig. 8.6 Optimized physiological activation during flow

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2. General arousal indicators As a certain pattern of arousal, namely a sympathetic-parasympathetic co-activation, was suggested to be typical for flow, one should take the current physiological arousal in a given moment into account. Starting from a state of boredom, when parasympathetic activation dominates sympathetic activation, an increase of arousal is required in order to reach a flowstate. Here, gentle physiological activity, such as going for a walk, or doing gymnastics, can help to increase sympathetic activation. In a state of stress, during which sympathetic activation exceeds parasympathetic activation, a downregulation of the physiological arousal is required. This can be reached through the application of relaxation techniques, such as deep breathing, meditation and the like. For long-term effects, these techniques can be practiced in a regular manner, as this increases a habitual parasympathetic activation and leads to better results in acute situations. In terms of cortisol, our normal diurnal rhythm provides moderately elevated cortisol levels in the morning, about 1 h after awakening. As cortisol decreases continuously during the day, the morning is, cortisol-wise, a good time to work in flow.

Directions for Future Research In this chapter, a framework for the psychophysiology of flow was proposed by linking popular stress concepts to flow theory. Examining flow from a physiological perspective is a young but growing field and since the previous edition, interest in the neuroimaging of flow has increased. Electrophysiological (EEG) and other imaging techniques (fMRI, PET-Scan) have been used to shed more light on the hypofrontality hypothesis (Dietrich, 2004; Dietrich & Stoll, 2010) and to answer other questions besides. Neural correlates of self, time and space seem to be promising variables in the investigation of flow experience. The role of dopamine has been investigated with genetics and neuroscience. Further pharmacological studies would help clarify the role of dopamine in reaching and sustaining flow. Further, results on facial EMG-measures indicating emotional arousal are still inconsistent and need future clarification. Regarding general arousal indicators of the ANS (e.g. ECG and EDA) or the endocrine system (e.g. cortisol), a u-shaped function of flow and arousal was proposed. However, contradicting findings exist. A potentially explanatory variable is the subjective relevance of the activity. A difficulty (and potential explanation for contradicting findings) is the huge variability of possible flow-inducing activities, that require different activation levels for “optimal functioning”, depending on the specific demands of the activity. In the years to come, with technological advancements, steps could be taken to identify individual and activity-specific patterns of physiological indicators reflecting flow instead of single indices (Peifer, Kluge, Rummel, & Kolossa, 2020). Overall, there is great

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potential in the psychophysiological investigation of flow experience and hopefully this chapter will further stimulate research progress.

Study Questions 1. Describe theoretical approaches regarding the psychophysiology of flow experience. (a) Based on results of Hamilton et al. (1984), it was hypothesized that flow is connected to a decrease in cortical activation, where a minimum of mental energy leads to maximum efficiency in highly practiced activities. Activation and inhibition of neural circuitry are fully adapted to momentary activity demands during flow experience and task irrelevant processes are downregulated (Csikszentmihalyi, 1990; Goleman, 1995; Hamilton et al., 1984; Marr, 2001). (b) The approach of Dietrich (2004) goes in a similar direction. He suggested that flow results from a downregulation of prefrontal activity in the brain (Hypofrontality; Dietrich, 2003): During flow, well-trained activities are performed by the implicit system without interference from the explicit system, which makes the process very fast and efficient. (c) Austin (2010) suggested that the thalamus plays a key role in effortless attention as experienced during meditation or flow. In the thalamic gateway hypothesis he argued that the thalamus serves as a filter selecting which events become aware and which are shielded from awareness. Further he explained that a deactivation of thalamic nuclei inhibits self-referential pathways in order to enter a state characterized by selflessness, effortlessness, and fearlessness as typical for flow. (d) Marr (2001) discussed the neurotransmitter dopamine as a possible neurophysiological correlate of flow experience. The mesolimbic dopamine system is regulating reward-related motivational, emotional and cognitive processes (Davis et al., 2009). Flow is a highly intrinsically rewarding state and engagement in rewarding activities creates positive memories and, therefore, these activities even gain salience for a subject. This process can be seen as an upward spiral of positive reinforcement that increases a subject’s motivation towards the rewarding activity. However, the idea of an upward spiral has not been tested yet. (e) Synchronization Theory was introduced by Weber et al. (2009), stating that during flow, areas associated with reward are synchronized with areas associated with cognitive control. (f) Based on research of Kivikangas (2006) and their own study, de Manzano et al. (2010) concluded that the physiology of flow consists of sympathetic activation and positive affect.

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(g) Based on the findings of de Manzano and colleagues, research on the relationship between flow and stress, and theoretical considerations drawn from the Flow Channel Model and the Transactional Stress Model, Peifer (2012) proposed that flow is characterized by an inverted u-shaped relationship of flow with sympathetic activation and cortisol, together with a sympathetic-parasympathetic co-activation. (h) In this chapter, summarizing prior theoretical and experimental papers led to the proposal of a particular physiological pattern typical for flow experience (see Fig. 8.6), that is (i) Moderate levels of sympathetic activation and moderate parasympathetic activation—i.e. a sympathetic-parasympathetic co-activation (ii) Moderate levels of cortisol (iii) Decreased activation in default networks of the brain (iv) High synchronization of brain regions involved in cognitive control and in reward (v) High activation of the brain’s multiple-demand system, which is involved in task-relevant cognitive functions 2. Please explain the term effortless attention in the context of flow experience and describe related findings. A feeling of effortless attention has often been described in the context of flow experience (Bruya, 2010b; Csikszentmihalyi, 1975, 1997, 1999). According to Bruya (2010b) it is a description of attention or action that (1) is not experienced as effortful or (2) involves exertion and, due to the autotelicity of experience, subjective effort is lower than in normal conditions, with effectiveness maintained at a normal or elevated level (p. 5).

Effortlessness as a subjective experience can be measured with questionnaires. Whether or not this subjective experience is accompanied by a decrease in objective effort can be measured with psychophysiological indicators. Related findings are reported in the following: (a) In an EEG-study, Hamilton et al. (1984) found that individuals scoring high on the intrinsic enjoyment scale, are better able to control and sustain their attention with less effort compared to controls. (b) In research concerning default networks in the brain, it was consistently found that blood flow in regional brain activity, like in the medial prefrontal cortex (mPFC), decreased in a passive, relaxing state compared to a taskfocused state (Goldberg et al., 2006; Gusnard et al., 2001; Raichle et al., 2001; Shulman et al., 1997). (c) Brefczynski-Lewis and colleagues (Brefczynski-Lewis et al., 2007) could show in an fMRI-study, that very experienced meditation practitioners need much less effort to sustain the attention focus during meditation compared to less experienced practitioners.

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(d) As shown by de Manzano et al. (2010), Keller et al. (2011) and Peifer et al. (2014), at least moderately increased sympathetic and endocrine activation— compared to a state of relaxation—is linked to flow. Based on these results, it seems that flow is linked to decreased default network activity in the brain and increased sympathetic and endocrine arousal. 3. How are stress and flow related? Compare Lazarus’ Transactional Stress Model with Csikszentmihalyi’s flow theory. Lazarus’ Transactional Stress Model, has important theoretical characteristics in common with flow-theory: (a) The conceptualizations of anxiety according to Csikszentmihalyi (1975) and stress according to Lazarus and Folkman (1984) seem equivalent, as they result if situational demands exceed the resources of an individual. Flow is experienced below anxiety (or stress respectively) and above boredom, in the small channel of an optimal challenge-skill-balance (Csikszentmihalyi, 1975). (b) The concept of challenge in the sense of Lazarus closely resembles the concept of challenge-skill-balance as a prerequisite of flow experience in the sense of Csikszentmihalyi (1975). Lazarus stated that in situations appraised as a challenge, the process of coping itself can be pleasurable (Lazarus et al., 1980). (c) Flow and stress are the result of an appraisal process. Referring to Lazarus (Lazarus & Folkman, 1984), Csikszentmihalyi (1990, 1993) states that stress can be transformed into flow experience through reappraisal of a negative situation into a pleasant challenge. Here, flow appears as a cognitive strategy to cope with stress (Weimar, 2005; see Fig. 8.2); Lazarus refers to flow experience as an emotion that helps to sustain coping efforts. ! for more detailed information please go back paragraph Flow in the Transactional Stress Model 4. Can physiological measures substitute self-report measures to investigate flow experience? Flow is a subjective experience, and therefore, physiological measures cannot completely substitute self-report measures, while they can help to better understand the concept. Whereas self-report measures, such as questionnaires and interviews, are retrospective by nature, the advantage of physiological measures is that they can be measured continuously during the activity and the person in flow does not need to be interrupted. ! for more detailed information please go back to paragraph Introduction: Benefits of a Psychophysiological Perspective to Study Flow

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Chapter 9

Autotelic Personality Nicola Baumann

Abstract This chapter reviews the search for more stable causes of flow experiences such as “flow personality” or “autotelic personality”. Although flow research is primarily concerned with flow as a motivational state, Csikszentmihalyi has introduced the concept of an autotelic personality, that is, a disposition to actively seek challenges and flow experiences. This chapter starts with an overview of Csikszentmihalyi’s conceptual ideas and phenomenological descriptions of autotelic personalities. Unfortunately, the rich concept was not complemented by an adequate operationalization. The chapter continues with a review of personality dispositions which can be conceived of as boundary conditions for flow experience. They reflect differences either in the need (achievement motive) or in the ability (self-regulation) to experience flow. The concept of an autotelic personality should encompass both aspects simultaneously. Next, the achievement flow motive (nAchFlow) is introduced which integrates need and ability aspects. As such, I propose nAchFlow as a way to operationalize an autotelic personality. The chapter offers a functional analysis of flow in achievement contexts within the framework of Personality Systems Interactions (PSI) theory. Finally, the chapter elaborates on flow in social contexts and gives an outlook for future directions.

Csikszentmihalyi’s Concept of an Autotelic Personality General Idea Flow is a state of intrinsic motivation in which a person is fully immersed in what he or she is doing for the sake of the activity itself (Csikszentmihalyi, 1975/2000, 1990). It is characterized by a merging of action and awareness, sense of control, high concentration, loss of self-consciousness, and transformation of time (Csikszentmihalyi, 1975/2000, 1990; Csikszentmihalyi & Larson, 1987; N. Baumann (*) Department of Psychology, University of Trier, Trier, Germany e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_9

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Csikszentmihalyi & LeFevre, 1989; Nakamura & Csikszentmihalyi, 2002; see Engeser, Schiepe-Tiska & Peifer, Chap. 1). Although flow research has so far been primarily concerned with flow as a motivational state, Csikszentmihalyi and colleagues also suggested the idea of an autotelic personality: Autotelic personalities tend to position themselves in situations which enable frequent experiences of flow states (Csikszentmihalyi, Rathunde, & Whalen, 1993; Nakamura & Csikszentmihalyi, 2002). They have a greater capacity to initiate, sustain, and enjoy such optimal experiences. Box 9.1 Csikszentmihalyi’s Definition of Autotelic Personality “‘Autotelic’ is a word composed of two Greek roots: auto (self), and telos (goal). An autotelic activity is one we do for its own sake because to experience it is the main goal. [. . .] Applied to personality, autotelic denotes an individual who generally does things for their own sake, rather than in order to achieve some later external goal” (Csikszentmihalyi, 1997, p. 117). “The mark of the autotelic personality is the ability to manage a rewarding balance between the ‘play’ of challenge finding and the ‘work’ of skill building” (Csikszentmihalyi et al., 1993, p. 80). Csikszentmihalyi’s concept of an autotelic personality is derived from his flow model. According to his original model (Csikszentmihalyi, 1975/2000), flow is experienced when an actor perceives a balance between the challenge of an activity and his or her own skills. In the revised model, Csikszentmihalyi and Csikszentmihalyi (1988) proposed that flow is experienced when both, challenges and skills, are high. Most flow research to date has started from these assumptions and operationally defined flow as experiences of balance (or high/high combinations; see Moneta, Chap. 2). Only recently have researchers begun to measure and experimentally manipulate challenges and skills separately and to test their relation to flow experience (Baumann, Lürig, & Engeser, 2016; Engeser & Rheinberg, 2008; Keller & Bless, 2008; Keller & Blomann, 2008; Rheinberg, Vollmeyer, & Engeser, 2003; see Barthelmäs & Keller, Chap. 3). Csikszentmihalyi’s definition of an autotelic personality was guided by the same balance assumption: Autotelic personalities have a greater ability to manage the intricate balance between the play of challenge finding and the work of skill building (see Box 9.1; Csikszentmihalyi et al., 1993). According to Csikszentmihalyi, challenge finding and skill building are supported by different, sometimes even opposing traits or processes which are simultaneously present in autotelic personalities: pure curiosity and the need to achieve; enjoyment and persistence; openness to novelty and narrow concentration; integration and differentiation; independence and cooperation (Csikszentmihalyi et al., 1993; Nakamura & Csikszentmihalyi, 2002). For example, the pleasure and fun associated with flow may be highly desirable. Nevertheless, flow activities also require concentration and a willingness to learn about the limits of one’s skills.

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Where non-autotelic individuals may see only difficulty, the deep sense of interest aids autotelic individuals to recognize opportunities to build their skills. They open their attention to new information (the play of challenge finding) and focus it on those units of information just far enough ahead of current skills to be manageable (the work of skill building). The autotelic personality is a conjunction of receptive (e.g., openness) and active qualities (e.g., engagement and persistence). The openness to detect and become interested in new challenges is receptive yet not entirely passive. It also involves active engagement and persistence in highly challenging activities. However, the engagement is not a mean to a specific goal. Csikszentmihalyi (1997) summarized these qualities as a capacity for “disinterested interest”. The term “disinterested” emphasizes a focus on task-inherent as opposed to purpose-related incentives as well as an orientation towards mastery as opposed to performance. Nakamura and Csikszentmihalyi (2002) describe similar core characteristics of autotelic personalities (i.e., curiosity and interest in life, persistence, and low self-centeredness) as metaskills. However, the relationship of such skills or traits with the frequency or intensity of flow experiences has rarely been tested. Csikszentmihalyi et al. (1993) proposed that these complementary (receptive and active) qualities in tandem produce a powerful autotelic combination. The simultaneous presence of complementary or even opposing traits fosters a dynamic, dialectical tension which is conducive to “optimal” personality development and the evolvement of complex individuals. Therefore, autotelic individuals should have a clear advantage in realizing the development of their talents to the fullest extent (Csikszentmihalyi, 1996; Csikszentmihalyi et al., 1993). The dialectical principal and the complexity inherent in autotelic experiences are often not only stimulated through the traits of a person but also through the environment: Autotelic personalities tend to have family and school environments which simultaneously provide challenge and support, independence and cooperation, flexibility and cohesion, integration and differentiation.

Previous Measurement Approaches Whereas the description of autotelic personalities and their developmental contexts is very rich and integrates general principles of self-growth from different theories, the operationalization of the construct is rather poor. There are two different approaches towards measurement. In the first approach, autotelic personalities are identified through frequency and intensity of characteristic experiences. Csikszentmihalyi (1997) assessed the frequency of high-demand/high-skill situations over longer periods of paging with the Experience Sampling Method - a technique developed for the purpose of obtaining self-reports of thoughts and feelings at random intervals during ongoing activities (see Moneta, Chap. 2). Individuals whose frequency of high-demand/high-skill experiences is in the upper quartile of the distribution (autotelic) are compared to those in the lower quartile

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(non-autotelic) in other outcomes of experience and behavior (Csikszentmihalyi, 1997). Findings indicate, for example, that autotelic individuals are not necessarily happier, but more often involved in complex activities which, in turn, make them feel better about themselves and increase their self-esteem (Csikszentmihalyi, 1997). Even when currently unemployed, individuals with a greater number of highdemand/high-skill activities report higher quality of life and sense of coherence (Hirao & Kobayashi, 2013). However, this measure of autotelic personality is problematic because high-demand/high-skill situations do not necessarily elicit flow (e.g., Engeser & Rheinberg, 2008; see Barthelmäs & Keller, Chap. 3). Additionally, Csikszentmihalyi and colleagues developed a flow questionnaire that assesses the frequency (0 ¼ ‘not at all’, and 1 ¼ ‘few times a year’ to 7 ¼ ‘few times a day’) of three flow characteristics (Asakawa, 2010; Csikszentmihalyi, 1975/ 2000, 1982; Csikszentmihalyi et al., 1993; see Moneta, Chap. 2). More recently, Jackson and colleagues (Jackson & Eklund, 2002; Jackson, Martin, & Eklund, 2008) developed a dispositional flow scale which assesses the frequency with which individuals experience the full range of typical flow characteristics (loss of selfconsciousness, transformation of time, sense of control, concentration on a task, etc.) within specified activities in general (see Moneta, Chap. 2). The scale is not only validated in physical activity settings but also in other performance-related domains (Jackson & Eklund, 2004; Wang, Liu, & Khoo, 2009) and in experience samples of flow (Johnson, Keiser, Skarin, & Ross, 2014). Furthermore, Ullén et al. (2012) measure flow proneness by the self-reported frequency of flow experiences in the three life domains of work, maintenance, and leisure time. However, mere frequency (as well as intensity) measures do not contribute to an understanding of the underlying causes of flow experience as has been the case for the conceptionalizations above. In the second approach, autotelic personalities are determined through their expected outcome of full talent development. Csikszentmihalyi et al. (1993), for example, derived autotelic personality patterns from traits that distinguish talented from average individuals: Autotelic (i.e., talented) personalities have traits conducive to concentration (e.g., achievement, endurance) as well as openness to experience (e.g., sentience, understanding). The traits were assessed with the Personality Research Form (PRF; Jackson, 1984). However, little is known about the role of such personality factors with respect to flow experience. More importantly, the measure is confounded with the outcome (i.e., talent development) which it was originally designed to explain (Csikszentmihalyi et al., 1993). Recently, Tse, Lau, Perlman, and McLaughlin (2018) developed a questionnaire that measures autotelic personality through a diverse set of attributes and meta-skills (curiosity, persistence, low self-centeredness, intrinsic motivation, enjoyment and transformation of challenge and boredom, attentional control). Taken together, the search for stable causes behind flow experience is appealing and has interested flow researchers from early on. However, the concept of an autotelic personality is awaiting a clear operationalization that is not confounded with its to-be-explained outcomes. Before offering such an operationalization, the existing literature on the relationships between personality traits and flow experience

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is reviewed in more detail. This review is designed to provide more insights into functional underpinnings of a flow personality.

Personality Traits as Boundary Conditions of Flow By introducing the concept of an autotelic personality, flow theory has acknowledged that some people are more likely to experience flow than others (Csikszentmihalyi, 1975/2000, 1990). Nevertheless, flow researchers have only recently begun to empirically test the relationship between personality traits and flow experience. The general frequency of flow experiences (i.e., dispositional flow or flow proneness), for example, correlates positively with the “Big Five” trait conscientiousness and negatively with neuroticism (Johnson et al., 2014; Ross & Keiser, 2014; Ullén et al., 2012). Even supportive environments do not facilitate flow experiences directly but through their impact on traits: Children’s perception of parents as accepting and moderately (but not overly) controlling was associated with higher conscientiousness and lower neuroticism and, in turn, with more flow (Mesurado & de Minzi, 2013). In motivational terms, conscientiousness has a strong overlap with the explicit achievement motive whereas low neuroticism indicates that, across motives, goal striving is not regulated by fear (Engeser & Langens, 2010). Other findings further support the assumption that flow experiences are systematically related to the achievement motive (Eisenberger, Jones, Stinglhamber, Shanock, & Randall, 2005; Engeser & Rheinberg, 2008; Schüler, 2007) and selfregulatory competencies (Baumann, Lürig, & Engeser, 2016; Keller & Bless, 2008; Keller & Blomann, 2008). I will elaborate on both traits below.

Achievement Motive Among the many traits proposed to be conducive to autotelic experiences, the achievement motive is a strong candidate for several reasons. First, Moneta and Csikszentmihalyi (1996) proposed that “the flow model may be more applicable to social contexts and activities where achievement plays a dominant role” (p. 393). Second, a consistent finding in motivation research is that the achievement motive moderates whether people perceive a challenge-skill balance (i.e., medium task difficulty) as positive or negative. According to Atkinsons’s (1957) risk-taking model, only individuals high in hope for success prefer medium task difficulty (balance) whereas individuals high in fear of failure try to avoid such balanced situations. The moderating role of the achievement motive has been empirically supported by findings from Eisenberger et al. (2005), Engeser and Rheinberg (2008) as well as Schüler (2007): Individuals high in hope for success and low in fear of failure do not only experience more flow, they especially experience more flow when they perceive a challenge-skill balance (medium task difficulty).

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The findings support the assumption that a high need for achievement (nAch) in its hope of success component is an important prerequisite for flow. In the studies cited above, hope for success was assessed with projective or semi-projective motive measures which tap into implicit (operant) motives. In contrast, questionnaire measures of achievement assess explicit goal orientations or self-attributed needs for achievement (sanAch). Congruence between these two distinct systems has been associated with self-determination and well-being (Brunstein, Schultheiss, & Grässmann, 1998; Thrash & Elliot, 2002) whereas incongruence has been identified as a hidden stressor associated with volitional depletion and psychosomatic symptoms (Baumann, Kaschel, & Kuhl, 2005; Kehr, 2004). Recent findings show that incongruence also has a negative impact on flow experience (Rheinberg & Engeser, 2018), especially when the potential conflict between nAch and sanAch is aroused by achievement incentives (Schüler, 2010). Taken together, the findings suggest that flow experience does not only depend on a strong need for achievement but also on its approach-oriented and self-determined implementation.

Self-Regulation The important role of self-regulation in flow can not only be indirectly inferred from goal-motive congruence. In studies by Keller and Bless (2008) as well as Keller and Blomann (2008) the role of self-regulation has been directly tested by assessing individual differences in self-regulation competencies such as action orientation (Kuhl, 1994) and internal locus of control (Rotter, 1966). The volatility-persistence component of action orientation reflects the ability to stay immersed in an ongoing activity (Kuhl, 1994). Whereas state-oriented individuals get quickly tired of interesting activities, take breaks, or work on other things in between (volatility), action-oriented individuals get fully immersed in interesting activities and persist for a long time with high concentration (persistence). Keller and Bless (2008) found this disposition to moderate the impact of challenge-skill balance on flow experience: Action- compared to state-oriented participants experienced significantly more flow when the task difficulty was dynamically adjusted to participants’ skill levels. This finding is especially noteworthy because challenges and skills were equally matched for state- and action-oriented participants and therefore skill levels per se could not explain the differences. Nevertheless, only actionoriented participants showed increased flow experience under balanced compared to unbalanced conditions. Similar findings were observed for an internal locus of control (Keller & Blomann, 2008). Individuals with an internal locus of control believe that outcomes are generally contingent upon the work and effort put into them and not so much on powerful others or chance (Lefcourt, 1991; Levenson, 1981; Rotter, 1966). Internal locus of control moderated the impact of a dynamically adjusted challenge-skill balance on flow experience (Keller & Blomann, 2008): Only individuals high in internal locus of control experienced higher flow under balanced compared to

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unbalanced task conditions (i.e., boredom or overload). In contrast, individuals low in internal locus of control had low levels of flow across all conditions. The findings confirm the assumption that flow does not arise for everybody as a result of optimal task conditions. Conceivably, it requires self-regulatory abilities to detect and utilize optimal task conditions even when they are externally provided. Baumann, Lürig, and Engeser (2016) further explored balance conditions on a micro-level of analysis. They found that slight overload and demand fluctuation further optimize flow compared to constant balance as created by Keller and colleagues. Furthermore, they perfectly replicated the finding by Keller and Bless (2008) that action- compared to state-oriented participants experience more flow under constant balance. In flow theory, skills have been typically described as person factors and challenges as environmental factors. However, the reviewed findings suggest that the perception and regulation of task demands may be a person factor as well. To summarize, the self-regulation findings suggest that autotelic personalities have a high ability to detect and utilize a challenge-skill balance when they encounter it (i.e., ability for flow). This is a necessary but not a sufficient prerequisite for frequent and intense flow experiences. In addition, the achievement motive findings suggest that autotelic personalities also have a strong motivation to actively seek and produce flow experiences (i.e., need for flow). Thus, a measure of an autotelic personality should integrate both, need and ability aspects: the need to seek difficulty (challenge) and the ability to master it (skill). Most personality approaches, in contrast, test only additive but no interactive effects of flow-conducive traits (Johnson et al., 2014; Mesurado & de Minzi, 2013; Ross & Keiser, 2014). Teng (2011), for example, identified novelty seeking (need) and persistence (ability) as flow-conducive traits without testing their interaction. However, the essence of an autotelic personality lies in the dynamic interaction of such opposing traits. Therefore, we need measures of autotelic personality that either consider trait configurations or integrate need and ability components within a single measure. I will first introduce a motive measure that integrates need and ability components into a single measure of an autotelic personality before elaborating on trait configurations.

The Achievement Flow Motive Behind Flow Experience Baumann and Scheffer (2010) proposed a stable motive disposition behind frequent and intense flow experiences in achievement contexts: the achievement flow motive (nAchFlow). It is the amalgam of the aroused need to master challenging tasks (seeking or seeing difficulty) and its mastery-approach implementation (mastering difficulty). The latter part of the definition reminds of Elliot’s 2  2 conceptual framework of goal striving which combines mastery versus performance goals with approach versus avoidance orientations (Elliot & McGregor, 2001). In contrast to Elliot, however, nAchFlow is not directly assessed via self-report but with operant measures which are based on apperception.

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Fig. 9.1 Three samples pictures of the Operant Motive Test (OMT)

The general idea is that in the process of apperception (e.g., when inventing stories to ambiguous pictures) people do not only give need-related interpretations of perceptual input which can be coded as need content (affiliation, achievement, power, autonomy). In addition, they provide implementation-related information on how they satisfy their needs (e.g., mastery-approach oriented in case of flow). The implementation component can be inferred from the mood of the protagonist and affective tone of the story. The assumption is based on research indicating that moods and affective processes are critical indicators for enactment-related determinants like mastery-approach or -avoidance, especially with regard to behavioral facilitation or inhibition (Baumann & Kuhl, 2002; Gray, 1987; Kazén & Kuhl, 2005; Kuhl, 2000; Kuhl & Kazén, 1999). As such, nAchFlow allows to operationalize the autotelic personality because it integrates ability and need aspects of flow. NAchFlow is conceived of as the intrinsic component of the achievement motive. The core aspect of the general achievement motive is to deal actively with an internal or external standard of excellence by changing an object towards a quality standard, improving it with respect to certain criteria, learning something or meeting a requirement (Kuhl & Scheffer, 1999; McClelland, Atkinson, Clark, & Lowell, 1953). The intrinsic component of the achievement motive is characterized by mastery- and approach-oriented strivings to meet internal standards of excellence (i.e., difficulty). These strivings are experienced as curiosity and interest in learning something.

Operant Measurement NAchFlow can be assessed with the Operant Motive Test (OMT; Kuhl, 2013; Kuhl & Scheffer, 1999; Kuhl, Scheffer, & Eichstaedt, 2003) which is a refined version of projective techniques like the Thematic Apperception Test (TAT; Murray, 1943; Schultheiss & Brunstein, 2010) and other picture story exercises. Participants are asked to write stories in response to ambiguous pictures which are coded for

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Table 9.1 Four hope components of the achievement motive in the OMT

Self-determined

Incentive-focused

Affective source of motivation Positive affect 1. Flow (a) Being immersed in a task (b) Interest, curiosity, fun (c) Learning something new 2. Standards of Excellence (a) Inner standards (b) Doing something well (c) Being proud

Negative affect 3. Coping with Failure (a) Perception of threat associated with active coping (b) Learning from failure (c) Disengagement 4. Pressure to Achieve (a) Social standards (b) Being the best (c) Relief after success (d) Meeting requirements

need- and implementation-related information. Sample pictures are presented in Fig. 9.1 and samples responses for coding nAchFlow are given in Box 9.2. The OMT differentiates four hope components (approach behaviors) for each motive on the basis of crossing two affective sources of motivation (positive vs. negative affect) with self-determined versus incentive-focused forms of motivation (see Table 9.1). For the achievement motive, the two components driven by positive affect/approach motivation are (1) self-determined flow (nAchFlow) and (2) incentive-focused standards of excellence. The two components driven by negative affect/avoidance motivation are (3) self-determined coping with failure and (4) incentive-focused pressure to achieve. For example, a story in which the protagonist feels relief after success indicates latent negative affect as a source of motivation for approach behavior (i.e., active avoidance). In contrast, positive affect as a source of motivation would trigger feelings of pride instead of relief (see Box 9.3 for further details of the coding procedure). For the assessment of nAchFlow, only flow (component 1) is relevant. Box 9.2 The Operant Motive Test (OMT): A Measure of Autotelic Personality In the OMT, participants are presented with up to 20 pictures (5 per motive) like the ones depicted in Fig. 9.1. Participants are asked to choose a main character, invent a story and give their spontaneous associations to the following three questions: 1. What is important for the person in this situation and what is the person doing? 2. How does the person feel? 3. Why does the person feel this way? (continued)

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Box 9.2 (continued) The first question is likely to elicit need descriptors (i.e., affiliation, achievement, power, autonomy). The second and third questions are likely to elicit implementation descriptors (e.g., mastery-approach, positive affect). Typical answers for coding the achievement flow motive are: Left picture in Fig. 9.1 (sitting person): 1. The fun of the game. The person is concentrated on the puzzle 2. Concentrated, elated 3. The person likes to solve difficult puzzles Middle picture in Fig. 9.1: 1. High concentration is important. The person is totally involved in climbing the steep mountain and focuses on holds 2. Invigorated, focused, and happy 3. Because the person is confident to master this challenge Right picture in Fig. 9.1 (person in the upper right): 1. Learning how to assemble the box; she is trying to do it on her own 2. Curious, absorbed in her work 3. The person wants to know what the thing is when assembled Only if participants show both types of answers, that is, indicate a need to get involved in challenging tasks and an implementation sequence characterized by positive affective and self-determination (i.e., mastery-approach), the score on achievement flow motive is given. In addition to achievement, the OMT differentiates four hope components for affiliation (intimacy; sociability; copingwith rejection; affiliation/familiarity), power (prosocial guidance; incidental impact; responsibility; dominance/inhibition), and autonomy (self-confidence; status; self-growth; self-protection). The intrinsic components of affiliation (intimacy) and power (prosocial guidance) may indicate tendencies to seek and experience flow in social domains (see Schiepe-Tiska & Engeser, Chap. 4). Similar to flow in the achievement domain, they entail a need and an ability component. However, some functional requirements for flow may differ between social and achievement domains (e.g., less difficulty orientation and more intuitive behavior control in the social domains). I will elaborate this assumption in a later section of this chapter. Finally, there is a classical fear component indicating a passive instead of an active avoidance for each motive (fear of failure; fear of rejection; fear of powerlessness; fear of self-devaluation).

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Box 9.3 The Four Steps of the OMT Coding Procedure 1. The OMT coding procedure starts by checking whether one of the three basic motives is present. If no need becomes obvious in the picture story, a “zero” is coded. 2. If a motive is present, the coding procedure continues by checking whether approach behavior (hope) or avoidance behavior (fear/passive avoidance) is present (components 1–4 vs. 5, respectively). Passive avoidance can be inferred from explicitly reported negative affect which is not counterregulated. 3. If an approach behavior is apparent, the next step is to code whether more internal, self-regulatory processes or more external triggers (e.g., incentives present in the situation) are involved in the motive-specific approach tendencies (components 1 and 3 vs. 2 and 4, respectively). For example, when a person in the story is confronted with a threat to need satisfaction, participation of the self is coded if he or she generates a creative solution. 4. The final step in the assessment is to code whether approach behavior is based on positive or negative affect (components 1 and 2 vs. 3 and 4). The affective source of motivation does not have to be explicitly reported in the story. Latent negative affect (active avoidance) which is not associated with self-determination (component 4), for example, can be inferred from rather “tight” or rigid forms of behavior even if negative affect is not directly mentioned (e.g., “she wants to be close to the other person”; “he just wants to beat his competitor”). In many cases, it may be easier to perform step 4 prior to step 3. See Kuhl and Scheffer (1999) and Kuhl (2013) for elaborated coding instructions.

Descriptives and Stability Scale Range In the OMT, no correction for length of story is necessary because only one of the 20 categories (4 motives  5 components) or a zero is coded per picture story. Thus, achievement flow motive scores could theoretically range from 0 to 20. However, only 5 of the 20 OMT pictures are designed to arouse primarily achievement. From the five achievement pictures, only one is designed to dominantly arouse achievement in its mastery-approach implementation so that individuals often score either zero or one on nAchFlow. Distribution Empirically, the distribution of OMT scores is rather screwed. Most people do not show a score on achievement flow motive at all (about 65–80%). Only a quarter of a sample shows scores of one (10–30%), two (5–10%), three (0–5%), or four (0–5%). This is consistent with estimates of an autotelic personality through mere frequency measures of flow (e.g., Hirao & Kobayashi, 2013; Ishimura & Kodama, 2009; Nakamura & Csikszentmihalyi, 2002). The sensitivity of the OMT

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could be increased by adding pictures that stronger stimulate the achievement flow motive and by removing those designed to assess other motive categories. To this point, it has to be left open if the flow motive is not distributed proportionally in the population indeed. Stability Flow research has paid only little attention to stable dispositions behind flow experience. Frequency and intensity measures of flow such as the Experience Sampling Method (Csikszentmihalyi, 1975/2000, 1997) as well as the dispositional flow scale (Jackson & Eklund, 2002) and the Swedish flow proneness questionnaire (Ullén et al., 2012; retest-stability of .75 over 3 weeks in a study by Yarar, 2015) have rarely been assessed repeatedly over longer test intervals. Thus, little is known about the stability of an inclination towards flow. In a twin study comparing 177 monozygotic with 267 dizygotic twin pairs, Mosing et al. (2012) found moderate genetic influences on flow proneness (as assessed by the mere frequency measure of Ullén et al., 2012). Interestingly, one common factor explained all genetic influences on flow proneness across work, maintenance, and leisure domains. The authors conclude that general mechanisms (the same underlying genes) influence flow proneness, independent of task and content. The heritability estimate for flow proneness (29–35%) was in line with, albeit slightly lower than, those typically found for “Big Five” traits (40–60%). However, the authors did not control for flowconducive “Big Five” traits (i.e., high conscientiousness and low neuroticism). Therefore, it remains unclear whether the genetic influences are specific for flow proneness or due to the strong overlap of flow proneness with conscientiousness and neuroticism (e.g., Ullén et al., 2012). Taken together, there is little empirical research on stable causes of flow. Investigating the stability of nAchFlow would therefore be an important contribution to flow research. Preliminary evidence by Baumann and Scheffer (2011) indicates that nAchFlow has a significant stability over a period of 2 years, rKendall’s Tau (27) ¼ .50, p < .007. This finding is an encouraging starting point when considering the length of the retest-intervall. However, it would be desirable to replicate the stability of nAchFlow in larger samples which do not only consist of psychology undergraduates.

Validity The assumption that nAchFlow offers a way to operationalize the autotelic personality was supported by its significant relationship with flow experiences using the experience sampling method. In a sample of 40 business students, there was a significant correlation between nAchFlow and flow experience across various tasks during an outdoor assessment center (r ¼ .37, p < .05; Baumann & Scheffer, 2010). The finding was replicated in a sample of 33 army officers (r ¼ .37, p < .01; Baumann & Scheffer, 2011). Neither the other OMT components of the achievement motive (see Table 9.1) nor a TAT measure of nAch were significantly correlated with

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flow experience. Furthermore, nAchFlow remained significant when controlling for the other achievement variables. Thus, nAchFlow is more than just nAch (seeking difficulty). It also comprises the ability to implement achievement needs in a selfregulated and affectively positive way (mastering difficulty). The findings link nAchFlow with past flow research which emphasizes frequent and intense flow experiences as a core element of an autotelic personality. However, in contrast to past flow research, nAchFlow is correlated but not confounded with the to-be-predicted outcome of frequent flow experience. Remember that the OMT assesses the need and ability to seek flow in the achievement domain and not the actual experience. In Csikszentmihalyi’s concept of autotelic personality, need characteristics may have been implied. However, they have not been assessed. Like other implicit motives, nAchFlow is based on extended cognitive-emotional networks of possible actions (derived from autobiographical memory) that can be performed to satisfy needs in a context-sensitive way across a variety of situations (Baumann, Kazén, & Kuhl, 2010; Heckhausen, 1991; Kuhl, 2001; McClelland, 1980; Winter, 1996). Because of the extended nature of the underlying networks, they are not (or only partially) consciously accessible. Therefore, the need to seek flow has to be assessed by apperception instead of self-report. Questionnaires assessing intrinsic interest in achievement may already tap into self-concepts of the need to experience flow. However, such explicit measures are conceptually distinct from implicit motives and rarely correlate with implicit measures (McClelland, Koestner, & Weinberger, 1989; Spangler, 1992). Taken together, the available findings support the assumption that nAchFlow offers a way to operationalize an autotelic personality. Before looking at first empirical findings on trait configurations and behavioral outcomes associated with nAchFlow, the functional basis of flow in achievement contexts will be analyzed within the framework of Personality Systems Interactions (PSI) theory (Kuhl, 2000, 2001; see also Baumann, Kazén, Quirin, & Koole, 2018).

A Functional Approach to Achievement Flow1 PSI Theory In a nutshell, PSI theory (Kuhl, 2000, 2001; Kuhl & Koole, 2004, 2008, Kuhl, Quirin, & Koole, in press) describes personality as the typical interaction between cognitive and affective systems: Positive and negative affects modulate the interactions among two high- and two low-level cognitive systems. The first modulation assumption explains how changes from low to high positive affect foster volitional

1 Consistent with flow theory, we do not propose different types of flow. The label achievement flow is simply used to indicate that our analysis is restricted to flow experiences in achievement contexts.

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A(+)

Intention Memory

Extension Memory

(Seeing Difficulty)

(Mastering Difficulty)

Analytical Problem Solving

Self-motivation

A+ Intuitive Behavior Control Intuitive Action

Fig. 9.2 Functional explication of achievement flow within the framework of Personality Systems Interactions (PSI) theory (Kuhl, 2000, 2001). A+ ¼ positive affect, A(+) ¼ reduced positive affect

efficiency: a smooth transition of intentions (intention memory) into action (intuitive behavior control). The second modulation assumption explains how changes from high to low negative affect foster self-growth: an integration of new, unexpected or even threatening experiences which are often represented as isolated “objects” (object recognition) into an extended, holistic, experiential network system (extension memory). Within the framework of PSI theory, achievement flow can be described as a smooth transition of intentions into action through positive affect. In the following paragraphs, the terms intention, action, and positive affect will be elaborated. The general idea is depicted in Fig. 9.2. Achievement Flow Involves Intentions One does not form an intention unless there is some difficulty associated with performing an activity. Without any difficulty one would simply go ahead and do it. Because flow activities are difficult and challenging (Atkinson, 1957; Csikszentmihalyi, 1990; Kuhl, 1978; Rheinberg & Vollmeyer, 2003), they activate an intention memory system (seeing difficulty). According to PSI theory, intention memory is a network of central executive functions involving active maintenance of an intention in working memory and inhibition of premature initiation of action in order to mentally simulate possible solutions to a problem (Goschke & Kuhl, 1993; Jostmann & Koole, 2006; Kazén & Kuhl, 2005; Kuhl & Kazén, 1999). It is supported by planning, analytical-sequential (left-hemispheric) information processing as well as convergent thinking and problem solving. The confrontation with difficulty (which is characteristic of achievement-related contexts) is typically associated with an initial dampening of positive affect (see Fig. 9.2). Vice versa, dampened positive affect (listlessness, frustration) activates intention memory and analytical problem-solving.

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Intentions Are Transferred into Action Through Positive Affect According to PSI theory, it takes positive affect (e.g., anticipation of success) to overcome the inhibition of action and recouple intention memory with its output system: intuitive behavior control (see Fig. 9.2). Intuitive behavior control is characterized by an execution of learned behavior sequences that combine information across multiple sensory modalities and integrate the finer details of sequential motor programming (e.g., Anderson, Husain, & Sumner, 2008; Doya, 2000; Lehéricy et al., 2006). In addition to the execution of automatic, pre-programmed behavioral routines, it consists of spontaneous, rather elaborated and flexible patterns, for example, intuitive parenting programms observed in early parent-child interactions (Papoušek & Papoušek, 1987). Positive affect can activate intuitive behavior control and stimulate a smooth transmission of intentions into action. Positive Affect Is Self-Generated in Extension Memory One might argue that, at least in achievement flow, positive affect is inherent in the activity itself because flow activities are fun and interesting. And indeed, positive affect is typically increased after flow activities (e.g., Rogatko, 2007). However, flow activities are also difficult and challenging. These characteristics might as well reduce positive affect during flow activities. According to PSI theory, a participation of extension memory in action control is necessary in order to maintain confidence in the ability to master difficulty and to self-generate positive affect (see Fig. 9.2). Extension memory is a network of central executive functions that is way more extended than intention memory. It operates according to connectionist principles (Rumelhart, McClelland, & the PDP Research Group, 1986) and is supported by intuitive– holistic (right-hemispheric) information processing (Beeman et al., 1994). This system gives an overview of extended semantic fields (Rotenberg, 1993), relevant episodes experienced (Wheeler, Stuss, & Tulving, 1997), and integrated selfrepresentations (Kuhl, 2000). The self-related part of extension memory can be regarded as the implicit self (Greenwald & Banaji, 1995). There is accumulating evidence that the self is a strong source of affect-regulation (Linville, 1987; Rothermund & Meiniger, 2004; Showers & Kling, 1996) which can even operate intuitively and outside of individuals’ conscious awareness (Jostmann, Koole, van der Wulp, & Fockenberg, 2005; Koole & Coenen, 2007). Action orientation, for example, is the ability to activate the implicit self in order to regulate affect—especially under difficult conditions (Jostmann & Koole, 2007; Koole & Jostmann, 2004). Thus, although individuals do not engage in conscious selfreflections during flow experiences, the self may be highly active at an implicit level. There is first evidence that the self is indeed more active in autotelic personalities (Baumann & Scheffer, 2011): Individuals with nAchFlow had a significantly reduced tendency to confuse unattractive assignments as self-selected goals compared to individuals without nAchFlow. Stated differently, they have better selfaccess and do not introject social demands. There Are Notions of Extension Memory in Flow Theory Access to extended associate networks of action alternatives derived from autobiographical memory may be the functional basis of a sense of control and a confidence in the mastery of

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difficulty inherent in flow experience (Nakamura & Csikszentmihalyi, 2002). Furthermore, extension memory is the basis for detecting semantic coherence (Baumann & Kuhl, 2002) and forming coherent, motive-congruent goals (Baumann et al., 2005). Thus, the experience of coherent, non-contradictory demands which is a defining component of flow (Csikszentmihalyi, 1975/2000) may not only be a function of the activity but also of the individual’s way of information processing. The parallel-holistic information processing format of extension memory and the extended nature of its associative networks enables individuals to satisfy multiple constraints simultaneously and to integrate even conflicting demands.

Achievement Flow Definitions The foregoing analysis shows that PSI theory explains the phenomenon of flow through specific interactions of cognitive and affective systems. Because of the mutual modulation of affect and cognition, there are several ways to summarize the functional analysis of achievement flow within the framework PSI theory. In Box 9.4, three summaries (definitions) are offered that emphasize different aspects of the cognitive-affective underpinnings of flow. The three definitions are not in contrast to each other but interchangeable. The first, more general definition is not a mere reiteration of the phenomenon because the foregoing analysis shows that the terms intention (e.g., its association with difficulty and its inhibitory component), action (i.e., intuitive behavior control), and self-motivation (e.g., the implicit self as an agent of affect regulation) can be functionally elaborated within PSI theory. Box 9.4 Three Definitions of Achievement Flow According to PSI Theory 1. General: Achievement flow is a smooth transition of intentions into action through self-motivation. 2. Cognitive: Achievement flow is an optimal coupling of intention memory and intuitive behavior control through extension memory. 3. Affective: Achievement flow is based on dynamic changes in positive affect.

The second definition focuses on the cognitive systems involved in achievement flow and is rather dense in jargon. The third definition offers a more parsimonious description of the functional underpinning of achievement flow by focusing solely on affect. It contains the same information as the other definitions because, according to PSI theory, cognitive systems are modulated by affect, and vice versa. The third definition of achievement flow in terms of affective change is in accordance with Csikszentmihalyi’s (1975/2000) conceptualization of flow as a motivational state which comes into play in situations which are neither overexciting

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nor boring, and thus yield an optimal arousal range. The affective change assumption is also compatible with classical conceptualizations of achievement motivation. According to McClelland and colleagues, hope for success and fear of failure are based on affective changes early in life that accompany doing well or failing to do well in various learning situations (McClelland, 1985; McClelland et al., 1953). Furthermore, achievement-related episodes typically start with a phase of reduced positive affect (when a person is confronted with difficulty) which turns into positive affect when the person anticipates or obtains success (Kuhl, 2001, p. 551; McClelland, 1985; McClelland et al., 1953). Finally, affective change is also inherent in the conceptualization of nAchFlow. Remember that the need to achieve encompasses a focus on seeing difficulty which is associated with reduced positive affect (Kazén & Kuhl, 2005; Kuhl, 2000, 2001) whereas its mastery-approach implementation is the ability to restore positive affect and enjoy difficulty (Baldwin, 2001; Harackiewicz, Barron, Tauer, & Elliot, 2002; McGregor & Elliot, 2002). In the next section, I review empirical findings on trait configurations associated with nAchFlow.

Trait Configurations Baumann and Scheffer (2010) started to test Csikszentmihalyi’s assumption of a dialectical principle inherent in autotelic personalities. More specifically, they tested the assumption that individuals high in nAchFlow have a combination of two kinds of traits. On the one hand, traits are needed that support an inhibition of positive affect and a focus on seeing difficulty. On the other hand, traits are needed that help to restore positive affect and to master difficulty. This specific combination of traits is proposed to stimulate an emotional dialectics that forms the functional basis of achievement flow. Traits Associated with Reduced Positive Affect Examples of traits associated with a chronic inhibition of positive affect are introversion, an independent, schizoid-like personality style (Kuhl & Kazén, 1997), and avoidant adult attachment (Brennan, Clark, & Shaver, 1998). Experimental analyses of the Big Five model have systematically demonstrated that introversion is related to a low activity of Gray’s (1987) reward system (Derryberry & Reed, 1994; Diener, Sandvik, Pavot, & Fujita, 1992; Gupta & Nagpal, 1978; Nichols & Newman, 1986). Similarly, an independent, schizoid-like personality style is characterized by low sensitivity to positive affect as indicated by reduced reward learning (Baumann, Kaschel, & Kuhl, 2007). Finally, avoidant individuals emphasize self-reliance and actively distance themselves from social partners and emotions (Bowlby, 1988; Mikulincer & Florian, 1998). Because perceived progress towards intimacy is a strong source of positive affect (Laurenceau, Troy, & Carver, 2005) this active distancing is also associated with an inhibition of positive affect. Taken together, despite their manifold

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Table 9.2 Autotelic trait configurations and behavioral patterns: NAchFlow is associated with high values in one of the left in conjunction with high values in one of the right variables

Traits

Behaviors

Seeing difficulty (reduced positive affect) • Independent, schizoid-like personality style • Introversion • Avoidant adult attachment • Decomposing and structuring tasks analytically • Generating hypotheses and plans to solve problems • Restraint from task-irrelevant social exchange

X X

X

Mastering difficulty (restored positive affect) • Mastery-approach orientation • Action orientation • Internal locus of control • Commitment to tasks and instructions • Spreading optimism and motivating the team • Staying power, good spirit in face of difficulties

Italicized variables have not been empirically tested for nAchFlow so far

differences, introversion, schizoid-like personality, and avoidance share the functional commonality of low sensitivity to positive affect. Traits Associated with Restored Positive Affect An orientation towards masteryapproach (Elliot, 1999) is associated with the ability to restore positive affect. For example, mastery-approach has been found to foster the maintenance of students’ interest over their college careers (Harackiewicz et al., 2002; McGregor & Elliot, 2002). Of course, there are more traits associated with the ability to restore positive affect. The prospective dimension of action orientation (Kuhl, 1994), for example, is most genuinely defined as the ability to self-generate positive affect (for an overview see Koole, Jostmann, & Baumann, in press). It even helps to counter-regulate the reduced well-being of schizoid-like individuals (Baumann et al., 2007). Similarly, the greater ability of individuals high in performance-related action orientation (persistence) and internal locus of control to actually utilize opportunities for flow also indicates a self-regulatory capacity (Keller & Bless, 2008; Keller & Blomann, 2008). However, their relationship with nAchFlow has not been tested so far. Emotional Dialectics In the studies by Baumann and Scheffer (2010), nAchFlow did not significantly correlate with any single trait but only with specific trait configurations conducive to dynamic changes in positive affect. More specifically, neither introversion, schizoid-like personality, and avoidance nor mastery-approach showed a significant relationship with nAchFlow. Only the high/high combinations of traits associated with low sensitivity to positive affect on the one hand and mastery-approach on the other hand were associated with higher scores on nAchFlow (see row “traits” in Table 9.2). The findings are consistent with Csikszentmihalyi’s assumption of a dialectical principle inherent in autotelic personalities.

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Behavioral Outcomes The dialectical principle inherent in autotelic personalities has not only been observed on a trait level but also in overt behavior (Baumann & Scheffer, 2011): During an outdoor assessment center, external raters coded participants’ behavior along several dimensions. Participants with high scores on nAchFlow showed a high/high combination of two sets of behaviors: an analytical focus on problems as well as an optimistic belief in mastery (see row “behaviors” in Table 9.2). Jointly activating or alternating between both sets of overt behaviors partially mediated the direct relationship between nAchFlow and flow experience. The finding supports the assumption that autotelic personalities have indeed access to more extended networks of action alternatives. Access to such a rich repertoire should not only be conducive to frequent flow experiences but also to performance—especially in difficult tasks which require efficient volitional regulation. On a macro-analytical level, the relationship between nAchFlow and volitional efficiency has been assessed with multisource feedbacks (Fletcher & Baldry, 1999) in actual work settings. According to multiple sources such as supervisors, colleagues, and customers, participants with higher scores on nAchFlow were better in decisiveness, doing whatever it takes, customer orientation, and management of resources (Baumann & Scheffer, 2011). In a cross-cultural study, nAchFlow predicted educational attainment across three cultures: Cameroon, Costa Rica, and Germany (Busch, Hofer, Chasiotis, & Campos, 2013). The effect occurred over and above of explicit achievement motivation and financial situation during childhood. Taken together, the findings support the assumption that nAchFlow is associated with complex behavioral patterns and high volitional efficiency which, in turn, may further stimulate the development of talent and autotelic personality systems interactions in the long-run. On a micro-analytical level, the relationship between nAchFlow and volitional efficiency has been assessed with the Stroop task (Stroop, 1935). In this task, participants are asked to name the color hue of incongruent color words (e.g., naming the blue color hue of the word “RED”). The task is difficult (and stimulates intention memory) because participants have to overcome the automatic tendency to read the word. The increase in reaction times compared to easy trials (e.g., naming the blue color hue of the control stimulus “XXX”) is called Stroop interference. Kuhl and Kazén (1999) and Kazén and Kuhl (2005) showed that the presentation of positive prime words (e.g., success) significantly reduces Stroop interference. The authors concluded that joint activation of intention memory and positive affect facilitates volition (i.e., the enactment of difficult intentions). Exactly this system configuration seems to be predominant and more easily activated in autotelic personalities. In a study by Baumann and Scheffer (2010), participants with high compared to low nAchFlow showed a significantly stronger removal of Stroop interference, that is, they had higher volitional efficiency.

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Affiliation, Power, and Autonomy The nAchFlow measure of an autotelic personality is restricted to flow in the achievement domain. However, flow experience may also arise from mutually coordinating one’s own activities with other people (see Schiepe-Tiska & Engeser, Chap. 4). For example, flow frequently occurs while dancing, making love, conversing, teaching, and playing games with children. Such activities can satisfy basic psychological needs. Furthermore, several studies show that psychological need satisfaction leads to greater flow experience to the extent that individuals have a matching implicit motive. Schiepe-Tiska and Engeser describe motives as drawing attention to different motive-specific incentives of an action opportunity (see Chap. 4). Thus, all motive domains may play a role in flow. Consistently, the moderating role of implicit motives for the relationship between need satisfaction and flow experience has been found not only for achievement (Schüler, Brandstätter, & Sheldon, 2013; Schüler, Sheldon, & Fröhlich, 2010) but also for affiliation (Schüler & Brandstätter, 2013) and autonomy motives (Schüler, Sheldon, Prentice, & Halusic, 2016). In these studies, Schüler and colleagues did not differentiate between intrinsic and non-intrinsic ways to enact implicit motives. Autotelic personalities should enact implicit motives in an intrinsic way and more actively seek and create conditions that optimally satisfy their needs. The OMT offers a way to measure the intrinsic enactment of affiliation (intimacy), power (prosocial guidance), and autonomy (self-confidence) motives. Thus, future studies can address the question whether there is a stable motive behind frequent flow experiences in social domains empirically: Do individuals who score higher on intimacy, prosocial guidance, and self-confidence in the OMT experience more flow in daily life? According to PSI theory (Kuhl, 2000, 2001), some functional requirements for flow may differ in affiliation compared to achievement contexts. Intention memory (and an initial reduction of positive affect) should be less relevant for social interactions because people can fully rely on intuitive patterns of behavior control (Papoušek & Papoušek, 1987). Intuitive behavior control is supported by positive affect. Individuals who are guided by own intentions and plans and lack positive affect while interacting with others may be perceived as rather irritating, stiff, or even manipulative. Thus, functions that are conducive to flow in achievement contexts may disturb a mutual tuning between interaction partners. Consistent with this assumption, Kazén and Kuhl (2005) did not find a removal of Stroop interference after positive affiliation primes (e.g., love) that was present after positive achievement primes (e.g., success). Affiliation does not seem to activate top-down control of behavior through intentions. Consequently, traits such as introversion may be an asset for autotelic experiences in achievement contexts but irrelevant or even maladaptive in affiliation and power contexts. Other functional requirements for the intrinsic enactment of motives may be similar across different motive domains. For example, self-regulatory abilities such as action orientation are conducive to the intrinsic enactment of achievement as well as affiliation and power motives (Baumann & Kuhl, in press). Hofer and Busch

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(2011) found action orientation to be significantly correlated with an intimate (and coping-oriented) enactment of affiliation (N ¼ 207, r ¼ .17, p < .05). Baumann, Chatterjee, and Hank (2016) found action orientation to be significantly correlated with prosocial guidance among future teachers (N ¼ 191, r ¼ .21, p < .01) and future psychologists (N ¼ 233, r ¼ .15, p < .05). Thus, dispositional action orientation appears to be an asset across domains because it enables intuitive, fast, and intelligent self-regulation of affect (Koole & Jostmann, 2004). Note that despite this general trend, there may be cases in which an action orientation is too much of the same. For example, if individuals have many other traits that further relax them and broaden their experience (e.g., low neuroticism, high openness to experience) the opposing pole of tense and narrow concentration may be underdeveloped. In this case, a state orientation may be more conducive to flow. Consistent with Csikszentmihalyi’s notion of autotelic personalities as encompassing opposing traits, PSI theory elaborates optimal personality functioning as dynamic interactions between antagonistic cognitive systems due to emotional dialectics (Kuhl, 2000, 2001; see also Baumann et al., 2018). Taken together, when researchers are interested in an overarching assessment of autotelic personality, they may aggregate the intrinsic enactment components in the OMT across motives. This measure may unravel especially the general, processoriented features of autotelic personalities, that is, “how” they are striving (see also Baumann & Kuhl, in press). When researchers are interested in a more fine-grained analysis of autotelic personality, they may focus on the intrinsic components of a single motive in the OMT. This adds information about content-specific features of autotelic personalities, that is, “what” they are striving for to derive satisfaction. In future research, it would be informative to further test trait configurations behind the intrinsic components of achievement, affiliation, power, and autonomy in the OMT.

Summary and Outlook Summary The present chapter shows that frequent and intense flow experiences may be driven by stable personality dispositions. Individuals experience flow not only if they encounter optimal task conditions but actively seek and create options for flow. Furthermore, individuals greatly differ in the need to seek and in the ability to create flow experiences. As such, personality traits are boundary conditions for flow experience. The chapter introduced an implicit measure of achievement flow motive (nAchFlow) which integrates need and ability aspects and offers a way to operationalize Csikszentmihalyi’s concept of an autotelic personality. The first empirical findings with nAchFlow are encouraging because the measure is relatively stable, valid in predicting flow experience, and supportive of central assumptions of flow theory. For example, the assumption of a dialectical principle inherent in autotelic personalities was supported by significant relationships between nAchFlow

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and high/high combinations of complementary or even opposing traits and behaviors. Within the framework of PSI theory, the dialectical principle can be functionally elaborated and parsimoniously summarized as dynamic changes between reduced and restored positive affect.

Outlook Despite the encouraging first steps in measuring and empirically investigating an autotelic personality, there is a host of open agendas for future research. In my view, important future directions are: 1. Developmental antecedents. The OMT measure of nAchFlow offers the opportunity to test Csikszentmihalyi’s rich conceptual ideas about autotelic personalities. For example: What are the central parental practices and environmental conditions that foster the development of an autotelic personality? How do autotelic personalities manage to unfold their talent? Preliminary findings stem from Scheffer (2005) who tested developmental antecedents of the intrinsic motive components in the OMT. Crosscultural findings show that younger siblings contribute to the development of prosocial guidance (Aydinli, Bender, Chasiotis, Cemalcilar, & van de Vijver, 2014; Chasiotis & Hofer, 2018). Other findings highlight parental support as a development antecedent of action orientation (Hirschauer, Aufhammer, Bode, Chasiotis, & Künne, 2018; Liesenfeld, 2018)—a core ability of autotelic personalities. Thus, the operationalizing via OMT opens the door for empirically testing developmental antecedents and core abilities of autotelic personalities. 2. Picture set. Implicit motives are best measured if the ambiguous pictures stimulate the relevant motive moderately. For achievement flow, the arousal potential of the present picture set is limited. Only one picture was designed to stimulate nAchFlow and actually does arouse flow answers in many people. Thus, the picture set could be extended for researchers who are primarily interested in nAchFlow. 3. Dynamic processes. The assumption that dynamic changes in positive affect or dialectical processes between opposing traits and behaviors are inherent in autotelic personalities has been analysed rather statically so far (for a notable exception see Ceja & Navarro, 2009). It is not clear whether individuals are able to focus on opposing aspects simultaneously or alternate between foci. What is the time course of alternation? Is there system or chaos behind patterns of fluctuation? These questions are not only of theoretical interest. It has important practical implications when trying to support the development of autotelic personalities. The findings by Oettingen, Pak, and Schnetter (2001), for example, show that positive fantasies about desired futures have to be repeatedly contrasted with reflections on difficulties in present reality in order to improve goal commitment. If only one component is stimulated or if the alternation does not start

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with positive fantasies, there is no improvement at all. Thus, it is important to learn more about dynamic process (Ceja & Navarro, 2009). 4. Negative affect. The analysis of achievement flow focuses solely on positive affect. According to PSI theory, negative affect is not involved in achievement flow. This analysis may be restricted to individuals high in nAchFlow. For individuals high in fear of failure, in contrast, negative affect may be present and disturb flow experience. The absence of negative affect (relaxation) may be an additional and necessary prerequisite for them in order to experience flow. This assumption is consistent with the findings by Engeser and Rheinberg (2008) and Schüler (2007) that individuals high in fear of failure are able to experience flow—albeit to a lower degree—when tasks are very easy or very difficult. Both task conditions reduce fear of failure because success is guaranteed (easy tasks) or failure not a shame (difficult tasks). Thus, for individuals high in fear of failure, low negative affect may be an additional prerequisite for achievement flow. Other individuals are less at the mercy of current conditions. Individuals high in the failure-related dimension of action orientation, for example, have the ability to down-regulate negative affect by themselves. In the OMT, this is evident in “coping with failure” which is coded when a protagonist is initially worried about failure but then confident to succeed. According to PSI theory, dynamic changes in negative affect are the basis for self-growth. Individuals who are sensitive to negative experiences and able to put them into broader perspective can learn from mistakes and grow as a person. Researchers may not want to restrict their definition of an autotelic personality to the need and ability for positive experiences but broaden their definition to the need and ability for coping with negative experiences. In this case, they may integrate coping-oriented components of the OMT into a measure of an autotelic personality. Future research should further investigate autotelic personalities as operationalized by (a) a single component of the OMT (e.g., nAchFlow; Baumann & Scheffer, 2010, 2011), (b) aggregating intrinsic and coping-oriented components within a single motive (e.g., intimacy and coping with rejection; Hofer & Busch, 2011), and (c) aggregating across intrinsic and/or coping-oriented components of all the three (or four) motives (e.g., Baumann, Kazén, & Kuhl, 2010; Baumann & Kuhl, in press). 5. Outcomes. The operationalization of an autotelic personality with an operant motive measure has consequences for the type of expected outcomes. Operant motives are predictive of operant (spontaneous, self-initiated) in contrast to respondent (planned, externally regulated) behavior. Thus, experimentally producing an optimal challenge-skill balance (e.g., Baumann, Lürig, & Engeser, 2016) or providing tasks that elicit moderate flow levels in most people (Baumann & Scheffer, 2010) may not be the best setting to test the predictive power of nAchFlow because it has a respondent component. Future studies should investigate if individuals high in nAchFlow tend to actively create flow experiences in the absence of such externally provided opportunities. Similarly, when investigating the relationship between nAchFlow and motivation, performance, and well-being it will be important to look at outcomes that are less influenced by social demands. Finally, it will be important to assess whether

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spontaneous behavior, initiative, and open performance-outcomes are appreciated or discouraged by the environmental setting (e.g., at school or at work). In the power domain, researchers have started to address some of these questions. For example, implicit prosocial guidance plays a role in spontaneous but not planned helping behaviour (Aydinli et al., 2014). Prosocial guidance is also associated with sustained volunteering—at least among parents (Aydinli, Bender, Chasiotis, van de Vijver, & Cemalcilar, 2015). Across cultures, implicit prosocial guidance is independent of prosocial norms and social desirability (Aydinli et al., 2015). In future research, it will be informative to investigate the type of outcomes and boundary conditions associated with autotelic personalities as assessed by intrinsic (and coping-oriented) components of the OMT.

Study Questions • Does everybody experience flow if task difficulty matches personal skills? Answer. Although many people do experience flow if task difficulty matches skills, not everybody does. Personality traits are boundary conditions for the ability to experience flow under optimal task conditions. The perception and sustainment of balance between challenges and skills is an active, self-regulatory process that some individuals are more capable of than others. • Is an autotelic personality just the same as having frequent and intense flow experiences? Answer. Frequent and intense flow experiences might be due to lucky circumstances (e.g., living in an optimal environment). Autotelic personalities, however, are not just lucky to be externally provided with optimal challenges. In addition, they actively seek and create optimal challenges (e.g., moderate task difficulty). Thus, autotelic personalities combine a need to see(k) difficulty with an ability to master it. • Why does flow in achievement contexts involve the formation of intentions? Answer. Flow occurs during challenging/difficult tasks. Without any difficulty, a task could simply be executed with preprogrammed behavioral routines. If such routines are not yet available, an intention is formed and premature action inhibited. This allows analytical problem solving (i.e., mental simulation of action alternatives and sequencing of several action steps) in order to prepare behavior. • When are intentions transferred into action? Answer. Intentions are transferred into action when positive affect (i.e., anticipation of success) indicates that a problem is solved or a difficulty overcome. • Why are flow activities not purely positive? Answer. Flow activities are not purely positive because they are moderately difficult which is associated with a dampening of positive affect. • Where does positive affect during flow activities originate?

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Answer. Positive affect has to be self-generated through a deep confidence in one’s ability to master difficulty (i.e., self-motivation). • Why do autotelic personalities have high access to the self although flow experience is defined as a state of low self-centeredness? Answer. It is important to distinguish between explicit, conscious reflections about the self (self as object) and implicit self-representations of own needs, goals, experiences, and action alternatives (self as subject/agent). Whereas selfreflections are reduced during flow, feelings of self-determination are increased and support the deep confidence in the ability to master challenges. • What do a schizoid personality style, introversion, and avoidant adult attachment have in common? Answer. These personality traits share a low sensitivity for positive affect which stimulates analytical problem solving and a tendency to see(k) difficulties. • Can flow be experienced in affiliation and power contexts? Answer. Flow is not restricted to achievement contexts. However, the functional underpinning of flow in social contexts may differ. For example, analytical problem-solving and intentional/planned behavior may be less adaptive during a romantic interaction with one’s love.

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Chapter 10

Social Flow Charles J. Walker

Abstract If you have played on an exceptional soccer or basketball team or were part of a highly engaging and productive business meeting, you may have experienced social flow. If you have been spellbound by the graceful synchrony of ice dancers, or awestruck by the flawless performance of a symphony, you may have witnessed social flow. If you have had these or similar experiences, you may agree that social flow is not the same as solitary flow. Solitary flow is an individual psychological phenomenon; social flow is a social psychological phenomenon. Both forms of flow explain intrinsic motivation and absorption; however, solitary flow is autotelic, where as social flow is syntelic. Social flow is a shared, contagious form of flow associated with highly interdependent and collaborative group processes. It is both a cause and an effect of synchronized performance within a human group. People who experience social flow enjoy it and want to repeat it. In this chapter I will further clarify the differences between social and solitary flow, describe the preconditions and group processes that cause and sustain social flow and the consequences and outcomes that document it has been achieved. Some practical applications of social flow will be discussed, and some provocative implications of social flow will be suggested for the enhancement of human performance in sports, arts, business and leisure activities.

Solitary and Social Flow Compared Especially when considering the early conceptualizations of flow (Csikszentmihalyi, 1975), there would appear to be no difference between solitary and social flow. Both require a challenge dispatched by relevant skills. However, the challenges and their requisite skills can be, and often are, quite different in social as compared to solitary situations. Solitary flow occurs when an individual experiences flow without the presence of others. Examples include a book writer cloistered in a remote cottage C. J. Walker (*) Department of Psychology, St. Bonaventure University, St. Bonaventure, NY, USA e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_10

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protected from distractions, an artist painting alone in her loft studio, or a solo kayaker enjoying a morning paddle on a secluded lake. Each finds it easier to be completely absorbed by their tasks without the interruptions or assistance of others (Bond & Titus, 1983). On the other hand, social flow occurs because of the presence of others (see Table 10.1). Many tasks and challenges are inherently social. With these situations, it is difficult to eliminate the presence of others, or because of the size or complexity of the task, it is impossible for a single individual to effectively dispatch it (Sawyer, 2003). Social situations stage at least two forms of social flow: Co-active and interactive. Co-active flow happens when individuals experience flow simply in the presence of each other. There is no communication or interaction required. It is a kind of socially facilitated flow, but individually and independently experienced. However, the mere presence of others may enhance or interfere with co-active human performance (Bond & Titus, 1983; Cottrell, Wack, Sekerak, & Rittle, 1968). Examples are cross-country ski racers in a loppet, cyclists in a peloton, or students passively listening to a highly engaging lecture. Interactive social flow, in contrast, involves deliberate cooperation, coordination and communication among members of a group or team. The tasks and challenges of the team cause group members to be agents of each other’s flow experiences. The coordination of interactants is usually synchronized sequentially, reciprocally or combinations of each. With sequential synchronization, one group member must complete his task successfully before another can do his part of the work. Examples are a triple play in baseball or serial flag twirls in a drill team. With reciprocal synchronization, group members have mutual concurrent, bi-causal agency of each other’s flow experiences. Examples are improvisation in a jazz group, seamless transitional offense by a basketball team, or story sharing and laughing with close friends. Reciprocal interactive social flow is the quintessential form of social flow. Solitary flow is the quintessential form of individual flow. Co-active flow is a hybrid located on a continuum of flow somewhere midway between solitary and social. For purposes of clarity, in this chapter, social flow will be defined exclusively as interactive social flow. When terms like group flow or team flow are used, it should be assumed that each is a variation of interactive social flow.

Theorized Social Conditions that Enable Social Flow Some of the enabling conditions for solitary flow (cf. Barthelmäs & Keller, Chap. 3) are also operative for social flow, specifically (a) tasks and emergent challenges are perceived to be important, (b) performance feedback is clear and immediate, and (c) competencies are relevant to handle challenges. However, because the primary unit of performance is a group, not an individual, several additional conditions must be in place for social flow to occur (Magyarodi & Olah, 2015; Walker, 2010, 2017). I will now describe and elaborate on each of these essential conditions.

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Table 10.1 Preconditions, processes and outcomes associated with social flow experiences Theorized preconditions and contexts for social flow • The unit of performance is a functional group or team. • The collective competency of the group is sufficient to dispatch challenges. • Group members are uniformly highly competent. • Each group member knows the competencies of every other member. • Challenges are consensually perceived as important & meaningful. • Tasks are divisible but prescribe collectiveaction. • Tasks require interdependence, coordination and cooperation. • Tasks are conjunctive and invite complementary engagement. Theorized transactions and group processes for social flow • Group members focus on each other as well as the task to receive feedback. • Task feedback is clear and immediate for individuals and the group. • High absorption and engagement with the task. • High attention to the task activities of other group members. • Group members become increasingly more group-centered than self-centered. • Emotional communication is expressed during task work. • Emotional contagion occurs within the group and to audiences outside the group. • Joy, anxiety, boredom and apathy are shared throughout task work in the pursuit of flow. Theorized outcomes and effects of social flow for individuals • An intense sense ofconnection with others is felt. • Social identity and personal identity merge. • Individuals feel invincible and powerful. • The emotions of happiness, joy and elation are experienced. • When in the group, individuals manifest symptoms of positive mental health. • Despair and dread are felt when an individual is ostracized. • Grief is felt when the group must disband. Theorized outcomes and effects of social flow for groups • The group performs excellently. • The experience builds meaning and a collective sense of purpose. • Group cohesiveness increases. • Identification with the group increases. • The group desires to the repeat the social flow experience. • Rituals, formal or informal, are established to normalize social flow.

The Unit of Performance Is a Functional Human Group Here, a group is defined as two or more individuals, ideally a collection of people with a common history and fate (Cartwright & Zander, 1968; Steiner, 1972). However, because social flow is a complex phenomenon that requires task and interpersonal feedback, the group should be small enough to allow immediate face-to-face, not asynchronous, communication (Snow, 2010). While social flow might be possible in large human groups such as corporations, educational systems or city governments, the human capital and other resource costs (e.g., improved employee selection and development, changes in reward & pay systems, or organization redesign and development)

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necessary for consistently achieving flow in large groups are likely to exceed its benefits (Smith, Koppes Bryan, & Vodanovich, 2012). The exception can be seen with flash mobs or spontaneous crowd reactions. If the life of a group is short-lived and the task is less challenging and skill requirement is low, then short duration large scale social flow is certainly possible. Nonetheless, it is more difficult for social flow to reliably occur in enduring, interconnected groups within large human organizations. As will be explained later in this chapter, team or group flow is more likely to happen, at acceptable levels of cost, in cohesive, efficacious small groups (Littlepage, 1991). The Competency of the Group Members Matches Its Challenges For social flow to consistently occur, every group member, and the group as a whole, must be sufficiently competent (Hackman & Morris, 1975; Salanova, Rodríguez-Sánchez, Schaufeli, & Cifre, 2014). The task knowledge and skill of group members, and the acquired acumen of the group (Lemon & Sahota, 2004), must be more than enough to cope with the topography of challenges confronted when the group pursues its most worthwhile goals (Hill, 1982). Included in the list of skills necessary at the level of individuals are social and emotional skills and, at the group level, shared norms and expectations about excellent performance, interpersonal and inter-group communication, leadership, and task feedback management (Karau & Williams, 1993; Katzenbach & Smith, 2015). Social and emotional skills are required because group members must, at least for task work, know each other sufficiently. They need not love or even like each other; however, they must know enough about each other’s idiosyncrasies to work effectively together (cf. Stoll & Ufer, Chap. 13). This requirement is especially needed in unstructured, unpredictable, or changing task environments; for example, in dynamic competitive team sports like soccer or hockey, or research and development teams in rapidly changing markets like those in digital technology or medicine. The Group Tasks Prescribe Valid Interdependency Tasks occasion and moderate social flow experiences. The properties of group tasks, more than the characteristics of group members, enable social flow (Campion, Medsker, & Higgs, 1993; Steiner, 1972; Van Schaik, Martin, & Vallance, 2012). Tasks must be divisible, not unitary, and conjunctive, not disjunctive. Tasks must stage reciprocal, not additive processing (Steiner, 1972; Walker, 2010). Tasks should be designed to give each member of the group unique work, but with complementary interdependencies (Borderie & Michinov, 2017). However, ideally group members should also understand and anticipate the work and roles of other members of the group. With this type of valid interdependency, cooperation and coordination should be unavoidable and feel natural and automatic. Well-designed group tasks may replace some leadership functions in a group (Hackman, Wageman, Ruddy, & Ray, 2000). When this happens, the group becomes autonomous and appears to lead itself. When these task conditions are met, not only are group flow and performance increased, but also social loafing and other symptoms of group dysfunction are substantially decreased (Karau & Williams, 1993). A couple examples may be useful to illustrate the importance of well-designed group tasks. The tasks of a social sport like basketball

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can readily produce social flow. On offense, a basketball team is comprised of the uniquely different positions of a point guard, shooting guard, small forward, power forward, and center. When a team executes an offensive strategy (e.g., a flex offense), players are in continuous motion interdependently pursuing the goal of helping at least one teammate acquire an open good shot at the basket. Wellcoached, mature teams execute their offensive strategies with beauty and grace. Equally obvious beauty and grace can be witnessed listening to a good jazz quartet. Again, the musicians have unique but interdependent roles playing a trumpet, piano, double bass, or drums. Quartets that have played together for many years can effortlessly improvise around sheet music because of their familiarity with each other (Gloor, Oster, & Fischbach, 2013; Hart & Blasi, 2015; cf. Harmat, de Manzano & Ullén, Chap. 14). Intimate conversations among trusted good friends often have similar social flow characteristics. In these heart-to-heart conversations, an upward spiral of engagement unfolds, motivated by reciprocal honest sharing that yields mutual insights, humor, and feelings of increased closeness. In several experiencesampling studies, highly engaging conversations were the most often reported examples of flow by participants (Csikszentmihalyi, 1990; Csikszentmihalyi & Larson, 1987).

Theorized Transactions and Group Processes of Social Flow A Shift from a Self-Centered to a Group-Centered Perspective Although it is uncommon in an individualistic culture (Earley, 1993), group members strongly identify with their group or team when social flow is achieved. Social flow is unlikely to be seen with groups of loosely coupled, self-conscious individuals. However, because under the right conditions, the shared joy and fun of experiencing social flow is so fulfilling, it is likely to be natural for individuals to be assimilated into the group (Snow, 2010). Therefore, it is predicted that groups that regularly experience social flow are better able to retain members than those that find flow to be more elusive. Note that some of the most successful musical groups or business teams have had little change in membership over decades of working together. With groups that regularly experience social flow it is likely that individual identity and group identity are indistinguishable. Is it possible to imagine the Rolling Stones without Mick Jaggar or Mick Jaggar without the Rolling Stones (Andersen, 2012)? An Abiding Attention to the Behavior of Other Group Members Achieving flow in a group or team is quite difficult, often more difficult than achieving flow as an individual. Social flow is more complex and tenuous than solitary flow (Armstrong, 2008). With social flow, group members must manage themselves and coordinate their performance with that of groupmates. So, in addition to being receptive to task feedback, they must also continuously be attentive to feedback from other performers, especially those to which their work is directly linked. The overall performance of a group will decline if there are lapses in attention. For

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example, a counter attack by a soccer team would be far less successful if one of the strikers was suddenly distracted by a vulgar insult from an opponent. Examples can also be seen with the performance of musicians (Bakker, 2005; Sawyer, 2006). A string quartet that normally experiences intense social flow playing Beethoven’s Opus 132 would be less likely to achieve it if the cellist was intermittently distracted by a sickness. Contagious Sharing of Emotional Reactions During Group Work Unlike solitary flow, with social flow, members of groups or teams pursuing social flow may express their emotions during, as well as after, task work (Christakis & Fowler, 2013; Sawyer, 2003, 2006; Walker, 2010). This is done to steer the team toward flow and keep it in the zone. Of course, joy is expressed when flow has been achieved or recovered; however, negative emotional expressions may also be useful for teams to seek and sustain flow. For example, when a team attempts an overwhelming challenge, anxiety or fear is shared; when challenges are underwhelming, boredom or apathy is shared. Interestingly, with social sports like ice hockey, soccer or basketball, this contagion of emotion is not limited to athletes; their audiences vicariously feel and share emotions too. Audiences receive the emotionalcommunications from their athletes and, in turn, express them back to help keep their teams in the flow zone (Walker, 2008, 2016, 2017). With visiting teams, as might be expected, audiences use distraction to keep competitors out of the flow zone (Totterdell, 2000).

Theorized Outcomes and Effects of Social Flow for Individuals An Intense Sense of Connection with Others Is Felt Cohesiveness is a signature characteristic of a highly performing group or team. While individual members of a group may have unique roles and functions, everyone comprising a successful team feels a sense of connection and with teammates, Cartwright (1968). Strong feelings of connection and unity with others are very likely to be consequence of social flow for individuals. Social Identity and Personal Identity Merge Because people join groups that they have something in common with, and because groups recruit and sustain the membership of individuals who fit their mission and purpose, with time, it is inevitable that social identity and personal identity overlap, Ashforth and Mael (1989). Social flow experiences probably strengthen and accelerate this process. The definition of the self will likely transform when individuals have reliable, satisfying social flow experiences while being a member of a group or team. Individuals Feel Invincible and Powerful Groups or teams that regularly experience social flow perform at a high level. They accomplish goals and do things far beyond the capability of a single individual member. However, without corrective

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feedback, they can develop unrealistic senses of invincibility and power, Paradis and Martin (2012). In decision making they can overestimate their efficacy and suppress useful contrasting viewpoints within a group, Turner and Pratkanis (1998). Individuals who experience social flow, compared to those who do not, are more likely to feel the power of a group, and for good or bad purposes, they may report feeling invincible. The Emotions of Happiness, Joy and Elation Are Experienced A group in social flow has a hyper sense of control. For individuals and groups, being in control is associated with the experience of positive emotions such as joy and elation (SaphireBernstein & Taylor, 2013). Moreover, the sharing of positive emotions gives feedback to members of a group on their successful pursuit of goals, thus reinforcing their perceptions of control and making challenging goals seem “fun” to achieve. For these reasons, individuals within social flow groups are liable to feel and express positive emotions such as happiness, joy and elation (Walker, 2010). Symptoms of Positive Mental Health Are Manifested During the peak moments of social flow, members of groups or teams feel fulfilled, vibrant and happy. They are likely to manifest some of the symptoms of flourishing, namely (a) expressing and experiencing positive emotions, (b) being engaged and absorbed, (c) enjoying relationships with others who matter to them, (d) realizing purpose and meaning, and (e) accomplishing important essential goals (Saphire-Bernstein & Taylor, 2013; Seligman, 2011). While some members of groups in social flow may not be mentally healthy when they are not in their groups, when they are in their groups, they temporarily manifest some of the signs of psychological well-being. Despair and Dread Are Felt When an Individual Is Ostracized Certainly, one of the worst fates for human beings is to be expelled, shunned, or ostracized (HoltLunstad, Smith, Baker, Harris, & Stephenson, 2015; Wesselmann, Nairne & Williams, 2012). People feel depressed and stunned when they are rejected from groups; however, because of the importance of social flow groups and the intensity of experiences they have in them, people are much more likely to feel despair and dread if they must be expelled or even anticipate being expelled. Grief Is Felt When the Group Must Disband In general, groups are less mortal than their members. With the exception of dyads, most groups have longer lifespans than the people who comprise them (Cartwright & Zander, 1968; Steiner, 1972). However, because of mistakes made or uncontrollable bad fate, some groups die early (Shepherd, Patzelt, Williams, & Warnecke, 2014). If a group meets the primary needs of individuals, its termination can cause distress and grief. Because of how rare high performing social flow groups are, by nature, they are, more transient and mortal. When they end or must dissolve, it is very likely that most of their members will experience grief because something important and special in their lives has been lost forever.

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Theorized Outcomes and Effects of Social Flow for Groups Groups or Teams Perform Excellently The seeking of social flow inadvertently sets high standards of achievement for a group or team. Moreover, the characteristics of group tasks associated with social flow (i.e., divisible, conjunctive reciprocally processed tasks), can be very challenging and therefore solicit the best efforts from members of a group (Littlepage, 1991). Indeed, self-reports of flow are positively correlated with higher levels of performance for both individuals and teams (Bakker, Oerlemans, Demerouti, Slot, & Ali, 2011). Increases in the Development and Maturity of Groups or Teams As discussed earlier, groups that regularly achieve social flow will have members who (a) strongly identify with their group, (b) trust other members, and (c) show increases in cohesiveness (Van den Hout, Davis, & Weggeman, 2018). However, these desirable positive outcomes also have been associated with negative outcomes such as groupthink (Janis, 1991) or resistance to change (Goldstein, 2001). It is interesting to note that these negative outcomes are likely to be counteracted by the tendency of individuals within groups who, according to flow theory, are likely to seek higher levels of challenge and boost their skills to meet new challenges to avoid the inevitable state of boredom (Csikszentmihalyi, 1975, 1990, 1997) The Propensity to Repeat, Stabilize and Normalize Social Flow Like solitary flow, successful group work that yields social flow will be perceived and remembered as purposeful and meaningful (Csikszentmihalyi, 2003; Nakamura & Csikszentmihalyi, 2003). Groups will want to repeat the experience, and consequently, will establish norms and rituals to make repetition more likely. The communities or organizations within which exceptional groups or teams reside will likely provide financial and other resources to help them develop and sustain their achievement of social flow. For example, towns with excellent soccer teams will provide the land and monies to build new stadiums, or cities with accomplished orchestras will construct and maintain performance halls or centers (Borland & Macdonald, 2003; Strom, 2003). As was described earlier, organizations and communities will take these supportive steps not only out of respect for the performers who directly experience social flow, but also their audiences who vicariously enjoy it. There are many reasons why stadiums or performance halls are built and maintained; the enjoyment of witnessing social flow is likely to be one of the reasons.

Some Practical Implications of Social Flow For Athletes in Social Sports When national and international committees convene to discuss changes and improvements in their sports, they might consider changes that are likely to increase social flow. For example, in competitive social

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sports like ice hockey, basketball, volleyball, American football, rugby, lacrosse, and soccer, new rules might be written that more strongly reinforce interdependent play and, at the same time, limit solitary play. Some sports like in ice hockey already have such rules. For example, the offside rule in hockey does not allow a single attacker to be ahead of the puck beyond the blue line. The offside rule in soccer is similar. Without such rules, soccer and hockey would lapse into a boring display of dyadic breakaways that would marginalize the rest of the team and decrease the opportunity for individuals or a team to experience flow (Engeser & Schiepe-Tiska, 2012). Modifications of scoring systems could also have positive effects on social flow. For example, assists might be more carefully operationalized and counted for assists to be a legitimate contributor to scoring and the outcome of a game. Specifically, when a game ended in a tie, the team with the most assists would be granted the win. Doing this would give the win to the team with the most team play, not the team with a dominant athlete. For example, in basketball, when considering the following pattern of scoring, which team is the superior team: Team A in which five players scored 20 points each, or Team B in which one player scored 64 points when her teammates scored only 9 points each? Both teams scored 100 points; however, Team A was more likely to have achieved its total score through social flow, while Team B probably experienced less social flow and perhaps even social loafing because of the dominance of a single performer. How games are managed and the venues in which they are played should also be reconsidered if social flow is to be increased, or at least not inhibited. Stoppage of play for any reason is a distraction and has the effect of getting athletes out of rhythm and their fans less engaged. American-style football, compared to soccer, allows less social flow in its athletes and probably less vicarious flow in its fans (Bakker et al., 2011). Play is stopped for huddles, penalties, commercial breaks, video reviews, and timeouts. Such disruptions of flow are very likely to make the game less enjoyable to play and watch. Interestingly, over the last decade viewership and ratings by fans of American football has been declining despite increases in profitability (Rovell, 2018). Large numbers of people watch social sports, many more than solitary sports (Statista, 2018). Unfortunately, businesses use this opportunity to insensitively promote their goods and services. Visual or auditory advertisements in sports venues are not only likely to disrupt the vicarious flow felt by audiences, but also such ads make it more difficult for audiences to send performance facilitating feedback back to their athletes. Feedback from fans should be spontaneous, timely and authentic. Prompts from jumbotrons and 360
LED advertisement rings may cause disingenuous audience reactions and actually discourage and decrease an audience’s engagement with its team. Without a valid connection between audiences and athletes, the home field or home court advantage could be weakened (Totterdell, 2000). Coaches and coaching staff should consider using social flow theory to train and prepare their athletes for competitive games (Jackson & Csikszentmihalyi, 1999). Jimmy Johnson famously claimed to use Csikszentmihalyi’s, 1990 book to inspire a win for the Dallas Cowboys in the 1993 Superbowl. However, social flow, as compared to solitary flow, has alternative complementary implications for coaching.

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For example, because athletes in social sports can benefit from learning about the idiosyncratic skills and habit of teammates, they should train most of the time in social situations with teammates, not alone. Having a professional agent and overuse of personal trainers could undermine social flow because stardom, not better team play, is pursued (Wachsmuth, Jowett, & Harwood, 2017). Practice sessions with simulated opponents and replications of home or away audiences should be a significant part of coaching. While most coaches realize these things, they may not concern themselves enough with the behavior of audiences, particularly home audiences. They should consider coaching their audiences as well as their athletes. For example, audiences in hockey, soccer and basketball react strongly and supportively to successful “showboating” by individual athletes even when it is risky to express these skills and is inconsistent with what has been trained during practice sessions. Coaches could meet with fans prior to games to discuss these issues and urge them not to reinforce showboating. Feedback from audiences should consistently reinforce and extend coaching, not interfere with it. Another less obvious implication concerns athlete replacement or substitution. With teams that achieve social flow regularly, any changes in their membership may disrupt social flow. For example, if just one athlete in a team of five is injured, and the coach replaces her with a similarly skilled individual, the entire team, not just the new athlete, must compensate, relearn and make other adjustments. Coaches may even have to change their “system” to assimilate a new player. This point about changes in group membership, of course, also applies with player substitutions during a competition. Each substitution imposes adjustments that must be made if the team desires to experience social flow with any consistency during competitions. For Musicians and Other Performers in the Arts Audiences of social sports generally do not expect athletes to always work beautifully and gracefully together; however, with performers like musicians and dancers, they do. These audiences hope to be emotionally moved and experience awe while listening to musicians or watching dancers. Musicians, dancers and other performers in the arts may have higher flow expectations and standards than do performers in most other areas of human expression (cf. Harmat et al. Chap. 14). On the other hand, because of the characteristics of their work, social flow is relatively easier to achieve by dancers or musicians. The music itself creates favorable conditions for bands, quartets, or orchestras to experience social flow (Sawyer, 2003). Likewise, the dance choreography for troupes usually prescribes synchronized movement that facilitates social flow. There are at least two interesting implications that follow from these observations. First, because organizational psychologists have found that the properties and design of work attracts certain kinds of workers (Tett & Burnet, 2003), it is likely that performers in the social arts may be more predisposed to seek social flow than performers in other areas of human creative expression. If solitary flow is sought by people with autotelic personalities, social flow may be sought by people with syntelic personalities (Walker, 2008, 2017). These individuals should tend to seek situations wherein social flow can be easily experienced, and they should be comfortable being assimilated into group, preferring not to be stars or celebrities.

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Indeed, it has been observed that back-up singers have these characteristics (Hartman, 2012). A second implication is that the desire to repeat a social flow experience by musical or dance groups may ultimately create boredom and smother the creativity of their members. Audiences may contribute to this surprising negative consequence. For example, a rock & roll band may at first enjoy playing a suddenly popular hit song and do so quite excellently basking in the thunderous reactions of adoring fans. However, after playing the hit song repeatedly over a 6-month tour, the group will likely to become bored with the song, long before its fans. Vicarious social flow, in this instance, may become a force that traps a group and constrains the band’s creative development. It may become an addiction, albeit a mildly damaging form of addiction, caused by adoring fans (Click, Lee, & Holladay, 2013). For Team Builders and Organizational Consultants Contingency models are useful to understand leadership, group performance and organizational behavior. Researchers and practitioners alike have discovered that there is no single reliable recipe for improving leadership, increasing group performance, or helping organizations function better (Locke, 2009). This wisdom should be heeded by consultants who promise to increase the experience of social flow within organizations. Social flow is much more likely to have stronger situational than dispositional causes (Bem & Allen, 1974; Mischel, 2004). Therefore, training interventions that focus on individuals without regard to the specific characteristics of their work or the organizational culture in which they labor, will have limited success (Swann, Piggott, Schweickle, & Vella, 2018; Van den Hout et al., 2018). Setting the achievement of social flow as a goal for workers without changing how their work is designed or their roles and interpersonal relationships will likely create more frustration than success. Even with supportive conditions, social flow is a delicate, fragile phenomenon (Armstrong, 2008). It can be easily overpowered by complex organizational systems. As was suggested earlier in this chapter, establishing the required conditions, such as immediate, continuous task and social feedback, favors small groups, not large organizations; as well, the work itself must prescribe conjunctive, reciprocal, complementary interdependencies for the experience of social flow to be reliably induced. A useful metaphor is a jazz group or string quartet, not a huge ungainly marching band. It should be relatively easy to get a small team to function gracefully, but in most cases, not worth the effort and cost to establish and maintain the conditions required for an entire large organization to manifest social flow with acceptable consistency and efficiency.

Hypotheses and Speculations Needing More Research and Development Assessing Flow The presence of flow in a group or team has usually been assessed through the self-reports of individuals on rating scales such as the Flow State Scale (Jackson & Marsh, 1996), the Flow State Questionnaire (Magyaródi, Nagy, Soltész,

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Mózes, & Oláh, 2013), or the Work-related Flow Scale (Bakker, 2001). Some researchers have developed and used their own Likert-style scales or checklists reflecting the fundamental preconditions and components of flow (Magyarodi & Olah, 2015; Walker, 2010). A few researchers have modified previously used scales to assess flow at the group level by converting personal pronouns, e.g., “I was absorbed.” to collective pronouns, e.g., “We were absorbed.” (Salanova et al., 2014; Zumeta, Basabe, Wlodarzyk, Bobowik, & Paez, 2016). Objective coding of verbal and nonverbal emotional expressions associated with joy, anxiety, boredom and apathy also has been done, albeit rarely (Borderie & Michinov, 2017; Walker, 2010), and objective measurements of neurochemical signatures of positive and negative emotions (e.g., Oxytocin and ACTH) have been used in research on flow in social contexts (Keeler et al., 2015; cf. Peifer & Tan, Chap. 8). Assessing the Causes and Effects of Social Flow An individual, not a group has been assumed to be the unit of interest with most of the available assessment tools and dependent measures on flow (Csikszentmihalyi & Rathunde, 1993; Guo & Poole, 2009; Moneta, 2012; Rheinberg, Vollmeyer, & Engeser, 2003). Very few investigators have used dependent measures to assess the presence of flow at the group-level, not individual-level (Swann et al., 2018). However, a couple researchers have developed dependent measures on flow at the group level, such as the Collective Efficacy Scale (Martínez, Guillén, & Feltz, 2011), or social network analysis (Gaggioli, Milani, Mazzoni, & Riva, 2011). Nonetheless, there is still a need for tools to investigate causal or correlational relationships between the group structure and process variables that define interactive relationships within groups and self-reported or coded observations of flow states. To quantify variables concerning group tasks, structures and processes, assessment tools or scales are needed to measure the perception that (a) all of the preconditions and factors predicted to support interactive social relations were operative, (b) the group processes variables predicted to facilitate interactive social flow were experienced, and (c) the consequences and outcomes of social flow situations were felt and perceived. A proposal for items that could be included in a scale or coding system to assess the preconditions, group processes and outcomes of social flow can be seen in Table 10.2. Assessing Social Flow as a Disposition Earlier in this chapter, the notion of a syntelic personality was presented. Individuals with syntelic dispositions, it was suggested, should tend to seek situations wherein social flow can be easily experienced, and they should be quite comfortable being assimilated into group. The possible existence of a syntelic disposition complementing an autotelic disposition raises some intriguing questions. Are some individuals more inclined than others to pursue social flow or enjoy it vicariously? Holding all other variables constant, will groups comprised of members with syntelic personalities fare better than those with autotelic personalities or a mix of personalities? How do people with syntelic personalities lead? Do they function well when their team is led by someone with an autotelic personality? Will someone who has both dispositions manifest higher levels of psychological well-being? There is a need for the development of a syntelic

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Table 10.2 Examples of items for a scale to assess the conditions, processes, and effects of social flow 1. My contribution to the group’s work was unique, no one else did exactly what I did. (divisible conjunctive task) 2. The task of the group was challenging. (challenge) 3. What my group strived to achieve was valuable and important to me. (goal importance) 4. What my group strived to achieve was important and valuable to other members of the group. (goal consensus) 5. The group I worked in had the right human resources to perform excellently. (collective competence) 6. I contributed knowledge, skill, and effort to the group’s work. (collective competence) 7. I was aware of the unique knowledge, skill, and effort that each member of my group contributed. (member competence awareness) 8. The group’s task was engaging and interesting. (absorption) 9. Time seemed to pass so quickly for us. (absorption) 10. It was easy and natural to give 100% of myself to my group. (assimilation of the self) 11. The performance of myself as an individual directly affected how well the group performed. (reciprocal conjunctive process) 12. We performed as a team, not a bunch of individuals trying to be stars. (altruistic cooperative processes) 13. Other members of my group not only knew what I was doing, they could easily see what I was doing and evaluate mywork. (mutual feedback) 14. Our group knew how well it was performing throughout ourwork. (continuous feedback) 15. I exerted a lot of effortto help the group achieve its goals. (altruistic cooperative processes) 16. All the members of my group worked equally hard. (cooperative process) 17. The task of the group required us to work side-by-side most of the time; we did not work alone. (reciprocal process). 18. We openly expressed our emotions during group work. (emotional communication) 19. Joy was shared by all when we were succeeding, anxiety was shared when we were not (emotional contagion). 20. My group performed excellently. (performance excellence) 21. As our work progressed, the group became more cohesive. (cohesiveness) 22. I was proud to be a member of the group. (group identification) 23. When we were done we knew we accomplished something important. (sense of purpose) 24. It was not easy to stop working, we wanted to keep working and do it again. (repetition of the experience) 25. Especially when we are in the zone, I feel a strong connection with others in this group. (psychological effect) 26. I identify strongly with this group. (psychological effect) 27. When I am in this group and we are performing well, I feel invincible. (psychological effect) 28. I feel fulfilled and happy when I am in this group. (psychological effect) 29. I would feel awful if this group expelled me. (psychological effect) 30. I would feel sad if this group ended or it had to break-up. (psychological effect) Please indicate your level of agreement using a five-point agree-disagree rating scale, where 1 ¼ strongly disagree, 2 ¼ disagree, 3 ¼ uncertain, 4 ¼ agree, and 5 ¼ strongly agree (the higher the number, the more you agree)

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personality scale to complement the autotelic scale. As well, the autotelic scale itself may need to be revised (Baumann, 2012; Tse, Wing-yan Lau, Perlman, & McLaughlin, 2018). Candidate items to comprise a syntelic personality scale might include items from a social flow scale (see Table 10.2) as well as items from scales on followership (Sy, 2010), need to belong (Leary, Kelly, Cottrell, & Schreindorfer, 2013) and self-monitoring (Snyder & Gangestad, 1986). Rewarding Social Flow in Organizations Where teams are the primary unit of performance in sports, the arts, and businesses, the excessive celebration of the accomplishments of stars or heroes may be a symptom of a dysfunctional organizational culture or a failure of management to support teams that consistently achieve high levels of social flow (Van den Hout et al., 2018). While at any one moment certain individuals might perform excellently and be rewarded for it, if social flow is taken seriously, excellence should be manifested by each team member, and the most meaningful rewards should be given to entire teams, not single individuals. So, if the achievement of social flow is valued, it should be reinforced by the middle and upper managers of organizations. Creating and Managing Social Flow in Large Organizations Social flow seems to be more of a small, than large group phenomenon (cf. Barthelmäs & Keller, Chap. 3). Research suggests that it may be more easily achieved in small than large human groups (Armstrong, 2008; Heyne, Pavlas, & Salas, 2011). However, the conditions that have been demonstrated to facilitate flow within a small group could be used as a model to create the large group conditions that should support intergroup forms of flow within organizations (Walker, 2010, 2017). Specifically, to achieve large group social flow, the mission of an organization would have to prescribe conjunctive, reciprocal, complementary inter-group relations and be maintained by unambiguous, continuous inter-group task and social feedback. An important compelling superordinate goal would be needed to stage cooperation and suppress conflict among participating groups. Examples of entire large organizations manifesting social flow are rare. However, there are some notable instances. When Grumman Corporation won the contract to build the Apollo Lunar Module, unprecedented levels of efficient coordination and collaboration were suddenly widespread (Francillon, 1989). Sports and education offer a few more examples. Some of the most consistently successful sports teams have been associated with excellent collaborative management, particularly when the head coach, assistant coaches and coordinators form a cohesive highly functioning administrative team (Jones & Kingston, 2015). Institutions in higher education are comprised of discipline-specific schools, divisions and departments. Creating and managing a truly integrated interdisciplinary general education curriculum that meaningfully involves distinct and autonomous units can be a challenge. However, when deans of divisions work closely together and are supported by a university provost or president, social flow can characterize their delivery of general education, albeit intermittently, at organizational levels above academic departments (Stalmeijer, Gijselaers, Wolfhagen, Harendza, & Scherpbier, 2007). Nonetheless, there is a strong need for systematic

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research on the organizational designs and structures that promote social flow not only within groups, but also between units and divisions. Some Good and Bad Effects of Social Flow Social flow is usually in the service of sanctioned prosocial goals; however, it can inadvertently serve deleterious goals. For example, within human groups, informal communication networks and grapevines can serve good purposes, e.g., disseminating useful news and information, or bad purposes, e.g., spreading unfounded disaster rumors, slander gossip, or misinformation (Friggeri, Adamic, Eckles, & Cheng, 2014; Rosnow & Fine, 1987). Making a bad situation worse, these human networks are now accelerated and enlarged by technology. Tweetstorms and viral posts on Facebook are likely to involve uncontrolled forms interactive or coactive social relationships. Deindividuation, a potentially adverse consequence of group identity, in this instance, may cause disinhibition and thus increase the swamping or flooding of innocent people with vicious comments on social media (Bandura, Underwood, & Fromson, 1972; Pfeffer, Zorbach, & Carley, 2014). Exactly how digital media can increase or decrease deindividuation and emotional contagion in social networks and the role social flow experience may play, is an important area for future research. Many other good and bad effects of social flow may be seen with spontaneous, selforganizing crowds. The Women’s March in Washington, DC in 2017 may provide a positive example of this simple kind of social flow experience. Only a couple thousand marchers were expected, but within a few hours the streets filled with over one million participants. The march was scheduled to begin at noon, but because of the enormity of the crowd, little movement was possible. Instead, the frustrated marchers spontaneously began rattling their protest signs and cheering. Loud waves of rattling and cheering spread outward thousands of yards from the Capital Building along all the streets and avenues that converged on this symbol of democracy. The effect on the marchers was positive, and in accord with predictions about social flow, was repeated several times until the crowd could finally move. However, such emotional contagion accompanying a social flow experience may have menacing expressions too (Hatfield, Cacioppo, & Rapson, 1993). The violent behaviors of nationalistic extremists at rallies, lynchings by racial bigots and massacres by soldiers are but a few of many possible examples of the potentially bad effects of social flow (cf. Zimanyi & Schüler, Chap. 7). Intraspecies and Interspecies Social Flow Are Homo Sapiens the only animals capable of expressing and experiencing social flow? Are the elegant and graceful murmurations of Starlings, the fluid kinetic silvery shapes formed by schools of Sardines, the balletic stampeding herds of Impalas or the harmonious choirs of wolf packs examples of social flow in the animal kingdom? The answer is “possibly”. It is not unreasonable to speculate that the evolutionary pressures that supported the appearance of social flow tendencies in Homo Sapiens may have also been operative in other social species. The Starlings in their murmurations may be intrinsically motivated too; they may flock together simply for its own sake. However, these captivating displays are more likely the result of evolved strategies to defend social

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species, Homo Sapiens included, from attacks by predators (Ball, 2009). Does our fascination with these displays say more about us than the creatures we behold? Like an audience that experiences flow vicariously at sporting or musical events, so too might be an audience that witnesses flow patterns in birds, fish, and other animals. The inclination to experience social flow vicariously may reveal a perceptual bias we have about human or nonhuman performance. For example, it has been suggested that a group itself can be in flow even when none of its members are in flow (Sawyer, 2003, 2006). A more parsimonious explanation may be that we are predisposed to project our dispositions about flow on to groups. In this instance, group-level flow may be more so a predisposition of social scientists, than a phenomenon distinct and separate from them. The inclination of human beings to work and play with some, but not all animals is also interesting. The pursuit of social flow experiences by our species may not be limited to the recruitment of other humans. Among domesticated animals, why have we selected only a few animals to work and play with us? The existence of interspecies social flow may partially answer this question. We may be particularly attracted to animals that we can share social flow experiences with. Consider these three examples: (1) a rider and a horse can become a graceful equestrian team, not unlike that of human teams such as ballroom dancers, (2) humans and dolphins can spontaneously become absorbed in seemingly unlimited creative play with each other (Kuczaj & Eskelinen, 2014), and (3) dogs and their owners can spur each other becoming completely engaged in endless games of fetch with sticks, balls or Frisbees. Do these examples provide evidence of an interspecies form of social flow? The answer awaits further research on this intriguing possibility. Social Flow Therapy Is there a place for social flow in the practice of clinical psychology? Can the positive effects of social flow help people with social anxiety disorders, those who are shy or lonely, or perhaps those trapped in primary social groups such as dysfunctional families (Cacioppo & Hawkley, 2009; Stein & Stein, 2008)? It could be therapeutic for individuals with abnormally low affiliative needs or those who are anxious in social situations to experience the joy of social flow (cf. Freire, Gissubel, Tavares, & Teixeira, Chap. 12). Counterconditioning and systematic desensitization have been proven to be effective with phobic individuals. Depending on the interests of clients, therapists could prescribe participation in group-based activities, such as drum circles, choirs, bands, theater companies, sports teams, discussion groups, or book clubs. However, this advice is not risk free. To avoid an unpleasant social experience, only groups that manifest the supportive conditions and group processes for social flow should be prescribed. Vicarious social flow is an alternative intervention that could give more control to therapists and clients. Specifically, again, mindful of the sensitivities and interests of the client, a series of short videos or movies could be shown that depict people expressing positive emotions during social flow. Supplemental instruction and explanation on the causes and characteristics of social flow should accompany all these interventions. Any information that gives a client a greater sense of control should be beneficial. Most clients would profit from learning about social flow because it

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would help them discriminate between social situations that are more likely to be positive than negative. While these suggestions for interventions seem plausible, clinical research would have to be done to demonstrate their validity and reliability.

Summary and Conclusion Social flow is a relatively new area of research on optimal experience. Conceptual and operational definitions of it have been in development for only a dozen years. Empirical research on social flow is quite limited. However, we know more about it today than we did when Mihaly Csikszentmihayli gave examples of various athletes experiencing flow during competitions and musicians performing together in his first book on flow (Csikszentmihalyi, 1975). In this book he also provided examples of artists and writers experiencing flow privately and alone during their creative solitary work. Since then it has become increasingly useful to distinguish two types of flow: solitary and social. While research and theory on solitary forms of flow has progressed at an even pace, the same cannot be said about social forms of flow. Research and theory on flow in teams, groups, organizations and other social situations has progressed somewhat too, but much more episodically. A lack of agreement on the theoretical and operational definitions of social flow has made research on it less fruitful (Swann et al., 2018). The current chapter attempted to provide clearer and more useful definitions of social flow. By taking a social psychological view, it described the differences and similarities of two forms of social flow: co-active and interactive. The case was made that the research literature in social psychology more strongly supports the notion that social flow is interactive, not co-active. Interactive social flow is a form of flow about which it can be asserted that all individual members of a group, can be a state of flow (Sawyer, 2003, 2006). Moreover, a social psychological perspective, it was argued, can more usefully direct the attention of basic and applied researchers to accessible situational variables that can be controlled or manipulated to increase or decrease social flow. The current chapter merely introduced the reader to some possibilities for utilizing a situational perspective in future basic and applied research on social flow in sports, the arts, businesses and organizations. It also described other potential applications and invited research on the possible influence of social flow on destructive collective behavior, audience and fan motivation, engagement with other social animals, and the design of new therapeutic clinical interventions. The horizon is broad and deep for future research on social flow. However, the provocative implications and interesting speculations offered in this chapter are no more than exactly that without systematic empirical research. Much more qualitative, correlational and experimental investigation is needed if we are to continue making progress on describing and explaining the intriguing phenomenon of social flow.

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Study Questions 1. Before you read this chapter, how did you define social flow? After you read this chapter, did your definition change? Social flow is a relatively new concept in research on flow. In science, the most interesting and useful concepts are heuristic and parsimonious. Is the author’s definition of social flow heuristic, yet parsimonious? How might his definition of social flow be changed to achieve a better balance between being heuristic and parsimonious? Explain why your definition of social flow will serve as a better guide for future research than the definition proposed by the author. 2. By drawing from classic and recent research in social psychology, the author makes the case that social flow is a social psychological phenomenon (i.e., more syntelic than autotelic). He appeals to classic research and theory on group processes, leadership, social facilitation and social contagion, and more recent research and theory from Positive Psychology on flow and meaning. However, does his inclusion of these literatures illuminate or cloud our understanding of what social flow is and why it happens? In fact, is the cause of social flow more situational than dispositional? Would some of the original concepts on flow based on individual differences and dispositions add clarity or confusion to our evolving understanding of social flow? 3. Why is social flow so delicate and ephemeral? Why is it difficult to create the conditions that cause it to emerge, and why does it require substantial attention and effort to sustain it? However, are all manifestations of social flow delicate and ephemeral? Like the concept of low flow or micro flow, are there varieties of social flow that are easy to create and maintain (e.g., spontaneous self-organizing crowds)? Is social flow much more accessible and easier to achieve in sports teams, musical groups and small groups within business organizations than the author claims? 4. In Table 10.1 the author lists the preconditions, components, group processes and outcomes of social flow. This table has an input, through-put, out-put structure derived from systems theory. However, a lot of variables in social psychology are bicausal or in nonlinear relationships. If this true, can some of the variables he lists be relocated or listed more than once? If you had to edit and reconstruct this table, what would your new list of preconditions, components, group processes and outcomes be? 5. Although throughout the chapter the author takes a strong social psychological position on social flow, he does offer the possibility that “syntelic personalities” may exist. He proposes that such individuals may seek and prefer membership in human groups, especially those in which social flow can be experienced. He suggests that older scales are biased to assess “autotelic” personalities and need to be revised to also assess “syntelic” personalities. If there is merit in this idea, what would a revised flow scale look like? What scores would it produce? How might these scores be used in basic and applied research on flow?

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6. Social flow appears to be more associated with the performance of groups in music or sports than business or government. If this is indeed so, why is it easier to achieve social flow in a jazz jam session or pick-up basketball game than a business meeting or political event? Are the situational variables inherently different and thus social flow can more readily emerge in some situations than others, or are there no meaningful situational differences from one human group circumstance to another, and instead, is it our culturally prescribed expectations that cause us to seek social flow in some but not all areas of human performance? 7. Are Homo Sapiens the only animals on earth that experience social flow? Mindful of the potential excesses of anthropomorphism, what other social animals are candidates for the expression of social flow? Are the conditions and indicators of social flow the same in all social species? If the conditions and indicators are not the same, why is this so? Why did social flow evolve in some animals but not others? Do animals that can experience social flow have a survival advantage over those that cannot? Can theories about the role that positive emotions play in evolution partially explain the unique appearance of social flow in some, but not all, social species (e.g., the Broaden & Build theory of Fredrickson, 2001, 2013)? 8. Solitary flow is a challenge to measure and assess (Moneta, 2012: cf. Moneta, Chap. 2) however, social flow presents additional challenges. The author proposes to measure social flow by constructing a self-report scale representing the preconditions, components, group processes and outcomes of social flow. However, is the development of a new assessment and measure system necessary? Are the advancements in the assessment of solitary flow relevant and sufficient for the assessment of social flow? If the established measures to assess solitary flow significantly correlate with those of social flow, is there a need for the development of new measurement procedures that go beyond those that assess solitary flow? If two measurement systems are necessary for future basic and applied research, how might they be similar and different? Would there be any benefits using social flow measures in solitary flow research, or conversely, solitary flow measures in social flow research?

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Chapter 11

Flow in the Context of Work Corinna Peifer

and Gina Wolters

Abstract Flow can be experienced both during leisure activities and during work and research shows that flow is even more often experienced at work. Considering its positive consequences, fostering flow is a relevant topic for employees and organizations. The consequences and antecedents of flow at the workplace as described in the literature can be conceived as falling into three spheres—the individual sphere, the job/task sphere and the organizational/social sphere—and their intersections. Regarding the consequences, studies find consistently positive effects of flow on measures of well-being and performance, making flow a positive experience relevant to both individuals and organizations. Regarding the antecedents, flow was found to be facilitated by individual resources (such as self-efficacy, optimism, hope, and resilience), by specific task characteristics (such as those described in the Job Characteristics Model, e.g., autonomy, skill variety and task identity) and by organizational/social factors such as the organizational climate, the leadership style of the supervisor and the interactions with colleagues. It is noticeable that many of the effects are bi-directional, with flow affecting resources that affect flow at a later point in time. Referring to person-environment fit theory, the chapter also highlights the important role of the person-environment interaction, which includes a fit of an individual’s attributes with the attributes of the job/task as well as with attributes of the organizational/social environment.

This chapter is based on the article: Peifer, C. & Wolters, G. (2017). Bei der Arbeit im Fluss sein: Voraussetzungen und Konsequenzen von Flow am Arbeitsplatz [Being in flow at work: Conditions and consequences of flow at the workplace], Wirtschaftspsychologie, 19 (3), 6–22. With permission of Pabst Science Publishers, the article has been translated, updated, substantially revised, and extended particularly with respect to the theoretical integration and framework. C. Peifer (*) Department of Psychology, University of Lübeck, Lübeck, Germany e-mail: [email protected] G. Wolters Faculty of Psychology, Ruhr-University Bochum, Bochum, Germany © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_11

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Introduction: Relevance of Flow at the Workplace Today’s Changing Work Demands, and Flow as a Healthy Path to Productivity While studies describe flow experiences in leisure activities as well as during work, there is evidence that flow occurs particularly frequently in work activities (Csikszentmihalyi & LeFevre, 1989; Engeser & Baumann, 2014; Schallberger & Pfister, 2001)—both in individual activities and in cooperative work activities (Magyaródi & Oláh, 2015). Moreover, it has been shown that flow has positive effects on well-being and performance (cf. Barthelmäs & Keller, Chap. 3; Llorens & Salanova, 2017) as well as on other, work-relevant factors (e.g., Demerouti, 2006; MacDonald, Byrne, & Carlton, 2006; Rivkin, Diestel, & Schmidt, 2018; Smith, Bryan, & Vodanovich, 2012; Zubair & Kamal, 2015a, 2015b). In our performance-oriented society, stress has become an increasing problem, as evidenced by rising rates of stress-related illnesses such as burnout or psychosomatic complaints (Lohmann-Haislah, 2012; Techniker Krankenkasse, 2016). This makes it all the more important to find a healthy path to performance that does not come at the cost of individual well-being. And all in all, taking care for employees’ well-being pays off for employees and organizations. Healthy employees are capable of longterm, sustainable performance, are more concentrated, have fewer absences and quit less frequently (Fritz, Yankelevich, Zarubin, & Barger, 2010; Van Katwyk, Fox, Spector, & Kelloway, 2000). Health saves high costs, which are caused, among other things, by absenteeism due to illness, new recruitment and training periods. A promising solution could therefore be to support the productive and, at the same time, well-being-promoting experience of flow at the workplace. In this chapter we first want to give a detailed overview of the consequences of experiencing flow at the workplace. In a second step, we report on what research already knows about the antecedents of flow and derive practical implications for the work context from this. In order to provide a systematic structure, we suggest the “Three Spheres Framework” of flow experience, distinguishing three spheres or levels of consequences and antecedents of flow-experience: (1) the individual sphere, (2) the job/task sphere and (3) the organizational/social sphere (Fig. 11.1).

Fig. 11.1 The three spheres framework of flow antecedents and consequences

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Work-Related Consequences of Flow Experience Consequences of Flow in the Individual Sphere Performance Studies in the laboratory (e.g., Christandl, Mierke, & Peifer, 2018; Engeser & Rheinberg, 2008) as well as in the field (e.g., Peifer & Zipp, 2019) consistently report positive associations between flow experience and performance. With regard to the question of causal direction, most studies assume that flow experience has a positive effect on performance. For example, a study by Demerouti (2006) found that people who reported that they frequently experience flow in their work showed greater performance in coping with the tasks assigned to them (so-called in-role performance). In addition, they showed even greater performance with regard to tasks that go beyond the range of tasks defined in the employment contract (so-called extra-role performance). By definition, these can be tasks such as supporting colleagues after work or taking over a voluntary additional project. Experiencing flow also has a beneficial effect on other performance-related variables: In a survey of service personnel, Kuo and Ho (2010) showed associations between flow and service quality, which consists of tangibility,1 reliability, responsiveness, assurance, and empathy towards customers. In line with this, Plester and Hutchison (2016) found that flow was related to greater engagement of employees in the performance of their work tasks—and thus to an attitude towards work associated with higher performance. Again in line with these findings, Smith and colleagues (2012) found in a cross-sectional study an association between flow and organizational commitment, which can be defined as the extent to which employees identify themselves as being a part of the organization (see also Van Dick, 2017). Rivkin and colleagues (2018) were able to confirm this in a diary study according to which flow had a positive effect on commitment. In addition, Bartzik and colleagues (Bartzik, Wolters, & Peifer, 2019), found in another diary study an effect of flow on task-related commitment, which mediated effects on performance. Creativity—another form of performance—was also found to increase as a consequence of flow. This is illustrated by a study in which music students were asked to compose a piece of music. The composers’ flow experience was significantly related to a subsequent expert judgement on the piece’s creativity (MacDonald et al., 2006). Further studies confirmed the associations between flow and creativity in the corporate context, for example among employees of a software Kuo and Ho (2010) define those constructs as follows: Tangibility ¼ “the degree to which the physical facilities, equipment, and employee’s appearance are satisfactory.” Reliability ¼ “the degree to which the employee has the ability to perform the promised service dependably and accurately.” Responsiveness ¼ “the degree to which the employee will help customers and provide prompt service. Assurance ¼ “the degree to which the employee has the knowledge, courtesy, and ability to inspire trust and confidence.” Empathy ¼ “the degree to which the employee cares and can provide individualized attention to customers.” (Kuo & Ho 2010, p. 538). 1

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development company and of a bank (see also Csikszentmihalyi, 1996; Zubair & Kamal, 2015a, 2015b). How can the effects of flow experience on performance be explained? There are various mechanisms of action: On the one hand, flow has an inherent positive effect on performance, as we are more concentrated during flow and stick to the task with greater perseverance. We ignore the non-task related, i.e. irrelevant thoughts, and focus all available resources on the completion of the task (see also Barthelmäs & Keller, Chap. 3). When we have finally completed a task in flow, it feels rewarding and good. As a result, we have a greater motivation to tackle such a task again, gradually increasing our skills and ‘getting better’. For example, Engeser, Rheinberg, Vollmeyer, & Bischoff (2005) and Schüler (2007) found that students who had experienced more flow during studying for an exam later achieved a better result in the exam. Similarly, Schüler and Brunner (2009) found that runners who had experienced more flow during training to participate in a marathon performed better in competition. Experiencing flow thus causes a greater motivation to practice and a subsequent improvement of our abilities.

Well-Being and Job Satisfaction Successfully mastering demands and noticeably improving one’s own abilities feels good. This sense of well-being is a possible explanation for why flow also has a beneficial effect on our well-being (Moneta, 2004) and why the concept is a central topic in the context of Positive Psychology. In Seligman’s PERMA model (Seligman, 2011), the experience of flow is assigned to the ‘Engagement‘pillar— one of the five pillars of well-being. The association of flow and well-being is in line with the assumptions of self-determination theory (Ryan & Deci, 2000, cf. Abuhamdeh, Chap. 5). It assumes that well-being depends on the fulfilment of three essential needs: competence, social inclusion and autonomy. At least two of these needs are met when performing tasks in flow. By increasing our skills and successfully completing challenging tasks, we experience ourselves as competent. Since we experience a high degree of control in flow, the need for autonomy is also met. Empirical studies consistently confirm the theoretical assumptions of a positive association between flow and well-being. For example, Fullagar and Kelloway (2009) conducted a longitudinal study with architectural students over 15 weeks and found positive effects of flow on positive mood. Studies in the work context also found positive associations between flow and well-being (e.g., Rivkin et al., 2018; Sahoo, 2015). Peifer and colleagues (Peifer, Syrek, Ostwald, Schuh, & Antoni, 2020) found that the frequency of flow at work in the past 2 weeks was positively associated with well-being in the same period of time. Plester and Hutchison (2016) even described flow as a form of work pleasure that encourages engagement at work. A field survey by Datu and Mateo (2015) showed that flow was positively associated with life satisfaction and length of employment (suggesting higher job satisfaction), and negatively with anxiety.

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Furthermore Demerouti, Bakker, Sonnentag, & Fullagar (2012) showed that flow at work leads to more energy after work during leisure time. Flow in leisure time also has a beneficial effect on work-related well-being; people who experienced more flow in their free time reported more positive affect, applied more problem-focused coping strategies and experienced less stress at work (Pinquart & Silbereisen, 2010). The authors interpret flow as a prerequisite and positive affect, coping, and less stress as consequences. In line with this, Peifer, Syrek, et al. (2020) found, that flow during an evening leisure activity had positive effects on well-being the next morning. Provided that long-term studies confirm these findings, it can be deduced that it has a positive effect on work activity if we regularly take time for a flow-promoting hobby. In addition, studies show that experiencing flow is associated with longer service at work (Datu & Mateo, 2015) and greater job satisfaction (Maeran & Cangiano, 2013). The latter, in turn, leads to better performance and other factors relevant to business success, such as engagement, commitment and fewer absences, as numerous studies and also meta-analyses show (Judge, Thoresen, Bono, & Patton, 2001; Wright, Cropanzano, Denney, & Moline, 2002). This means that job satisfaction could mediate effects of flow on performance, as well as vice versa, that performance could lead to increased flow and, consequently to increased well-being. Possibly there is an upward spiral of flow, job satisfaction and performance, an intertwined relationship that has not yet been investigated as such.

Self-Efficacy Self-efficacy is an individual characteristic that is less stable than a trait but more stable than a state (Bandura, 2000; Luthans, Avolio, Avey, & Norman, 2007). It refers to judgements of one’s own skills and abilities to successfully cope with future demands” (Bandura, 1977, 1983). Perceived self-efficacy can change over time depending on an individual’s experiences. Just such an experiential source of selfefficacy is personal mastery (Bandura, 1977)—an experience that is typically created by flow (cf. Baumann, Chap. 9). Accordingly, self-efficacy should increase as a consequence of frequent flow experiences. Furthermore, flow is characterized by a strong feeling of control over the task at hand. This feeling of control should contribute to the confidence of mastering upcoming challenges, i.e. to self-efficacy. Empirical studies indeed confirm a positive relationship between flow and selfefficacy, for example self-efficacy and team-efficacy were significantly related to the frequency of flow of athletes (Pineau, Glass, Kaufman, & Bernal, 2014) as measured with the dispositional flow scale (Jackson & Eklund, 2002). In the work context, a longitudinal study with school teachers found that work-related flow enhanced work-specific self-efficacy at a later point in time (Rodríguez-Sánchez, Salanova, Cifre, & Schaufeli, 2011; Salanova, Bakker, & Llorens, 2006). Besides individual flow, collective flow was also found to facilitate collective efficacy beliefs in workteams (Salanova, Rodríguez-Sánchez, Schaufeli, & Cifre, 2014).

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Consequences of Flow in the Organizational/Social Sphere Flow affects not only the respective person’s behavior and experience, but also the people surrounding them. Bakker (2005) showed that experiencing flow is ‘contagious’. In one study, music students reported more flow in practice when their teachers also experienced more flow in playing their instruments. In this study, the passion for the activity was transferred from the teachers to their students. In another study, Culbertson and colleagues (Culbertson, Fullagar, Simmons, & Zhu, 2015) investigated the so-called crossover of flow experiences from teachers to their students and from classmates directly to fellow students during class. Using a multi-level approach, they found that both the flow of teachers during teaching and the flow of classmates in class influenced the flow of students surveyed. As a possible theoretical explanation, Culbertson and colleagues (Culbertson et al., 2015) assume that experiencing flow results in a certain teaching climate, which in turn has a positive effect on the flow experience of the other participants. As Bakker (2005) points out, these findings are also in line with the phenomenon of emotional contagion, which is defined as the “tendency to automatically mimic and synchronize facial expressions, vocalizations, postures and movements with those of another person and, consequently, to converge emotionally” (Hatfield, Cacioppo, & Rapson, 1994, p. 5). As a positive psychological experience, it is thus likely that the contagious effect also applies to flow. Many empirical studies found support for emotional contagion, among them studies in the work context (Bakker, Demerouti, & Schaufeli, 2003; Bakker & Schaufeli, 2000; Bakker, Schaufeli, Sixma, & Bosveld, 2001; Westman, 2001). Accordingly, and although the reported findings on contagious flow originate from the educational context, it is likely that they also occur in the work context, as they generally underline the transferability of flow between individuals. Thus it can be assumed that a transfer of flow from supervisors to their employees as well as among the employees themselves also takes place. This transfer and the associated effects in the work context should be investigated in future studies. Similar to the findings just presented, the study by Plester and Hutchison (2016) also came to the conclusion that experiencing flow leads to greater engagement with the team as well as with the organization. Furthermore, Aubé, Brunelle, and Rousseau (2014) showed that flow in the team affected the team performance—especially when there was an intensive exchange of information between the team members. The effect of flow on performance was mediated by team commitment (Aubé et al., 2014). In a similar vein, an 8-week study of Salanova and colleagues (2006) found that flow at work leads to increased social resources in terms of supportive relationships within the organization. In summary, it can therefore be said that the flow experience of the organization members probably promotes a climate which in turn leads to more cohesion among the organization members, supports engagement and causes more flow in the performance of the work tasks. This creates a positive upward spiral of flow

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experience, commitment and mutual support, which needs to be examined in longitudinal studies.

Consequences of Flow in the Task Sphere Besides having effects on the person and his/her social environment, flow also affects task or job characteristics, thereby changing the structure and content of the tasks. From a theoretical perspective, the broaden-and-build theory of positive emotions (Fredrickson, 1998, 2001) predicts that positive emotions (recall that flow leads to positive emotions) lead people to actively seek and build resources. In line with this, Tausch and Peifer (2019) found that employees who reported experiencing more intense flow at work were more likely to wish for technological support, i.e. they were more keen on using new work methods that could possibly improve their work. Also Mäkikangas and colleagues (Mäkikangas, Bakker, Aunola, & Demerouti, 2010) found that experiencing work-related flow and the building of resources are closely intertwined. Salanova and colleagues (Salanova et al., 2006) found that flow at work had a positive effect on the building of resources such as the orientation towards clear goals and rules. In line with this argumentation, it is likely that flow also supports job crafting, i.e. “the physical and cognitive changes individuals make in the task or relational boundaries of their work” (Wrzesniewski & Dutton, 2001, p. 179). However, only the reverse direction of this relationship has been argued so far (Bakker & Van Woerkom, 2017) and only one cross-sectional study exists that investiged the suggested relationship (Ko, 2011). This study found that flow and job crafting co-occurred in most of the participants (Ko, 2011).

Conclusion on the Consequences of Experiencing Flow The consequences of experiencing flow in the workplace described in the literature relate to the individual sphere (performance, well-being, work satisfaction and selfefficacy)—to the organizational/social sphere, and to the task sphere. Evidence shows numerous positive effects of flow on performance criteria (in-role & extrarole behavior, customer orientation, creativity, ability growth, reliability, problemfocused coping), as well as on variables that have a positive effect on performance and corporate success (work engagement, organizational commitment). Flow also has a beneficial effect on well-being; in addition to direct effects on mood, flow at work spills over to leisure time where we have more energy during after-work hours. Flow at work reduces the stress reaction and leads to higher job satisfaction. Also, flow creates mastery experiences, which support the development of increased selfefficacy—a construct with many work-relevant positive outcomes. Positive effects of flow also exist at the organizational/social level: flow is likely to be contagious

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between organizational members, and it leads to more commitment with the team and the organization, as well as to team performance. On the task level, flow leads to active seeking and building of resources (e.g., clear goals, technological support) and is assumed to promote job crafting, thereby changing the structure and content of the tasks.

Antecedents of Flow at Work The positive consequences of experiencing flow presented here make us ask what exactly leads to experiencing flow. Can we specifically induce or promote this state? Certainly, experiencing flow is not possible ‘at the push of a button’, but we can make some adjustments to make it more likely. Already Csikszentmihalyi (1975) described three conditions that foster flow: clear goals, clear feedback, and a balance between the skills of the individual with the challenges of the task, as operationalized in the Flow-Channel Model (cf. Moneta, Chap. 2). Since then, researchers have identified many further factors that promote or hinder flow, and Barthelmäs and Keller (Chap. 3) provide a general overview of these factors. In the following, we will describe the antecedents of flow with relevance for the work context.

Antecedents in the Individual Sphere Personality Csikszentmihaliyi and colleagues (Csikszentmihalyi, Rathunde, & Whalen, 1993; Nakamura & Csikszentmihalyi, 2002) proposed that personality is associated with the probability of experiencing flow and introduced the “autotelic personality” (cf. Baumann, Chap. 9). An autotelic personality is the disposition to actively seek situations that enable flow experiences. Since then, several studies have confirmed that flow has a trait component. For example certain personality factors have been found to be associated with flow experience. With respect to the big five personality factors, studies outside the work context found a negative association of flow with neuroticism, and a positive relationship of flow with extraversion and conscientiousness (Ullén et al., 2012). Demerouti (2006) found in her study that conscientiousness as a personality variable moderates the positive effect of flow experience on workplace performance. The negative association of flow with neuroticism is supported by the findings of Heller and colleagues (Heller, Bullerjahn, & von Georgi, 2015), as well as by our own research in which we used the work-related personality questionnaire of Hossiep and Paschen (2012) and found a negative relationship of flow with the subscale stability (Wolters & Peifer, 2017). This subscale is a measure of self-confidence and frustration tolerance, based on the concept of neuroticism (Hossip & Krüger, 2012).

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Beyond the big five personality traits, the achievement motive also seems to play a role in flow experience and the relationship between flow and performance (Baumann & Scheffer, 2011; Engeser & Rheinberg, 2008). Baumann and Scheffer (2011) found, for example, that the achievement motive is related to flow experience, but also to work efficiency. We found support for this association in our own research, where work-related performance motivation (operationalized in the subscale commitment, i.e. the extent to which work goals are pursued—based on the concept of performance motivation; Hossiep & Paschen, 2012) was positively associated with flow experience and performance (Wolters & Peifer, 2017). Furthermore, experimental studies show that flow is further related to action orientation (Baumann, Lürig, & Engeser, 2016; Keller & Bless, 2008) and that action orientation buffers negative effects of adverse conditions on flow experience.

Self-Efficacy We have listed self-efficacy already in the consequences of flow, as flow creates feelings of control and mastery, which are sources of self-efficacy. The opposite causal direction, i.e. that self-efficacy also promotes flow, has also been suggested in literature (e.g., Rodríguez-Sánchez et al., 2011). As Rodríguez-Sánchez and colleagues (2011) point out, self-efficacy affects how individuals evaluate the challenges they pursue, how much effort they expend, how much they persist when facing difficulties and how they feel during the activity. Accordingly, high selfefficacy should increase the probability of positive appraisals when task demands are high, and should instead lead to evaluations of pleasant challenge. According to Csikszentmihalyi (1990), a positive evaluation of task demands as pleasant challenge promotes experiencing flow instead of stress (compare Peifer & Tan, Chap. 8). In line with this, studies were able to show causal effects of self-efficacy on flow: In a recent experimental study, we found that manipulated self-efficacy after a mental arithmetic task led to increased flow in a subsequent similar (but more difficult) task (Peifer, Schönfeld, Wolters, Aust, & Margraf, 2020). In the work context, Rodríguez-Sánchez and colleagues (2011) found in an 8-month longitudinal study with 258 teachers effects of work-related self-efficacy on flow experience at work. Also applying a longitudinal design (three measurement points at intervals of 1 week), they identified collective work-related self-efficacy as a predictor for flow in the team (Salanova et al., 2014). Interestingly, these effects were found to be bidirectional, i.e. more self-efficacy at an earlier measurement point led to more frequent flow at a later time point and vice versa. This means that self-efficacy and flow mutually enhance each other, suggesting an upward spiral of frequent flow at work and increased work-related self-efficacy (compare Salanova et al., 2006).

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Psychological Capital Flow has also been associated with psychological capital (PsyCap; Zubair & Kamal, 2015a, 2015b), which is composed of the facets of self-efficacy, optimism, hope and resilience (Luthans, Luthans, & Luthans, 2004). Luthans and colleagues (2007) state that the four facets of PsyCap share one higher order core construct, which represents “one’s positive appraisal of circumstances and probability for success based on motivated effort and perseverance” (p. 550). This essence of PsyCap is assumed to enhance performance and satisfaction through its positive effects on cognitive and motivational processes. As such, authors suggest that the effects of the combined facets go beyond the effects of each of the facets (e.g., self-efficacy) alone. Accordingly, to explain possible effects of PsyCap on flow, the argumentation is the same as the argumentation for self-efficacy as described above, complemented by the positive cognitive and motivational effects of optimism, hope, and resilience. Hope adds, for example, motivational energy to pursue a goal (Luthans et al., 2007)—and goals have been found to be flow conducive. Optimism adds a realistic sense of what can be accomplished (Luthans et al., 2007). This can save an individual from disappointment because of being unsuccessful—which could reduce self-efficacy and consequently flow. Resilience adds the capability to grow even after adverse events (Luthans et al., 2007). As Bandura states, renewed effort after failures is a source of success (1998). Accordingly, resilience contributes to becoming an expert in a certain activity—another antecedent of flow. Confirming these theoretical considerations, a cross-sectional study among employees of software houses found that each of the PsyCap facets was positively correlated with flow experience (Zubair & Kamal, 2015b). Another study among journalists found associations between flow and optimism, and with another concept related to self-efficacy, namely, the internal locus of control (Emanuel, Zito, & Colombo, 2016). These findings were also supported by results from Zito, Cortese, and Colombo (2016). Their study showed that for journalists working as freelancers, flow experience was associated with the internal locus of control. Another study with primary school teachers showed that flow experience correlated positively with job-related optimism (Beard & Hoy, 2010). However, until now only a few crosssectional studies have been performed, and future research should test the effects of PsyCap interventions on flow.

Antecedents at the Job/Task Level Numerous findings show that flow experience is more frequent in work activities than in leisure activities (Csikszentmihalyi & LeFevre, 1989; Engeser & Baumann, 2014; Rheinberg, Manig, Kliegl, Engeser, & Vollmeyer, 2007; Schallberger & Pfister, 2001). This finding was also confirmed when one distinguishes between active and passive leisure activities and compares flow experience at work only with

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active leisure activities (Engeser & Baumann, 2014). However, the so-called paradox of work describes the finding, that although employees report more flow at work, they nevertheless state that they prefer to engage in leisure activities. As Engeser and Baumann (2014) show, this can probably be explained by the difference in negative affect, which is higher at work than in leisure. However the fact that flow is more likely to occur at work is probably due to the fact that work tasks provide more of the flow-promoting antecedents. Next, we will thus look at the antecedents of flow in the job/task sphere.

Job Dimensions According to the Job Characteristics Model In the literature on the antecedents of flow at work it is noticeable that the predictors examined are often based on the Job Characteristics Model (JCM; Hackman & Oldham, 1975) (cf. Demerouti & Mäkikangas, 2017; Maeran & Cangiano, 2013) or at least have large overlaps with it. The JCM describes five core job dimensions of motivational work: Skill variety, task identity, task significance, autonomy, and feedback. The JCM and flow theory have much in common. While the JCM predicts motivation and satisfaction at work, flow has an inherently motivating character, which is why it is also called autotelic experience (Csikszentmihalyi, 1975) and has been defined as a form of intrinsic motivation (Rheinberg, 2008). Accordingly, factors that predict work motivation are likely to also affect flow experience. As described in detail by Maeran and Cangiano (2013), all five job dimensions of the JCM can be referred to the flow model. For example, feedback has been described as an antecedent in both models. Autonomy is described as an antecedent of motivated work in the JCM, while a high feeling of control is a defining characteristic of flow. It is likely that autonomy as an objective and adaptable job dimension will promote the feeling of control in the acting person. While skill variety as a job dimension refers to the range of work activities requiring different skills, it is more likely that a person finds a challenge-skill balance (an antecedent of flow) when there is a broad range of skills that can be used. Hackman, Oldham, Janson, and Purdy (1975) even describe skill variety as “the degree to which a job requires the worker to perform activities that challenge its skills and abilities” (p. 59). Task identity according to Hackman and Oldham (1975) relates to the degree to which the job asks for an identifiable product and has a visible outcome. This job dimension resembles the flow-antecendent clear goals, as clear goals are meant to identify exactly what piece of work should be accomplished. However, task identity goes beyond clear goals, as it further asks for the completion of a “whole” piece of work, that is done from beginning to end. Finally, task significance reminds of a quote of Csikszentmihalyi’s (1990) in which he explains that the best experiences occur “when a person’s body or mind is stretched to its limits in a voluntary effort to accomplish something difficult and worthwhile.” (p.3; compare Maeran & Cangiano, 2013). Accordingly, also task significance should foster flow.

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We found some studies that tested the core job dimensions in this or very similar combinations as antecedents for flow experience (Bakker, 2005, 2008; Demerouti, 2006; Kuo & Ho, 2010; Maeran & Cangiano, 2013; Mäkikangas et al., 2010; Salanova et al., 2006). If the core dimensions are included in the analyses as an overall package, there are always significant connections with flow (Demerouti, 2006; Maeran & Cangiano, 2013; Mäkikangas et al., 2010). This also applies to the associations between the psychological states resulting from the core dimensions: the experienced meaningfulness of one’s own work activity, the experienced responsibility for the outcomes of one’s work and the knowledge of the actual results of the work activities are significantly related to flow (Maeran & Cangiano, 2013). In a study by Bakker (2005), the connection between the core job dimensions and flow was mediated via the challenge-skill balance. Looking at the relationships between the core job dimensions with flow in detail, a more differentiated picture emerges, which varies depending on the study and target group. For example, Maeran and Cangiano (2013) in their study with employees from different professions found that flow was particularly predicted by task significance and feedback. In this study, task identity was only associated with the flow component ‘autotelic experience’. The task significance has been confirmed in further studies as a predictor of flow: Shernoff, Csikszentmihalyi, Schneider, and Shernoff (2003) also found that people reported flow especially when the job was assessed as personally relevant. Bassi and Delle Fave (2012) were also able to show in a long-term study with teachers that more flow was experienced with subjectively significant tasks. Also regarding autonomy, which means that individuals are responsible and in control of their actions (compare e.g., Karasek, 1979), empirical studies found that having control over an activity was related to flow experience (e.g., Emanuel et al., 2016; Fagerlind, Gustavsson, Johansson, & Ekberg, 2013; Kuo & Ho, 2010; Rau & Riedel, 2004; Shernoff et al., 2003; Zito et al., 2016). Emanuel and colleagues (2016) also found evidence that the existence of autonomy increased the link between other resources (innovation climate and social capital) and flow experience. In a 15-week longitudinal study with architecture students, Fullagar and Kelloway (2009) likewise showed causally that autonomy—together with skill variety (see also Kuo & Ho, 2010)—facilitates flow. The core job dimension feedback has also received much research attention as a prerequisite for flow. Csikszentmihalyi (1975) already counted feedback as a flowcomponent. Feedback was later classified by Landhäußer und Keller (2012; see also Barthelmäs & Keller, Chap. 3) as an antecedent of flow, as it is a circumstantial condition that does not describe the experience itself, but facilitates it. Empirical studies have confirmed a relationship between feedback and flow (Maeran & Cangiano, 2013) and between being informed about the results of one’s work and flow (Rau & Riedel, 2004). In the context of feedback, clear goals also play an important role. They can be understood as a prerequisite for clear feedback, because only if clear goals are given can performance be reviewed and evaluated in terms of clear, unambiguous feedback (see also Keller & Landhäußer, 2012). Initial correlative findings support the assumption that goal clarity promotes flow experience (Pratt, Chen, & Cole, 2016). Feedback can on the one hand be inherent in the task,

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e.g., when the desired effect is achieved when programming software. On the other hand, feedback can also be of a social nature, for example when the supervisor praises the performance of an employee. Here it plays an important role that the goals are first clearly formulated (Csikszentmihalyi, 1975; cf. Barthelmäs & Keller, Chap. 3), for example in regular employee discussions (e.g., using Management by Objectives, Drucker, 1955). In this way, transparent performance feedback can be given based on the achievement of goals. Further studies deal with job or task characteristics, but do not explicitly relate to the JCM (Hackman & Oldham, 1975). For example, a number of studies exist that investigate which tasks or jobs are more likely to elicit flow. For example Llorens, Salanova, and Rodríguez (2013) found that teachers experienced more flow than tile layers. The authors interpreted their results to mean that the work of teachers contains more flow-promoting resources and that working with other people is more flow-promoting than working with “data or things”. From the perspective of the JCM, one could assume that teaching is the more complex task, involving a higher degree of skill variety and, thus, leading to increased flow. Even within professions there are differences in the different work-related activities. Bryce and Haworth (2002) found in a cross-sectional study among office workers that women reported flow primarily during problem solving, conflict management, meeting deadlines/achieving goals, planning meetings, negotiating with customers and learning from new experiences. Men reported flow primarily during problem solving, meeting deadlines/achieving goals, order acquisition and handling complaints. Also in this study, the high degree of skill variety in the reported flowtasks is noticeable, and routine tasks are not part of that list. In line with that, Allison and Duncan (1987) likewise found differences between the professions in a study conducted exclusively with women; women scientists reported more flow at work than blue collar workers. Both occupational groups generally showed less flow in repetitive, simple and lengthy activities. These included only a small part of the work of female scientists, but the majority of the work of female blue collar workers. In a study with employees of a private accountancy firm and a public elder care organization, activities such as planning, problem solving and evaluation predicted the experience of flow, but this was not the case with brainstorming (Nielsen & Cleal, 2010). Furthermore, in this study there were significantly different flow levels in the two companies observed: in elder care, the flow levels were higher than in the accountancy firm. In terms of the JCM (Hackman & Oldham, 1975), this finding maybe explained by the higher level of task significance in elder care compared to accountancy tasks.

Stress-Related Task Demands Stress-related task demands—also called as stressors—are mentioned as factors that affect flow experience. At first sight, it could be assumed that stressors hinder flow— but the association is more complex. Csikszentmihalyi (1975) already observed flow with rock climbers—a risk sport. Rheinberg, who had investigated many different

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flow-related activities during his research, found the highest flow values in a study of illegal graffiti sprayers (Rheinberg, 2008)—which is an activity involving a certain risk of being caught. Also Weimar (2005) showed in a study with teachers and trainees that flow occurred in stress-related teaching situations. Accordingly, some degree of stress or arousal seems to promote flow. This fits to the operationalization of flow in the Flow-Channel Model, which depicts flow as an experience located between the low-arousal state of boredom and the high-arousal state of anxiety (cf. Peifer & Tan, Chap. 8). Assuming that transitions from boredom to flow and from flow to anxiety come along with a continuous increase in arousal, arousal should be moderately elevated during flow. This pattern is confirmed by physiological studies on flow (cf. Peifer & Tan, Chap. 8). Moderate physiological activation (as opposed to relaxation) was found to be associated with high flow (De Manzano, Theorell, Harmat, & Ullén, 2010; Keller, Bless, Blomann, & Kleinbohl, 2011; Peifer, Schulz, Schächinger, Baumann, & Antoni, 2014). The relationship was found to be not linear, but inverted u-shaped; while moderately increased values of sympathetic activation and the stress hormone cortisol led to higher flow values, values beyond a moderate level resulted in lower flow experience (Peifer et al., 2014; Peifer, Schächinger, Engeser, & Antoni, 2015). Moderate (“positive”) activation, such as that caused by challenging tasks, seems to promote flow, while too much activation hinders it. Looking at work-related stressors, Emanuel and colleagues (2016) found positive correlations of workload with flow experience among self-employed journalists. However, there is evidence that work-related stressors differ in their quality and that not all kinds of stressors have positive effects on flow at moderate levels. For example, we recently found an inverted u-shaped relationship between unfinished tasks at work and flow at work; however, the pattern of the curve rather revealed that moderate levels of unfinished tasks were not yet detrimental to flow at work, while higher levels of unfinished tasks were negatively related to flow at work (Peifer, Syrek, et al. 2020). At the same time, unfinished tasks at work and during studying negatively affected flow during an evening leisure activity (Peifer, Syrek, et al. 2020). In another recent study we found that multitasking is negatively associated with flow at work (Peifer & Zipp, 2019). Furthermore, there is evidence that flow is best achieved when employees come to work rested. Debus and colleagues (Debus, Sonnentag, Deutsch, & Nussbeck, 2014) showed that recovery outside working hours is an important prerequisite for experiencing flow at work. The authors of the study found that people who started work less rested had a linear decrease in their flow experience throughout the working day, while those who started the day rested showed an inverted u-shaped development in their flow experience. The flow of rested people increased continuously during the morning and then decreased again until the end of the day. It can also be stated that flow at the workplace leads to less exhaustion and more energy at the end of the working day—but only if people manage to ‘switch off’ (Demerouti et al., 2012). In summary, this means that adequate recovery and mental detachment from work are important to experiencing flow during the day at work and to recovering well in the evening—a positive upward spiral that has not yet been

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fully investigated. There are also long-term studies missing to investigate the influence of flow on stress-related diseases as a function of recovery. The relationship between recovery and flow suggests that recovery is an important parameter for the sustainable use of flow in the workplace. In this context, an experimental study by Baumann and colleagues (Baumann et al., 2016) is informative. The authors found that the interplay of tension and relaxation also plays a central role for flow in the direct execution of activities. It was not a perfect balance of demands and skills which was the best possible flowtriggering factor in a computer game, but flow was rather most likely when the requirements slightly exceeded the abilities (high, but achievable demands) and when there were regular short recovery phases (low demands) in between. Therefore, not only after work, but also during work activities, regular breaks should be taken or less demanding tasks should be processed in order to increase the probability of flow.

Antecedents at the Organizational/Social Level Experiencing flow is often described in social contexts (cf. Walker, Chap. 10), be it in teamwork (Aubé et al., 2014; Hout, Davis, & Walrave, 2016; Magyaródi & Oláh, 2015; Salanova et al., 2014), in team sports (Mosek, 2009) or in music-making in a band (Gloor, Oster, & Fischbach, 2013). Walker (2010) found in a survey study that there are higher flow values in joint as opposed to individual activities. In two experiments he could further show that individuals had more flow when playing the same computer game with a partner (social condition) compared to a solitary condition and that flow was higher with higher interdependence of partners in a game. A theoretical approach that includes effects of social resources (as well as of other kinds of resources and demands) on motivation and strain is the Job DemandsResources (JD-R) model (Bakker & Demerouti, 2017; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). The model assumes that job resources promote motivation and buffer effects of demands on strain. The JD-R Model has in the past been used to explain empirical findings that work resources also positively affect flow as a positive and rewarding work experience accompanying motivated task engagement (Habe & Tement, 2016; Salanova et al., 2006). Social support is one of the most frequently named resources in the Job DemandsResources (JD-R) Model, and at the same time one of the most studied social factors that facilitate flow at work. Knowing that colleagues are at our side when things get tight has a beneficial effect on the frequency of flow in the workplace (Bakker, 2008; Salanova et al., 2006). In line with this, Fagerlind and colleagues (Fagerlind et al., 2013) found that social capital is a collective resource related to flow. The term ‘social capital‘ refers to positive social relationships, trust and mutual support in the working group and the sharing of information and resources between colleagues and the supervisor in informal networks (Kouvonen et al., 2006). It was further found

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that collective flow in a work team promoted collective self-efficacy. This in turn had a beneficial effect on future collective flow experience—leading to a positive upward spiral of collective flow and collective self-efficacy (Salanova et al., 2014). Besides work-related social support, also fun among colleagues fosters flow as it enables restful breaks and creates positive affect (Plester & Hutchison, 2016). At best, there should be a certain time and/or space to have fun—for example having a break room—as too frequent and unwanted interruptions can hinder concentrated work in flow (Peifer & Zipp, 2019). Furthermore, the supervisor can also contribute much to the employees’ flow. In addition to constructive feedback (see above), coaching by the supervisor—another frequently named resource in the context of the JD-R model—was also identified as flow-promoting (Bakker, 2005; Emanuel et al., 2016). In a study by Bakker (2005), this connection was mediated via a larger demand-skill balance. Coaching, as operationalized by Bakker (2005), focuses on the support of the supervisor, be it in problem-solving processes or in using one’s own influence to help employees. In the study by Zito and colleagues (2016) as well, the support provided by the supervisor was associated with the flow experience of the employees. Zubair and Kamal (2015a) found connections between authentic leadership style and flow experience of employees in a cross-sectional study with employees from the banking and the IT sectors (software development). Authentic leadership is a management style in which the manager is aware of his or her own values, promotes the strengths of the employees, is reliable and open in his or her thinking, and tries to create a positive and pleasant working environment (Avolio & Gardner, 2005; Gardner, Avolio, Luthans, May, & Walumbwa, 2005). Similarly, Smith and colleagues (Smith et al., 2012), in a cross-sectional study with non-scientific university staff, found that an authentic leadership style, as well as a transformational leadership style, are related to flow experience. Although the studies mentioned are crosssectional studies, the authors assumed that the leadership style influenced flow experience. According to Zubair and Kamal (2015a), this can be explained by the fact that authentic managers help their employees to gain intrinsic motivation and they stimulate positive emotions such as optimism (Ilies, Morgeson, & Nahrgang, 2005; Zhou & George, 2003). This presumably strengthens the endurance and concentration of the employees—a central prerequisite for experiencing flow. Furthermore, when looking at the description of authentic leadership, this form of leadership implies the promotion of resources, which have been identified as antecedents of flow. For reasons of completeness, we need to repeat here the finding that transfer effects of flow between individuals also exist, which were empirically found between teachers and students; which can be explained using emotional contagion theory (Hatfield et al., 1994); and which are likely to be found in the constellation of supervisors and employees (see section ‘Consequences at the organizational/social level’; Bakker, 2005; Culbertson et al., 2015). Accordingly, flow can be regarded here both as a prerequisite and as a consequence. For the work context, this should still be explicitly empirically verified.

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As Culbertson and colleagues (2015) suggest for the school context, the flow experience of the other organization members could also have a lasting positive effect on the organizational climate or departmental climate and thus create an overall flow-promoting working environment. Csikszentmihalyi already assumed that the social and organizational climate (e.g., the security climate) influenced flow experience (Csikszentmihalyi, 1990). Presumably these kinds of organizational climates foster antecedents of flow. Current empirical results are available, for example with regard to the innovation climate of organizations (Fagerlind et al., 2013): organizational openness to new ideas (which is related to high autonomy), joint exploration, and the questioning of routines (which is related to skill variety) were found to be related to increased flow. In addition, Smith and colleagues (2012) found that the experience of flow also correlates with the safety climate in the company; the higher the employees’ assessment of the priority of occupational safety over other organizational objectives, the more flow they experienced during work. A climate of organizational safety potentially promotes the feeling of control that is a component of flow. Rau and Riedel (2004) further found that when work environments were more advantageous in terms of learning potential, responsibility, and cooperation/communication, employees were more likely to experience flow at work.

Antecedents at the Intersections Psychology in general—and work and organizational psychology in particular—has a long tradition in investigating interactions between individual and environmental factors. A prominent theory in this respect is the person-environment fit (P-E fit) theory (e.g., Caplan, 1987; Edwards, Caplan, & Harrison, 1998; Edwards & Cooper, 1990). In the work context, P-E fit “. . .refers to the degree of compatibility or match between individuals and some aspect of their work environment” (Kristof-Brown & Guay, 2011, p. 3). This match encompasses all possible combinations of individual attributes like personal interests, values, traits, preferences, knowledge, skills, abilities, needs, goals, and attitudes on the one side with environmental attributes, including job/task attributes (e.g., characteristics, demands and resources) and attributes of the social and organizational environment (e.g., organizational culture or attributes of supervisors and colleagues; Kristof-Brown & Guay, 2011) on the other. While the original P-E-fit theory particularly looks at the experience of stress as an outcome of P-E misfit, one of these person-environment fit combinations has already been defined as an antecedent of flow experience in the Flow-Channel Model (Csikszentmihalyi, 1975): namely, the fit between skills of a person with the demands of a task. In accordance with P-E fit theory, Csikszentmihalyi (1990) predicted stress to occur when demands exceeded skills—which corresponds to a misfit according to P-E fit theory. Accordingly, while a misfit of personal and environmental attributes leads to stress, we propose that a fit of both may be an antecedence of flow.

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Antecedents at the Intersection Between the Individual and the Task Spheres Balance Between Individual Skills and Task Demands The so called challenge-skill balance has been described as one of the central antecedents of flow and constitutes the Flow-Channel Model (Csikszentmihalyi, 1975). The challenge- or demand-skill balance has since been confirmed in many studies as a prerequisite for flow (including Bakker, 2005; Engeser & Rheinberg, 2008; Fullagar, Knight, & Sovern, 2013; Keller & Bless, 2008). Advancing the initial concept of demand-skill balance as an antecedent of flow, later theoretical models assume that flow results particularly when demands and skills are not just in balance but when both are high (Massimini & Carli, 1988). This assumption is also referred to as the expertise effect of flow experience (Rheinberg, 2008) and was confirmed in empirical studies (Fullagar et al., 2013; Llorens et al., 2013). Llorens and colleagues (2013), for example, found the expertise effect in a large-scale study with 957 teachers and tile workers for both occupational groups. In this respect, Eisenberger, Jones, Stinglhamber, Shanock, and Randall (2005) found that the special combination of high demands and high skills led to a greater positive mood, more interest in tasks, and increased performance only among those employees who had a high achievement motive. This finding again supports research indicating that a congruence of personality, personal motives, and needs with the possibilities of the situation facilitates flow (Schiepe-Tiska & Engeser, 2012, cf. Schiepe-Tiska & Engeser, Chap. 4). Another advancement of the demand-skill balance as an antecedent came from Baumann and colleagues (Baumann et al., 2016), who found that not a static, but a dynamic balance leads to the highest flow values. In their experiment the dynamic balance was operationalized by demands being periodically slightly higher than the skills, which was balanced with periods of low demands during one session of a computer game.

Fit Between Personality Traits and Task Characteristics While some personality traits were found to have main effects on the probability to experience flow, personality traits often function as moderators of the relationship between task characteristics and flow experience. One example is a finding of Schüler, Sheldon, Prentice, and Halusic (2016), who found that autonomy only promotes flow at work when a person possesses a need for autonomy. Another example is polychronicity, which is a personality trait reflecting the preference to multitask (also called multitasking preference). While multitasking in general was found to have negative effects on flow (Peifer & Zipp, 2019), we found that multitasking had a positive effect on flow for individuals with high multitasking preference (Wolters, Bartzik, Luhmann & Peifer, submitted). Similarly, action orientation moderated the relationship between multitasking and flow, with

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individuals low in action orientation showing the largest negative association between multitasking and flow, while individuals high in action orientation showed no association. This finding underlines that action orientation buffers negative effects of adverse conditions on flow (Wolters et al., submitted). In addition, this finding further illustrates that effects of task characteristics on flow differ as a function of individual characteristics. In line with this, Schiepe-Tiska and Engeser (Chap. 4) speak about motive-corresponding action opportunities that foster flow.

Interest, Subjective Value and Personal Relevance Bricteux, Navarro, Ceja, and Fuerst (2017) showed that interest is a prerequisite of flow experience, and moderated the occurrence of flow as a consequence of a demand-skill balance: the greater the individual’s interest in an activity, the more likely a demand-skill balance would lead to flow. Similarly, Barthelmäs & Keller (cf. Chap. 3) suggest the subjective value of an activity as a predictor of flow intensity, interacting with a perceived fit of demands and skills. They understand subjective value in the sense of Higgins (2006) as the strength of motivational force and attraction of an activity itself (not of the consequences). Engeser and Rheinberg (2008) found that a demand-skill balance particularly led to flow if the task was of little personal relevance. This result was found in a study in which students either took a written exam within their study program (high personal relevance) or played a computer game (low personal relevance). In the high relevance task, flow was more likely experienced if the skills exceeded the demands. Accordingly, interest, subjective value and subjective task relevance serve as moderators of the relationship between the demand-skill ratio and flow. Depending on their level, different demand-skill ratios can be beneficial for flow. These findings further show that multiple interactions between individual attributes (e.g., skills and subjective relevance) and task attributes (e.g., demands in terms of difficulty) can be involved in the prediction of flow—findings which open many new routes for research. Supporting the effects of relevance on flow, we found in a recent study preliminary evidence that physiological arousal—in terms of cortisol increase—was linearly increasing with difficulty under conditions of high relevance, and it had an inverted u-shaped relationship with difficulty when relevance was low (Peifer, Lehrich, Ingwersen, Schächinger, & Antoni, 2016 & submitted). Accordingly, arousal could be a mediator transmitting effects of high or low relevance on flow experience. This association, however, remains yet to be tested.

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Antecedents at the Intersection Between the Individual and the Organizational/Social Spheres While findings of Walker (2010) (as described in the section on the organizational/ social sphere) suggested increased flow in social compared to solitary activities, it has still not been investigated if this finding is moderated by personality. Given the assumptions of P-E fit theory, one would rather expect the social context to interact with personality, for example that individuals scoring high on extraversion should be more likely to experience flow in social situations. The other way around, individuals scoring low on extraversion should be more likely to experience flow in solitary situations. When investigating flow in a social context, a distinction can be made between cooperative and competitive situations. While in working groups, all parties work towards the same goal (hopefully), parties in competitive working situations pursue different goals. Flow has already been investigated in competitive situations in sports (e.g., a marathon run, Schüler & Brunner, 2009), however it was not compared if flow levels during the competition vs. joint activity differed. Also, studies in competitive compared to joint work situations are missing. A possible application context here is, for example, the context of negotiation, which represents an interesting future object of investigation. Also here, one could rather expect to find a fit between personality and the social situation, for example individuals with a high prosocial motive will more likely experience flow in joint, cooperative tasks, while individuals with a high egoistic/competitive motivation will more likely experience flow in competitive tasks. Furthermore, in line with P-E fit theory, it is likely that not just the differentiation between social vs. solitary or cooperative vs. competitive context predicts flow, but more attributes of fit will contribute to the resulting experience. P-E fit theory makes specific predictions with respect to the fit between individual attributes and attributes of the organizational (P-O fit) and group context (P-G fit). P-O fit is defined as “the compatibility between people and organizations that occurs when (a) at least one entity provides what the other needs (complementary fit), or (b) they share similar fundamental characteristics (supplementary fit), or (c) both” (Kristof-Brown & Guay, 2011, p. 7). Accordingly, an example for a good P-O fit in terms of premise (a) is when an organization offers opportunities for a specific career development that a person seeks; in terms of premise (b), an example is a congruence between personal and organizational values. The P-G fit describes the compatibility of the attributes of an individual with the attributes of its peers or the work team (Kristof-Brown & Guay, 2011). This refers to a fit (supplementary and or complementary) of, for example, personality traits, goals and values, skills and knowledge, or preferences for working climates. A perfect combination of the relevant attributes may lead to social/team flow as a shared experience.

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However, to our best knowledge, the interaction of personal and peer/organizational attributes has not yet been investigated and should clearly receive research attention in the future.

Antecedents at the Intersection Between All Spheres In an integration of P-E fit approaches, Kristof-Brown and Guay (2011) highlight P-E fit as a multidimensional construct, describing an individual’s fit with, at best, all kinds of environmental attributes (and ultimately both job/task attributes and organization/social attributes, are kinds of environmental attributes). They outline that “the most rewarding experiences are those in which multiple types of fit exist simultaneously.” (p. 13). Accordingly, a holistic fit of both the relevant attributes of an individual with the attributes of the job/task sphere and the attributes of the organizational/social sphere should provide the greatest flow potential.

Conclusion on the Antecedents of Flow The large number of studies and findings on the antecedents for experiencing flow in the work context shows that there are numerous starting points for promoting flow experience at work (Fig. 11.2). These starting points concern the individual sphere, the job/task sphere, and the organizational/social sphere (for details see Fig. 11.2), as

Fig. 11.2 The three spheres framework of flow-antecedents

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well as their intersections. In accordance with the P-E fit theory, the likelihood of flow should increase the more attributes of the individual are compatible with the attributes of the job/task and of the organizational/social environment.

Implications for Practice On the basis of the described antecedents of flow at the workplace, recommendations for the practical promotion of flow experiences can be derived. The interventions can be distinguished according to their target area within the Three Spheres Framework—an overview is given in Fig. 11.2. In each sphere, interventions can encompass work design, training, management tools and organizational development. As such, promoting flow can be a holistic framework for organizational development that enhances sustainable performance and well-being as typical outcomes of flow.

Implications in the Individual Sphere Within the individual sphere, psychological capital (self-efficacy, optimism, hope and resilience) is a predictor of flow experience. There are a number of validated interventions to increase psychological capital (Luthans, Avey, Avolio, Norman, & Combs, 2006; Luthans, Avey, Avolio, & Peterson, 2010; Luthans, Avey, & Patera, 2008) and which are accordingly promising interventions to foster flow. There is a large research tradition on self-efficacy that is independent from psychological capital. According to Bandura (1977), self-efficacy can be increased via four sources: (1) performance accomplishment, i.e. positive mastery experiences, (2) vicarious experience, i.e. having role models who are good at something, (3) verbal persuasion, i.e. positive feedback and support, and (4) psychological and physiological states, e.g., body signals of arousal or relaxation that can be interpreted in terms of confidence to cope with upcoming demands. These sources can be used to support self-efficacy and, consequently, flow experiences for example as follows. In terms of management tools, positive feedback can be used for verbal persuasion. A positive effect of positive performance feedback on self-efficacy was, for example, confirmed by Reynolds (2006), who also found that negative feedback reduces self-efficacy. Accordingly, supervisors should regularly provide positive feedback. In staff meetings, they should always also emphasize positive aspects when giving feedback in addition to possibly necessary criticism. Another management tool based on the enhancement of self-efficacy is being a role-model to create vicarious experiences. Furthermore, and in line with the above mentioned implications, goal setting according to the abilities of a person will further create positive mastery experiences, thereby facilitating self-efficacy. Psychological states can be adapted by applying cognitive framing techniques, which can be learned in

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psychological training and coaching programs. Physiological states, for example in response to a demanding situation, can be manipulated using relaxation techniques spanning from deep breathing to meditation and progressive muscle relaxation. When these techniques are trained on a regular basis, acute downregulation of physiological arousal can occur in a fast and efficient manner. Aside from affecting self-efficacy, physiological states can also directly affect the probability of flow experience. As described above, moderate physiological arousal facilitates flow (cf. Peifer & Tan, Chap. 8). Depending on the current physiological state, such a moderate physiological activation can be reached either by downregulating physiological activation with relaxation techniques (see above) or by upregulating physiological activation with physical activities such as walking or yoga. For those employees who work at a computer, it will already help to use height-adjustable desks, as standing creates more arousal than sitting. This could, for example, be done after lunch, when parasympathetic activation is increased to cope with digestion. Against the background of the effects of stressors, recovery, and detachment on flow experience, it can be concluded that ‘switching off’ after work and sufficient recovery are important antecedents for experiencing flow on the next working day. Reading or answering emails after work, taking work home or even working at the workplace for too many hours are obstacles to successful recovery. This is certainly not completely avoidable sometimes, but if it is the rule rather than the exception, it impairs long-term recovery and, thus, also the probability of flow experiences. To counteract this, innovative organizations have already developed their own approaches. Some companies (e.g., Telekom, Bayer, E.on, Henkel) explicitly communicate to their employees that no emails should be answered in their leisure time. Volkswagen is taking an even more radical approach by switching off mail servers for smartphones overnight (Kaufmann, 2014). In fact, both sides—organizations and employees—can do their part. Managers should design the demands for their employees in such a way that there is enough time for rest after work. And every employee should also take care of himself/herself and search for adequate periods of rest. Even if many employees initially believe that they are doing the organization a favor by working overtime, this is—for both sides—a fallacy in the long term.

Implications for the Job/Task Sphere and Its Interactions with the Individual Sphere With regard to the positive effects of the core job dimensions of the Job Characteristics Model on flow, work design should realize skill variety as well as task identity. Tasks should further be complex and challenging, being in balance with the person’s abilities. In order to ensure such a balance, regular staff meetings (appraisal interviews) are suitable in which, among other things, the core job dimensions should be addressed; supervisors should discuss with their employees to what degree these job

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dimensions are fulfilled and how they can be improved. One question in these meetings should for example be: how do employees feel about the demands of their job? If the required workload is experienced as too high, it should be adjusted accordingly, or strategies to reduce stress should be jointly developed. If the tasks are perceived as too difficult, it is possible to face them with further training. It is worthwhile to adapt the demands to the skills and qualifications of the employees. People who regularly experience flow at work increase their performance in the long term; overburdened employees, on the other hand, reduce their performance in the long term and have an increased risk of stress-related illnesses and burnout. A certain amount of autonomy to manoeuvre and make decisions as well as feeling responsible for one’s own work results can help employees experience more flow. In the case of high demands, autonomy acts as a buffer so that flow can also occur in the case of increased demands. Appraisal interviews can also be used to underline the importance of the employees’ activities in the organization and thus strengthen the perceived relevance of their own role. During these appraisal interviews, it is also possible to discuss which tasks and activities are of special interest to the employees and to redistribute them accordingly—since interest also facilitates flow experience. Furthermore, the setting of clear goals and regular feedback is a prerequisite of flow. Therefore, target agreements as well as feedback should be part of the appraisal interviews. Only on the basis of clearly formulated, transparent goals can clear and constructive feedback take place. Here, setting goals and giving feedback should take place on a regular basis, not just once a year within the annual staff assessment procedure. It should be anchored directly in the culture of the organization and implemented as a consistent principle in all tasks. A positive and supportive attitude of the supervisors towards the employees is additionally conducive to flow. In this respect, leadership training for learning an authentic or transformational style of leadership can be recommended, which also specifically includes practice of how to conduct appraisal interviews with employees.

Implications in the Organizational/Social Sphere and Its Interactions with the Individual Sphere Overall, the social context in every organization plays a major role in the experience of flow at work. Mutual social support is relevant, which is why a climate of trust should be created in which cooperation pays off and collective performance is emphasized more than individual performance. To what extent a certain degree of competition can also be beneficial to flow has not yet been conclusively clarified. In any case, there should be time and space for fun among colleagues, as this provides refreshing breaks that keep one productive and increase the probability of flow in subsequent activities. To promote fun and trust among colleagues, team trainings, but also joint after-work/leisure activities are efficient tools. Also helpful is having a

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shared kitchen or another place away from the work place were joint breaks can occur. As authentic and transformational leadership styles were found to be flowconducive, giving training to supervisors and managers is a further promising approach to foster flow among employees. Furthermore, supervisors should apply management tools to provide flow-promoting contextual conditions (see above), such as clear goals, autonomy and feedback; those conditions should likewise be addressed in leadership training. It was also shown that supportive behavior and coaching from the supervisor fosters flow. An innovative approach that was developed together with Mihaly Csikszentmihalyi is the serious game called FLIGBY, which was designed to teach flow-based leadership skills (Buzady & Almeida, 2019; Marer, Buzady, & Vecsey, 2016). Last but not least, a climate of innovation and security should be promoted. This can be done by systematically assessing the status quo using established questionnaires, from which interventions to improve the climate can be derived in a second step with the participation of employees.

Directions for Future Research There are already meaningful empirical findings on flow experience in the work context. However, most studies are based on cross-sectional surveys, so that causal conclusions are often not possible. To validate the findings, longitudinal studies, studies considering dynamical patterns across time (Baumann et al., 2016; Ceja & Navarro, 2017), and experiments are required. In order to be able to derive statements on the flow experience in the work context on the basis of experiments, paradigms as close to work as possible should be developed and used. Besides these methodological issues, several implications for research have been proposed throughout the manuscript, and this section summarizes and extends those research ideas. What should be addressed in future flow research is the fit of a person’s attributes (e.g., personality, interests, motives and needs) with the attributes of the environment. First studies show that these individual attributes moderate if a person experiences flow, but far more evidence is needed to derive practical recommendations based on individual characteristics. In this respect, it is also likely to be flowpromoting if there is a fit of a person’s character strengths (Harzer & Ruch, 2013, 2015; Peterson & Park, 2006) with the possible strength use in a respective activity (Seligman, Rashid, & Parks, 2006) or job (Bakker & Van Woerkom, 2017)—this has however not yet been investigated. The lack of investigation concerning a fit between individual and environmental attributes also encompasses the social and organizational environment. Many possible interactions have been suggested in the section ‘Antecedents at the intersection between individual and organizational/social spheres’. One of them was to investigate how competitive situations affect flow. A special form of competitive social

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situation is negotiation. Negotiations are common situations in the work context— but currently nothing is known on their relationship with flow. Is it rather flowpromoting if negotiations are framed as joint problem-solving tasks or if they are framed as a competitive game? Possibly, again, personality may play a role here, particularly if a person rather has a competitive, egoistic motivation or a competitive pro-social motivation (De Dreu & Carnevale, 2003). Within social situations it was further found in the school context that flow is contagious and that there is a crossover of flow from teachers to students and from students to students (Bakker, 2005; Culbertson et al., 2015). Although it is likely that this crossover also works from supervisor to employee and from employee to employee, this remains to be tested. There is as yet scarce systematic knowledge about how different kinds of (workrelated) stressors relate to flow. Different (work-related) stressors have different psychological qualities and different outcomes (e.g., Lepine, Podsakoff, & Lepine, 2005). Thus, the effects of different stressors on flow should be distinguished to make clear recommendations regarding the prevention of stress in favor of a promotion of flow. This is of particular interest considering today’s working conditions with increasing work-related stressors such as time pressure, multitasking, and task difficulty. Studies explicitly investigating the relationship between those stressors and flow are inconsistent (task difficulty), very rare (multitasking, unfinished tasks) and/or merely exploratory (time pressure). Modern work is changing due to the increase of digitalization and automation and the development of artificial intelligence. Automation has many positive effects, for example it supports us by taking on simple or repetitive and therefore unpleasant tasks. At the same time, in many professions work becomes more complex and, as a consequence of automation, there are fewer possibilities to alternate complex activities with simple ones. In other professions, automation takes away the charm of core activities: for example for a pilot, the autopilot takes over many challenging tasks which actually make up part of the profession. Similarly, many people experience flow during driving a car. The use of automation and artificial intelligence in selfpropelled cars takes away the opportunity to experience flow during driving—be it driving as part of one’s job or during leisure time. Other occupational groups are also affected by artificial intelligence, be it mechanics (e.g., repairing modern cars increasingly involves computers instead of manual skills) or physicians (e.g., computer-based diagnosis of skin cancer). Accordingly, the professions as a whole will need to newly define their tasks and roles. When artificial intelligence is established in organizations, human needs and motives and the possibility to experience flow at work should be taken into account in order to maintain humane work environments. For example physiological flow indicators (cf. Peifer & Tan, Chap. 8) could serve to identify the subjective state during work (e.g., production work), and work characteristics (e.g., difficulty, task variety, etc.) could be automatically adapted to increase flow (Peifer, Kluge, Rummel, & Kolossa, 2020). These and similar new developments are an important field of flow-research in the years to come.

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General Conclusion The research on experiencing flow at work provides numerous interesting findings for organizations and their members. We know that flow has positive effects on the individual him/herself in terms of well-being and self-efficacy, as well as on the contextual and social conditions of employees. Importantly, we already know a lot about the antecedents for experiencing flow in the workplace. There are many and practicable approaches for holistic flow-conducive organizational development including work design, training, coaching, management tools and cultural change. Fostering flow helps to maintain and promote the well-being and productivity of employees in the long term and can thus make a significant contribution to ensuring the success of companies.

Study Questions • Which are the four sources of self-efficacy according to Bandura (1977) and how can they be addressed? 1. Performance accomplishment, can be addressed by positive mastery experiences 2. Vicarious experience, can be addressed by having role models who are good at something 3. Verbal persuasion, can be addressed by positive feedback and support 4. Psychological and physiological states, can be addressed by cognitive framing and relaxation techniques that help to adapt body signals of arousal or their interpretation so they are perceived in terms of confidence to cope with upcoming demands • Which are the components of psychological capital (Luthans et al., 2004) and how is psychological capital related to flow? Psychological capital was found to be positively related to flow. Its components are: 1. 2. 3. 4.

Self-efficacy Optimism Hope Resilience

• Which are the core job dimensions according to the Job Characteristics Model (Hackman & Oldham, 1975)? The Job Characteristics Model describes five core job dimensions of motivational work: 1. Skill variety

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Task identity Task significance Autonomy Feedback

• What are the three spheres in the “The Three Spheres Framework of FlowConducive Conditions”? Please report (at least) one intervention to foster flow in each sphere. 1. Individual sphere Potential intervention: positive feedback to increase self-efficacy 2. Job/task sphere Potential intervention: increasing autonomy 3. Organizational/social sphere Potential intervention: leadership training. Further interventions can be found in the section ‘Implications for Practice’. • Why should flow be promoted at the workplace? In our performance-oriented society, stress has become an increasing problem, as evidenced by rising rates of stress-related illnesses such as burnout or psychosomatic illness. This makes it all the more important to find a healthy path to performance that does not come at the cost of losses in well-being. All in all, taking care of employees’ well-being pays off for employees and companies: healthy employees are capable of sustainable, long-term performance, are more concentrated, have fewer absences and quit less frequently. Health saves high costs, which are caused, among other things, by absenteeism due to illness, new recruitment and training periods. Promoting flow is a promising solution, as it has been shown to have positive effects on well-being and performance as well as on other, work-related factors (compare section ‘Introduction’).

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Ullén, F., de Manzano, Ö., Almeida, R., Magnusson, P. K., Pedersen, N. L., Nakamura, J., et al. (2012). Proneness for psychological flow in everyday life: Associations with personality and intelligence. Personality and Individual Differences, 52(2), 167–172. Van Dick, R. (2017). Identifikation und Commitment fördern. Göttingen: Hogrefe. Van Katwyk, P. T., Fox, S., Spector, P. E., & Kelloway, E. K. (2000). Using the job-related affective well-being scale (JAWS) to investigate affective responses to work stressors. Journal of Occupational Health Psychology, 5(2), 219. Walker, C. J. (2010). Experiencing flow: Is doing it together better than doing it alone? The Journal of Positive Psychology, 5(1), 3–11. Weimar, D. (2005). Stress und Flow-Erleben. Eine empirische Untersuchung zur Bedeutung von Kognitionen, Emotionen und Motivation bei Lehramtsstudierenden, Referendaren und Lehrern. Berlin: Logos. Westman, M. (2001). Stress and strain crossover. Human Relations, 54, 717–751. Wolters, G., Luhmann, M., Bartzik, M. & Peifer, C. (submitted). Working productively under (positive) stress? Moderators of the relationship of multitasking and flow experience. Manuscript submitted for publication. Wolters, G. & Peifer, C. (2017). Weil wir die Herausforderung brauchen – Die Effekte von Multitasking auf das Flow-Erleben und die Leistung bei der Arbeit unter Berücksichtigung arbeitsrelevanter Persönlichkeitsmerkmale. [Because we need challenge – Effects of multitasking on flow experience and performance at work depending on work-relevant personality traits]. Talk at the 2nd Conference of the Geman Society of Positive Psychological Research, Trier, Germany. Wright, T. A., Cropanzano, R., Denney, P. J., & Moline, G. L. (2002). When a happy worker is a productive worker: A preliminary examination of three models. Canadian Journal of Behavioural Science/Revue Canadienne Des Sciences Du Comportement, 34(3), 146. Wrzesniewski, A., & Dutton, J. E. (2001). Crafting a job: Revisioning Employees as active crafters of their work. Academy of Management Review, 26(2), 179–201. Zhou, J., & George, J. M. (2003). Awakening employee creativity: The role of leader emotional intelligence. The Leadership Quarterly, 14(4–5), 545–568. Zito, M., Cortese, C. G., & Colombo, L. (2016). Nurses’ exhaustion: The role of flow at work between job demands and job resources. Journal of Nursing Management, 24(1), E12–E22. Zubair, A., & Kamal, A. (2015a). Authentic leadership and creativity: Mediating role of workrelated flow and psychological capital. Journal of Behavioural Sciences, 25(1), 150. Zubair, A., & Kamal, A. (2015b). Work related flow, psychological capital, and creativity among employees of software houses. Psychological Studies, 60(3), 321–331.

Chapter 12

Flow Experience in Human Development: Understanding Optimal Functioning Along the Lifespan Teresa Freire

, Keith Gissubel

, Dionísia Tavares

, and Ana Teixeira

Abstract In this chapter, we discuss the idea of flow experience in human development. We consider that the study of flow and its complexity should take a developmental and ecological framework into consideration since the experience of flow occurs in the interaction between the individual and his/her daily contexts. Besides this, we show how flow is, by its nature, anchored in developmental science and developmental psychology, contributing to the development of new skills and resources that help the individual to mature, grow and reach an optimal level of functioning. Research has shown that flow plays an essential role in buffering psychopathology and enhancing mental health and well-being. Hence, we discuss how it can be intentionally applied in psychological interventions to promote positive human development, presenting some relevant and recent therapeutic applications of flow. We also present the main findings on flow research across the lifespan. Despite being initially researched in adolescence and leisure, flow has been empirically studied across several contexts and populations (from infants to the elderly), using different methodologies (e.g. the experience sampling method). By summarizing the results for each developmental period, we identify the specificities concerning flow experience in each age group, considering the tasks, challenges and the contexts in which individuals are involved in their developmental stages. We can also see how the influence of external and internal factors in flow experience evolves and changes across development. Finally, the authors conclude with the relevance of studying flow experience and its applications within a developmental and ecological perspective in order to better understand and foster positive human development.

T. Freire (*) · K. Gissubel · D. Tavares · A. Teixeira School of Psychology, University of Minho, Minho, Portugal e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_12

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Flow Experience in Human Development This chapter aims to present the topic of flow experience and how it relates to human development in order to contribute to the understanding of optimal human functioning. Within the chapter, we will provide a broad and systematic overview crossing the two main topics of flow experience and human development, keeping our main goal of the underlying relevance of how flow experience and human development are matched according to the perspectives of developmental science and developmental psychology. As for flow experience, and in line with this book, we follow the original definition of Csikszentmihalyi (1975, 1990), being defined as an optimal psychological state where a deep absorption, full concentration, enjoyment and intrinsic motivation exist in the activity. Different kinds of definitions can be found in literature, emphasizing particular aspects or dimensions of the flow experience. For a deeper understanding of the concept and definitions of flow experience, see Engeser, Schiepe-Tiska, and Peifer (Chap. 1). Firstly, we will present some of the core developmental concepts and approaches to understand flow in development based on the ecology of human development. We will then focus specifically on the flow experience and its relation to developmental processes that produce human complexity, thus contributing to the understanding of optimal experience in human development. The emphasis on the optimal side of human development will be underlined along with the benefits flow experience theory can add to developmental psychology. Following the developmental perspective, a section on applications and interventions will show how the flow experience model and approach can be applied towards interventions aimed at promoting or sustaining optimal experiences and thereby contributing to the promotion of positive human development. A later section will be illustrating several empirical studies that demonstrate how flow research is applied in studies throughout the different ages. A lifespan approach will organize the presentation of these studies and their highlights to demonstrate how flow experience is related to the development of the human being, from birth to death. Finally, a conclusion section will highlight our main findings and reflections and will also open new insights and research issues for future research.

From Developmental Science to Flow Experience: Through the Lens of Ecological Human Development Human developmental processes and behaviors have always been a point of curiosity for both science and lay people. Questions about why and how we develop, why some people thrive while others struggle, or how external influences shape internal states, have been timeless. They are still largely unanswered, even though it has been

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part of scientific enquiry for over a century and philosophical enquiry for several millennia. From a scientific perspective, developmental psychology has been one of the main pillars inside the psychological domains interested in human growth and patterns of maturity. The developmental perspective has been the largest contributor to furthering a deeper understanding of the psychological and behavioral dimensions in human growth. Among countless contributions to our current knowledge, some impactful issues based on the ecological perspective on human development have been those on “nature vs nurture”, “continuity vs discontinuity”, and “stability vs instability” about human functioning (cf. Lerner, 2006). The pioneering work of Bronfenbrenner postulating an ecological perspective to understand human development (Bronfenbrenner, 1995) was expressed in his “bioecological model of human development” (Bronfenbrenner & Morris, 2006), defining development “as the phenomenon of continuity and change in the biopsychological characteristics of human beings, both as individuals and as groups” (p. 793), over the course of life and throughout historical time. Therefore, he defined four properties to characterize the bioecological model into the following: (1) process, (2) person, (3) context, and (4) time, making them the basic elements to analyze and to understand the diversity of developmental pathways. Several developmental researchers have focused on the lifespan theory, dealing with the study of individual development (ontogenesis) from conception to adulthood (Baltes & Reese, 1984), stating that “the basic premise of life span developmental psychology is that ontogenesis extends across the entire life course and that lifelong adaptive processes are involved” (Baltes, Lindenberger, & Staudinger, 1998, p. 1029). Although, in past decades, the study of human development has been exclusively under the umbrella of developmental psychology, today it is much deeper and broader and has become more multidisciplinary, with more scholars of human development starting to refer to their field as developmental science. In fact, in a recent issue of Nature devoted to the study of adolescence, Dahl, Allen, Wilbrecht, and Suleiman (2018), discuss the importance of investigating adolescence from the perspective of developmental science, reflecting its relevance in the actuality. There, they define developmental science as being “the study of the patterns and processes of biological, cognitive and behavioral changes that occur as an organism grows and matures” (p. 441). Previously, in the “Handbook of child psychology: Theoretical models of human development”, Richard Lerner (2006) presented a chapter on “developmental science, developmental systems, and contemporary theories of human development,” which discussed the theoretical evidence for a new perspective based on positive human development, presenting main assumptions to support our own chapter on flow experience in human development. The author embraces, highlights, and integrates the concepts and models associated with the developmental systems theories to assume a contemporary framework in understanding human development. By aggregating several main concepts highlighted by other authors in line with this framework, he defined the main features of developmental systems theories as the need for a relational metatheory, making the integration of all the levels of

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organization relevant within the ecology of human development. These levels range from the biological and physiological through the cultural and historical, influencing the regulation of development through mutually individual and context relations across ontogeny. These integrated actions constitute the basic unit of analysis within human development, making temporality and plasticity main aspects of human development, as well as diversity. In line with these concepts, Lerner states “the promotion of positive human development may be achieved by aligning the strengths (operationalized as the potentials for positive change) of individuals and contexts” (p. 3). Finally, he also emphasizes that multidisciplinary and interdisciplinary knowledge that supports the need for change-sensitive methodologies. The above concepts can directly assist our understanding of flow experience and its relationship with development throughout the lifespan. Flow theory is a holistic and broad perspective that integrates multiple levels of self-organization, emphasizing the importance of the level of subjective experience through the successive articulation between the individual and the tasks at hand (both in the moment and along several moments). According to Rathunde and Csikszentmihalyi (2006), the flow model itself is a developmental model. Understanding why and how the flow model fits a developmental framework is the main aim of this chapter, with all further sections contributing to this goal.

Flow in Development and the Experience of Human Complexity: Towards the Optimal Experience From an ecological perspective of development, flow experience has been deeply studied in order to understand human complexity (Nakamura & Csikszentmihalyi, 2009; Massimini & Delle Fave, 2000). In fact, flow experience is clearly associated with the concept of psychological complexity, understood as “. . . the self-regulative capacity to move towards optimal experiences by negotiating a self-environment fit that is integrated and differentiated, or a fit that achieves an optimally arousing balance of order and novelty” (Rathunde & Csikszentmihalyi, 2006, p. 472). Following this idea, growing in complexity implies a continuous reciprocal exchange of information between the individual and the environment. It is within this exchange and through repeated processes of integration and differentiation that psychological development occurs (Delle Fave & Massimini, 2005). Although a person’s behavior and personality can change within each passing moment, in different situations the sequence of seeking higher and higher levels of complexity can show us how there is a consistency within the variance, and it demonstrates how healthy development can proceed in an ever-upward direction of greater psychological complexity. Looking to the challenge-skill balance that characterizes flow experience and thinking of the disequilibrium signaled by boredom or anxiety, one can understand how optimal experiences cannot be recaptured through a regression of skills and challenges, but solely through their progression

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(Csikszentmihalyi, 1990; Csikszentmihalyi & Rathunde, 1993). This means the movement into continuously increasing complexity in terms of the person-environment fit and the achievement of optimal experiential states. The experiential perspective associated with flow has been a major contributor to developmental science, with scholars highlighting how this perspective can add new insights to the knowledge of developmental processes (cf. Csikszentmihalyi, 1990, 2014; Delle Fave, Massimini, & Bassi, 2011). For instance, “adopting this experiential perspective highlights core aspects of human nature that—if nurtured and allowed to flourish—provide opportunities to exert influence on the course of development” (Rathunde & Csikszentmihalyi, 2006, p. 469). These authors underline the fact that focusing our interest on one’s experience does not mean “taking the person out of the context”. On the contrary, it allows for a complementary view and understanding of the human condition, which proves useful for understanding how individuals could maximize their potential, given their biological predisposition and lived external conditions. The focus on the experience itself highlights the concept of subjective experience, which is strictly related to the study of flow experience in order to understand its (flow experience’s) optimal conditions to foster development (Massimini & Delle Fave, 2000). Subjective experience integrates both internal and external variables of daily experience, which means considering individual cognitive, affective and motivational (internal) processes, on one side, and places, companionship, and activities one is involved (external), on the other side. From the point of view of an individual’s developmental path, by integrating internal and external aspects of the experience, the individual develops their self-regulation for development, determining the quality of their subjective experience at the moment (daily-based), and also along the entirety of their lifespan. For Massimini and Delle Fave (2000), the concept of subjective experience is strictly associated with the one of psychological selection. From a bio-cultural perspective, this is fundamental to understanding the role of flow and optimal experience in individual development. These authors stated that optimal experience promotes individual development because of the replication process involved when searching for increasingly complex challenges in the associated activities that, in turn, improve their skills accordingly. They define this process as “cultivation”. Therefore, optimal experience fosters the growth of complexity, not only in the performance of flow activities but also in individual’s behavior as a whole. The lifelong process of psychological selection, centered on the preferential replication of optimal experience and associated activities, results in the individual’s life theme, as defined by Csikszentmihalyi and Beattie (1979). The positive developmental side of flow is all about the concept of optimal experience. In literature, optimal experience has been reported as one of the most positive and meaningful experiences an individual can have in their daily life. Several studies focus on the antecedents and consequences in the daily life of flow and its outcomes, using the Experience Sampling Method (ESM; Hektner, Schmidt, & Csikszentmihalyi, 2007; Larson & Csikszentmihalyi, 1983; Reis, 2012). The ESM is a real-time measurement used to assess and investigate the subjective experience of individuals in relation to their lived milieu. Real-time measures, in

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contrast with retrospective ones, deeply investigate the characteristics of daily experience, increasing knowledge about flow and its role in development. Although methodological aspects are not an aim of this chapter, measurement issues about flow experiential dimensions is a current topic. Although used synonymously in literature, flow and optimal experience need to be deeply analyzed in terms of their conceptual meaning. Recently, Freire (2013) discussed this topic, stating that “flow experience” corresponds to an experiential state occurring during the course of the action, whereas “optimal experience,” is about the representation of the action in the individual’s socio-cognitive map. For the author, “only when the individual becomes aware of this action and it is represented in his/her socio-cognitive map, it is possible to speak about optimal experience” (p. 60). This kind of differentiation between flow and optimal experiences has great implications for intervention designs, but they are also relevant to explain different empirical results associated with flow/optimal experiences and its characteristics when using real-time or retrospective measures. Thus, for the author to understand how experiential states become represented, appraised, interpreted, and incorporated in the self, from the course of the action/experience to socio-cognitive processing, is still an open research issue. Nevertheless, a consensus does exist about the many kinds of benefits that optimal experience can have in the individual-environmental relationship. Flow experience can optimize individual growth along the life course, contributing to worthy lives that connect the self within lived life contexts and environments. This is why flow experience embodies the concept of developmental trajectory—the path along which one progresses from the past and into one’s future—a main concept in developmental science. When considering developmental concepts along with an experiential perspective, we can then ask about the course of momentary experiences, building a trajectory where the self acquires meaning as a person, which underlines the development into personhood, a concept assumed by Rathunde and Csikszentmihalyi (2006). This concept refers to the developing person or the process of becoming a person. For these authors, higher levels of personhood are possible, with people acquiring different qualities at different moments in life. They highlight the process of becoming a person as being complex and varying across times and places in the life cycle, with the social practices being facilitators or obstacles of this process.

Applications: Psychological Interventions to Foster Flow and Optimal Experience An overview of flow experience in development makes a discussion about what is happening below the surface of development and beyond its normative line relevant. This means that, moving in one direction away from the normative line, we can reach

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Potentialities Strengths Flourishing

Fig. 12.1 Understanding ecological human development: The experiential path between psychopathology—normative—flourishing

the area of the disorders, deficits, or psychopathology; and, in the opposite direction, is where potentiality, strength, and flourishing occurs (see Fig. 12.1). Traditionally, the focus of psychology was leaned in the direction of deficits and disorders. In recent decades, the perspective to understand the entire and continuous life-outline is increasingly becoming empirically supported and discussed (Nakamura, 2011; Seligman & Csikszentmihalyi, 2000; Wood & Tarrier, 2010). Life moves back and forth along this spectrum, integrating negative or adverse aspects with positive and optimal conditions, both internally and externally. Developmental studies have taken the lead in understanding the successive levels of human complexity, integrating biological, cognitive, emotional, and behavioral components of human functioning, along with contextual and cultural components. These studies ask for an integrative perspective as is the actual ones on developmental psychopathology (Kerig, Ludlow, & Wenar, 2012; Pollak, 2015) and positive development (Tolan, Ross, Arkin, Godine, & Clark, 2016; Ungar & Lerner, 2008). The reciprocity of these processes underlines the need to understand the normative developmental processes, as well as to understand how its absence or its disordered existence leads to the onset or reinforcement of psychopathology, compromising an individual’s health development; or how flourishing processes can be informative of potentialities, also becoming guidelines for promoting normative or preventing psychopathological pathways. Figure 12.1 illustrates and stresses the dynamic and permeability of these processes (from the negative to positive sides, and vice-versa). From a developmental point of view, this is actually about understanding typical or atypical, healthy or unhealthy, positive or negative developmental trajectories, where the optimization of developmental conditions and outcomes (internals or externals) emerge as fundamental in understanding the human condition. In this framework, interventions are a main part of the whole picture. According to developmental systems theories (Lerner, 2006), interventions to enhance the character of a human’s developmental trajectories are important and make achieving positive human development possible by aligning the strengths of individuals and their contexts. Flow experience can be conceptualized as either an intervention tool or as an aim or outcome of the intervention process, targeting either the individual or the context. In this line, Nakamura & Csikszentmihalyi (2002, p. 99), distinguished between interventions seeking to shape activity structures and environments, in order to foster flow or obstruct it less, and those interventions

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attempting to assist individuals in finding flow. Both types of interventions mirror the ecological perspective of human development by considering both internal and external aspects of individuals’ lives. Keeping in mind the experiential path in between psychopathology, normativity and flourishing, it becomes relevant to look at the role psychotherapy plays in assisting individuals in the process of finding flow in their lives. According to this perspective, and adopting a developmental framework, there has been a clear amount of work on a variety of therapies that follow a developmental flow-based perspective. Initial studies focused on developing therapeutic interventions aimed at transforming the structure of daily life toward a more positive experience. These studies used the ESM under the scope of flow theory as a tool for identifying patterns in everyday experience with the possibility to monitor one’s success in transforming these patterns (Delle Fave & Massimini, 1992; Inghilleri, 1999; Massimini, Csikszentmihalyi, & Carli, 1987). These topics are still present in recent clinical studies (Freire, Teixeira, Silva, & Matias 2015; Teixeira & Freire 2020). The well-known flow contributions to individuals’ well-being, positive affect, self-esteem, happiness, quality of life, life satisfaction and personal growth make its inclusion in psychological interventions relevant (Csikszentmihalyi & Csikszentmihalyi 1988; Csikszentmihalyi & Hunter, 2003; Hektner & Asakawa 2000; Nakamura & Csikszentmihalyi 2002). Psychological interventions provide a safe and ideal context to identify and foster flow, contributing to the development of autotelic skills that, in turn, will enhance mental health and prevent relapses (Riva, Freire, & Bassi, 2016). As an example, the Flow Therapy (Riva, Rainisio, & Boffi, 2014) and the Optimal Functioning Therapy for Adolescents (OFTA, Teixeira & Freire, 2020) in the therapy setting, and the Challenge: To be+ (Freire, Lima, Teixeira, Araújo, & Machado, 2018) in a group intervention setting, use the experience of flow both as an intervention tool and an outcome. More specifically, these interventions aim to identify flow experiences and flow inducing activities in individuals’ everyday lives; to find opportunities for flow in daily life contexts (peers, family, leisure, school); and to increase the frequency and quality of the experience to practice skills. In OFTA, adolescents learn how to gradually increase challenges and complexity in flow activities to develop new skills and improve performance (Teixeira & Freire, 2020). The use of flow as a tool aims to buffer psychopathological symptoms and improve optimal functioning and mental health (Riva et al., 2016; Teixeira & Freire, 2020). In Flow Therapy, flow is also conceptualized as a therapeutic experience that may be shared between the therapist and the client during the therapy session. In this therapy, the therapist learns how to promote and share flow with patients during sessions, improving the therapeutic relationship (Riva et al., 2016). In another therapy, specifically in the Positive Psychotherapy (Seligman, Steen, Park, & Peterson, 2005), flow is considered an outcome to be achieved but not as an intervention tool. According to Seligman, Rashid, and Parks (2006), engagement and flow can be promoted through the identification of signature strengths (individual essential strengths and talents) and by finding opportunities to often use these strengths in daily life, while in various life contexts (school, work, leisure). Hence,

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flow occurs when individuals use their strengths and talents to meet and overcome perceived challenges. All those therapies and interventions integrate an ecological and developmental perspective where the individual skills, the environmental factors and the interactions of the two are used as needed resources to achieve healthy developmental patterns and positive life trajectories. The use of flow principles allows interventions to be reoriented towards building on interests, strengths, and thriving, taking advantage of the personal growth that attends flow experience, and thus enabling the individual to reduce or transform psychopathological symptoms or life patterns, as a consequence of his/her growth/ complexity associated to the emergence of flow experiences (Nakamura & Csikszentmihalyi, 2002). The emergence of flow experience in daily life through intervention processes allows the possibility to foster optimal functioning, and thus to improve better social structures and conditions aligned with the development of human complexity.

Flow Experience Across the Life Span: Empirical Findings For the past few decades, a growing body of research has been studying flow experience in various ages of life and various contexts and populations. Although the initial studies on flow were with adolescents (Csikszentmihalyi & Larson, 1987), researchers have been centering their attention more and more on the rest of the developmental lifespan, from infancy to the elderly. In the following sections, a brief introduction about major developmental issues of each age range will be presented, followed by the related empirical findings on flow experience. At the final of each age section, we will present a summary of the associations found and conclude with some observations about the methodologies used for that age group. The method used to classify the participants into one age range over another was based on either how the author(s) explicitly classified them in the article, or wherever they best fit, given the traditional classification of ages.

Infants, Toddlers and Children The main developmental concerns during these early years of life are exploring the world, oneself, and, towards the end of this age group, the beginning of formal education. Some developmental and personality theorists find this time to be the most influential and most important in our developmental trajectories. The studies included in this age period cover ages that begin in the first years of life and extends approximately to 15 years old. The few studies that have looked at flow experience during this developmental time-period have focused on domains such as exploration, education, computerbased activities, or a mix of them all (e.g. Custodero, 2005; Inal & Cagiltay, 2007).

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In these studies, researchers have found that specific educational techniques, materials or technology can influence the experience of children in educational settings. Inal and Cagiltay (2007) found that some elements of games, such as challenge and complexity, had a significant effect on children’s flow experiences and that flow tended to occur more among children that were playing a game as a group. Issaka and Hopkins (2017) found that music technology and specialized pedagogy in music education led to a better quality of experience in pediatric patients, namely more engagement and enjoyment, both characteristics associated with flow experience. Custodero (2005) considered flow experience a framework for observing and analyzing musical engagement in infants, toddlers, preschool, and school-aged children. She observed different developmental trajectories or age-based variation for observable flow indicators such as self-assignment, focused and controlled gesture, expansion of challenge, and awareness of adults and peers. This suggests that young children use age-relevant strategies to engage with music. These results showed that learners are agents of their own growth by using particular engagement strategies and that the ability to engage in an activity to the point of flow experience requires a combination of temperament, environment and developmental trend (Custodero, 2005). Another study on children in a musical activity, conducted with 3 and 4 year-olds, showed that flow experience at this age was influenced by both individual characteristics, such as whether the child was shy or more outgoing, and the nature of musical activities (Chen-Hafteck & Schraer-Joiner, 2011). These results were found in both typical-hearing children and deaf children, suggesting that flow experience, even for disabled children, is associated with the development of skills and creativity. In a study with middle school students, autonomy-supportive teaching resulted in higher intrinsic motivation and flow experience, when compared with a more controlling-teaching behavior (Hofferber, Basten, Großmann, & Wilde, 2016). However, the effect varied slightly depending on how interesting the lesson was to the children. Additionally, some studies investigated the use of online or digital educational games for fostering a better quality of experience during various learning processes. Results showed that flow experience enhanced the students’ learning of traditional Chinese in an online game (Hong, Hwang, Tai, & Lin, 2017), the learning of mathematics using a specific operational “learnware” (Sedig, 2007), and the students’ creativity and manual skills when using a digital game-based environment versus a traditional classroom environment (Hsiao, Chang, Lin, & Hu, 2014). Another study found that flow experience has a significant positive direct effect on the attitude towards the use of social network systems at this young age (Kristianto, 2017). When it comes to excelling in music, it was found that genuine interest and willingness to practice can foster a child’s autotelic behavior (a known precursor to experiencing flow in later ages), resulting in progress of their musical skill (Valenzuela & Codina, 2014). Some studies have focused on the different traits and contextual factors that can influence or are associated with flow or optimal experience. Mesurado (2009), and Mesurado, Cristina, and de Minzi (2013), for example, conducted two empirical studies with children, aged from between 9 to 15 years old, and found that the child’s

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perception of a healthy relationship with his/her parents (one with acceptance and moderate control) indirectly affected flow experience via the child’s personality traits: specifically, extroversion, openness to experience, and conscientiousness. Mesurado et al. (2013) were also able to identify that a child’s perception of having an authoritarian or negligent relationship with his or her parent(s) has a direct effect on the personality trait neuroticism, which in turn has a direct negative effect on flow experience. In addition, structured tasks were associated with higher levels of optimal experience, while performing the tasks alone had a detrimental effect on flow experience at this age (Mesurado et al., 2013).

In Summary In this age group, we see the beginning of a trend of focusing flow experience studies on education, playing, and/or learning. Learning is particularly important at this age group since it incorporates the timespan between which humans are acquiring language and the time when they first begin school. The above studies reported a large set of dimensions related to flow experiences, such as intrinsic learning, creativity, and manual skills, and interest and willingness to practice enhanced autotelic behavior. Perceived relationship with parents in this group has a direct relation with some of the personality traits that appear to be directly related to flow experience. Additionally, individual characteristics, environment, an individual’s developmental trend and structured activities, such as playing musical instruments and educational games, also appear to be related to flow experience. Methods and measures of flow experience used in studies with this age group are mainly observational methods for infants and toddlers, questionnaires, and interviews for children.

Adolescents The time of adolescence is a major transitional period physiologically, neuronally and psychologically, and should be seen as a time of strength and resilience (Dahl, 2004; Srivastava, Tamir, McGonigal, John, & Gross, 2009; Worthman & Trang, 2018). The period of adolescence is lasting longer today than it ever had in history. Besides that, it is much more likely for an adolescent to engage in potentially risky behaviors or make choices that could affect health consequences later in life than at any other period in development (e.g. Srivastava et al., 2009). Therefore, it becomes important to study this age group in relation to developmental science (Dahl et al., 2018). The participants’ age of the studies included in this section range from approximately 13–20 years old. Delle Fave and Massimini (2005) investigated how the characteristics of optimal experience and apathy differed by age, culture, and life conditions. The authors

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found no differences between adolescents and the other samples (young adults, adults, disabled adults, Italian and Nepalese) regarding the features of these two subjective experiential states. A stable cognitive core (i.e., concentration and sense of control) characterized optimal experience and apathy (although in opposite directions), around which some affective and motivational dimensions (i.e., happy, goals, wishing to do the activity) varied widely across daily activities. Rathunde and Csikszentmihalyi (2005) also conducted a study using the ESM with early adolescents and then compared the results between a traditional middle school and a Montessori school. The results of their study revealed positive support for the Montessori students, who reported more instances of flow experience, higher positive affect, higher engagement and intrinsic motivation while involved in academic work. Other studies on what inhibits flow experience in students while in school found that student disengagement was promoted by a reported lack of challenge or meaning, as was frequently reported during the traditional lecture-formatted lesson (Shernoff, Csikszentmihalyi, Schneider, & Shernoff, 2003). Another study from Bassi and Delle Fave (2012) examined adolescents’ levels of self-determination during schoolwork activities when classified as optimal (high challenges and high skills) or non-optimal. The authors found that perceived autonomous regulation (“I wanted to do it”) that is considered to be high self-determination, is not an essential aspect of schoolwork as an optimal activity; and that controlled regulation (“I had to do it”) classified as low self-determination can be associated with flow experience. However, the best quality of experience when doing schoolwork as an optimal activity occurred when adolescents reported moderate to high levels of selfdetermination. A study of Korean high school students (Choe, Kang, Seo, & Yang, 2015) identified several factors that the students said were hindrances from being able to experience flow in learning. Some of these hindrances were sleepiness, conflicts with others, academic pressure (from others and themselves), and a messy room. In music education research, Freer (2008) found that middle school students reported higher levels of flow during choral rehearsals when teachers exhibited greater use of instructional scaffolding language. Freire, Tavares, Silva, and Teixeira (2016) compiled research that showed that adolescents reported school activities as not being related to having flow experiences as much as when they were engaged in a leisure activity. Paradoxically, however, those school activities were also reported as being more important to their future goals in life than leisure activities, which in turn were not rated as being important for their future goals. Studies unrelated to formal education are fewer in number but still have a large impact on flow literature and on understanding the multidimensional relationship between flow experience and adolescent development. A study on adolescent relationships with their parents (Rathunde, 1997) revealed that greater complexity and quality of interactions between adolescents and their parents resulted in higher frequencies of interest and flow experiences, as well as potentially several other positive developmental qualities and identity shaping traits, such as conversation complexity, self-reliance, stronger identities, role-taking skills, superior ego development, and higher moral reasoning. One study that focused on the psychological

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health of nearly 600 adolescents (Albert-Lőrincz, Albert-Lőrincz, Kádár, Krizbai, & Lukács-Márton, 2011) showed that psychological immune systems, or the ability to protect oneself from internal or external psychological harm, complements and enables flow experience. Bassi, Steca, Monzani, Greco, and Delle Fave (2014) found that openness to experience is the sole personality trait correlated with flow experience among adolescents, and the higher the frequency of flow, the higher their satisfaction with life, hedonic balance, and psychological well-being was. To that end, those same authors (Bassi et al., 2014) recognized that personality traits, in general, are becoming more of a stable and permanent part of the individual by this age. Besides, parents and educators should be encouraging adolescents to be more open to new experiences, as this could lead them to more flow experiences and a more positive developmental trajectory. Other studies on social media showed that some elements of flow experience on Facebook, namely, concentration (i.e., complete immersion) and playfulness (e.g. happiness, satisfaction, excitement, and hopefulness), resulted in higher online regret experience of adolescent users aged between 13 and 18 years (Kaur, Dhir, Chen, & Rajala, 2016). Yang, Lu, Wang, and Zhao (2014) also showed that flow experience leads to more exploratory behavior on the internet but also increase high school students’ addiction to internet use. Another study demonstrated that the internet café social environment (e.g. talking to each other and helping other players while playing) and some gaming characteristics (e.g. game genre, goal, and visual appearance) increased the flow experiences and motivations of adolescents (Kara & Cagiltay, 2013). More recently, Tavares, Freire, and Faria (2019) investigated how some cognitive and emotional internal psychological states (i.e. challenge-skill perception, effortless attention, positive affect and negative affect) interacted with contextual features in explaining adolescents’ daily optimal experiences. These authors found that perceiving more high challenges and high skills, and having more positive affect, had a positive influence on optimal experience. Experiencing more negative affect was negatively associated with optimal experience. Results also showed that contexts such as the type of activity (leisure, school activity or socializing) or the type of company (family, friends or alone) not only had a direct influence on adolescents’ optimal experience but also moderated the associations between internal psychological states and optimal experience. Moreover, these authors presented preliminary evidence about the possible mediating role of effortless attention on the relationships between internal states and optimal experience. In another recent study, Clementson (2019) analyzed the young adolescent (middle school) flow experiences using a mixed method approach. The author was interested in identifying the factors that contribute to experiencing flow in a music band. Results showed that both internal dimensions, such as self-determination, and external dimensions, such as activity type (in this case, rehearsing a concert repertoire) independently predicted flow experience. Despite this, some results from ESM and qualitative data were contradictory, suggesting that flow is an individualized experience and that younger adolescents may not conceptualize flow as their older adolescent counterparts.

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In Summary In the sample of studies reported here, the factors that are associated with flow experience and that contribute to the development of adolescents are a stronger psychological immune system, a more complex relationship with one’s parents, autonomy in learning, the acquisition of new skills, higher self-esteem, and openness to new experiences. Also reported as factors, were higher positive affect, higher engagement and intrinsic motivation while involved in academic work, and engagement in leisure. Disengagement was found for those who had a lack of meaning and challenge, more extrinsic motivation, were sleepy, had high academic expectations and conflicts with others. Other associations with flow experience that were not related to education were stronger identities, superior ego development, high selfesteem, low anxiety, low negative affect, search for a future career, high levels of engagement, effortless attention, satisfaction with life, hedonic balance, and psychological well-being. Research also showed that the cognitive core of flow experience seems to be more stable than the volitional components of affect and motivation, which are more sensitive to contexts. This age group introduces the use of the experience sampling method in several studies. This method and retrospective self-reports are the most widely used for this age group. Less commonly used is the interview method.

Adults The adult age group in developmental research sometimes includes university students, and we did the same in the current chapter, specifically when the authors reported the sample as being an adult sample. Studies included used participants ranging in age, from 17 to 60 years old. Since there are university students included in some of the samples here reviewed, we can therefore expect to see a continuation of the focus on education and its relationship to flow experiences. We also find, however, a large focus on flow experience in the workplace. Just as school dominates the time of an adolescent and the young adult, the workplace is where the majority of adults spend their time, and it is, therefore, possible for the workplace context to make a significant impact on the development of the individual. In studying university students, Asakawa (2010) found that high self-esteem, low anxiety, a use of a variety of active coping strategies, active commitments to college life, search for a future career, high levels of engagement, and concentration in daily activities were all found in students who experienced more often flow in their daily lives. In a previous study, Asakawa (2004) had also found that, between the autotelic and non-autotelic participants, the autotelic ones were those who sought higher challenges that matched their skills. They also were focused for longer on the tasks that invoked the flow experience, were able to extract more meaning from

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experience and also made sure the challenges were increasing as their skills developed. In a study about online learning environments, a mostly adult student sample reported that flow experience is directly related to using good and effective learning strategies, and, when mediated by satisfaction, it can increase learner retention (Lee & Choi, 2013). If the students are studying alone, they are more likely to report having flow experiences, as are those who are working, solving tasks, and engaging in a creative activity alone (Magyaródi & Oláha, 2015). These same authors also found that if they are in a social situation, and they perceive a high level of cooperation happening, then flow is more likely to occur when engaged in a work and sport activity. Moreover, recent research has shown that flow is also positively associated with an adult’s social identity through the participation in self-defining activities (Mao, Roberts, Pagliaro, Csikszentmihalyi, & Bonaiuto, 2016), and that the flow experience, in this case, will have a larger impact on the complexity of their social identity than it will on their personal identity. Bonaiuto et al. (2016) have found that flow experience was also positively and significantly associated with place identity. Results suggest that, through participation in self-defining activities, adults can develop a sense of place identity that may help them in attaining flow experience. However, future research should test directionality in these associations. In a diary study of self-control demands, flow experience and well-being in the workplace revealed that adults who reported higher daily levels of flow not only had higher overall well-being, but it was also effective at buffering the negative effects of high self-control demands—a common stressor in the workplace (Rivkin, Diestel, & Schmidt, 2016). Ilies et al. (2017) performed a study where they supported the previous findings of regular flow experiences at work. Those who had flow experiences while at work were also more often engaged in the planning, problem-solving and evaluation of activities (Nielsen & Cleal, 2010) and would also report a higher quality of life than those who did not report having flow experiences (Csikszentmihalyi & LeFevre, 1989). The results in this previous study, however, were moderated, at least partially, by the personality trait openness. In a recent study, Peifer, Syrek, Ostwald, Schuh, and Antoni (2020) showed that a high level of unfinished tasks is negatively associated with flow experience and wellbeing at work/study, while low to medium levels of unfinished tasks were not associated with flow. It seems that finishing tasks during the day can be a facilitator for having flow experience at work and non-work activities and, in consequence, it can improve adults and college students’ well-being. Unrelated to education or work environments are the pursuits of more leisurely activities. Harmonious passion, versus obsessive passion, was positively related to flow experience while participating in an activity that one particularly enjoys (Carpentier, Mageau, & Vallerand, 2012). Moreover, these researchers found that flow during that activity was shown to increase the individual’s overall well-being. Nature hikers in Korea reported experiencing more flow experience when they had a higher level of leisure involvement (Cheng, Hung, & Chen, 2016), and this increased their feelings of self-expression and pleasure. They also reported a higher psychological commitment to the activity that mediated the relationship between leisure

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involvement and flow experience. In the field of radio broadcasting, researchers also found that the concept of flow can be useful to understand why an audience continues listening to a radio program. A study by Ruth, Spangardt, and Schramm (2017) found that musical complexity and familiarity of the songs were associated with higher levels of flow experience and a positive appraisal of radio listeners. Moreover, the more flow they have experienced, the more they have enjoyed the radio program. In addition, listeners with more musical skills had experienced more flow when the complexity of the program was high. Research also shows that flow experience while using gaming devices for adults also has healthy developmental effects. Such effects were found by Barry, van Schaik, MacSween, Dixon, and Martin (2016), who reported that adults who used body movement-controlled exercise games (such as games using a Nintendo Wii or XBOX Kinect) had higher positive feelings and achieved more of Csikszentmihaly’s (Csikszentmihalyi, 1990) nine dimensions of flow than did adults who performed similar exercises while at a traditional gym. This exergaming tool could be a very useful alternative for rehabilitation (Barry et al., 2016), physical interventions and exercise promotion (Thin, Hansen, & McEachen, 2011), and even as a treatment supplement for people diagnosed with multiple sclerosis (Robinson, Dixon, Macsween, van Schaik, & Martin, 2015). Research also focused on the negative aspects of flow experience, specifically in online gambling (Trivedi & Teichert, 2017). These authors found that specific dimensions of flow influenced online gambling addiction in different ways: a sense of control and concentration influenced it negatively, whereas the transformation of time and autotelic experience influenced it positively. Another study in online gaming found that achievement and immersion motivation are crucial for players to attain a higher level of flow experience (concentration, time distortion, and telepresence or loss of self-consciousness) while playing an online pet game (Li & Luh, 2017). A greater effect on flow was found when the players had more than one type of game motivation.

In Summary Formal education and the workplace are well researched in this period of life with regards to flow experience. When adults were alone, it was observed that flow experiences were more likely to occur if they were studying, working, solving tasks, or engaging in a creative activity. In the workplace, flow experiences contributed to adults’ overall well-being, the buffering of negative effects from high selfcontrol demands, while planning, problem-solving or evaluating activities, and it also contributed to perceiving a higher quality of life, job satisfaction, and autonomy. Research also has shown that some circumstances in work or study, such as unfinished tasks are negatively associated with flow experience. Once again, we also see the mention of the personality trait openness as being a moderator. Some research also highlighted the autotelic personality trait as an important factor for experiencing more flow. A more recent line of research showed that flow experience

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was related to an adult’s social and place identity through participation in selfdefining activities. Finally, when adults were using body movement-controlled games (which is effective as a rehabilitation aid, physical interventions, and for people diagnosed with multiple sclerosis), they had high positive feelings and achieved more of the dimensions of flow than someone doing the same exercises in a traditional gym context. However, some research also showed some detrimental aspects of flow experience, specifically in online gambling addiction. Methodologically, the studies mentioned above used either the ESM or retrospective self-reports.

The Elderly As we move out of the period of adulthood and enter into the final developmental years of life, research on development is mostly focused on free or leisure activities and also on taking care of one’s health. To date, there has been very little flow experience research that has focused on the older adult or elderly population. Those that exist relate flow experience with leisure activities, health quality and “active aging,” as these are the main concerns and focuses at this period of life. Studies included here used samples aged from 55 and above. Hirao, Kobayashi, Okishima, and Tomokuni (2012) found that the physical health of elderly Japanese was significantly better for those who reported being more often in either a flow state (high challenge, high skills) or a “relaxed” state (low challenge, high skill) during daily activities, as opposed to those who rated more often being in an “apathetic” state (low challenge, low skill). Marston, Kroll, Fink, and Gschwind (2016) used an exergame system called “iStopFalls” with games that are specifically geared towards older adults to produce flow experience and help increase physical activity to prevent age-related physical impairments, such as falls in the home. Results showed a positive trend in the measure of flow and enjoyment of the experience in two of the three cities where this experiment was run. Other research has analyzed flow experience in the everyday lives of this age group. In a study that focused on the leisure activities for retirees, Chang (2017) found that those who engaged in “serious leisure” activities (ones that are considered meaningful and fulfilling, as opposed to watching TV or listening to music), and particularly those activities that were done alone, were more likely to elicit flow experiences. One study showed that retirement was negatively associated with flow, meaning that people who still are working experienced flow more often than people who are not working (Heo, Lee, Pedersen, & McCormick, 2010). However, when flow for a retiree did occur, it was most likely going to be in the context of their home, which is where they also reported spending most of their day. Contrary to previous findings, Heo and colleagues found that flow experiences did not occur during serious leisure activities, but only during casual leisure activities, such as watching TV. This type of activity was referred to as a micro-flow activity, or an “ordinary, unstructured”

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activity, where flow is experienced. To these contradicting data, Heo and colleagues suggested that the activity itself does not weigh as heavily as does the meaning someone places on it. Also, in another study, if the senior was given a choice in their activities and were given goals to set for that activity, they were likely to place more meaning on that activity, and therefore were more likely to experience flow during that activity, no matter what the activity was (Myllykangas, Gosselink, Foose, & Gaede, 2002). Another study found that flow experience, combined with the Japanese concept of IKIGAI (loosely translated as “reason for living”), positively predicted the combination of comprehensibility, manageability and meaningfulness in older Tai-chi practitioners. This result persisted even after a year in a follow-up study (Iida & Oguma, 2013, 2014). Finally, Collins, Sarkisian, and Winner (2009) also explored flow experience in daily life experiences but turned the focus on its relationship to mood. Results of this study showed that flow was positively associated with high-arousal positive affect and negatively associated with low-arousal negative affect, and their findings also linked more flow experience to higher life satisfaction. Kim, Lee, and Bonn (2017) found that flow experience had a strong effect on subjective well-being and purchase intention in seniors using mobile social network sites. They also showed that intrinsic motivation (enjoyment and self-efficacy) had more influence on seniors’ flow experience than extrinsic motivation (usefulness and social interaction). These results highlighted the importance of attending to the subjective experience of this population to understand their consumer behavior within the context of tourismrelated products, services, and activities.

In Summary One important area related to flow experience in this age group concerns the achievement of better physical health. Retirement seems to be negatively associated with flow experience frequency when compared with the actively working life period. However, those retirees who did experience flow reported higher positive affect and higher life satisfaction than those who did not. Moreover, they reported flow more frequently when at home and when performing serious leisure activities, although some contradictory results showed more flow in unstructured leisure activities. Additional studies found that more than the characteristics of the activity itself is the meaning attributed to the activity that is central for attaining flow experience in this age, along with having the possibility of choosing some features of the activity and setting goals for it. A more recent research interest concerns seniors’ use of mobile social network sites. Results in this field showed that flow experience could be an important factor explaining consumer behavior within this developmental period. The methodology used most often for this age group is the experience sampling method, though one study used the daily reconstruction method. The other studies tend to use traditional retrospective self-report measures.

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Conclusion The main goal of this chapter was to present some of the core developmental concepts and approaches to understand flow in development and flow as development, based on the ecological perspective of human development. For this, we discussed main concepts anchored in the approaches of developmental science, developmental psychology, and the experiential perspective associated with the flow theory. Throughout this chapter, we aimed to better understand how flow experience encompasses the relations between the individual and his/her environment, how contextual conditions determine individual characteristics to achieve a process of sequential complexity, and how flow and optimal experiences, because of these characteristics, sustain and produce a movement into positivity, expressed in optimized life trajectories. This complex theoretical framework made the role of interventions to enhance positive human development through flow or by promoting flow relevant. This means that, to facilitate the internal (individual) or external (contexts/ environments) conditions for development to occur, maximizing strengths and potentialities along life is a key point. The other main aim of this chapter was to explore the current studies on flow or optimal experience across the lifespan, from early to late ages. Of note, the fact that the empirical literature mentioned here, and the studied variables associated with flow experience, are not meant to be exhaustive. They are a sample drawn from literature with no systematic criteria or set inclusion/exclusion list other than being studies that focused on flow experience within a specific age group, with relevance in terms of the developmental characteristics for the individual(s). From a lifespan perspective, we can conclude that, in fact, flow is experienced differently across all ages, and that the role of the contexts (in terms of opportunities, tasks and activities, or relations), as well as of the individuals (based on multiple aspects, such as biological, cognitive, affective and motivational), is also changing and weighing differently at each age group. We can see how, in early life, the main focus has been about learning, playing, cooperating, and becoming more autonomous, while, in adolescence, aspects on personality, personal complexity, and psychological resources, throughout school and leisure, become most important. In adulthood, well-being, self-expression, and work in relation to other areas of life become more relevant variables. However, in the later years of life, the focus turns to leisure time, meaning-making and health. Intrinsic motivation, concentration, and engagement seem to be consistently present in flow experiences across development, as well the influence of personality traits (e.g. openness to experience). The overview of the studies across the different developmental periods was one of the main novelties of our chapter, though it is still grounded in a cross-sectional aggregation and comparison of the studies. Our afterthoughts make us now look to the future of research on flow and developmental science. The Experience Sampling Method (ESM) appears as an effective assessment tool, being a common method used in the sample of studies

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presented in this chapter, though mainly from early adolescence and into the elderly. The ESM allows intensive longitudinal studies to be performed (Bolger & Laurenceau, 2013) by collecting within-subject information along a brief but intensive period of time (e.g. a week) and combining it with momentary situations. Although the benefits of these methods are clear in literature (cf. Mehl & Conner, 2012), longitudinal studies in flow that include intensive repeated assessments along with more widely temporal intervals, seem to be scarce. If it appears as a limitation, it also emerges as a challenge for the future since longitudinal methodologies are one of the main pillars in developmental science. To not have studies that focus on the intraindividual processes of transformation and change over the period of extended time is a significant gap in flow research that must be valued in future research. Finally, based on the relationship of all contents in this chapter, to match studies on flow and development appears to be an important aspect to better understand daily life experiential paths, and how flow turns into optimal experience with datasupported developmental benefits. Nevertheless, the need to go further is an important endeavor and research should aim at investigating flow in and throughout development, and thus challenging the developmental flow researchers to understand and improve optimal functioning in human development.

Study Questions • Comment on the following sentence: “The flow model itself is a developmental model”, based on the perspective of Rathunde and Csikszentmihalyi (2006). The flow model integrates multiple levels of self-organization, showing the importance of the successive articulation between the individual and the task (s) at hand (in the immediate moment and along several moments). This means that individuals can evolve into levels of higher complexity through the achievement of flow experience. Individual development, in turn, deals with a diverse range of components, from the biological and physiological dimensions to the cultural and historical ones, making the emergence of subjective paths across the lifespan relevant. Thus, a parallelism exists between flow experience and developmental paths since they both influence the regulation of development through individual and contextual relationships. • How can flow experience be explained through Bronfenbrenner’s ecological perspective? According to Bronfenbrenner’s model (the bioecological model of human development; Bronfenbrenner & Morris, 2006), development is defined “as the phenomenon of continuity and change in the biopsychological characteristics of human beings, both as individuals and as groups”, considering four properties: process, person, context, and time. From this bioecological perspective, these four properties are the basic elements to analyze and understand the diversity of developmental pathways. In the same way, flow experience integrates these elements through a process that matches the person and the context

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(by aligning individual characteristics, or skills, and tasks at hand, or challenges) across time, making the achievement of higher levels of personal development possible. • Explain how flow can be applied to psychological interventions either as a strategy or an outcome. Psychological interventions can use flow experience as a strategy to achieve another specific outcome (e.g., optimal functioning) or as an outcome itself (e.g., to promote the achievement of flow experience). These interventions constitute unique contexts for an individual to learn about flow experiences, in terms of how to identify flow in their daily life, how to look for opportunities to increase it in their natural settings (e.g. with family, in school, with peers, or during leisure), and how to increase the match between successive challenges and skills. Like this, promoting flow experience through psychological interventions improves optimal functioning, mental health and counteracts psychopathological functioning, facilitating the identification and practice of individual strengths and potentialities in relation to everyday life contexts and opportunities. • How can we relate psychopathology, normative development, and flourishing, taking into consideration Fig. 12.1? While traditional psychology has focused on the study of deficits and disorders that are associated to negative life trajectories, in recent decades the study of potentialities, strengths, and flourishing has gained a renewable interest. This change of focus is the result of a growing research on what occurs along the entire spectrum of development, moving back and forward from and through the normative experiential pathway. According to recent developmental perspectives, it is essential to understand this spectrum in an integrative way when discussing the relationships among psychopathology, normative functioning and potentialities. The interaction between adverse and optimal conditions in the continuous life outline contributes not only to prevent psychopathology, but also to promote flourishing pathways. • How do the influences of external (contexts) and internal (individual) factors in flow experience change along age ranges? External factors seem to be crucial in the earlier ages of life, as for example the kind of relationships and activities parents have or plan with their children. In adolescence and adulthood, the characteristics of some external contexts, such as leisure or school/work gain relevance. At the same time, the influence of internal factors takes an increasingly larger role, as it is the case of intrinsic motivation, concentration, and engagement along the life span. Also, the influence of personality traits on flow remains significant from the first years of life (childhood temperament) until adulthood (openness to experience). In addition, other individual characteristics become more salient such as self-esteem, identity, and the autonomy and capacity to match challenges with skills (autotelic personality) in adolescence and adulthood, and the process of meaning-making in the elderly. It is the combination of internal and external factors that differently frame the occurrence of flow experience along the life span pathways.

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• What are the main measurement methods used in flow research? Are they specific to any certain developmental stage? Real-time and retrospective measures are methods commonly used in flow research. Real-time measures, such as the Experience Sampling Method (ESM; Hektner et al., 2007; Larson & Csikszentmihalyi, 1983; Reis, 2012), allow researchers to collect within-subject information for an intensive period of time (e.g. a week). Contrary to the traditional retrospective self-report measures, realtime measures collect information on the daily subjective experiences of individuals in their moment-to-moment situations. This real-time assessment tool is mostly used, starting from early adolescence, and going through to the elderly years of life. Less common are the use of observational methods and interviews, which are employed especially to investigate flow experience in infants, toddlers, and children.

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Chapter 13

Flow in Sports and Exercise: A Historical Overview Oliver Stoll

and Michele Ufer

Abstract Originally, Csikszentmihalyi studied activities such as rock climbing, playing chess, composing music, modern dancing, playing basketball or conducting a surgery. Csikszentmihalyi’s interest was to determine, why people pursue these activities even though they might offer little, if any extrinsic rewards. He claimed that if we better understood, what makes us put a lot of effort into something that is seemingly lacking an extrinsic reward, then it may help us be less dependent on extrinsic rewards (cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1). Competitive sports, as well as physical exercise (in terms of prevention) are often linked to extrinsic rewards (e.g. performance & money in competitive sports, or gaining and stabilizing health in prevention settings). Nevertheless, there are a lot of sports activities, which can’t be explained with extrinsic rewards, such as marathonrunning as a hobby. Since the early 1990s, flow-experiences were often in the focus of sports- and exercise psychology. The aim of this chapter is to describe the historical development of flow research in sports and exercise settings and furthermore methodological as well as theoretical advances (e.g. neuro-cognitive aspects) related to sports and exercise will be reported and discussed.

Introduction: Flow in Performance Sports Flow is a mental state where athletes have a laser-like focus up to being completely absorbed in the activity. Attention and performance seem to happen effortless and spontaneous, like on autopilot, without any distracting or negative thoughts at hand, thus flow is considered a highly functional state that underlies superior performance in sport (Jackson & Roberts, 1992). Due to its links with peak performance, and psychological concepts, such as positive subjective experience (Csikszentmihalyi, 1975, 2002), enhanced well-being (Haworth, 1993) and self-concept (Jackson,

O. Stoll (*) · M. Ufer Institute of Sports Science, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale), Germany e-mail: [email protected]; [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_13

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Thomas, Marsh & Smethurst, 2001) it is of great interest for athletes of all levels, coaches and researchers to understand the concept of flow and to know how athletes can experience this highly desirable state more often and intensely (Jackman, Crust & Swann, 2017). While climbers and basketball players were included in Csikszentmihalyi’s original work (1975), the first empirical studies that explicitly adopted flow research into sport were published in 1992 (Jackson, 1992; Jackson & Roberts, 1992; Kimiecik & Stein, 1992). Since then numerous studies have been conducted in all kind of sport settings, while “elite athletes have been the population of primary interest” (Jackson and Kimiecik, 2008, p. 385). For two reasons this is not surprising. In elite sport, athletes compete at the highest level. They may face intense pressure and important rewards are at stake. Even small improvements in high performance settings can have dramatic impacts on the outcome in terms of success or failure (Nicholls, Polman & Holt, 2005). So knowledge about mental states that underlie and influence peak performance is crucial. In addition, the “elite level also represents the domain from which most can be learned from an applied perspective” (Swann, Keegan, Piggott, Crust, and Smith, 2012, p. 808). It seems more likely that athletes with a lower performance level learn from elite athletes than vice versa Based on the systematic review on flow research in elite sport conducted by Swann, Keegan, Piggott, Crust, and Smith (2012) and some more recently published studies the following section describes how athletes experience flow, what key factors facilitate, disrupt or prevent the occurrence of flow, how flow can be controlled and manipulated and how flow influences performance in sport

Part 1: The Experience of Flow in Sports The current description of flow generally suggests nine dimensions to describe the phenomenon (Csikszentmihalyi, 2002; Jackson & Csikszentmihalyi, 1999, cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1). Three of these dimensions are proposed to be the preconditions through which flow occurs: challenge-skill balance, clear goals, and unambiguous feedback. The remaining six dimensions are supposed to be the characteristics of flow, describing the subjective experience during flow: concentration on the task at hand, action-awareness merging, loss of self-consciousness, sense of control, transformation of time, autotelic experience (e.g. Nakamura & Csikszentmihalyi, 2002). This conceptualization has been widely supported by qualitative and quantitative research in the field of sport (e.g. Aherne, Moran & Lonsdale, 2011; Chavez, 2008; Jackson, 1996; Stavrou, Jackson, Zervas & Karterliotis, 2007; Swann, Crust, Keegan, Piggott, and Hemmings, 2015). However, in some studies athletes have reported concepts that go beyond Csikszentmihalyi’s dimensions of flow, e.g. Jackson’s (1996) and Sugiyama & Inomata’s (2005) athletes mentioned the following aspects: aware of effort, feel out of body, as if watching self, endless supply of energy. Elite golfer reported a kind of (self-)

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Table 13.1 Flow vs. clutch states Shared characteristics

Differences

Flow Absorption Altered perceptions Enjoyment Perceived control Effortless experience Effortless attention

Clutch Absorption Altered perceptions Enjoyment Perceived control Intense effort Deliberate focus

awareness of being in a flow state while it occurred (Swann, Piggott, Crust, Keegan, and Hemmings, 2015) Researchers should consider the possibility that the original flow model based on nine dimensions may not represent exhaustively flow experience in sports and thus be open for refinements and adjustments of the original flow model. Swann (2016) suggests that at least an additional dimension accounting for kinesthetic perceptions of flow experience seems evident (Swann, 2016) The demand for a further investigation into refining the concept of flow experience in sport is strongly supported by recent findings. Swann, Crust and Vella (2017) raised concerns about existing knowledge on flow in sports. They found that during superior performance flow or a second, overlapping but different “clutch state” can be experienced. The latter happens in moments, when competitive athletes experience and are fully aware of high pressure but anyway perform at their best through skilled actions (Jackman, Crust & Swann, 2017). Theses clutch states are considered to underlie “clutch performance”, defined as “any performance increment or superior performance that occurs under pressure circumstances” (Otten, 2009, p. 584). Flow and clutch states share several aspects, like absorption, altered perceptions, enjoyment, perceived control, but they also differ in various dimensions: intense effort instead of effortless experience, and deliberate focus instead of effortless attention (see Table 13.1).

Flow Occurrence Based on their in-depth review of flow studies in elite sport Swann et al. (2012) found ten factors, which are associated with the occurrence of flow in sport. The combination and interaction of the following internal states, external influences, and behaviors facilitate the occurrence of flow: focus, preparation, motivation, arousal, thoughts and emotions, confidence, environmental and situational conditions, feedback, performance, team play and interaction. Depending on whether these factors occur prior or during a performance, they can facilitate, prevent or disrupt the flow experience. If these factors appear in their negative form, they prevent flow, if they appear during flow they are likely to interrupt the experience. It is not yet clear what exactly makes each of these factors negative, nor what level or intensity of a

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facilitator (e.g. of arousal) is needed to promote flow, which can be seen in line with Hanin’s (1997) concept of individual zones of optimal functioning. Also it appears that not every aspect is experienced during every flow state and it is not clear why a certain factor is experienced in one flow state but not in another one (Swann et al., 2012). Surprisingly the three dimensions that are considered preconditions for flow (challenge-skill balance, clear goals, and unambiguous feedback) were not reported as flow facilitators by elite athletes. Possibly, these were just taken for granted in high performance settings (Swann et al., 2012). While the ten influencing factors seem rather general in nature and apply to many sports, some sport specific facilitators have also been found. In a study with jockeys, Jackman, Van Hout, Lane, and Fitzpatrick (2015) found that an optimal relationship between horse and jockey and a positive horse demeanor and performance promote flow. Swann, Piggott, Crust, Keegan, and Hemmings (2015) found that in elite golf the caddie has an important influence on the occurrence of an athlete’s flow. While situational factors influencing the occurrence of flow have been extensively researched, to date individual differences have widely been neglected or were used for vast explanation of inconsistent data (Swann et al., 2012). But due to the fact that flow is a subjective state, it is of vital importance to understand how individual differences affect the occurrence and experience of flow. A couple of studies addressed this issue, though. Canham and Wiley (2003) found that expert rock climbers were more likely to experience flow dimensions, such as automatic performance, unambiguous feedback, clear goals, and time transformation than novel climbers. Catley and Duda (1997) report a positive correlation of skill level and flow in golf, which is in line with Engeser and Rheinberg’s (2008) general assumption that “it is likely that individuals with higher abilities have higher flow values” (p. 161). Due to the fact that elite athletes regularly experience and have to cope with highly challenging and competitive situations they may also develop exceptional mental skills during their career, that facilitate flow (Jackson, 1996). This assumption is supported by Crust and Swann (2013) who report an association of dispositional flow and mental toughness and Jackson et al. (2001) who found positive associations between flow and the use of psychological skills such as emotion and thought control, as well as maintaining an optimal arousal level through adequate activation or relaxation. In addition flow was shown to be correlated with the level of perceived ability (Jackson & Roberts, 1992) and athletes with a low level of anxiety and a positive attitude towards one’s own emotion were more likely to experience flow (Wiggins & Freeman, 2000) So far, the current findings (e.g. the summary in Barthelmäs & Keller, Chap. 3) are mainly descriptive in nature and cannot offer causal explanations for flow occurrence. Kimiecik and Stein (1992) note that “It is one thing to know, for example, that a flow experience is accompanied by focused concentration, feelings of control, and clear goals. It is quite another to know why or how the flow experience actually occurred . . . (and) the mechanisms underlying the experience” (p. 148). Despite all progress that has been made “there is a degree of uncertainty as to when flow states occur” (Chavez, 2008, p. 71). That’s why Swann (2016) highly recommends moving from describing the factors (e.g. internal states, external

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influences, behaviors, skills, personality traits) that are associated with the occurrence of flow to causal explanations how exactly these factors influence flow occurrence and what mechanisms and processes lead to flow experience in sport Attentional aspects are repeatedly suggested as crucial in order to get into a flow state (e.g. Singer, 2002, cf. Barthelmäs & Keller, Chap. 3). While Jackson’s (1992) athletes report focus as very important to find flow, Swann et al. (2012) also found concentration on the task at hand and action-awareness merging to be the most reported aspects. Harris, Vine and Wilson (2017) conclude that the best approach to understand the mechanisms underlying flow experience in sport and to advance theoretical models is to focus on attentional processes. But to date theoretical explanations are at an initial stage and empirical findings are rare (cf. Barthelmäs & Keller, Chap. 3), mainly from outside sport and they are contradictory. E.g. Dietrich & Stoll (2010) argue that prolonged physical activity, like longdistance running, leads to a temporal reduction of activity in the prefrontal cortex (hypofrontality) which accounts for central flow characteristics, such as effortless attention, time distortion, action-awareness merging. On the other hand, in a study with runners using electroenzephalography, Wollseiffen et al. (2016) demonstrated hypofrontality with increasing activity, but this did not correlate with flow. Based on findings using functional near-infrared spectroscopy (fNIRS) Harmat, de Manzano, Theorell, Högman, Fischer and Ullén (2015) argue that a general mechanism of hypofrontality to explain flow may be too simplistic, because in their study no hypofrontality during flow was found. Nevertheless it seems that higher-order attentional networks play an important role and flow is associated to reduced activity of those neural networks that are linked to self-referential processing (Harris et al., 2017), so that future explanations focusing on attentional processes seem promising to explain flow occurrence and its underlying mechanisms

Controllability of Flow in Sports The aim of research concerning the controllability of flow is to raise understanding if and how the frequency and intensity of flow experience can be systematically increased. In a couple of studies scientists asked elite athletes about their perceived control over flow and over the factors that influence the occurrence of flow (e.g. Chavez, 2008; Jackson 1992, 1995; Sugiyama & Inomata, 2005). Aherne et al. (2011) sees flow-experiences in sport as illusive and unpredictable (Aherne et al., 2011), because it still cannot be predicted when exactly, for how long and how deep an athlete enters a flow state, 66% of the elite athletes perceive flow to be within their control, whereas 26.5% of the athletes find flow to be difficult or impossible to control. 81% of the participants think they are able to restore flow after a disruption (see Swann et al. 2012 for an overview), and elite golfer report that they are able to prolong their flow experience through the use of positive distractions or the dissociation from the task (Swann, 2016). However, findings remain unclear. They are based on limited data and are somewhat contradictory, as some influencing factors,

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e.g. concentration, optimal arousal, positive/negative attitude, motivation, are perceived both controllable and uncontrollable, depending on the athlete. This again clearly indicates that more research on the role of individual differences underlying flow experience is needed. And even if some facilitators were more consistently perceived as controllable, Swann et al. (2012) points out that “just because these factors are perceived to be controllable as well as related to flow does not mean that they cause flow to occur, or guarantee its occurrence” (p. 816) and concludes that we first need to get a detailed understanding of the mechanisms of flow occurrence and in a next step can test how factors that seem to be within the control of an athlete can enhance flow Some studies investigated, if flow experience can be systematically enhanced through psychological interventions, e.g. hypnosis (Lindsay, Maynard & Thomas, 2005), imagery (Nicholls et al., 2005), a combination of pre-competition imagery and music (Pain, Harwood, and Anderson, 2011) or a 6-week mindfulness training (Aherne et al., 2011). But the results were somewhat mixed. Again, from our position, a key challenge seems to be that so far no sound explanation exists of how flow occurs, so that at this point “interventions are, by necessity, quite speculative” (Swann et al., 2012). Another reason for the somewhat mixed results could be the fact that the interventions did not refer to those dimensions and factors that research so far has found to be associated with flow occurrence. Future studies should take these arguments into account and develop intervention settings that match the athlete’s personality and include facilitating aspects or skills like preparation, focus on the task, manipulation of arousal and goal-setting These recommendations are strongly supported by findings on goal-setting strategy as potential causal mechanism of flow experience. While specific goals focus on objective and measurable outcomes, open goals are rather exploratory, e.g. “see how well I can do”. Setting specific goals is considered best practice to enhance performance (Locke and Latham 2013; Maitland & Gervis, 2010). Recent qualitative studies revealed that the type of goal pursued seems to influence whether flow or clutch states occur. Athletes reported that open goals preceded flow, while specific goals precede more effortful clutch states (Swann et al., 2017b). Schweickle, Groves, Vella and Swann (2017) confirmed that the type of goals an athlete pursues appears to influence the occurrence of flow. In an experimental setting, participants were assigned to one of three goal condition (specific, open, do-your-best goals) and had to perform a cognitive task. Those athletes who pursued open or do-your-best goals reported significantly higher levels of flow than those athletes prescribed specific goals, who in turn, reported significantly higher level of clutch state. The findings seem to confirm that the type of goal-setting in advance of a task has an impact of flow occurrence in the way that open goals may be a reliable intervention to induce or achieve higher levels of flow.

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Flow and Performance in Sports Given the key characteristics of flow, e.g. strong focus on the task with a high level of control and effortless, intuitive action, since the beginning of flow research, flow is considered a highly functional state that is very likely to have a positive impact on performance. Also an indirect effect of flow on performance is likely. Flow is generally perceived as a positive experience. This leads to an increased motivation to practice, which results in a better performance. However, the empirical findings in sports are mixed. Some researchers found positive links of flow with performance, some didn’t. A key element in performance sport is for athletes to achieve peak performance, whether it is winning a competition, beating an opponent, finishing an extremely challenging event or achieving a personal best. While flow research has been mainly focused on the characteristics of flow and its antecedents, surprisingly only little investigations have been undertaken to analyze the effects of flow on performance in sport. From the beginning flow theory has assumed a positive relationship between flow and performance, and there are good reasons for this assumption. Given the key characteristics of flow, e.g. strong focus on the task with a high level of control and effortless, intuitive action, flow can be considered a highly functional state that is very likely to have a positive impact on performance. This view is supported by Eklund (1994, 1996) and Williams & Krane (1997), who consider the mentioned flow characteristics to be significant drivers of performance in sports. Privette (1981) also concludes in her work on excellence in sports that flow experience should have a positive impact on performance, because “Csikszentmihalyi’s term ‘flow’ [. . .] is an elegant fit for the whole, graceful, and directed behavior athletes described as characteristic of peak performance in sports” (p. 55). In addition to this direct impact of flow on performance, also an indirect influence is shown (Schüler & Brunner, 2009). Flow is usually experienced as very pleasant. This can act as an incentive, increasing the motivation to practice in order to experience flow again. The increased practice leads to increased training effects which in turn enhances performance While from a theoretical point of view there are many reasons, for flow to have a direct positive effect on performance, so far the empirical findings in sports do not show a clear picture. Some researchers found positive links of flow with performance (e.g. Jackson et al., 2001; Jackson & Roberts, 1992; Stavrou et al., 2007; Swann et al., 2017a). However, this contrasts with work in which the postulated direct relationships between flow and performance could not be confirmed (e.g. Schüler & Brunner, 2009; Stoll & Lau, 2005), although in the field of longdistance running Schüler and Brunner (2009) found an indirect effect of flow on performance: flow during a marathon race had a positive effect on the future motivation to run. An increase in training time was related to a better performance during future competitions. In a recent study on the effects of goal types on flow and clutch states, Schweickle et al. (2017) found that participants scoring high on clutch state performed significantly better than those who scored high on flow, who, however, reported a higher perceived performance despite objectively performing worse

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Stoll and Lau (2005) see the reasons for the inconsistent findings in the fact that the available studies are very heterogeneous. The theoretical foundations are not always clear and problematic in terms of research methodology (small samples, problematic operationalization of flow and performance. Henk (2014) shares this point of view and explains that the flow measurements were carried out very differently. Sometimes flow assessments took place during or immediately after an event, sometimes years after a certain performance. Sometimes psychometric scales were used, sometimes career-based or event-related interviews. Performance was measured either on the basis of objective data, e.g. rankings or finish times, or based on individual pre-competition expectations or through subjective post-event assessments (e.g. referring to a performance which an athlete remembered better than average) It is highly recommended to further investigate the effects of flow on performance. But performance is not the final criterion in sports. As we have shown before, flow is a positive experience associated with well-being and confidence but also able to motivate people to exercise further. These are important findings we can use in the field of leisure, health and prevention sports.

Part 2: Flow in Primary and Secondary Prevention Settings A current research question in health psychology is how to motivate individuals to maintain exercise behaviour in order to gain the beneficial health effects connected to long-term exercising in primary or secondary prevention settings. Preventive healthcare strategies are described as taking place at the, primary, secondary, and tertiary prevention levels. Primary prevention summarizes methods to avoid occurrence of disease either through eliminating disease agents or increasing resistance to disease. Examples include immunization against disease, maintaining a healthy diet and exercise regimen, and avoiding smoking. Our main focus in this section is the especially the question of maintaining of an exercise regimen and its relation to the occurrence of flow. Secondary prevention summarizes methods to detect and address an existing disease prior to the appearance of symptoms. An example includes e.g. the treatment of hypertension with e.g. sports-therapy, but also with regard to psychosomatic problems. In primary prevention settings, flow-experiences are rarely reported or discussed. Elbe, Strahler, Krustrup, and Stelter (2010) explored whether inactive individuals (in comparison to active individuals) can experience flow, a rewarding, psychological state, during an exercise intervention and if there are differences according to the type of intervention they experienced. Furthermore, they investigated if experiencing flow is connected to physiological improvements attained during the exercise intervention. The 12- to 16-week interventions included six randomized intervention groups (in a sense of primary prevention), two female and four male groups performing continuous running, football, interval-running and strength training. The results indicate that all six randomized exercise intervention groups experience

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rather high levels of flow regardless of whether the intervention is a team or individual sport. Differences in experiencing flow, worry and exertion as well as physiological improvements could be found for the different types of sport and the two genders, with the male football group having the highest score for physiological improvement and the lowest score for worry. A connection between experiencing flow and physiological improvement could not be found (Elbe et al., 2010). Stein, Kimiecik, Daniels, and Jackson (1995) investigated three psychological antecedents of flow in sports in a recreational setting. They measured goals, competence and confidence as well as flow in three different studies with tennis players, basketball players in college activity classes and hobby-golf player. The first study had the participants rate flow characteristics in a scale, whereas the second and third study used the experienced sampling method to measure flow. In the “learning environment” (basketball class), students in flow experienced greater enjoyment, satisfaction, concentration and control than their counterparts in boredom, apathy, or anxiety. In a more competitive environment (tennis and golf), athletes in flow or boredom states had a better quality of experience than individuals in apathy or anxiety states. They interpreted these results that contextual differences influence why an athlete perceives a situation as optimal. And they conclude that the antecedents of flow remain unidentified, neither goal, competence, nor confidence predicted the flow experience. Flow-experiences are also in the focus of sports therapeutic settings (such as an approach in psychotherapy; Reinhardt et al., 2008) as well as in the treatment of pain patients (Persson, 1996), survivors of war with PTSD (Ley, Krammer, Lippert & Barrio, 2017) and in occupational therapy (Emerson, 1998) and nowadays also with regard to exergaming and/or virtual reality in therapy (Riva, Castelnuovo, & Mantovani, 2006). These settings clearly belong to secondary preventions. Persson (1996) conducted an explorative study designed to further the understanding of a creative activity group from a “doing perspective”. Play- and flow theory were chosen as the primary theoretical reference emphasizing this “doing perspective”. Congruent elements from these theories provided the attention focus for the identification of “play/flow” and “non-play/non-flow episodes” in the performance of five chronic pain patients within one of the six group sessions included in the study. Methods for data sampling used were videogram, microethnographymethods as well as focused interviews. The results from the observations as well as from the interviews corroborate that this activity group promotes playing and experiences of flow, which was helpful in the coping-process with pain. This means that flow-experiences may moderate functional coping-processes. Ley et al. (2017) performed a single case study of a war and torture survivor, who was diagnosed with posttraumatic stress disorder (PTSD) and depression, and who was participant of the sport and exercise therapy program Movi Kune. Participant observation was conducted as well as semi-structured interviews with the participant and his psychotherapist. Data analysis resulted in the proposal of different processes: Beside the focus on bodily sensations related to an exposure effect, contributing to improvements in body awareness, coping behavior, and affect regulation, whereas the focus on playing related to an improved performance, presence, enjoyment, and

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here especially flow as well as mastery experiences, pointing toward distraction and motivational-restorative effects. Again, especially the motivational effect of the flow-experience seems also to be functional in a PTSD-therapeutical setting. In a study with patients, suffering from depression, Reinhardt, Wiener, Heimbeck, Stoll, Lau and Schliermann (2008) investigated, if flow-experiences are the consequence of a downregulated prefrontal cortex and if this downregulation can be induced by high-intensive endurance workload. This approach could be effective in a running therapy context for the treatment of depression. One symptom of depression is that patients suffer from rumination, which is dependent on prefrontal cortex activity. Reinhardt et al. (2008) ran a study using the above-mentioned workload regulation to induce flow to 31 adult volunteers with moderate depressive disorders. Using a load-oriented and speed regulating bicycle ergometer, the participants were kept within an individual demand level and cycled in this condition for 40 continuous minutes. Flow state was measured using the Flow-Short Scale (Rheinberg, 2015) during the activity. Effects of mood variations were assessed immediately before and after the training using a profile of mood-questionnaire. According to the main results, the participants reported (confirming study 1) a continuous, deep and stable flow experience. The effects of mood variation can thus be illustrated in the form of an iceberg profile, which means that negative emotions decreased, and positive emotions increased. In summary, the here reported studies show clearly that flow-experiences can play a central role in a health-psychological context. Based on the results of Schüler & Brunner (2009), flow experience may contribute to the long-term maintenance of exercising by positively rewarding the sport activity and thus enhancing the probability to perform it again. With this, two kinds of well-being could be reached simultaneously, the immediate positive experience quality connected to flow and the beneficial health effects in the long run (Schüler & Brunner, 2009). If, and how flow can mediate or moderate also physiological benefits in sports therapy settings remains unclear. It can be hypothesized that flow inhibits more physical activity, which then leads to other positive consequences.

Measuring Flow in Sports Assessing flow is a key challenge. If we understand flow as a non-reflective absorption in an activity where attention is focused entirely on the task execution, then it follows that when being in a flow state, we can hardly think and respond to questions about that flow state. This deliberate reflection and distraction of attention away from the task executing can prevent, interrupt or terminate the flow experience (de Manzano, Theorell, Harmat & Ullén, 2010; Deutsch, Debus, Henk, Schulz & Thoma, 2009; Peifer, 2012; Rheinberg, 2004; Youssef, 2013). This assumption is supported by the neurophysiological work on the default mode and self-awareness networks (Jonson, Baxter & Wilder, 2002; Lou, 2015). Challenging task management reduces the cortical activity of the network of self-awareness and thereby

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inhibits the ability to introspect or self-reflect (Harris et al., 2017; Ulrich, Keller, Hoenig, Waller & Grön, 2014; Ulrich, Keller & Grön, 2016). Furthermore, flow diagnostics in sports is made even more difficult by the fact that flow usually seems to be a relatively short, rare and from the view of athletes an unpredictable state (Aherne et al., 2011; Swann, 2016). In addition, Nisbett & Wilson (1977) point out, that mental states and processes are only partially accessible to self-reflection, so that Jackson and Kimiecik (2008) come to the following conclusion: “One of the greatest challenges in flow research is finding ways to assess the experience itself accurately and reliably” (p. 395). One possible solution could be the use of psychophysiological and neuroscientific measurements (cf. Peifer & Tan, Chap. 8), which is increasingly the case in non-sport domains, e.g. computer gaming (Harmat et al., 2015; Peifer et al. 2014) or arithmetic challenges (Ulrich et al., 2014, 2016). This would be of great advantage, because the explicit questioning of the subjects would be dispensed and the occurrence and intensity of flow could be measured directly, live and online during the activity without any self-reflection and disorder of the activity (cf. Peifer & Tan, Chap. 8). Keller (2016) reports first approaches in the context of experimental studies. The activities, however, are again computer games and arithmetic tasks that require no or only minimal physical activity and do take some minutes only. But the theoretical basics explaining flow on a (neuro-)physiological basis are still under discussion. Prominent models, like the transient hypofrontality theory (THT) (Dietrich, 2004), seem to be too simplistic (Harmat et al. 2015). Studies using psychophysiological and neuroscientific measurements have great potential but are at the beginning and have so far revealed only few and partly contradictory empirical findings on the correlates of flow (Harris et al. 2017, cf. Peifer & Tan, Chap. 8). In addition, psycho /neurophysiological measurements are more difficult to realize in sport settings for two reasons. On the one hand, the use of corresponding devices may not be permitted due to the competition regulations or may just contradict the requirements of the activity. Also, many of the technical possibilities are not yet sufficiently mature to perform reliable live measurements during intensive physical activity. That’s why in sport settings so far flow is mostly assessed retrospectively (after an event), which means all information relies on subjective memory (Swann, 2016). The following approaches are usually used in sports in isolation or in combination: interviews, experience-sampling and questionnaires.

Interviews Studies by Jackson (1995, 1996), that are based on interviews, are among the first and also most cited about flow in sports and exercise and have produced valuable insights, followed by numerous works in different sport settings, e.g. golf (Swann, Crust, et al. 2015; Swann et al., 2012), swimming (Bernier et al., 2009), tennis (Young, 2000). Interviews are still the method of choice when it comes to obtaining precise information from athletes about their subjective flow experience (Stavrou et al., 2007). This may be of particular value in exploratory research of previously

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unstudied sport settings, as well as in refining our knowledge about flow (Swann, 2016). However, interviews also have a big disadvantage. The greater the period between the experience and the reflection about the experience, the greater is the danger of memory gaps and distortions. Some interviews on flow refer to events that may have occurred years ago so there is a real chance that athletes have forgotten certain details or their memory is biased (Brewer, Van Raalte, Darwyn, Van Raalte, 1991). That’s why Jackman et al. (2017) suggest conducting event-related interviews as fast as possible after the activity. In addition interviews with an athlete should be conducted across multiple events in order to get more detailed information about possible differences in flow experience and thus a deeper understanding on personal and situational factors that influence the occurrence of flow (Jackman et al., 2017, Stavrou et al., 2007).

Experience Sampling Method In order to gain more detailed and reliable information, when and under which specific conditions flow occurs with certain intensity, and what consequences this might have, the Experience Sample Method (ESM) was developed (Csikszentmihalyi & Larson, 1987; Csikszentmihalyi, Larson & Prescott, 1977). The overall goal was to move the flow detection as closely as possible to the respective state, permitting to assess flow states “online” or “real-time” during the activity, thus when it happens. During ESM studies participants are equipped with electronic signal transmitters (for example a pager or mobile phone), which are carried throughout the activity. These signalers ask the participants at irregular intervals or randomly chosen time points to briefly interrupt their activity and capture their flow, mood and other information based on a brief survey. Compared to retrospective interviews, this experience data is obtained directly from the course of the action and activities are interrupted only briefly, but can then be continued. This leads to the fact that the ESM is relatively complex to carry out, but ecologically highly valid (Rheinberg & Engeser, 2018). While ESM has been used in a variety of flow studies worldwide. ESM is only of limited use in sport (Swann, 2016). In many sport settings, wearing a signaler or (randomly induced) interruptions of the activity are forbidden, or obviously inconvenient or impossible. For these reasons, only very few ESM studies or ESM-inspired research designs were conducted in sport, e.g. in rock climbing and high-altitude mountaineering (Aellig, 2004; Delle Farve, Bassi & Massimini, 2003) or long-distance running (Schüler & Brunner, 2009). Ufer (2017) set up a study design in the field of extreme ultramarathon races avoiding the main disadvantages of the ESM and naturally integrating data acquisition into the event. Flow was repeatedly assessed at various time points during and immediately after completing several competitions lasting up to 250 kilometers, e.g. in the Amazon rainforest, Kalahari Desert or at the polar circle in winter. Athletes didn’t have to wear a signaler during the races. Instead, they were asked

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to report their flow experience when finishing a section or at official checkpoints throughout the race. Checkpoints are an important part of the race infrastructure: athletes naturally stop their running in order to provide themselves with fresh food and water, treat blisters etc. Also, they were asked by organizers and the medical staff how it is going, how they feel, and it is checked whether their health condition allows them to continue the race. Although “checkpoint management” is, like boxing stops in formula one, an important part of these races, checkpoints seem well suited for assessing flow: athletes are not disturbed and interrupted during their run. Instead, flow is assessed when they naturally stop running and have to switch their attention away from running to more (self-) reflective activities, e.g. responding to the medical doctors, reflecting what to eat, body/equipment check, etc. Athletes were not asked to report their current flow experience in the moment of the survey but retrospectively with respect to the last running section in mind. This procedure avoided potential interference of the questioning with the flow experience. Finally, repeated assessments during the events allowed capturing flow very close to the experience and from a “fresh” memory. In addition they also allow for longitudinal analysis.

Questionnaires Most studies in sport, exercise and beyond used questionnaires to investigate flow (Engeser and Schiepe-Tiska, 2012). Validated psychometric scales are the preferred method of choice when the goal is to capture the intensity of flow and to analyze correlations between flow and other constructs. Numerous quantitative instruments have been developed to assess flow, but those developed by Rheinberg, Vollmeyer & Engeser (2003) and especially Jackson and colleagues (2008) are the most widespread. In English-speaking countries, the Flow State Scale (FSS) and the revised version FSS-2 have been most widely used to assess situational flow in sports and beyond (Jackson & Eklund, 2002, 2004; Jackson & Marsh, 1996). It refers the nine dimensions of flow (Csikszentmihalyi 2002) and analogously consists of nine subscales with 4 items each, so a total of 36 items. A short version of the FSS-2 (Short Flow Scale) was also developed for brief assessments (SFS, Jackson et al., 2008). In addition to the situational flow experience, the Dispositional Flow Scale (DFS, Jackson & Eklund, 2002, 2004; Jackson et al., 2008) captures the construct “autotelic personality,” the general tendency to experience flow. The questionnaires have been translated into Greek (Stavrou & Zervas, 2004), French (Fournier et al., 2007), and Japanese (Kawabata, Mallett, & Jackson, 2007). In the German-speaking area, the Flow Short Scale (FKS, German version) was developed by Rheinberg et al. (2003). The goal was to provide an instrument that can detect flow in its various components and at the same time is compact enough to easily use it in everyday life. In addition, the scale should be able to apply across different situations for any activity. The resulting FKS consists of 10 items that take

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less than a minute to complete and yet capture all components of the flow experience first described by Csikszentmihalyi (1975). The instrument has been translated into various languages: English, French, Italian, Danish, Czech, Turkish, Dutch (Rheinberg, 2015) and tested in different settings, e.g. sport, work, learning, so that the findings also permit comparison across different contexts (Rheinberg et al., 2003). Nevertheless, it is difficult to define whether a person really was in a flow or not. Quantitative measures result in mean scores. They provide information about the intensity of the respective flow experience but do not answer the question of whether flow was actually experienced or not (Moneta, 2012, cf. Moneta, Chap. 2). It is hard to determine how high a value should be to distinguish a flow state from a non-flow state or a micro flow from a deep flow (Deutsch et al., 2009, Swann, 2016). Kawabata & Evans (2016) addressed this issue and suggest that a mean score of 3.4 and 3.3 for the preconditions of flow (demand-skill balance, clear goals, unambiguous feedback), measured by the FSS-2, may differentiate flow from non-flow experience. But in a recent multi-measurement study using scales and questionnaires, the mean scores suggested by Kawabata & Evans (2016) were not able to differentiate people that were in a flow state from those who did not experience flow (Jackman et al., 2017). Another problem is that in the course of an activity to which a survey response relates, the experience can vary. It can be assumed that flow is not always experienced at a constant level. But then how is the flow experience transferred into a single value? Does an assessment refer to the moment with no or little flow? Or the time point with intense flow? Or is a kind of average built? Stavrou et al. (2007, p. 454) summarize the limits of quantitative scales: “Trying to quantify athletes’flow has certain limitations, because it cannot portray the subjective nature of the phenomenon”. Last, not least, questionnaire development is based on central assumptions and descriptions on flow and the scales actually used in flow research generally refer to Csikszentmihalyi’s dimensions and characteristics of flow. But Jackman et al. (2017) raised concerns about the discriminant validity of the existing flow scales. In recent studies Swann et al. (2017a, b) discovered that during superior performance flow and a similar, though different “clutch” state with overlappings of characteristics can be experienced but existing scales do not differentiate between flow and clutch states. That’s why Jackman et al. (2017) recommend the revision of existing scales or the development of new instruments to better assess flow and differentiate flow from clutch states.

Conclusion All methods described above have advantages and disadvantages. “No single measurement approach will be able to provide trouble-free assessments of the flow experience” (Jackson and Kimiecik, 2008, p. 395). Psychophysiological and neuroscientific measures may be an attractive way to assess an athlete’s flow objectively

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“online” during intense exercise in the future. But so far, sound theoretical foundations and/or technical devices that reliably record data during exercise are still missing. The ESM is of limited use in many sport settings, because it is inconvenient or can even turn dangerous to interrupt athletes from task execution for assessments. But if the data capturing process can be integrated more “naturally” into the course of an event and thus minimize distortions or interruptions, the ESM approach is an interesting option to get experience-near data, because the data is assessed from an external person, filling out the questionnaire, while the individuum can be active. Questionnaires are most widely used but may lack discriminant validity because they do not differentiate between flow and clutch states. Also they only present mean scores but do not distinguish, if someone really was in a flow state or not. Interviews offer in-depth information on athletes subjective flow experience but there is a risk of memory distortion and data relies on the quality of self-reflection. We can conclude that „a gold measurement standard for flow has yet to be reached” (Moneta, 2012, pp. 23–24). But whatever method is used, with all limitations, the assessment of flow should always take place in an “event-focused” way (Swann, 2016), as close as possible to an event, without disturbing or interrupting an athlete from task execution. And in addition Jackson (2000) suggests that a mixed method approach could be an appropriate way to capture the greatest breath of information in flow studies.

Part 3: Flow in Team Sports Collective or team flow is a relatively new research topic in sport psychology. A nice description of team flow delivers Sawyer (2006): “One might say that they have a good chemistry, or that things are clicking or in sync. For just about any sports team, one can speak of the group spirit, the team spirit, or the esprit de corps. A commentator might say they gelled as a unit or that they displayed good teamwork. All of these metaphors focus on the entire group and on their performance together as an ensemble. Even if the individual performers are prepared and focused, a good ensemble performance doesn’t always emerge.” (Sawyer, 2006, 157f). In competitive team sports, different terminology is used for comparable phenomenon, like e.g. “the hot hand phenomenon” or “playing in a momentum”. Only few studies are published so far. Jackson (1995) was one of first studies investigating flow not only in individual, but also in team sports. Nevertheless, in this study, she studied the phenomenon not really in a collective perspective, because the individual flow-experiences of the players were measured. Cosma (1999) was the first researcher, introducing the term “Team Flow”. She studied five College Football-Teams and found out that flow is higher and more stable, when players report acting in a “playing tune”. Despite this finding, this study is problematic from a methodological point of view. She modified the Flow-State-Scale 2 (Jackson & Eklund, 2002) in a way, just modifying the items from an “I-perspective” in a “Weperspective” (e.g. “I know clearly what I wanted to do” in “We knew clearly, what we wanted to do”) and called this scale “Team Flow Short Scale” (TFSS). And the

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measurements happened retrospectively, weeks after the games (and so the flowexperiences). Both above mentioned studies did not measure a real collective experience. Nakamura and Csikszentmihalyi (2002) also explored this phenomenon and called it “shared flow”. They clearly circumscribed individual from “shared flow” and noted that nothing really is known about “shared flow”, neither about the circumstances, nor about its dimensions. Lazarovitz (2003) studied 114 female ice-hockey players, measuring individual (FSS-2) and Team Flow (TFSS) and found differences in the experiences flow between the players. Reinhardt (2017) studied one youth-soccer-team (18 players) over a whole season, measured the individual flow-experiences post-hoc each game (using the Flow Short Scale, FKS, Rheinberg et al., 2003), documented the demand-ability fit and the results of each game. He could show that the players can experience flow in the games. The latest study in sports was the study from Vurgun et al. (2016). The aim of this study was to determine the flow states of elite handball players and to examine its effects to other variables. 34 athletes competing in Turkish Handball Super League participated in the study and as a result of a total of 17 matches, 142 participations were included into this retrospective analysis. They measured Flow (FSS-2, Turkish version) and their perceptions of the difficulty levels of the competition. As a result of the study, they found that female players in a team context had higher flow experiences than male players in teams. Flow experiences of handball players aged 30 and over were found to be significant at a higher level than those of handball players aged 30 and under. They concluded that the flow-experiences in teams is dependant to the, duration of game, gender and age. Summarizing the findings so far, the empirical data in the sport context is relatively small and heterogenous.

Theoretical Assumptions to Explain Team Flow Players of a team share common experiences (same games, same coach, same results). Because of league structures in competitive team sports, all teams in one league are comparable regarding their performance level. The same precondition applies for all teams of a league. Correspondingly the relationship of demand and ability could be comparable for all individuals of a team. Probably many players have comparable demand-ability appraisals in one game. This would be the demandability fit, we know from individual flow experience precondition, which simply happens cumulative. There is evidence to belief that the demand-ability fit shifts in group activities and so effects performance in sports. Ryu and Parsons (2012) could show that players of computer games in a group setting tend to make more risky decisions than playing on their own. This also can be assumed for players in sport teams, because the cognitive demands of these computer games are similar to these of teams in sports. The shared information could be one reason that a player in a group setting can expand his performance over his individual possibilities. In the following three possibilities for the explanation of team flow will be discussed.

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1. Opioids and Neurotransmitters: Cohen et al. (2010) showed: “compared with training alone, group training significantly increases pain threshold, suggesting that synchronized activity somehow heightens opioidergic activity. While it is possible that the effect on pain threshold of being in a group is independent of (an additive with) the opioid-mediated effect of exercise, we favour the simpler explanation that group exercise stimulates greater opioid production. 2. “Risky-Shift-Phenomenon”: The above-mentioned explanation is one-dimensional and explains possible shifts in an appraisal simply on changes in neurotransmitters in social performance-related situations. In social psychology, “group polarization” refers to the tendency for a group to make decisions that are more extreme than the initial inclination of its members. These more extreme decisions are towards greater risk if individuals’ initial tendencies are to be risky and towards greater caution if individuals’ initial tendencies are to be cautious. The phenomenon also holds that a group’s attitude toward a situation may change in the sense that the individuals’ initial attitudes have strengthened and intensified after group discussion, a phenomenon known as attitude polarization or “Risky-Shift” (Stoner, 1961, 1968). 3. Emotional Contagion: This is the phenomenon of having one person’s emotions and related behaviours directly trigger similar emotions and behaviours in other people. One view developed by Hatfield et al. (1994) is that this can be done through automatic mimicry and synchronization of one’s expressions, vocalizations, postures and movements with those of another person. When people unconsciously mirror their companions’ expressions of emotion, they come to feel reflections of those companions’ emotions (Hatfield et al., 1994). Emotions can be shared across individuals in many different ways both implicitly or explicitly. For instance, conscious reasoning, analysis and imagination have all been found to contribute to the phenomenon (Hatfield et al., 1994). Emotional contagion is important to personal relationships because it fosters emotional synchrony between individuals. A broader definition of the phenomenon was suggested by Schoenewolf (1990) is “a process in which a person or group influences the emotions or behaviour of another person or group through the conscious or unconscious induction of emotion states and behavioural attitudes” (Schoenewolf 1990). So far, we are still at the very beginning of understanding, describing and explain team flow in sports. From our point of view there are different ideas of possible mechanism as well as phenomenon of team flow discussed in the field. In this area, we definitely need a clear research focus and more empirical work as well as theoretical reflections to better understand team-flow in sports and exercise.

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Part 4: Discussion and Future Perspectives This chapter showed that flow research has a more than 25-years longing tradition in the area of sports and exercise psychology leading to important insights. We showed that flow experiences can be beneficial from a motivational point of view and that it helps to stick with the rehabilitation regimen and that it can be hypothesized that flow experiences are associated with other constructs known in positive psychology. But one of the main interests in sport psychology—the possible relationship of flow with performance cannot be answered clearly until today. With regard to the “mechanism”, we have to state that there are different assumptions. The more cognitive theory focuses on the skill-challenge fit as the mechanism to produce flow. This could be shown in different empirical studies. The transient hypofrontality theory, as a more neuro-cognitive approach, and its explanation for the inhibition of flow experiences are inconsistent from an empirical study point of view. The results of the studies reported here show indirect evidence in favour of the hypothesis that prolonged exercise might result in a state of transient hypofrontality. Only a few studies reported indirectly a downregulation of the prefrontal cortex during exercise (e.g. Dietrich, 2004). Additionally, other neuroimaging measures such as optical imaging or EEG combined with other selective neuropsychological measures in sports- and exercise-settings are needed to further explore the complex interaction between exercise and mental function. Other more physiological variables, like e.g. the heart rate variability might be worth considering in the context of flow experiences in a more complex view. As far as the “measurement problem” concerns, especially in sports and exercise settings, there is also still discussion. Our suggestions are not new, but should be discussed in the future. Initially, Csikszentmihalyi (1975) used qualitative data through personal interviews to explore a new model for intrinsic rewarding behavior, which led him to the development of the concept of flow. Further qualitative and quantitative (i.e. questionnaires) research techniques were used, facing various challenges in measuring the construct, conditions, and occurrence of flow (Swann, 2016). One challenge to address these limitations is to measure flow as close as possible to the moment of occurrence. Therefore, the Experience Sampling Method (ESM) was used applying a questionnaire when receiving a signal. However, this method interrupts the momentary experience and might not be as appropriate for sports as for other task domains (Swann, 2016). Seifert and Hedderson (2010) took an event-focused approach; they observed skateboarders in a skate park then approached them directly and interviewed them individually about their experiences. Furthermore, Swann, Piggott, et al. (2015) found that elite golfers were able to recognize flow in other players, concluding that flow could be observable. Furthermore, the authors stated that “observations may be a useful avenue for flow research” (p. 230). Observation may be useful in appraising flow elements in sport and exercise through bodily expression (e.g. joy, a sense of safety or fluency of movement; movement analysis methods of Dance/Movement Therapy). ESM method could be combined with interviews in order to appraise individual perceptions soon

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after the flow occurrence. Taken together, it appears that the ESM method may be a better method of measuring flow experiences in sports and exercise, especially if this data can be combined with observational data as well as psycho-physiological data. A complete new area in the flow-related sport psychology is the research in team flow. There are only very few cross-sectional studies conducted and from our knowledge just one longitudinal study (Reinhardt, 2017), who followed a youth soccer team over one complete season and assessed (individual) flow and performance. He could show that the players can experience flow in the games and that there is a correlation between flow and performance of the team. Nevertheless, even this study is weak from a point of methodological rigor. We need more studies with a field-experimental study design to study this possible research question. Furthermore, we need to clarify the possible mechanism, responsible for the occurrence of team flow. Currently, we do not know if it is a more psychos-physiological explanation, if it is connected to the “risky-shift-phenomenon” or it is simply emotional contagion. More research in this area could be extreme beneficial for the advancement in flow research in sports and exercise.

Study Questions • What are the ten main factors influencing the occurrence of flow in elite sport? Under which conditions do they facilitate, prevent or disrupt the flow? The ten core factors that influence the occurrence of flow in elite sports are focus, preparation, motivation, arousal, thoughts and emotions, confidence, environmental and situational conditions, feedback, performance, team play and interaction Swann et al. (2012). Depending on whether these factors occur prior or during a performance, they can facilitate, prevent or disrupt the flow experience. If these factors appear in their negative form before flow occurs, they prevent flow, if they appear during flow they are likely to interrupt the experience. It is not yet clear what exactly makes each of these factors negative, nor what level or intensity of a facilitator (e.g. of arousal or concentration) is needed to promote flow. • How does flow affect performance? Given the key characteristics of flow, e.g. strong focus on the task with a high level of control and effortless, intuitive action, since the beginning of flow research, flow is considered a highly functional state that is very likely to have a positive impact on performance. Also an indirect effect of flow on performance is likely. Flow is generally perceived as a positive experience. This leads to an increased motivation to practice, which results in a better performance. However, the empirical findings in sports are mixed. Some researchers found positive links of flow with performance, some didn’t. • Explain how flow is measured in sports. What are the advantages/disadvantages of the methods?

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Psychophysiological and neuroscientific measures, like EEG may be an attractive way to assess an athlete’s flow in the future. But so far, sound theoretical foundations and/or technical devices that reliably record data during exercise are missing. The ESM is an interesting option to get experience-near data but of limited use in many sport settings, because it is inconvenient or can even turn dangerous to interrupt athletes from task execution for assessments. Other methods, also using questionnaires, but not in action, are easy to administer and most widely used. But they may lack discriminant validity because they do not differentiate between flow and clutch states (Jackman et al., 2017). Also they only present mean scores but do not distinguish, if someone really was in a flow state or not. Interviews offer in-depth information on athletes subjective flow. Whatever method is used, the assessment of flow should always take place as close as possible to an event, without disturbing or interrupting an athlete from task execution. A mixed method approach could be an appropriate way to capture the greatest breath of information in flow studies (Jackson, 2000). • What are the possible mechanisms in team-sports currently under discussion? Currently there is a discussion about opioids and neurotransmitters, about the “risky-shift-phenomenon” and about emotional contagion as possible mechanisms to induce team flow experiences.

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Chapter 14

Flow in Music and Arts László Harmat

, Örjan de Manzano

, and Fredrik Ullén

Abstract The aim of this chapter is to discuss the literature on psychological flow experiences in relation to artistic creation and performance. In the first section, we review studies on state flow in music and dance. In the second section, we discuss collective flow experiences (‘group flow’) in artistic performances. The third section elaborates on the neurobiological underpinnings of creative cognition in relation to flow, and the relationship between flow, creativity, and quality of performance. In the fourth section, we discuss the relationship between dispositional flow (‘flow proneness’), expertise, and artistic creation. In summary, the literature on flow and artistic creativity is still relatively small, and more studies would be important to test key hypotheses and resolve inconsistencies in the literature, in particular concerning relations between state flow and creative output. We conclude by suggesting some possible future directions for work in the field.

Introduction The psychologist Mihaly Csikszentmihalyi introduced the term ‘flow’ in his studies of visual artists, who reported to frequently have flow experiences while painting, and that these experiences were important for their motivation to continue as active visual artists (Nakamura & Csikszentmihalyi, 2005; cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1). These observations motivated further research on flow per se with a desire to understand the nature and conditions of the phenomenon. The research continued with studies of chess players, rock climbers, dancers, and other professionals who emphasized intrinsic enjoyment of the activity as the main reason for pursuing their vocational interests (Csikszentmihalyi, 1975, 1990, 2000). Several L. Harmat (*) Department of Psychology, Faculty of Health and Life Sciences, Linnaeus University, Växjö, Sweden e-mail: [email protected] Ö. de Manzano · F. Ullén Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_14

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studies have been published on flow experience in the musical domain (for a review see Chirico, Serino, Cipresso, Gaggioli, & Riva, 2015). Music performance, as well as other artistic activities (Delle Fave & Zager Kocjan, 2017; Fullagar, Knight, & Sovern, 2013; Sawyer, 1992) may be conducive to flow since they present good opportunities to take on challenges that match the current skill level, provide continuous feedback to the performer, and capture attentional processes (Bakker, 2005). The aim of this chapter is to discuss the literature on flow experiences in relation to artistic creation (i.e. musical improvisation and composition, visual arts and writing) and performance in music and dance. In addition, we will explore how the dispositional tendency to have frequent flow experiences may promote practice and creative achievement. The first section after this brief introduction reviews studies about subjective state flow experiences in music and dance performances. We also present emerging research on collective flow (‘group flow’) in artistic performances, that is, a collective dimension of the flow experience that can only be observed within a group activity. In the third section of this chapter, we discuss the relation between flow states and artistic creativity. This section further elaborates on the neurobiological underpinnings of creative cognition in relation to flow, and the relationship between subjective flow experiences, creativity and quality of performance. In the fourth section, we discuss relationships between dispositional flow proneness, personality traits, expertise, and artistic creation. The fifth section suggests some directions for future research and discusses practical implications.

Individual and Collective Flow Experiences in Music and Dance Individual Flow During Music and Dance Performances State flow has been investigated in musicians with different levels of expertise and in a variety of contexts such as during live performance auditions and musical jam sessions (Hart & Di Blasi, 2015; Wrigley & Emmerson, 2013), as well as in experimental settings (de Manzano, Theorell, Harmat, & Ullén, 2010). We have previously used piano playing as a flow-inducing behavior in order to analyze the relationship between subjective flow reports and psychophysiological measures (de Manzano et al., 2010). Professional classical pianists were asked to play one of their favorite musical pieces while we collected psychophysiological data, and then retrospectively rate state flow on nine component dimensions (cf. Engeser et al., Chap. 1 for a description of the nine characteristics of the flow state). The performance was repeated five times, and as the participants got more comfortable with the experimental setting across trials, flow ratings increased. This was further associated with increases in both deep breathing and sympathetic arousal, which indicates a combination of relaxation and readiness for action. Our results thus suggested that flow is a state

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of ‘effortless attention’, which can be experienced during task performance as a result of an interaction between emotional and attentional systems (see also Peifer & Tan, Chap. 8). Wrigley and Emmerson (2013) measured state flow in a live musical performance context in 236 musical students from five instrument families (i.e., strings, piano, woodwind, voice, and brass). It should however be pointed out this study focused on examination performance, rather than music performance in general. The Flow State Scale-2 (FSS-2) (Jackson & Eklund, 2004) was used to assess subjective flow experience. The findings showed that the examination was not generally perceived as a rewarding experience and that a majority of the students rated low scores on most of the subscales in the FSS-2. Apart from the subscale “clear goals”, state flow did not vary substantially with instrument type, year, level, or sex. The results also suggest that performance anxiety during a demanding situation may interfere with subjective flow (Fullagar et al., 2013; Kirchner, 2011). Two qualitative studies have investigated the factors that facilitate and inhibit state flow during dance performances. Jackson (1992) investigated elite figure skaters and found that the most important factors for flow in this domain included a positive mental attitude, positive competitive affect, maintaining focus, physical readiness, and for some pair dance skaters, a sense of unity with the partner. The factors that were perceived to disrupt flow included physical problems or mistakes, inability to maintain focus, negative mental attitude, and lack of audience response. Hefferon and Ollis (2006) used a qualitative approach called interpretive phenomenological analyses (IPA) (Reid, Flowers, & Larkin, 2005) to analyze the subjective experience of flow in professional dancers. The analyses were based on in-depth, semi-structured interviews of nine professional dancers. The respondents identified several environmental and individual/personal factors, which they believed facilitated flow in dance, such as choreography, music, costumes and make-up, familiar stage settings, confidence to perform the movement sequence, and pre-performance routine. In addition, the presence of others on stage increased the performers’ ability to experience flow. Furthermore, the performers’ positive relationship with their partners was perceived as a vital factor for facilitating flow, unity and feeling as “one” on stage. These results could potentially be generalized also to other theatre performers such as actors (Martin & Cutler, 2002). Another IPA-based study was conducted on professional ballet dancers (PanebiancoWarrens, 2014). Three dimensions were found to be most predictive of flow in dance: action-awareness merging, loss of self-consciousness, and autotelic experience. In addition, most of the dancers reported that experiencing flow was facilitated by live orchestral music. However, disliking the performed or recorded music may inhibit the flow experience. These qualitative studies suggest important facilitators and inhibitors of the subjective flow experience in dance performances, which would be of interest to study further using objective measurements and quantitative analyses. Positive relationship and unity with the partner (i.e. feeling as “one” on stage) may also be important factors to consider in relation to collective flow experiences during dance.

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The next section, we discuss concepts and findings about group flow experiences in musical artistic performances. Group Flow Experiences During Musical Performances Flow experiences have predominantly been investigated in individual performers. However, there is a growing research interest in the quality of flow in social contexts, i.e. group flow experiences (Sawyer, 2007), also referred to as shared flow (Csikszentmihalyi & Csikszentmihalyi, 1988), social flow (Walker, 2010; cf. Walker, Chap. 10), combined flow (Hart & Di Blasi, 2015), or team flow (van den Hout, Davis, & Walrave, 2016). Sawyer (2006) was the first scholar to describe the existence of group flow during musical collaborations and defined group flow experiences as a “collective state of mind” when the performers were in “interactional synchrony” (Sawyer, 2003, 2007). He suggested that group flow is an optimal collective experience that occurs when members develop a feeling of mutual trust and empathy that allows individual intentions to harmonize with those of the group. Consequently, it has been suggested that group flow is different from individual flow (Nakamura & Csikszentmihalyi, 2005). Hart and Di Blasi (2015) measured the characteristics, outcomes, and practical applications unique to group flow during musical jam sessions. The authors interviewed six musicians who had extensive experience of group jam sessions. Seven out of the nine key characteristics of individual flow (Csikszentmihalyi, 1990; Jackson & Marsch, 1996) also applied to group flow. These were ‘challenge-skill balance,’ ‘action-awareness merging,’ ‘concentration on the task at hand,’ ‘sense of control,’ ‘loss of self-consciousness,’ ‘transformation of time’ and ‘autotelic experience’ (cf. Engeser et al., Chap. 1 for a description of all nine characteristics of the flow state). Based on the analyses of the interviews, the authors suggest that group flow may develop through different stages, and that the shared experience induces empathy between group members. Gaggioli, Chirico, Mazzoni, Milani, and Riva (2016) measured group collaboration and the relationship between flow, social presence, and performance in 15 musical bands during rehearsals. The authors applied their previously developed Network Flow model (Gaggioli, Mazzoni, Milani, & Riva, 2015; Gaggioli, Riva, Milani, & Mazzoni, 2013) in order to determine whether group flow is associated with specific patterns of interpersonal coordination among band members. According to this model, group collaboration is enhanced, when the members of the team experience high levels of social presence. When high social presence has been achieved, participants can enjoy an optimal state that maximizes the creative potential of the group. Social presence was measured by the Networked Minds Social Presence Inventory (NMSPI), developed by Biocca and Harms (2011). The measure includes several facets such as (a) perceived attentional engagement; (b) perceived emotional contagion (the transfer and sharing of emotional states between group members); (c) perceived comprehension; (d) perceived behavioral interdependence. Consistent with the main hypotheses of the NF model, the authors found that there were strong positive relationships between flow, social presence, and intrinsic motivation, and that when in flow, the group members felt more emotionally

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connected. Furthermore, when players perceived higher mutual comprehension, as measured with the NMSPI, they also felt more in control and that they got more clear feedback. Perceived emotional contagion (i.e. transfer of positive emotions) was positively correlated with the dimension of autotelic experience from the flow state scale (Jackson & Eklund, 2002) and the authors suggested that this may indicate a “flow contagion” among players. These results are similar to those reported by Bakker (2005) who also studied emotional contagion and to what extent flow experiences may transfer from one person to another in a music school. The authors found a positive relationship between flow among music teachers (absorption, work enjoyment, and intrinsic work motivation) and the experience of flow among their students. The authors suggested that this may partly reflect emotional contagion. Possibly, such states of shared positive emotion during collaborative music activities could also contribute to increased social cohesion which may be beneficial to consider in educational and therapeutic settings (Koelsch, 2013; Koelsch, Offermanns, & Franzke, 2010).

State Flow Experiences in Relation to Artistic Creation and Performance State Flow and Creative Cognition During Artistic Performance The literature suggests that there is more than one way in which flow could be causally related to creativity. Firstly, the experience of flow has been proposed to play a role as a motivating factor for task engagement and long-term achievement, which would apply in practically any domain. This will be discussed more deeply in section ‘Trait flow proneness and artistic creativity’. Secondly, creative thinking might involve cognitive processes and possibly a certain state of mind that is more intimately linked to the flow experience. This will be further elaborated now. One reason to expect an association between flow and creativity is that there are several elements of flow that are also important for creative thinking (Csikszentmihalyi & Nakamura, 2010). One such element is positive affect. Positive affect increases cognitive flexibility and performance on tasks that require rapid shifting and updating of information in working memory and broader access to remote semantic associates (Nijstad, De Dreu, Rietzschel, & Baas, 2010). One proposed explanation for this, is that positive affect shifts cognition to a more “diffuse” mental state, which is characterized by less gating of information and reactive control (Vanlessen, De Raedt, Koster, & Pourtois, 2016). In the context of creative activities, this mental state would thus benefit free association, fluency of performance, and output that is more original. Consequently, finding flow and experiencing intrinsic enjoyment might be a way of promoting creative processes. The notion of a particular mental state that is conducive to creative thinking is relevant also in relation to recent neuroimaging studies, which indicate that creative cognition is underpinned by a characteristic combination of brain networks,

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i.e. executive control networks and networks related to imagination (Raichle, 2015). This view of a singular creative mental state has however been challenged (Pinho, Ullén, Castelo-Branco, Fransson, & de Manzano, 2016) based on the fact that reallife creative performance can involve different cognitive strategies, which also implies different neural underpinnings. Different creative activities/strategies might then hold different potential for flow experiences. To give an example, Clarke (1988) described three principles/strategies for producing a musical improvisation: (1) Current behavior may be part of a hierarchical structure, to some extent worked out in advance, and to some extent constructed in real-time; (2) current behavior may be part of an associative chain of actions; (3) current behavior may be selected from a number of actions contained within the performer’s repertoire. Given that flow is associated with effortless attention and a sense of automaticity, it seems reasonable to hypothesize that the first strategy, which presumably involves a great deal of conscious elaboration, is less associated with flow experiences than the second strategy, which relies on free association. The existence of different creative strategies, and what neural correlates they might have, has been studied by Pinho et al. (2016). They asked professional pianists to perform musical improvisations on a keyboard under different experimental conditions (different instructions), while measuring brain activity using functional magnetic resonance imaging. Their results showed that creative cognition could indeed be biased towards at least two general cognitive strategies or control modes that differ with regard to the involved brain networks. When the pianists were given an instruction to play only on certain keys (focus on the pitch set), they engaged an extrospective network characterized by conscious executive control and activity in working-memory regions. When the pianists instead were given the instruction to convey a certain emotion (happy/fearful), they engaged an introspective network, similar to that described for creative cognition above. In the latter network, executive control requires less mental effort because control comes from internalized semantic and syntactic rules that can guide free-association in more domain-specific brain systems. This of course requires a certain level of expertise and automaticity (Pinho, de Manzano, Fransson, Eriksson, & Ullén, 2014). Artistic creativity, in contrast to for instance scientific creativity, relies heavily on spontaneous associations, emotional involvement and the expression of affect (Eysenck, 1995; Feist, 1999), and could therefore be expected to depend largely upon processing in the introspective network. This also means that many forms of artistic creativity are facilitated by a decreased activity in the extrospective network, which fits well with first-person narratives of artistic creation. Chemi (2016) for instance reported on the cognitive, emotional and relational elements of creative processes based on interviews with 22 artists from various domains. Several of them described the surprise in looking at the artwork as it took shape, as if they had been suspending their conscious presence and suddenly came back to consciousness. This appears to correspond well with the cognitive prerequisites of flow, which as mentioned involves the subjective experience of effortless but high attention, action-awareness merging and automaticity of responses (Csikszentmihalyi, 1990).

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All of these phenomena are markers of a reduced influence of extrospective processing. Further indirect support of a relation between artistic creativity and introspective processing can be found in an early study by Csikszentmihalyi and Getzels (1971). In this study, 31 advanced art students were observed in a quasi-naturalistic setting of an art school while carrying out an assignment to produce a still-life drawing. The finished artwork was independently evaluated by an expert panel on three dimensions: overall value, originality, and craftsmanship. Two overall problem-solving strategies were found to be used by the students, which map well onto the two forms of processing (top-down controlled versus free associative) described above. Some students produced drawings that were recognizable from the start—the basic structure did not change from beginning to end. These drawings were the result of the artist having a clear idea of what he or she wanted to end up with. Other students produced drawings that had no resemblance to how the drawing started. They typically had a strong emotional response to the objects they chose to draw and the drawings developed organically—they evolved during the process of drawing. These latter drawings were rated by the expert panel to be more original, interesting, and valuable than the former, though the craftsmanship of the two groups of students was deemed equal. Viewing this in relation to what was explained above, one interpretation could be that the students who depended more on a cognitive strategy that involved free association and emotional involvement, i.e. relied on the introspective network for creative imagination, were also deemed more creative by the expert panel. Another point of discussion concerns the observation that state flow is associated with reduced self-awareness, a function ascribed to the medial prefrontal cortex in the frontal lobe, which is part of the introspective network. Regions in this area interface between percepts, cognitive context and core affect. This has also been shown in relation to music (Koelsch, 2010). Janata et al. (2002) were able to reveal a topographic representation of tonality structure in the medial prefrontal cortex and— in a later study—that the same area may enable associative processes between music, emotions, and memories (Janata, 2009). Such processes are further used to provide predictions about specific outcomes associated with stimuli, choices, and actions— especially their moment-to-moment value based on current internal states (reviewed in Rudebeck & Murray, 2014), as for instance during musical improvisation (Pinho et al., 2016). One hypothesis is therefore that when a person is in flow, and attentional resources and skills are completely and continuously directed towards meeting the challenges of the task at hand, there are simply no neural resources left for self-referential thought (cf. Peifer & Tan, Chap. 8). If such reflections are made, performance will break down, since explicit cognitive control can interrupt fluent extemporaneous behavior in much the same way as attending to the components of a well-learned skill can impair concurrent performance (Beilock, Carr, MacMahon, & Starkes, 2002; Gray, 2004). Thus, for artistic activities that load highly on cognitive flexibility, benefit from expertise, and gain from fluency of performance, it might be that flow is associated with a certain state of mind that is particularly conducive to

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creative thinking. It is also conceivable that seeking flow in such activities, in itself, could be a strategy to promote creative cognition. State Flow and Quality of Creative Performance A natural question to ask, given the discussion thus far, is whether the quality of creative output is indeed associated with flow. There are to date, however, only a few studies that have tested this experimentally. Cseh, Phillips, and Pearson (2015) investigated the link between state flow and performance, using a visual creative task and a sample of non-artist participants. The data collected were based on a measure of state flow (post-task), mood measures (pre- and post-task), and performance ratings. Their findings showed that the experience of flow was related to more positive affect during the task, and to higher self-perceived performance. However, there was no relation to the externally rated subjective or objective measures of performance. MacDonald, Byrne, and Carlton (2006) explored whether flow in music students during a group music composition task (45 students working in groups of three) was associated with the quality of creative output, as rated by lecturers and postgraduate students. The compositions belonging to the groups who had reported higher flow was rated as more creative on average by the staff. Gaggioli et al. (2016) studied group collaboration in musical bands and found group flow to be a significant predictor of self-reported performance (including ratings of interpretation, technique, global group performance and perceived competence), but not of expert-evaluated performance. This finding would indicate that flow is more important for subjective motivation than real-time creative performance. Though all of these findings are interesting, it is clear that more research is needed to fully understand the causal relation between state flow and quality of creative performance.

Trait Flow Proneness and Artistic Creativity The findings discussed in the previous section all deal with the relation between flow experiences, i.e. flow as a psychological state, and artistic cognition/performance. Another possible link between flow and creativity exists at the trait level, where flow proneness, i.e. the tendency to have frequent flow experiences, may impact artistic creativity in several ways. This tendency can be operationalized as flow proneness, i.e. a measure of the self-reported frequency of flow experiences in daily life (Jackson & Eklund, 2002; Ullen et al., 2012; Ullén, Harmat, Theorell, & Madison, 2016). First, since flow experiences are intrinsically enjoyable, flow proneness is important for intrinsic motivation, which by definition refers to motivation based on enjoyment of the activity as such. It appears likely that individuals with a higher flow proneness are more intrinsically motivated, since they more frequently

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experience enjoyable flow states. Intrinsic motivation stimulates practice and engagement and, consequently, expertise in a field, which is essential for creativity (Csikszentmihalyi, 1997a). Creative thinking is critically shaped by our acquired knowledge and experiences. Without previously stored concepts and a context, there are no mental elements out of which to form new ideas, and no frame of reference compared to which the ideas can be identified as either novel or useful. Csikszentmihalyi introduced the term autotelic personality (Csikszentmihalyi, 1997a, 1997b) for the trait tendency to invest time and energy in activities which are flow promoting and hence enjoyable and meaningful in themselves. In line with this, we have found that flow proneness, as measured with the Swedish Flow Proneness Questionnaire (for further details see Ullen et al., 2012) is correlated with intrinsic motivation and self-determination, as measured with the Global Motivation Scale (Ullén et al., 2016). Self-report studies indicate that intrinsic motivation and selfactualization are indeed important motivational factors for individuals engaging in artistic fields such as writing and the visual arts (Harrington & Chin-Newman, 2017). Interestingly, the relation between flow proneness and engagement may be domain specific. In later studies, we have used an updated version of the Swedish Flow Proneness Questionnaire, which includes a subscale specifically measuring frequency of flow experiences during musical activities (Butković, Ullén, & Mosing, 2015). We have found that time spent on music practice showed a substantial relation (r ¼ 0.46, p < .0001) to flow proneness specifically during musical activities, whereas there was no relation with general flow proneness in everyday life (r ¼ 0.01, n.s.) (Butković et al., 2015). Relations between flow proneness and creativity in the arts may also involve personality. In a large cohort of Swedish twins, we have found (Ullén et al., 2016) substantial associations between general flow proneness in everyday life and the main personality dimensions of the Five Factor Model (McCrae & Costa, 1990), i.e. positive for openness (r ¼ 0.13), conscientiousness (r ¼ 0.48), extraversion (r ¼ 0.33), and agreeableness (r ¼ 0.31), and negative for neuroticism (r ¼ 0.41). Of these dimensions, openness shows the strongest and most well-replicated link to artistic creativity. This trait is related to artistic interests, cognitive flexibility and a positive attitude to new ideas (Feist, 1999; McCrae & Costa, 1990), and many studies have shown that it predicts creativity in both the arts and science (Feist, 1999; Ivcevic & Brackett, 2015; King, Walker, & Broyles, 1996). For music, several studies have also found correlations between openness and time spent on practice (Butković et al., 2015; Corrigall & Schellenberg, 2015; Corrigall, Schellenberg, & Misura, 2013), indicating that the relation with creative achievement in part may be mediated by time spent on active engagement in a field. For the other Five Factor Model dimensions, the patterns of correlations with flow, on the one hand, and artistic creativity, on the other hand, appears less clearcut. Agreeableness, for instance, is positively related to flow proneness but has been reported to be negatively associated with creative achievement (King et al., 1996). Furthermore, although conscientiousness, openness, and low levels of neuroticism all predict scholastic achievement (Briley, Domiteaux, & Tucker-Drob, 2014), artists are lower on conscientiousness than non-artists (Feist, 1999). King and coworkers

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found indications that creative achievement may depend on trait interactions where the effects of conscientiousness on achievement are positive at low levels of divergent thinking ability, but negative at high levels (King et al., 1996). A general observation is that associations discussed above are likely to reflect both pleiotropy, i.e. common genetic influences, and environmental effects. Individual differences in flow proneness itself depends on genetic influences (Mosing et al., 2012), and we have found the associations between music flow proneness, openness, and music practice to involve significant influences of genetic as well as non-genetic factors (Butković et al., 2015). Overall, results from behavior genetic studies thus suggest that both environmental factors and genetic liability influence how much flow an individual experiences.

Conclusion and Suggestions for Future Research In the first section of this chapter, we discussed the relations between individual and group flow experiences in music and dance. Several recent studies have started to address the interesting phenomenon of flow experiences in a group. These studies indicate that a common experience of flow in a group of interacting individuals may be important for artistic performance and creativity in collective settings. Further work would be important to understand how flow in a group setting differs from flow in other situations, both subjectively and in terms of physiological mechanisms. Furthermore, the group flow experience—unlike flow in solitary activities—appears to involve a component of empathy. Further studies are also needed to investigate the psychological and biological mechanisms behind group flow, and the relationship between emotional synchronization (e.g. empathy, emotional contagion) and collective flow experiences. Another general observation in this context relates to the well-established finding that artistic activities can increase group cohesion and bonding (Dunbar, Kaskatis, MacDonald, & Barra, 2012). In fact, in evolutionary terms, such effects on group sociology are likely to be fundamental for why humans engage in arts to begin with (Dunbar, 2004). An interesting hypothesis is therefore that positive effects of artistic activities on group cohesion, and its underlying neurobiological mechanisms, are mediated by experiences of group flow. This should be tested further in empirical studies Taken together, the empirical work discussed in this chapter clearly suggests that flow is an important factor for creativity in the arts. We can also identify several questions where more empirical work would be of great interest. As we discussed in the third section, existing studies indicate that state flow may be beneficial for creative cognition during artistic activities. Furthermore, neuroimaging studies suggest that a reason for this may be that artistic creativity relies primarily on automated, free associative processing in introspective brain systems involving the medial prefrontal cortex. An important next step would be to further explore this notion

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by incorporating measures of state flow in neurophysiological studies of the brain mechanisms underlying artistic creativity. Several large-scale studies show that flow proneness is important for long-term engagement in artistic fields and creative achievement. An interesting observation in this context is that, at least for music, associations with practice and achievement appear to be highly specific for flow proneness within a domain, rather than general flow proneness. What matters is not a general tendency to experience flow in everyday life, as much as having frequent flow experiences during the creative artistic activity. This suggests that separate analyses of domain-specific and domain-general effects may be essential for future analyses of the complex relations between flow proneness, personality and creativity in different domains. In summary, this chapter has reviewed the literature on flow in relation to music and other fields of arts. We have discussed both state and trait flow in artistic creation and the similarities and differences between individual and group flow experiences in music and dance. The literature on state flow and creative output is small and inconsistent. Although some findings suggest that flow states may be conducive to artistic creative cognition, more studies would be needed to test this hypothesis. The link between trait flow proneness and long-term artistic engagement and achievement is more well-supported, and likely to involve personality traits such as openness.

Practical Implications Taken together, the findings discussed in this chapter suggests that actively finding strategies to promote flow experiences may be beneficial for performing artists in several ways. During practice and preparation, flow increases intrinsic motivation and thereby makes it easier to engage in the tasks for long periods of time. Some studies also suggest that flow experiences during performance itself are related to a high level of artistic quality and creativity, although the literature is inconsistent and more well-controlled studies are needed to investigate this question. Since flow is characterized by enjoyment and reduced self-awareness, findings ways to increase flow on stage could potentially be beneficial for performance anxiety. Finally, group flow may be of practical interest since it appears to be related to social bonding. Conceivably, artistic group activities could therefore be one means to reduce conflicts and increase group cohesion e.g. in educational settings.

Study Questions 1. A phenomenon which has received increased interest in flow research during recent years is group flow, i.e. collective flow in group activities. Please describe some of the distinctive aspects of this experience as compared to individual flow!

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While the basic characteristics of an individual flow experience appear to be present also during collective flow, the latter in addition involves an experience of increased empathy, mutual trust, and emotional connection between the participants. Interestingly, this suggests that collective flow may stimulate prosociality and group bonding. 2. Some studies indicate that flow states are associated with high quality of artistic performance. Please discuss possible reasons for why this may be the case! One possibility is that the positive affect associated with state flow increases cognitive flexibility and fluency of performance. Moreover, while problem solving in STEM subjects typically relies on extrospective creative strategies that require effortful top-down control of cognition, artistic creativity is more characterized by spontaneous associations, bottom-up driven (i.e. less effortful) attention, reduced self-referential thought and emotional involvement. This so-called introspective creative strategy may be more conducive to flow states, and their associated feelings of effortlessness, control, automaticity, and lowered selfawareness. 3. In what ways may trait flow proneness, i.e. a constitutional tendency to frequently experience flow states, be beneficial to artistic creativity? High flow proneness is related to higher intrinsic motivation, which in turn is essential for long-term engagement and practice. Furthermore, flow proneness correlates with the personality dimension openness-to-experience, which in turn is an important predictor of artistic interests and engagement. Finally, if flow states are conducive to artistic creativity - as discussed above—a higher flow proneness would imply that a person more frequently experiences psychological states that are beneficial for artistic creation.

References Bakker, A. B. (2005). Flow among music teachers and their students: The crossover of peak experiences. Journal of Vocational Behavior, 66(1), 25–44. Beilock, S. L., Carr, T. H., MacMahon, C., & Starkes, J. L. (2002). When paying attention becomes counterproductive: Impact of divided versus skill-focused attention on novice and experienced performance of sensorimotor skills. Journal of Experimental Psychology-Applied, 8(1), 6–16. https://doi.org/10.1037/1076-898x.8.1.6. Biocca, F., & Harms, C. (2011). Networked minds social presence inventory (scales only, version 1.2). East Lansing: MIND Labs, Michigan State University. Retrieved from http://cogprints.org/ 6742/ Briley, D. A., Domiteaux, M., & Tucker-Drob, E. M. (2014). Achievement-relevant personality: Relations with the Big Five and validation of an efficient instrument. Learning and Individual Differences, 32, 26–39. https://doi.org/10.1016/j.lindif.2014.03.010. Butković, A., Ullén, F., & Mosing, M. A. (2015). Personality and related traits as predictors of music practice: Underlying environmental and genetic influences. Personality and Individual Differences, 74, 133–138. Chemi, T. (2016). The experience of flow in artistic creation. In L. Harmat, F. Ørsted Andersen, F. Ullén, J. Wright, & G. Sadlo (Eds.), Flow experience: Empirical research and applications (pp. 37–50). Cham: Springer.

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Chapter 15

Flowing Technologies: The Role of Flow and Related Constructs in Human-Computer Interaction Stefano Triberti

, Anna Flavia Di Natale

, and Andrea Gaggioli

Abstract A promising field of application to analyze and better comprehend the impact of the flow concept is Human-Computer Interaction (HCI), in that the analysis and the design of computer interfaces pose notable challenges to its application. For example, in which cases the experience of flow should be promoted in the users, and in which other cases it should be avoided; or, whether and how an overall user engagement can be related to challenges/skills balance or imbalance. The first section of the present contribution provides a critical overview of the integration of flow in HCI, along with its relationship with other important constructs in the field such as presence/immersion, embodiment, breakdown and readiness-tohand. In the subsequent sections, this contribution explores the role of flow in the interaction with specific technologies, namely video games, that constitute the most interesting example of complex interactive interfaces where a certain balance (or imbalance) between challenges and skills should be explicitly designed; virtual reality (VR) as the more immersive digital technology, which recently gained renovated importance because having become a commercial product thanks to the emergence of innovative devices on the global market; and other contexts, with a specific focus on the role of flow in the use of new technologies to promote health and well-being (Positive Technology). The last section will identify important guidelines for future research on the topic of flow and HCI, introducing the more

S. Triberti (*) Department of Oncology and Hemato-Oncology, University of Milan, Milano, MI, Italy Applied Research Division for Cognitive and Psychological Science, IEO European Institute of Oncology IRCCS, Milano, MI, Italy e-mail: [email protected] A. F. Di Natale Department of Psychology, University of Milan-Bicocca, Milano, MI, Italy A. Gaggioli Department of Psychology, Università Cattolica del Sacro Cuore, Milano, MI, Italy Applied Technology for Neuro-Psychology Lab, I.R.C.C.S. Istituto Auxologico Italiano, Milano, MI, Italy e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_15

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inclusive and updated concept of Human Computer Confluence (HCC) and selected new lines of research on the transformative potential of digital technologies.

Introduction Psychological flow can be defined as the feeling of fluidity and continuity in consciousness and action (Csikszentmihalyi, 1988; Massimini & Delle Fave, 2000). More specifically, flow or optimal experience is an emotionally-positive state of deep involvement, absorption, and enjoyment. The basic feature of this experience is a perceived balance between high environmental action opportunities (challenges) and adequate personal resources used to cope with them (abilities or skills). Additional features include the perception of clear rules for action, deep concentration, unambiguous feedback from the task to the activity, a loss or decrease of self-consciousness, altered time perception and intrinsic motivation (ibidem). In the last decades, flow research has become widespread in a large number of topics and areas (Csikszentmihalyi, 2013; Engeser, 2012; Harmat, Andersen, Ullén, Wright, & Sadlo, 2016; cf. Engeser, Schiepe-Tiska & Peifer, Chap. 1); among them, an interesting one is the interaction between computers (technology) and human users. Historically, Human Computer Interaction emerged as a strictly-rationalist approach. According to Card, Moran, and Newell (1983), the best computer is the one which is easy to understand. By using the “language” of computers (e.g.: writing code), one should easily understand if everything is working correctly: by knowledge, observation, and reasoning the users are able to employ computers for their everyday tasks effectively. Today, it is easy to see that this conception is not acceptable anymore. Computers users (basically almost everyone in developed countries) have not specific competences or the ability to reason about hardware and software’s functioning: even toddlers can use specific apps on the tablet, and modern computers are not only used by humans but they also connect inanimate things. The new conception of computers may be lead back to the work by Winograd and Flores (1986), who considered the work by the German philosopher Martin Heidegger and sustained that the best computer is the one that is ready-to-hand. Basically, interactive technologies are good when we are able to act trough them without thinking, such as they were “transparent” to our cognition. The evolution of computers undoubtedly followed this line of thinking: the invention of devices such as the mouse, folders, icons and graphic interfaces demonstrates that technology has learned to speak our language instead of the opposite. It is easy to see how these features of technologies are strictly related to the concept of flow: if good technologies are those used without involving conscious awareness (which, instead, is brought into action when the user experiences a breakdown, namely when something in the technology does not work as expected), this means that using interactive technologies already poses people in situations of fluid interaction. For this reason, Human Computer Interaction can potentially be an extremely florid ground for the emergence of optimal experiences.

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Indeed, the concept of flow has been included very soon in this specific field, and it has been often considered as a quality indicator of interactive technologies. It has been found that numerous technology-mediated activities are able to induce flow experiences, for example e-learning (Davis & Wong, 2007), playing videogames (Triberti & Argenton, 2013), on-line gaming (Hsu & Lu, 2004), using computers in the workplace (Ghani & Deshpande, 1994) and web surfing (Pace, 2004; Skadberg & Kimmel, 2004). Different researchers have studied flow in the context of information technologies. For example, Hoffman and Novak (Hoffman & Novak, 1997) have described flow during web navigation as a cognitive state where high balance between skill and challenge is maintained and characterized by high levels of focus attention and arousal, as well as interactivity and telepresence. Pace (2004) conducted interviews with internet users and he confirmed the presence of the most important characteristic of flow as described by Csikszentmihaly and used them to suggest some tips for web designers to promote flow, by creating appealing interfaces that engage the user, piquing his/her curiosity. Agarwal and Karahanna (2000) investigated flow in information technology users and compared the state of flow to a state of cognitive absorption characterized by five dimensions: temporal dissociation, focused immersion, enjoyment, control and curiosity. Ghani and colleagues (Ghani, Supnick, & Rooney, 1991) analyzed flow during computermediated interactions confirming the importance of skill-challenge balance and further emphasized two other characteristics of flow such as enjoyment and concernment. In other words, multiple data and studies seem to suggest that flow is an important component of the experience with technologies, and that it could be considered an important criterion for quality assessment. However, deeper investigations of the role of flow in human-computer interaction show that this is not the whole story. On the one hand, the opportunity to experience flow certainly has specific effects on the quality of experience; humans tend to prefer and to pursue activities able to promote flow, a process known as psychological selection (Delle Fave & Massimini, 2005; Massimini & Delle Fave, 2000). Anyway, this doesn’t mean that flow experience is good “per se” or it is in any context (cf. Zimanyi & Schüler, Chap. 7). In some cases, flow could be counterproductive to pursue in the context of human-computer interaction. For example, Webster, Trevino, and Ryan (1993) analyzed the dimensionality of flow and its correlates in work-related human-computer interaction tasks. Their interesting results pointed out that flow is related to playfulness perceived in the tasks, which can positively influence the quality of the final products as well as creative abilities and attitudes. However, if a task is playful, pleasant and able to promote flow, this could also negatively influence time of completion because of users’ over-engagement in the activity. Also, in order to empower work-productivity (cf. Peifer & Wolters, Chap. 11), promoting engagement and flow in specific activities could be not always the right choice. Indeed, multiple-system environments require not only the ability of the operator to be involved and concentrated on his task, but also to manage interruptions, because it could be necessary to monitor multiple activities and systems at a time (Mcfarlane & Latorella, 2002). If an

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operator/user is profoundly engaged in an optimal experience, it is possible he will not be able to manage sophisticated contexts of activity. Finally, flow has been often associated with the topic of “engagement” in positive psychology (Davis & Wong, 2007; Hong et al., 2013; Pearce, Ainley, & Howard, 2005), and these constructs are certainly related; indeed, if I experience flow doing something, I will be probably committed to the activity because it is an inherently pleasant task. Moreover, because of the already-mentioned psychological selection process, I will be prone to return to the flow-promoting activity again and again. However, flow and engagement are probably not the same concept, or in other words the promotion of flow is not a sufficient condition (or not the only one) to engage people in what they are doing. Indeed, people can be committed and involved in activities that are not inherently pleasant, continuous and “flowing”; the best example of this kind of engagement, as well as an interesting source of debate on the role of flow in human-computer interaction, is that of video games.

Flow and Video Games Among numerous technologies which have been associated with flow, video games have an important place. Indeed, such interactive, digital media seem to be a particularly fertile ground for analyzing the flow process as well as optimal experiences; they are directly related to entertainment and enjoinment, and multiple studies noticed that they are able to generate an unprecedented amount of psychological engagement (Przybylski, Rigby, & Ryan, 2010; Skoric, Teo, & Neo, 2009). We can feel concentrated and involved in video game playing more than in book reading or film viewing. This kind of engagement can be explained by considering multiple factors (e.g., motivational and contextual ones, or simply the interactional nature of the technology), but psychological flow may be a very useful construct to understand it. Indeed, video games have numerous properties that can be easily linked to flow and its characteristics and requisites. According to Jones (1998) and Cowley, Charles, Black, and Hickey (2008), these properties can be resumed as such: video games provide challenge by nature and the necessity to be fully concentrated in an activity (immersion properties); video game activities have specific rules to play and clear goals within structured systems, and also clear feedback to the activity is always provided; a feeling of full control can be guaranteed by mastery of devices and controllers; even the altered sense of time is promoted by video games, in that events tend to be adapted to narrative and game mechanics necessities (e.g., entire war battles are conducted in minutes). In other words, the exact nature of video games seems to be “flow-like designed”, so that optimal experience could be a recurring and typical event within the interaction with such digital media, and the possibility of flow experience one of the main reasons for their success. Indeed, some research seem to confirm that flow is typical and recurrent in video games (Argenton, Triberti, Serino, Muzio, & Riva, 2014; Cowley et al., 2008; Gee, 2005; Klasen, Weber, Kircher, Mathiak, & Mathiak, 2012).

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However, recent research on the topic challenges simplistic assumptions (Triberti, 2016). Indeed, video games are more complex than what it seems. For example, they should be considered a technology whose main goal/property is not to make the player succeed nor engaging him/her in fluid streams of actions, rather it is to make him/her fail and try again continuously (Juul, 2013; Lorentz, 2014); this aspect is important to enjoy video games, in that the possibility to fail and try again is what allows players to feel self-effective and to actually become better gamers. Although optimal experiences of fluid interaction may occasionally appear in video games, this is probably not sufficient to explain their multifaceted appeal for players. Also, recent research is challenging the classic skill-challenge equilibrium hypothesis, in that challenges which are always at the same level of skills may generate boredom instead of engagement, especially in the long term (Baumann, Lürig, & Engeser, 2016; Løvoll & Vittersø, 2014); this is very common in video games, in that challenges tend to be constantly varying instead of fixed. Another aspect that may be interesting to explain the role of flow in video games is related to arousal. It has been investigated the possibility that flow can be associated with different arousal states depending on the level of balance between challenge and skills (Engeser & Rheinberg, 2008; cf. Abuhamdeh, Chap. 5). When the challenge is too high compared to the subject’s ability then he will experience anxiety, and thus, high physiological arousal. On the other hand, if the skills exceed the challenge, the individual will feel relaxed or bored, experiencing low level of arousal. If flow occurs when the balance between skill and challenge is matched, that implies that this state should be correlated with moderate or optimal physiological arousal. De Manzano, Theorell, Harmat, and Ullén (2010) suggested that the relationship between flow and arousal might be described by an inverted u-shape where low and high level of arousal are associated with low flow values, while flow experience is linked to moderate levels of arousal. This hypothesis was tested and confirmed in a study by Peifer, Schulz, Schächinger, Baumann, and Antoni (2014; cf. Peifer & Tan, Chap. 8) that showed an inverted u-shaped relationship between flow and the stress system, namely sympathetic and HPA-axis activation. In this sense, it appears that a “pure” experience of flow is related to specific values of arousal. However, sophisticated interactive technologies such as video games feature numerous and sophisticated content that may interfere with arousal continuum (Triberti, 2016; Villani et al., 2018); for example, when one plays a video game with a compelling narration and/or emotional contents, this probably influences his arousal fluctuations independently of skills/challenges balance featured by the interactive tasks, as typical of media fruition in general (Bartsch, Vorderer, Mangold, & Viehoff, 2008; Schramm & Wirth, 2010). In conclusion, flow can be used as one of multiple quality criteria while evaluating user experience of a given technology; however, when considering engagement, other factors (e.g., emotional, contextual, social) should be taken into consideration, in that they could influence or even predict engagement despite interfering with an optimal experience as understood by flow theory. In addition to this ongoing debate, a specific mention could be given to social aspects in video games playing. Multiple authors have identified that emergent types

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of flow experience can be attributed to groups and not only to individuals (Borderie & Michinov, 2017; cf. Schiepe-Tiska & Engeser, Chap. 4 and Walker, Chap. 10). On the one hand, such a phenomenon can be related to individuals experiencing flow together; but, usually such conceptions point out that when a group is entirely focused on a specific activity, it is possible that a positive optimal group experience of engagement emerges, both in a real and in a virtual setting. Sawyer (2010) investigated this phenomenon as “group flow”. When group members are involved in a specific task at the same time, performing correlated and coordinated actions, it is possible that such an experience occurs, generating optimal group performances. According to the Networked Flow theory (Gaggioli, Milani, Mazzoni, & Riva, 2011; Riva et al., 2016a, 2016b), groups experiencing a shared experience of flow are creative or motivated and equipped to generate new artifacts and products; in the field of video games, this may result, for example, in optimal strategies and solutions to cope with challenges emerging within the game instances. Massive multiplayer games and Virtual Worlds (i.e., large virtual environments online that multiple users could utilize by means of avatars) have been identified as an interesting resource to design interactive and creative activities that could be used to promote networked flow, this way capitalizing on social relations for health and well-being (Triberti & Chirico, 2016) (see Box 15.1 for the phases of Networked Flow). Box 15.1 The Six Staged Model of Networked Flow According to Gaggioli et al. (2011), networked flow develops over six phases: 1. Meeting (Persistence): a certain number of individuals start sharing an interactive context; 2. Reducing the distance: individuals recognize to share similarities and begin to form a collective intention; 3. Liminality-parallel action: individuals experience a high level of social presence; they define group’s boundaries by means of group activity and conventions; 4. Networked Flow: optimal collective experience; 5. Networked Flow—creation of the artifact: the collective activity generates a creative product (physical or abstract); 6. Networked Flow—application of the artifact: the artifact is taken into the pre-existing, enlarged social context.

Flow and Virtual Reality Several authors have pointed out the potential of virtual reality (VR) as a flowinducing technology. Specifically, VR is particularly interesting because it allows to recognize and appreciate flow’s relationship with other phenomena typical of the Human-Computer Interaction field. VR is a technology that broadens the spectrum of human experience, allowing users to perceive, feel and interact in a manner that is

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similar to a physical place. This is achieved by combining stimulation over multiple sensory channels—such as sight, sound and touch—with force-feedback, motion tracking, and control devices. Gaggioli, Bassi, and Delle Fave (2003) highlighted some crucial characteristics of VR that suggest its potential effectiveness in fostering flow: 1. opportunities for action—In the virtual environment situations and tasks can be designed involving a wide range of human gestures and of perceptual and cognitive functions. The complexity of tasks can be gradually modified so that the individual can start to face the simplest situations and step towards more difficult ones; 2. skills—The tasks presented in the virtual environment can require specific skills, such as cognitive and practical ones, that can be refined and gradually increased during the sessions; 3. feedback—VR systems can offer a multimodal feedback to individuals’ actions and behavior (such as video games); 4. control—Individuals can experience control of the situation while interacting in the virtual world and using their abilities. In other words, VR offers challenges that can be gradually increased, simultaneously allowing the individual to gradually improve his/her skills: Therefore, it can be a potential source of optimal experience. In this dynamic process the feedback the person receives from VR, and the control perceived during the session also come into play. To better understand the possible link between VR and flow, a useful starting point is the analysis of the concept of presence, broadly defined as “the perceptual illusion of non-mediation” within an artificial reality (Lombard & Ditton, 1997). When users feel “present” in a simulation, they feel that the technology has become part of their bodies and that that they are experiencing the virtual world in which they are immersed. Crucially, when they feel present in VR, they react emotionally and bodily (at least to some extent), as if the virtual world exists physically. Historically, the concept of presence was first introduced by Marvin Minsky (1980) to describe the sense of being physically “transported” to a remote place that is often reported by teleoperators. Successively, Sheridan (Sheridan, 1992) defined telepresence as “feeling like you are actually ‘there’ at the remote site of operation” and “virtual presence” as “feeling like you are present in the environment generated by the computer” (p. 120). More recently, the term telepresence has been used to refer to any medium-induced sense of presence, while reserving “presence” strictly for referring to the natural perception of an environment. Starting from these early definitions, several theoretical accounts of presence have been developed over the years, which can be grouped into two main perspectives. The first conceptualization sees presence as an experience resulting from the interaction of a user with a given medium. According to this view, presence is a psychological state that is shaped, although not completely determined by, the immersive properties of the medium (Slater & Usoh, 1993). The second perspective conceives presence as an embodied process, which is linked to the organization and control of action, so not necessarily related to technical properties of a medium: according to this view, the sense of

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“being there” is strictly related to the subject’s ability to successfully “act there” (Riva, Mantovani, Waterworth, & Waterworth, 2015; Zahorik & Jenison, 1998), in the space where he/she is situated - either “physical” or “virtual”. Thus, the more a virtual environment allows the user to successfully engage in an intended behavior, the more present he/she will feel. According to Riva and Waterworth (Riva & Waterworth, 2014; Waterworth & Riva, 2014), presence is not a property derived from a given technological medium, but a neuropsychological process that has a specific evolutionary function, that is, allowing to define the boundaries of action by means of the distinction between “internal” and “external” within the sensory flow. More specifically, according to this model, presence has three functions: (1) to permit the subject to position himself in a space—real, virtual, or social—through the distinction between “internal” and “external” and the definition of a boundary; (2) to check the efficacy of the subject’s actions through the comparison of intention and the result of the action; (3) to identify the Other and to attribute to him an ontological status—“the other similar to the self”—and allow interaction and communication through the understanding of the Other’s intentions. The latter aspect refers specifically to social presence, that is, how users perceive the presence of other social entities (living or synthetic) in a virtual environment. This brief overview of the key features of presence allows to highlight the phenomenological aspects that this experience shares with flow. First, both experiences are described as absorbing states, characterized by a feeling of being “transported in another reality”, and an altered perception of time. Furthermore, both flow and presence are usually associated with the subjective perception of high concentration and involvement, focused and sense of control on the activity at hand. Furthermore, other theoretical models of presence, such as the PresenceInvolvement-Flow Framework (Takatalo, Häkkinen, Kaistinen, & Nyman, 2010) include as subdimensions of presence other key elements of flow, such as the level of arousal, concentration, and time distortion. On the basis of these commonalities, it has been proposed that presence could be regarded as a specific type of flow experience, arising from concentrating on a virtual reality task (Draper, Kaber, & Usher, 1998; Klimmt & Vorderer, 2003) or a pre-requisite of flow (Takatalo et al., 2010). Other scholars have also highlighted specific differences between the two experiences. For example, Heeter (2003) has argued that flow is generally associated with positive emotions, whereas one can feel present in unpleasant, unsatisfying situations (for example, when a phobic patient is exposed to a feared situation, his/her level of presence may be high but he/she might experience the negative emotion of fear). Fontaine (1992) has highlighted two further conceptual distinctions between flow and presence. The first is that optimal experience involves a narrow focus of attention on a limited range of task characteristics, whereas presence is associated with a broader awareness of the task context. Also, while optimal experience requires feelings of control, presence has been reported with unfamiliar ecologies involving a lack of predictability that make feelings of control difficult. Many studies explored the relationship between presence and flow. When trying to explain the factors involved in effectiveness of augmented reality-based e-learning materials, Kye and Kim (2008) applied a

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structural equations model and found that sensory immersion and manipulation (i.e., interactivity with the system) predicted presence, and presence predicted flow variables, which directly influenced positive outcomes in learning. More recent research employing qualitative methods in educational video games (Scoresby & Shelton, 2011) highlighted the role of additional factors (emotions, virtual environment content, motivation, engagement) in the relationship among presence, flow and enjoinment. Similarly, Weibel et al. (2008) suggested that the experience of flow mediates the link between feeling of presence and enjoinment when playing computer games, against other players especially. According to Riva and Waterworth (2014), the link between presence and flow is related to the efficacy of the (inter)action: the greater level of presence a subject experiences in an activity, the greater the organism’s involvement in the activity will be, and this increases the probability of the activity ending well (the transformation of the intention into action). When this happens, the optimal experience of flow more likely arise. A similar analysis applies when considering the context of social presence: in this case, the resulting optimal (social) experience is the specific group-level mental state called “networked flow” (Gaggioli et al., 2011) which has been introduced in the previous paragraph. Thus, according to these theoretical views, presence—intended as the feeling of being situated in a body and in the space around it—is an important condition for the occurrence of flow, either in mediated or non-mediated environments. The potential for VR to foster flow/networked flow via the generation of presence/social presence, could be used for designing VR-based applications in various domains—such as learning, creativity, sports and healthcare—in which it is proven that this optimal experience provides an added value. For example, Riva and Gaggioli (2009) have proposed that VR could be used to activate a “transformation of flow” for rehabilitation purposes. Successful rehabilitation exercises—i.e., in stroke rehabilitation—requires the active participation of patients, as well as patients’ ability to identify novel opportunities for action matched with challenges that are proportioned to the residual functional skills. The “transformation of flow” approach consists in the following methodological steps: first, to identify an enriched environment that contains functional real-world demands; second, using VR to enhance the level of presence of the subject in the environment and to induce flow; third, allowing cultivation, by linking this optimal experience to the actual experience of the subject. A prototypical example of this approach is the VR-based Rehabilitation Gaming System (RGS) proposed by Cameirão, Bermudez y Badia, Oller, and Verschure (2010) for the treatment of upper extremity disorders following stroke. This gamebased rehabilitation system is designed to provide a dynamical and real-time adjustment of the difficulty of the task to the residual skills of the patient, thus allowing a perfect match between skills and challenge: on the one hand, this avoids patient’s frustration (since the difficulty of the exercise is never excessive with respect to the patients’ abilities); on the other hand, this increases patient’s intrinsic motivation and feeling of control. A similar approach is followed with the “Positive Bike” system (Fig. 15.1), an immersive biking experience for physical and cognitive training of frail elderly patients (Pedroli et al., 2018). The system consists of a cycle-ergometer

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Fig. 15.1 The Positive Bike Rehabilitation System: cave setup, schematic representation of hardware, and usage session

integrated in an immersive virtual reality system (CAVE) which allows combining motor and cognitive exercises according to a “dual-task” paradigm. To achieve transformation of flow, the level of challenges within the rehabilitation games can be dynamically adjusted to match the skills of the patient. An interesting feature of the system is that it provides a full-body experience (because the patient is physically exercising while immersed in the virtual world), thus enhancing the embodiment illusion that contributes to generate a feeling of presence. Preliminary assessment of the Positive Bike experience indicated that patients’ reported high levels of flow in performing the rehabilitation exercises.

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The transformation of flow concept highlights that flow can be used as a design guideline to develop technologies aimed at improving users’ abilities, health and well-being. This relates to the concept of positive technology.

Flow in Positive Technologies Nowadays the use of technologies has rapidly increased leading to massive changes in our everyday life and getting the attention of many researchers, especially of those in the psychological field. We can refer to Cyberpsychology to indicate a new branch of psychology studying precisely the relationship between digital technologies and human behavior. In particular, a new striking challenge within the context of Human Computer Interaction is to understand how to use technologies to promote our psychological well-being and positive functioning. An attempt to do so comes from an emergent discipline, known as Positive Technology (Riva, Baños, Botella, Wiederhold, & Gaggioli, 2012). We can define the Positive Technology as the use of technologies to manipulate our personal experience by structuring it, increasing it or replacing it, in order to improve our well-being. This new scientific paradigm reflects the key concepts of the Positive Psychology, a psychological approach that emphasizes the need for psychology to focus on humans’ strengths and on making people’s lives fulfilling, rather than focusing on disease and on treating pathologies (Seligman & Csikszentmihalyi, 2000). In his book, “Authentic Happiness” (Seligman, 2002), Martin Seligman, founder of the Positive Psychology, identifies three pillars of our personal well-being, or “happy lives”. The Pleasant Life that one may reach by experiencing as many positive emotions as possible. The Engaged Life that one may reach by getting involved in satisfying and pleasant activities. Finally, the Meaningful Life that one may reach by knowing what his/her highest strengths are and by using them in the service of something larger than he/she is. Later, Seligman developed his ideas and implemented the PERMA model (Seligman, 2011), a model explaining five core elements that can help people reach a happy and fullof-meaning life: positive emotions, engagement, relationships, meaning and accomplishment. This model introduces the importance of the social dimension that, together with the emotional and psychological dimensions, contributes to one’s personal well-being. To synthetize these aspects we can say that the Positive Psychology identifies three characteristics of the personal experience that can be manipulated in order to increase wellness: the emotional quality, the engagement or realization and the social connection. In line with the Positive Psychology paradigm, the Positive Technology paradigm classifies technologies based on their impact on these three characteristics of the personal of experience (see Box 15.2).

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Box 15.2 Positive Technology Levels • Hedonic Technologies: or technologies used to induce positive emotion. For example, it has been demonstrated that it is possible to induce a specific emotional response, such as anxiety, relaxation or joy, with the use of Virtual Reality or other video and audio technologies (Botella et al., 2012; Graffigna, Barello, Wiederhold, Bosio, & Riva, 2013; Riva et al., 2016a, 2016b). • Eudaimonic Technologies: or technologies used to support individuals in engaging and self-actualizing experiences. • Social/Interpersonal technologies: or technologies used to promote connectedness and to improve social relationships and collaboration among individuals or groups. To sum up, the core idea underlying the Positive Technology approach is the possibility to use technologies to influence all the levels of our personal experience—positive emotions, engagement and connectedness—to elicit optimal experiences that might induce personal long-lasting changes. However, in order to do so, technologies must guarantee an enjoyable, positive and engaging subjective experience. They should be ready-to-hand allowing the user to fluently interact with them without consciously thinking about every single step to take while using them (Winograd & Flores, 1986). For example, a person using a laptop trying to accomplish any kind of tasks, from opening a folder to web surf, should be able to do it easily and intuitively. He won’t have to know the language, or code, of the computer to do it but he will have to interact with a human-friendly interface that allows him/her to do what he/she wants without over thinking. This concept of fluid interaction recalls the one of flow experience, described in fact as intrinsically rewarding and motivating activity that people experience when they are completely focused and absorbed by the activity itself and when they feel balance between the perceived challenge and their own skills. In this case, usability is a fundamental characteristic of a technology able to promote flow experiences and should be considered the starting point for technology design (Triberti, Chirico, & Riva, 2016). Nevertheless, this idea is not sufficient to explain the complicated role of flow in technologies. Despite flow has been considered to be a crucial aspect for technologies to promote satisfying experiences, Finneran and Zhang (2005) suggested that the experience of flow is related to the task that the user is trying to accomplish rather than to a specific technology. According to this perspective the main point is to clarify what aspects of a technology and the task at hand are fundamental to promote a state of flow in the user. While different factors, such as cognitive strategies, personality traits and contextual variables, have been suggested to influence the emergence of flow experiences in the context of technology use (Bassi, Steca, Monzani, Greco, & Delle Fave, 2014; Cavanagh & Shernoff, 2014; Schmidt, Shernoff, & Csikszentmihalyi, 2014), it is important to understand what aspects of

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a technology may be useful to properly respond to the users’ goals, guaranteeing a fluid interaction and thus promoting flow. In particular, Triberti et al. (2016), suggest that the possibility to enter a flow state, while interacting with a technology, is strictly linked to presence and intention enacting. In fact, Riva, Waterworth, Waterworth, and Mantovani (2011) suggest that when a technology fluently responds to the user’s goals, he/she can reach the highest level of presence, a necessary condition that, together with a positive emotional state can induce optimal experiences (Riva et al., 2012). This happens as the technology satisfies his/her hierarchical structure of intentions (Pacherie, 2006): the distal intentions, which are about reasoning about means and future plans; the proximal intentions, which concerns what to do in the present to achieve the distal intention; and finally the motor intentions, that corresponds to the motor aspect of the action implementation. According to this perspective, the sense of presence, or the feeling of “being there”, is what enables a person both to locate himself or herself in the physical space and to feel present by enacting his/her intentions. Based on the idea that there is a link between the intentional hierarchy of the user and the characteristic of the technology, Triberti and Riva (2015, 2016) developed the Perfect Interaction model (PIM), that defines the processes triggered when user’s intentions meet technology (Fig. 15.2). First the user reaches a level of extended presence, when the technology addresses his/her distal intention: the user wants to achieve something and the technology represents an opportunity to extend user’s action in order to reach that purpose (DC level: for example, a user wants to be a writer, and the design concept at the basis of a computer with a text editor software dovetails with such intention). Then the user transforms the distal intention in a proximal intention, reaching a level of core presence: the structure and functions of the technology allow the user to accomplish all the tasks and action needed to pursuit the distal intention in the here-and-now (PS level: e.g., the wannabe-writer user wants to write the first words of his book, and the structure of the technology, namely keyboard, screen, etc. dovetail with such here-and-now intention). Finally the user actually executes the action, satisfying the motor intention and reaching a level of proto presence when he physically interacts with the technology interface with success (MI level: the writer moves one of his fingers, and a key is pressed). The Perfect Interaction Model represents the interaction in which everything functions as it should: a perfect “dovetailing” develops among the generative features of technology and the user’s hierarchical structure of intentions. Such a model can be used as a main guideline for technology design; indeed, often only the motor component (or the interface) of technologies tend to be taken into consideration by engineers and designers. But, interface usability is not sufficient as a requisite for the technology to promote a state of flow. Despite interface can be easy to understand and to use, users may not understand the function of the technology, or try to use it to achieve improper intentions, or also just exploring its function without a clear objective. An optimal experience of interaction could establish only when the technology responds to user’s intentions at the high level of general objectives too. In this sense, in order to reach an optimal experience or, better, a perfect interaction, recent literature suggests to employ research methods

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Fig. 15.2 The Perfect Interaction Model. MI motor/interface level, PS proximal/structure level, DC distal/concept level

not only for the evaluation of already-designed and implemented technology, but rather to analyze users’ intentions even before design in order to provide data-based design, eradicated in users’ needs. User Centered Design could be considered the gold standard of User Experience (Garrett, 2010; Lowdermilk, 2013), both for promoting flow in human-technology interaction when needed, but also to understand users’ needs and intentions and evaluate whether flow is a desirable state or a psychological process to be avoided in specific cases.

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Future Research: Flow in Human-Computer Confluence and Transformative Experience Design In this contribution we have seen how the flow construct made its way through the field of technology and experience; it has been found and measured in different kinds of interaction, and it has reached the status of positive evaluation criteria for the analysis of experiential quality. However, the present contribution also highlighted that the debate on the topic became more complex in the last years; flow may be not always positive or desirable for Human-Technology Interaction, especially if technologies are used in the context of multi-task activities, or when a more sophisticated type of engagement should be called into play, such as in the case of video games and interactive media use. Basing on these premises, a complete contribution on the role of flow in Human-Technology Interaction should provide some information (or even guidelines) to prefigure such a role in the context of innovative technologies too. Indeed, the next years’ scenario promises to bring important innovation to the field of new technologies. For instance, the term “Human-Computer Confluence” has already been proposed to substitute that of “Interaction” (Ferscha, Resmerita, & Holzmann, 2007; Gaggioli, Ferscha, Riva, Dunne, & Vlaud-Delmon, 2016), especially because of the emergence of technologies going by the names of Ambient Intelligence (AmI), Smart Environment, or Pervasive Computing. These classifications regard multiple technological solutions that have in common the idea of computers surrounding people, usually in a non-intrusive way because they are supposed to be integrated into existing environmental features and possibly invisible; practically, these are based on the embedding of micro-computers and multiple sensors (pressure/strain, image processing, sound, motion, physiological signals, ...) in order to allow the distributed system to recognize, analyze and monitor people present in the environment and their activities; then, integrated computational elaboration permits to understand users’ needs and to respond accordingly with online modifications of the environment itself. According to literature (Acampora, Cook, Rashidi, & Vasilakos, 2013; Bravo, Cook, & Riva, 2016; Cook, Augusto, & Jakkula, 2009), AmI applications share some specific characteristics; they are context aware (making use of information drawn on the here-and-now situation); personalized (tailored on the individual); anticipatory (predicting user’s needs/behavior); adaptive (modifying its own functions on the basis of user’s habits); ubiquitous (embedded and distributed in the environment); transparent (working without direct action, nor perception, nor knowledge by the human user). So, AmI systems are expected to support the users in their everyday activity but, in the strictest sense, they exclude the concept of “interaction” in that they may completely lack of interface. On the contrary, humans and computers are merging (confluence) in a single entity directed towards a given outcome. This is basically a starting point and the ultimate agenda of those who develop AmI technologies. However, the main criticalities of Ami approaches in the literature are usually focused on the limitations of these technologies in analyzing users’ needs and behavior (Friedewald, Vildjiounaite, Punie, & Wright, 2007; Rubel

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et al., 2004; Triberti & Barello, 2016). Considering these premises, the role of flow in the context of AmI technologies is not related to interaction with them, rather it will be that of everyday activities which could be empowered by technology. In other words, AmI designers of the future should develop an understanding of everyday activities that goes beyond the user experience-like evaluation of technology, on the contrary it presumes technology as present in the environment and supporting activities. According to Gaggioli (2005), promoting optimal experiences should be considered a guiding principle of AmI development; specifically, dedicated research methods such as experience sampling method (Csikszentmihalyi & Larson, 1987; Konradt & Sulz, 2001) or ecological momentary assessment (Alcañiz, Rodríguez, Rey, & Parra, 2014) could be used in order to assess agents’ daily activities and tailor AmI technology design on them. Another important development which may affect innovation in technology at large is the concept of transformative experience, namely experiences with a strong emotional value which have a direct influence on behavior and could cause immediate change in people (transformation). Profound and rapid change in attitudes and behavior is related to transformative emotions, such as awe, which is an emotional response characterized by intense feelings of wonder and astonishment that arise from witnessing (at a physical or abstract level) something extraordinary, which transcends previous mental schemas or expectations (Keltner & Haidt, 2003). In the last years, the research on positive technology included studies on the promotion of transformative emotions in order to recover the “meaning” factor from Positive Psychology. In this sense, technologies may be used to induce not only “simple” emotional states but also these rich emotional experiences with a potential important role in behavioral change. This pertains to the innovative field of Transformative Experience Design (Gaggioli, 2016; Gaggioli, Chirico, Triberti, & Riva, 2016) or the effort to develop simulation technologies able to induce transformation in users in controllable situations such as the laboratory. As previously hinted at, awe is the first transformative emotional experience to be analyzed by theoretical and experimental literature related to simulative technology (Chirico et al., 2017; Chirico, Ferrise, Cordella, & Gaggioli, 2018; Gallagher, Reinerman-Jones, Sollins, & Janz, 2014; Triberti, Chirico, La Rocca, & Riva, 2017). These studies could be considered the first steps in the field; anyway, the Human-Computer Interaction of the future will probably include technologies able to convey extraordinary experiences, with potentially enormous consequences in terms of identity construction and behavioral change. According to pioneer studies, flow itself could be considered a transcendental experience, although at a low level of physiological activation (Chirico & Gaggioli, 2018); in this sense, the research on technology and flow could probably offer important insights for future studies on the effect of technologies on human experience. On the other hand, as suggested previously, if flow is inherently associated with a specific type of arousal profile, it is possible that new constructs would be introduced to explain emergent phenomena in HCI. Flow is one recurrent experience in HCI that have found reasonable research interest and could capture important aspects; however, some new constructs emerged and it is still mandatory to understand their relationship with optimal experience, such as presence and

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intentions, sophisticated emotions, and the role of flow in user research for user centered design of computers and technology.

Conclusions This contribution explored the role of flow and related constructs in the field of Human Computer Interaction. On the one hand, flow made its way through this field to finally become a quality criterion of interaction; to date, numerous technologists and technology designers still use to analyze it in order to assure that a given device or interaction is usable, well-functioning and positive. However, we also highlighted that Human Computer Interaction is a complex and various field, in which flow is not always desirable and positive (cf. Zimanyi & Schüler, Chap. 7). Broadly speaking, we highlighted that: • Multiple tasks (e.g., using devices to monitor multiple technologies and activities) could be possibly hindered by flow-related over-engagement in a sole specific technology usage (Mcfarlane & Latorella, 2002); • Technologies of the future (e.g., Ambient Intelligence) will require to consider flow, but not necessarily in interaction, rather in everyday activities which would be supported by ubiquitous and invisible technologies (Gaggioli, 2005); • Some interactive technologies/media (e.g., video games, and transformative technologies) bring along specific features which are fundamental for evaluating interaction quality but that one risk to overlook if considering flow primarily (Triberti, 2016). This contribution may be useful for scholars working in the field of Human Computer Interaction, in order to keep in mind the importance of flow to explain interactional engagement, but also to appreciate the complexity and variety of phenomena that may be important to fully-comprehend the profound involvement between devices and their users.

Study Questions • What is the role of flow in Human Computer Interaction History? Broadly speaking, Human Computer Interaction developed from an approach focused on rationalism (e.g., computers should be easy to understand) (Card et al., 1983) to one focused on “readiness-to-hand” (e.g., computers should be easy to use as they were “transparent” to cognition) (Winograd & Flores, 1986). In this sense, effective technologies are already conceptualized as able to engage users in fluent streams of interaction. For this reason, flow has been included soon in studies about technologies, often as a measure to analyze design and interaction quality. However, one should consider that sophisticated technologies may

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feature experiential aspects that hinder the concept of flow itself (e.g., see response to next question), and that not always flow is desirable to achieve in the interaction (e.g., if one has to monitor multiple technologies and/or tasks to complete his activity, over-engagement in one technology use may be distracting and negatively influence global performance) (Mcfarlane & Latorella, 2002). • Why do some complex technologies (e.g., video games) challenge the traditional concept of flow? Video games could certainly engage users in an unprecedented way. However, they usually do not feature skill/challenge balance but challenges are constantly varying. Moreover, video games feature narrative properties that engage people independently of mere interaction, for example moving and tragic contents. According to literature on tragedy (Triberti, 2016), these aspects could engage users in counterintuitive ways, but they also tend to promote arousal profiles (i.e., physiological activation) which are not consistent with that typical of optimal experience. • What is the relation between flow and typical phenomena of immersive digital technologies such as virtual reality (e.g., sense of presence)? Sense of presence could be defined as the sensation of “being” in real or virtual place. According to literature, sense of presence comes from successful interactivity and the impression of being able to enact one’s own intentions (Riva & Waterworth, 2014). In other words, sense of presence is strictly related to interaction quality. Therefore, experiencing flow while in virtual reality is an indicator that one is experiencing sense of presence. Moreover, both experiences are described as absorbing states, characterized by a feeling of being “transported in another reality”, and an altered perception of time. Furthermore, both flow and presence are usually associated with the subjective perception of high concentration and involvement, focus and sense of control over the activity at hand. Furthermore, other theoretical models of presence, such as the PresenceInvolvement-Flow Framework (Takatalo et al., 2010) include as subdimensions of presence other key elements of flow, such as the level of arousal, concentration, and time distortion. • Why should one consider flow when planning rehabilitation interventions with technologies? Can you state an example? Riva and Gaggioli (2009) have proposed that VR could be used to activate a “transformation of flow” for rehabilitation purposes. Successful rehabilitation exercises—i.e., in stroke rehabilitation—requires the active participation of patients, as well as patients’ ability to identify novel opportunities for action matched with challenges that are proportioned to the residual functional skills. The “transformation of flow” approach consists in the following methodological steps: first, to identify an enriched environment that contains functional realworld demands; second, using VR to enhance the level of presence of the subject in the environment and to induce flow; third, allowing cultivation, by linking this optimal experience to the actual experience of the subject. A prototypical example of this approach is the VR-based Rehabilitation Gaming System (RGS) proposed by Cameirão and colleagues (Cameirão et al., 2010) the treatment of

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upper extremity disorders following stroke. This game-based rehabilitation system is designed to provide a dynamical and real-time adjustment of the difficulty of the task to the residual skills of the patient, thus allowing a perfect match between skills and challenge: on the one hand, this avoids patient’s frustration (since the difficulty of the exercise is never excessive with respect to the patients’ abilities); on the other hand, this increases patient’s intrinsic motivation and feeling of control. • What is the Perfect Interaction Model and how does it work? The Perfect Interaction Model (PIM) (Triberti & Riva, 2016) is an innovative approach to technology evaluation and design. It is based on the idea that a “dovetailing” process should establish between the generative characteristics of a technology (concept, structure of functions, physical interface) and the user’s hierarchy of intentions (distal intentions or general objective, proximal intention or effect to be obtained now, motor intention or how to physically move). The consistency between these three layers of agency/interaction (I) guarantees the achievement of high-quality technology-mediated performance, (II) could be used as a guide to perform technology evaluation, informing about the types of existing issues, and (III) could be used as a guide to perform User Centered Design of the technology itself. Designing technology with the PIM as a reference could allow designers to predict users’ intentions and interaction, therefore it enhances the possibility that sense of presence and optimal experience will be achieved. • What can you say about the possible role of flow in future technologies? Can you state an example? An interesting field to discuss is that of Pervasive Technology. In the future decades, Ambient Intelligence (AmI) technologies, or invisible devices surrounding people and supporting their activities, may be more widespread. In this context, flow will not regard interaction, because AmI technologies could function autonomously and lack interface; on the contrary, AmI designers should be able to analyze users’ activities in everyday life, and technologies should be able to preserve or even support optimal experience achievement in everyday activities (Gaggioli, 2005).

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Chapter 16

Theoretical Integration and Future Lines of Flow Research Corinna Peifer

and Stefan Engeser

Abstract The final chapter provides a short summary of all chapters of the book and points to similarities between the chapters and what these imply for future research. Future research topics that are discussed include the core components of flow, the differentiation of frequency and intensity of flow, and the duration of and dynamics within and between flow episodes. We further look at antecedents of flow beyond the demand skill balance and at the role of intrinsic and extrinsic reasons for the emergence of flow. For the development of autotelic personality, it is proposed that we can apply existing evidence regarding personality factors related to flow. Further, the chapter addresses a potential agreement regarding the measurement of flow using a combination of the Flow Questionnaire and the Componential approach to assess flow as a yes-or-no continuous phenomenon. We discuss the Experience Sampling Method (ESM) and provide ideas on how to make use of the full potential of ESM data. Finally, some speculation about consequences of flow, and the application of flow interventions, are addressed. The chapter ends with a personal view on the role of flow in development.

Summary of the Chapters of This Book The first chapter of Engeser, Schiepe-Tiska & Peifer presents the concept of flow and draws the historical lines of flow research. In introducing the concept of flow, the components of flow are described. It is pointed out that there is a high level of agreement on these constitutional components, and only small variations and differences have emerged in the flow research since the first comprehensive description by Csikszentmihalyi (1975). The starting point of flow research was a shift to viewing C. Peifer (*) Department of Psychology, University of Lübeck, Lübeck, Germany e-mail: [email protected] S. Engeser Institute of Psychology, University of Trier, Trier, Germany e-mail: [email protected] © The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4_16

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the experience of performing enjoyable acts on its own terms. Csikszentmihalyi used various methodological approaches, which ultimately led to the discovery of a recurrent experience in diverse activities, which he labeled flow. Besides this new paradigm and methodology, the formulation of a model that explained why activities are enjoyable in themselves has been a milestone. Evidence of the importance of the experience on its own terms emerged in details of the work of Rheinberg in Germany, with a similar but independent research agenda and methodology. Two further important steps of flow research were the study of flow in daily experiences and a focus on the relation of flow with well-being and creativity (cf. Harmat, de Manzano & Ullén, Chap. 14), which are still major concerns in current flow research. The concept of flow was further examined by Moneta in Chap. 2. He presents the main approaches to measure flow and describes how the measurement and the concept of flow are interrelated and influence each other. In working out the strengths and weaknesses of each approach, he provides us with a very helpful guide regarding when to use a certain approach. This also provides a solid base upon which to improve the measurement of flow and to look for possible new ways to tackle weaknesses in order to achieve a more valid measurement of flow and address new research questions. As the measurement of flow and the concept of flow are interrelated, he also points out that new measurements need conceptual work to account for the complexity of flow. Moneta also looks at the emerging process approach to measure flow, introducing the cusp catastrophe model of flow (Ceja & Navarro, 2011, 2012). He argues that complex, non-linear models should be considered in future flow research to deepen and broaden our understanding of the concept. Barthelmäs and Keller, in Chap. 3, look at the antecedents, boundary conditions and consequences of flow. They critically discuss some aspects of what Moneta presents as the second main approach to measure flow (“Capturing Flow in Daily Experience”). Regarding the antecedents, they advocate that demands instead of challenges should be used to capture the balance of challenge/demands and skills, and they argue that challenge already implies an assessment of skills. Based on further methodological considerations, they also call for the subjectively experienced balance to be assessed rather than measuring demands and skills separately and calculating the balance. This would solve methodological problems and simplify research without departing from theoretical assumptions. However, if the research addresses how challenges/demands and skills determine the experience of flow, both have to be assessed separately (cf. Moneta, Chap. 2: “The Regression Modeling Approach”). Further, Barthelmäs and Keller propose a model of flow with value as a main antecedent of flow. Regarding the boundary conditions of flow, they discuss the roles of personality and situational factors as factors that moderate the perception of a demand-skill fit. Regarding the effects of flow on positive affect as well as on performance, Barthelmäs and Keller call for further research. The authors argue that these effects likely depend on the activity as well as on personal and situational characteristics. Schiepe-Tiska and Engeser (Chap. 4) address a lingering problem that the flow model seems to be more applicable for achievement situations. To address this, Schiepe-Tiska and Engeser take recourse to the broader understanding of challenge

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and skills as opportunities for action and accompanying action capabilities (cf. Engeser, Schiepe-Tiska, & Peifer, Chap. 1, Box 1.2 and “An exploratory model of play”). It is argued that individual preferences in the forms of the affiliation motive and power motive help to structure the situations. This provides support for individuals to choose between action opportunities and to know what to do next while being engaged. The authors further extend this perspective to social situations and teams. Abuhamdeh (Chap. 5) provides a conceptual framework for the integration of flow theory and the cognitive evaluation theory (CET) as a part of self-determination theory (Deci & Ryan, 1985). Both theories are meant to explain intrinsically motivated behavior and have been considered as being two sides of the same coin. In reviewing the empirical findings, he works out that both theories hold the greatest explanatory power for distinct aspects of behavior. Flow theory can best describe enjoyment in already intrinsically motivated behavior. In this case, optimal challenge (as the central part of the flow theory) will lead to enjoyment of enacting. CET, on the other hand, is more powerful to explain how intrinsically motivated behavior develops on the basis of perceived competence. The discussion of the CET’s perceived autonomy proposition points to a similar conclusion. Flow theory applies for activities which are freely chosen (i.e. self-determined) and CET explains how the feeling of self-determination leads to intrinsic motivation. In Chap. 6, Abuhamdeh focuses on the relationship between flow and enjoyment—a relationship that is widely discussed in flow research with greatly varying viewpoints. He first disentangles those views and their conceptual history. He concludes that flow is enjoyable and intrinsically-motivating, although we are not aware of that when we are in flow. Abuhamdeh further outlines that enjoyment during flow is not of the “happy-smiley” type, but the enjoyment is caused by feelings of efficacy and perceived competence. Zimanyi and Schüler (Chap. 7) take a closer look at potential negative effects of flow. They outline that (1) neglecting further goals and values, (2) narrowing the focus of attention, (3) being overoptimistic and (4) neglecting temporal information could lead to negative effects of flow. Theoretical considerations and empirical results on the topics of addiction and risk-taking provide first (but strong) evidence that there can be negative effects of flow. It has to be further outlined that flow itself is a rewarding experience, irrespective of the ethical acceptability of the activity. Here, Zimanyi and Schüler bring the example of flow in combat situations, which is still a rewarding experience, with potentially undesirable consequences. Thus, the self-rewarding nature of flow is the basic mechanism that motivates people to engage in an activity without regard for potential negative or ethically questionable consequences. This constitutes a kind of paradox, in terms of a positive experience having negative effects. Therefore, we have to bear in mind the dark side of flow to decide when to foster and when to prevent flow experiences. Peifer and Tan (Chap. 8) provide an overview of the research regarding the psychophysiology of flow and develop a conceptual framework that integrates the diverse findings. They consider conceptual similarities between flow and stress and argue that flow is one way to cope with potentially stressful demands. Findings

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suggest an inverted u-shaped relationship between flow and stress-related physiological indicators, with flow being associated with moderate arousal. Accordingly, flow is discussed as a positive and moderate form of stress, linked to the concept of eustress. When also taking into account findings on brain processes during flow, it seems that flow is a very efficient state in which only those physiological processes are active that are needed for task fulfilment, while all other processes are switched off. Accordingly, performance during flow occurs at low metabolic costs. The physiology of flow is an emerging area of research, and Peifer and Tan point to several directions of future research agendas. They argue that psychophysiological methods and findings have the potential to reach a deeper understanding of flow; physiological measures can extend existing measures of flow by providing the opportunity for real-time continuous measurement of dynamic changes during an activity. Baumann (Chap. 9) reviews the concepts of the autotelic personality as provided by Csikszentmihalyi and others. She comes to the conclusion that the existing measures of the autotelic personality cannot fully capture the complexity required to account for the theoretical concept. Subsequently, she outlines the personality dispositions that serve as boundary conditions of flow experience: the need (achievement motive) and the ability (self-regulation) to achieve flow (cf. Barthelmäs & Keller, Chap. 3, and “Directions for Future Methodological Research” by Moneta in Chap. 2). Baumann offers a new measure that covers these aspects, and the first data on its validity are very promising. Additionally, she presents a functional approach that addresses the relationship between affect and flow as a controversial aspect of flow research (cf. Barthelmäs & Keller, Chap. 3); she argues that flow emerges from dynamic changes in positive affect. Baumann explains that positive affect has to be down-regulated in order to face challenges and acknowledge the difficulties which are posed by challenging activities. During the mastery of the activity, positive affect will boost self-motivation and persistence. This functional approach also nicely provides the basis upon which to specify personality dispositions that are prone to these flow fostering affect regulations: seeing and mastering difficulties. Walker (Chap. 10) addresses social flow–a topic enjoying a huge rise in interest in recent years. An increasing number of studies looked at flow in social interactions, such as in work teams, sports teams and music groups. Walker argues that social flow is more than flow in social situations. This leads to various challenges in operationalization of social flow, and he outlined that operationalization of “social flow” differed very much in the diverse studies, from solitary flow in social contexts, called “co-active flow,” to joint flow in social contexts, called “interactive flow”. In his chapter, Walker tries to disentangle those forms of social flow, and compares the characteristics of solitary vs. interactive flow, thus contributing to a common understanding of social flow in the research to come. In Chap. 11, Peifer & Wolters review the literature on flow in the work context. They report antecedents and consequences of flow in the spheres of (1) the job/task, (2) the individual, (3) the organization/social environment and (4) on the interactions between all these spheres. In line with Barthelmäs & Keller (Chap. 3) and with reference to the Person-Environment Fit theory, they argue that flow will most likely

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occur when there is a fit between individual and situational characteristics. In the work context, situational characteristics apply to the social and organizational context as well as to the task/job context. Peifer and Wolters derive possible workplace interventions to foster flow in all spheres and in their interaction. Finally, they outline challenges for future research, also in the context of new work in the age of digitalization. Freire, Gissubel, Tavares, and Teixeira (Chap. 12) focus on flow from a developmental perspective. In particular, they look at the role of flow in the development of new skills and resources that lead to individual growth and maturation (compare also Csikszentmihalyi & Rathunde, 1993). In line with Massimini and Delle Fave (2000), they describe the underlying mechanism as cultivation, meaning that flow leads to the search for increasingly complex challenges in the respective activities, which will improve long-term skills and an individual’s behavior as a whole. Freire and colleagues review findings from different periods within one’s lifespan, from infancy to old age, and identify specificities at each age group. Further, they look at the role of flow for healthy development and as a buffer of psychopathology. Also, they introduce flow-based therapeutic interventions that aim at positive human development. Stoll and Ufer (Chap. 13) address flow in the sports context. They look at flow in exercise, in competitive sports, as well as in recreational sports. The sports context was one of the first contexts investigated in the early years of flow research. Jackson (1995; Jackson & Roberts, 1992) developed a flow scale for athletes, which has been widely used and applied to other contexts. Accordingly, research on flow in sports has influenced flow research in general, and thus the chapter provides many references to other topics of this book. For example, Stoll and Ufer discuss psychophysiological measures to study flow (cf. Peifer and Tan, Chap. 8). Also, flow in social situations is an important topic when investigating team sports (cf. Walker, Chap. 10). Stoll and Ufer add theoretical explanations for the occurrence of flow in teams, including emotional contagion, the risk-shift-phenomenon, and opioids and neurotransmitters. In Chap. 14, Harmat, de Manzano, and Ullén look at creative performance in music, dance, and visual arts. While painters and dancers were among those individuals who were interviewed by Csikszentmihalyi (1975) and who inspired his research, their particular professions remained largely ignored since then. Only a few studies address flow in visual arts, while only a few more address music composition, music improvisation, and dance. In their chapter, Harmat and colleagues review the existing literature and outline directions for future research. Similar to Stoll and Ufer, they also dedicate parts of their chapter to the social component (cf. Walker, Chap. 10) of flow in arts, and to the psychophysiological mechanisms of flow (cf. Peifer & Tan, Chap. 8), which underlines the importance of these branches of flow research. In addition, they discuss the role of dispositional flow in the creation of art, i.e. referring to possible individual characteristics (cf. Baumann, Chap. 9). Last but not least, Triberti, Di Natale and Gaggioli (Chap. 15) focus on the role of flow in human-computer interaction. They describe how to (and discuss when to) apply the concept of flow in designing user experiences. In more detail, they use the

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concept of a challenge-skill balance in video games and virtual reality settings. As a particular context of application, they discuss flow for rehabilitation purposes, e.g. in treatments following a stroke. With the inherently motivating and enjoyable character of flow, we see huge potential in the approach to using flow for rehabilitation as an emerging field with much research yet to come.

Overarching Topics and Open Research Questions Are There Core Components of Flow? As we discuss in Chap. 1 (Engeser et al.), the components of flow as originally described by Csikszentmihalyi in 1975 have only been marginally modified over the years. While some components were added in the early years of flow research (cf. Chap. 1, Box 1.1), the components were later rather reduced: they underwent a deeper analysis of whether they are a core component of flow, an accompanying phenomenon, an antecedent, or a consequence of flow. In that vein, Barthelmäs and Keller (Chap. 3) understand a perceived balance of demands and skills as a core component of flow and they outline how goals and feedback contribute to that perception. Accordingly, they understand clear goals and feedback as antecedents of the flow-component perceived demand-skill balance (Barthelmäs & Keller, Chap. 3). At this point, we would like to disentangle some aspects that are often misunderstood: the components clear goals and feedback as described by Csikszentmihalyi are meant to be the experience of clear goals and feedback, i.e. they represent a subjective perception during an activity. In the same situation, one person can experience clear goals, while the other does not (or, at least, experiences less clear goals)—which depends, for example, on the level of expertise, but also on personality or on transient factors like mood, etc. Accordingly, there are antecedents that we can implement in order to promote flow, such as providing goals and feedback, but the experience itself depends on many other factors. We agree that goals and feedback are antecedents of flow while the experience of clear goals and feedback are components of flow. At the same time, the experiences of clear goals and feedback make up part of the perceived demand-skill balance. We thus propose together with Barthelmäs and Keller (Chap. 3) that perceived demand-skill balance subsumes related experiences and in this sense it could be regarded as a core component of flow (compare Table 16.1). Another component that is closely related to the perceived demand-skill balance is the feeling of control. When individuals perceive their abilities to be in balance with the demands, they most likely experience a feeling of control. However, a feeling of control can also be achieved when skills surpass the demands. The perceived demand-skill balance adds an important aspect to that: the feeling that one can make use of one’s skills to just the right degree. Accordingly, the feeling of control in a demanding task is a certain aspect of or closely related to the perceived demand-skill balance (compare Table 16.1).

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Table 16.1 A proposal of core flow components Components of flow in early years Merging of action and awarenessa Centering of attentiona Loss of self-consciousnessa Absorptionb,c,d Distortion of temporal experience of timee,f,g Feeling of controla,b,c Experience of coherent, non-contradictory demandsa Experience of clear goalsf Experience of unambiguous feedbackf Experience of challenge-/demand skill balancea,b,c,f Autotelic naturea Intrinsic motivationd Enjoymentd,j

Proposal of core components Absorption

Perceived demand-skill balance

Enjoyment

a

Csikszentmihalyi (1975) Rheinberg, Vollmeyer, and Engeser (2003) c Engeser and Rheinberg (2008) d Bakker (2005) e Csikszentmihalyi and Csikszentmihalyi (1988) f Jackson and Marsh (1996) g Nakamura and Csikszentmihalyi (2005) h Barthelmäs and Keller, Chap. 3 i Engeser, Schiepe-Tiska, and Peifer, Chap. 1 j Abuhamdeh, Chap. 6 b

Further flow components that were mentioned by Csikszentmihalyi (1975) and other flow researchers are absorption, loss of self-consciousness, action-self merging, and time distortion. Analogous to perceived demand-skill-balance, we suggest that absorption largely subsumes the other aspects and in this sense can be regarded as a core component of flow. When we are totally absorbed with what we do, all task-irrelevant stimuli are shielded from attention. In most flow activities, selfreferential thoughts are not needed or even contra-productive, and they are consequently masked. This goes along with an experienced loss of self-consciousness during flow. Similarly, when we are absorbed, everything we do serves the activity at hand, which explains the feeling of action-self merging. Also, while being totally absorbed, we do not notice time passing. Thus, loss of self-consciousness, actionself merging, and time distortion are certain aspects of (or are at least closely related to) absorption. Absorption can accordingly be understood to subsume these related experiences and in this sense it can be regarded as a core component of flow (cf. Table 16.1). In line with that, Rheinberg, Vollmeyer, and Engeser (2003) and Engeser and Rheinberg (2008) found that within the flow short scale, the items “I am completely lost in thought” and “I don’t notice time passing” both load on the same meta-factor, which the authors call absorption. Other components of flow as described in the past are the autotelic nature of flow (Csikszentmihalyi, 1975), intrinsic motivation (Bakker, 2005), and, with some

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controversial discussion, enjoyment (Bakker, 2005; cf. Abuhamdeh, Chap. 6). As outlined by Engeser and colleagues (cf. Chap. 1), flow was introduced as a concept that explains intrinsically motivated engagement in tasks without extrinsic rewards. The component that can explain the self-reinforcing character of flow is enjoyment (cf. Abuhamdeh, Chap. 6). In fact, already Csikszentmihalyi (1975) explained the autotelicity of activities with enjoyment: “The process of making their products was so enjoyable that they were ready to sacrifice a great deal for the chance of continuing to do so” (pp. xii; compare Abuhamdeh, Chap. 6). This is also in line with Bakker’s (2005) understanding of the flow-component intrinsic motivation as “(. . .) the need to perform a certain work-related activity with the aim of experiencing the inherent pleasure and satisfaction in the activity” (p. 28). And although there exists a major ongoing discussion about the question of whether enjoyment is part of flow or not (cf. Abuhamdeh, Chap. 6), we suggest that enjoyment is indeed a component of flow. Thus, we only call an experience flow when the experience is enjoyable. As shown in Table 16.1, we suggest enjoyment as the overarching term subsuming autotelic nature and intrinsic motivation (for a more detailed understanding of enjoyment see Abuhamdeh, Chap. 6). Taken together, we suggest that flow can be described with three core experiences (1) absorption, (2) perceived demand-skill balance, and (3) enjoyment. Based on that, flow can be defined as the enjoyable experience of full absorption in an activity in which the demands are perceived as optimally compatible with one’s skills. However, we propose to keep the originally described components of flow as aspects in order to more holistically define, describe, and measure flow so as not to miss subtle aspects of the experience of flow as outlined by Csikszentmihalyi.

Frequency, Intensity, Duration and Dynamics of Flow-Episodes Frequency vs. Intensity Following another major question that is presented by Moneta (Chap. 2) and by Barthelmäs and Keller (Chap. 3), flow research could and should take into account frequency and intensity of flow. However, their assessment touches a basic problem in flow research that has not yet been fully addressed. To illustrate the problem, we present in Fig. 16.1 three possible relationships between flow and its components. First Possible Relationship If flow is a yes-or-no phenomenon, it will not be experienced until the components of flow are sufficiently presented. An individual either experiences flow or not, and there is no in-between, like a little bit of flow. When discussing this with laypersons, this is how they intuitively understand flow. We also sometimes think about flow as a dichotomous phenomenon and the (first) interview studies of Csikszentmihalyi also seem to suggest this understanding. A

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Fig. 16.1 Possible relationship between flow components and the experience of flow

person is in flow or not, and in this case, we can count frequency but we cannot measure intensity. Second Possible Relationship That flow may not be a yes-or-no phenomenon was already addressed by Csikszentmihalyi (1975) when he looked for flow patterns in everyday life. He stated that “In fact, the flow model suggests that flow exists on a continuum from extremely low to extremely high complexity” (p. 141). We illustrated this in Fig. 16.1 with flow being a continuous phenomenon: The more the flow components are present and pronounced, the more intense is the experience of flow. Csikszentmihalyi labeled less intense flow experience as “microflow” in contrast to “deep flow” (cf. “shallow” and “deep flow” in Moneta, Chap. 2). He argued that the components of flow are less intensely present during microflow. For example, the person is not fully absorbed by one activity, or the balance between demands and skills is not fully perceived. Nevertheless, microflow is positively valenced (component of enjoyment) and the person feels “free” to engage in the activity without anxiety or boredom. In the case of a continuous phenomenon, a differentiation of intensity and frequency is again not possible: this time we can measure intensity but cannot measure frequency—unless we build categories such as high and low (or high— medium—low), thereby treating flow again as a yes-or-no phenomenon. Third Possible Relationship In Fig. 16.1, we depict another possible pattern, in which flow is termed a yes-or-no continuous phenomenon. Flow is not experienced when factors fostering flow are below a certain threshold. Above this threshold, a person experiences flow and the intensity of the experience becomes even more intense the more the components are present and pronounced. This third pattern allows researchers to assess frequency and intensity of flow. In contrast to the

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continuous phenomenon, only “real” flow experiences are taken into account and the measure of intensity is only applied to such instances. In looking at existing measures of flow described by Moneta (Chap. 2), one could use the Flow Questionnaire (Csikszentmihalyi & Csikszentmihalyi, 1988) and combine it with a measure of the “componential approach” (such as the flow short scale, Engeser & Rheinberg, 2008). Alternatively, like Barthelmäs and Keller (Chap. 3), one could use a purely componential approach. They counted flow frequency when the intensity of the measure surpassed a certain threshold and took the intensity of the measure for these instances as additional information. The delicate aspect concerned here is that appointing the exact threshold is not clear-cut based on our current knowledge but needs further investigation (cf. Moneta, Chap. 2) or even individual calibration, as each person may have individual thresholds for the components.

Frequency and Intensity of the Components of Flow To complicate matters of frequency and intensity of flow, the different components of flow (cf. Engeser et al., Chap. 1, Box 1.1) may follow different patterns and they may contribute differently to the holistic experience of flow. At the same time, the different components offer a way to focus research on one aspect in order to study possible patterns that are associated with flow. We would expect the components to follow the pattern of the continuous phenomenon, e.g. people can experience more or less control, more or less goal clarity and so on. Looking at the componential approach presented by Moneta (Chap. 2), empirical results support this assumption. But how each component contributes to the holistic feeling of flow is less clear, as pointed out by Moneta when discussing the third weakness of this approach (summing up the means on each component may not always capture flow). In the yes-orno pattern, flow will be experienced when each component is beyond a certain threshold (which might differ between the components), and in the continuous pattern, it would increase in a linear fashion as the components are more pronounced. Finally the yes-or-no continuous pattern will be a mixture of the two patterns, i.e. flow emerges when all components have surpassed their threshold, and the more they are pronounced, the more intense is the experience. Research might tackle this problem empirically. The holistic experience of flow would have to be assessed in line with the Flow Questionnaire (Csikszentmihalyi & Csikszentmihalyi, 1988) as presented in the section “Capturing Flow in Special Endeavors” by Moneta (Chap. 2) and serves as the dependent variable predicted by a componential flow measure. As simple as it sounds, it will pose methodological challenges. Researchers would need many data points to estimate the relations. Also, there could be interaction effects of the components, e.g. the high intensity of one component could compensate for low intensity of another component. To investigate such interaction effects, even more data are required (cf. McClelland & Judd, 1993). Nevertheless, a research program addressing this issue seems worthwhile. Our understanding of the components and their unique contribution could be further enhanced by using psychophysiological measures, as they allow for the generation

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of continuous data during the experience without having to interrupt the participant (cf. Peifer & Tan, Chap. 8). There are measures that apply particularly to specific components or correlates of flow, such as to enjoyment (e.g., activation of zygomaticus major, the smiling muscle), or to mental effort (e.g., high frequency heart rate variability). Considering the different components could also address the suggestions made by Barthelmäs and Keller (Chap. 3) to induce flow via selectively manipulating one component of flow.

Duration of Flow Episodes When talking about frequency and intensity of flow, a factor that has so far been neglected in flow research is the duration of a flow episode. One person might be in flow several times a day but for a short period of time and another person might be in flow once a day but for a long period of time. In a sense, they experience the same time being in flow, but one person experiences it more frequently. Csikszentmihalyi (1975) already pointed to this possible differentiation (p. 158). This differentiation also allows for an interesting research question: we would expect longer time periods to be more rewarding than more frequent shorter periods, the rationale for this being that the longer periods indicate or provide more order in the person’s life (cf. Csikszentmihalyi, 1975, p. 158ff). At the same time, it is interesting how long a flow-episode can be and what duration can even be recommended. As discussed by Peifer and Tan (Chap. 8), flow can be considered a positive form of stress, which should be alternated with phases of relaxation. In line with that, studies found that only when employees are well recovered in the morning can flow occur over the whole course of the day (Debus, Sonnentag, Deutsch, & Nussbeck, 2014; cf. Peifer & Wolters, Chap. 11).

Dynamics Within Flow Episodes Another interesting aspect when flow is defined as a yes-or-no continuous phenomenon is the variation of flow intensity within a flow-episode. While flow is clearly an experience that lasts over a certain time, to the best of our knowledge nothing is yet known about the dynamics of flow intensity that occur during flow. Common methods to assess flow rely on self-reports that relate to a certain time period in the past, e.g. the last week(s), the last day, the last hour, or the just-interrupted activity. In each case, the self-report is retrospective, with presumably more accuracy the shorter the time interval between report and the experience. In none of these cases can the experiential dynamics within a flow-episode be assessed. While the experience sampling method (ESM, cf. Moneta, Chap. 2) can at least make predictions of flow patterns over the course of one day, the person in flow needs to be interrupted and the progress of the current flow episode is disturbed. Thus, ESM measures can provide only one data point per flow episode, and one cannot measure fluctuations within the episode. To get deeper insights into these dynamics, it would

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be helpful to have measures that assess flow without interruption, such as observational methods or physiological methods (cf. Peifer & Tan, Chap. 8). Physiological measures could even be used as real-time flow measures, such that intelligent systems can react to changes in flow dynamics to keep the user in a state of flow or to intensify the state. These could be new challenges for flow-research in the years to come.

Agreement on a Common Flow Measure Nakamura and Csikszentmihalyi (2005, p. 101) expressed the hope that we are nearing a “consensual ESM measure to facilitate the accumulation of knowledge”. We agree that this is an important endeavor for future flow research, in order to be able to fully compare empirical results from our studies. At the same time, in reviewing the chapters of the present book, we believe that a consensus on the components of flow is not too far away; this chapter tries to integrate the different views and to come even closer to a consensus. At the same time, many different measures still exist which complicate the interpretation and integration of the studies utilizing these different measures. One possible solution could be using a combination of the Flow Questionnaire (Csikszentmihalyi & Csikszentmihalyi, 1988) with an adapted version of the Experience Sampling Form (ESF; cf. Moneta, Chap. 2). The ESF should be supplemented (or replaced) by items for all components of flow, such as those items provided by the Flow Short Scale (in the Appendix, Engeser & Rheinberg, 2008). As some components of flow are already measured in the ESF, this would mean adding only a few items, with the great advantage that flow is measured with all its components [this has already been realized, as Moneta (Chap. 2) presented]. As discussed in the previous section, the Flow Questionnaire distinguishes between flow or not-flow, while the componential approach measures its intensity to eventually achieve a measure of flow as a yes-or-no continuous phenomenon (see above). We expect that most or all researchers on flow could consent to these extensions. For special research questions concerning moderators or antecedents of flow (e.g. value of the activity, motivational orientation), researchers, of course, need to add the respective measures to the experience sampling form. While most research questions on flow may well be addressed with self-report data, these may in the future be complemented with physiological measures especially when it comes to analyzing the dynamics within flow episodes as described above. In reviewing the research on ESM, however, it seems to us that the potential of this method is often not being fully utilized. Most notably in this respect is the rare use of the longitudinal structure of the data (e.g. Ceja & Navarro, 2011), which, thanks to advancements in analytical tools, is nowadays relatively easy to analyze. One could address research questions such as how flow will influence affect at later measurement points, or how the experience at work will influence the experience in leisure activities (Engeser & Baumann, 2016; Peifer, Syrek, Ostwald, Schuh, & Antoni, 2020; cf. Peifer & Wolters, Chap. 11). Both are examples of questions at the

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heart of flow research. Similarly, the use of longitudinal designs would also provide information about fluctuations of different experiences. As mentioned by Engeser and colleagues (Chap. 1), a meaningful alternation of different states such as flow and relaxation may be optimal, and the contributions of Peifer and Tan (Chap. 8) also point in this direction (cf. Nakamura & Csikszentmihalyi, 2005); flow shares aspects with stress and is even called a moderate and positive form of stress. By disregarding relaxation, one would risk exhausting resources, without having the chance to recharge. According to this rationale, life satisfaction should be high for persons who experience more flow, but only when periods of relaxation are experienced, too. Otherwise, the experience of flow might even harm life satisfaction (at least from a long-term perspective). In order to gain more information about temporal patterning (this time, not within but between flow episodes and other experiential states), it would be helpful to sample data with short and long intervals: if only relatively similar time intervals are sampled, the total range of information could not be extracted from the data (see Oud & Delsing, 2010). To additionally gain insights into sequential relationships, it would be helpful to sample data under special circumstances where the sequential influences are expected to be particularly strong or represent the research interest (e.g. transitions from work to leisure). ESM has one disadvantage: rare activities are too infrequent to be represented and analyzed properly. To tackle this, Aellig (2004) or Delle Fave, Bassi, and Massimini (2003) measured flow in special activities that are known as being particularly flowconducive, such as climbing and mountaineering. This is of special importance for flow research as, for example, deep flow is a rare phenomenon. Similar research methods as the Day Reconstruction Method may also be helpful, as participants can relate to their flow-activities instead of relating to whatever activity during which they were beeped (cf. Moneta, Chap. 2; Barthelmäs and Keller; Chap. 3). Another promising approach that can also tackle this ESM-disadvantage again implies physiological methods. Once research has identified a flow-typical physiological pattern, physiological sensors can be programmed to detect that pattern, and the ESM-beep can be linked to that. Through this, the chance to sample flow-relevant episodes increases significantly. Realizing such a research design will bring deeper insights into flow activities. This is of even higher relevance as research shows that post-hoc self-reported flow-ratings can easily be impaired by post-task circumstances, such as through a false feedback on the duration of the task (Christandl, Mierke, & Peifer, 2018) or the need to leave a task unfinished (Peifer & Syrek, 2015).

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Antecedents of Flow Balance of Demands and Skills and Its Moderating Factors Many of the chapters of this book address the antecedents of flow in general (e.g., Barthelmäs & Keller, Chap. 3) or in particular contexts (e.g., Peifer & Wolters, Chap. 11; Stoll & Ufer, Chap. 13; Harmat et al., Chap. 14). A common agreement within those chapters is that the objective balance of demands and skills is a central antecedent of flow. A further agreement is that this balance alone cannot explain the experience. Therefore, the flow model (Csikszentmihalyi, 1975) presents a central aspect of the emergence of flow but not the whole picture. We suppose that most researchers on flow would go along with this view, as Csikszentmihalyi himself has pointed to this since the beginnings of flow research (e.g., in the concept of the autotelic personality). The prevalent view is that personal factors and situational factors serve as moderators of when and how a demand-skill balance will lead to flow. Examples for moderating personal factors are regulatory fit, need achievement (cf. Barthelmäs & Keller, Chap. 3), self-efficacy, stress management competencies (Peifer & Wolters, Chap. 11), action orientation and further personality dispositions as discussed by Baumann (Chap. 9; cf. also Zimanyi & Schüler, Chap. 7). Baumann further outlines dynamic and transient aspects of affect regulations to explain when challenges are recognized and approached. Other moderators of flow as discussed in the chapters of this book relate to the perception of the task or activity as personally valuable, relevant or interesting, and whether there are intrinsic or extrinsic reasons for action (e.g., Barthelmäs & Keller, Chap. 3; Abuhamdeh, Chap. 5; Peifer & Wolters, Chap. 11). Moneta (Chap. 2) and Abuhamdeh (Chap. 5) point to the state-level moderators of motivational orientation (intrinsic or extrinsic nature of the task) and Abuhamdeh additionally discusses perceived outcome importance and perceived self-determination. Barthelmäs and Keller extend the flow model with the value attributed to an activity as an additional factor that predicts flow. This approach is in line with considerations by Nakamura and Csikszentmihalyi (2005) on interest: Nakamura and Csikszentmihalyi (2005) state that the more a person is interested in an activity, the more likely it is that the person will experience flow when there is a balance between challenges/demands and skills (cf. Abuhamdeh, Chap. 5). They further propose that past flow experiences foster the interest in an activity. Consequently, past flow experiences will foster flow experiences in the future through increased interest. The same could be inferred from the concept of values as proposed by Barthelmäs and Keller (Chap. 3). The value of an activity depends on the hedonic experience the person had with an activity in the past. As the hedonic experience should be higher with the experience of flow, flow itself would be a predictor of flow during the activity in the future. The appeal of this assumption is that the prediction of flow could be made within a single theoretical approach, and for reasons of parsimony we would like to encourage this assumption.

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Antecedents Beyond a Demand-Skill Balance Besides moderating factors that affect whether or not a demand-skill balance leads to flow, research has investigated other flow antecedents, such as individual characteristics (cf. Schiepe-Tiska & Engeser, Chap. 4; Peifer & Wolters, Chap. 11), task characteristics (Peifer & Wolters, Chap. 11) and characteristics of the social situation (cf. Schiepe-Tiska & Engeser, Chap. 4; Walker, Chap. 10; Peifer & Wolters, Chap. 11; Stoll & Ufer, Chap. 13; Harmat et al., Chap. 14). In particular, Schiepe-Tiska and Engeser suggest that individual preferences will structure activities independently of a balance of demands and skills. They propose that individuals can experience flow not only in achievement situations but also in social situations, and that flow emerges when an individual’s motives correspond to the motivespecific incentives in a situation. Accordingly, they argue for an interplay between individual and social characteristics which predicts flow experience, and that a compatibility of both enhances the likelihood of experiencing flow. Regarding the social situation, Walker (Chap. 10) differentiates solitary situations from social situations, and even concludes from his literature review that flow is more intense in social situations. In line with Schiepe-Tiska and Engeser (Chap. 4), this should only be true for individuals with high levels of social motivation. While literature on social flow did largely neglect that research question, Schiepe-Tiska and Engeser (Chap. 4) provide a first indication that waits to be complemented by further research on this issue.

An Integrative Framework of Flow Antecedents Based on the suggestion of Schiepe-Tiska and Engeser (Chap. 4) that there is an interplay between individual and social characteristics, one can suggest a more general principle for the emergence of flow: Peifer and Wolters (Chap. 11) suggest in their Three Spheres Framework of Flow Antecedents that flow occurs when there is a fit between attributes of the individual and attributes of the social situation/ organization and with attributes of the task/job. This approach is compatible with a demand-skill balance as an antecedent of flow, with demands being a task attribute and skills being an individual attribute. At the same time, other compatible constellations of task or social attributes with individual attributes are possible, such as high autonomy in combination with a high autonomy motive (Schüler, Sheldon, Prentice, & Halusic, 2016) or social situations together with high social motivation. Thus, the framework integrates the demand-skill balance as well as the just-reported approaches that go beyond the demand-skill balance (Fig. 16.2) as antecedents of flow. The Three Spheres Framework is theoretically based on the person-environment fit theory (e.g., Caplan, 1987; Edwards, Caplan, & Harrison, 1998; Edwards & Cooper, 1990) and suggests that the fit encompasses all possible combinations of individual attributes like personal interests, values, traits, preferences, knowledge, skills, abilities, needs, goals, and attitudes on the one side with environmental

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Fig. 16.2 The three spheres framework of flow antecedents

attributes, including job/task attributes (e.g. characteristics, demands and resources) and attributes of the social and organizational environment (e.g. organizational culture or attributes of peers and supervisors/teachers, depending on the context) on the other. While Peifer and Wolters relate their framework to the work context, it can be generalized to all contexts of flow research.

Intrinsic and Extrinsic Reasons for Action Since the beginnings of flow research, Csikszentmihalyi acknowledged that reasons other than intrinsic ones might serve as the starting point for action before flow is experienced (cf. Engeser et al., Chap. 1 and Schiepe-Tiska & Engeser, Chap. 4). Thus, the experience during the activity is not necessarily the initial reason for action (cf. Barthelmäs & Keller, Chap. 3, on intrinsic motivation). Conversely, it might be the activity itself that provides the reason for action (i.e. to experience flow), but extrinsic reasons would suggest not carrying out the activity (cf. the potential negative outcomes in Zimanyi & Schüler, Chap. 7). Technically speaking, this means that intrinsic and extrinsic reasons for action can be dissociated and are partially independent of each other (Abuhamdeh, Chap. 5). We will go into some implications of this dissociation, because this seems to us a lingering question in flow research. We assume the question is lingering because it is somehow paradoxical that an intrinsically rewarding experience could be triggered by extrinsic reasons. Just to take an illustrative example, we could pay money for the engagement in an activity in which a person would have never been interested. While doing the activity, the

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person will become familiar with the activity and possibly may experience flow. Once the person experiences flow, this may become a reason for action in itself as flow is a rewarding experience. This means that an initially extrinsically motivated behavior could become intrinsic during its performance and this becomes an intrinsic reason for acting upon it in the future. This phenomenon closely resembles the “functional autonomy” proposed by Allport (1937). The theory of biocultural evolution by Massimini, Csikszentmihalyi and Delle Fave (1988; cf. Engeser et al., Chap. 1) also suggests this basic principle as important, and Abuhamdeh (Chap. 5) points to this principle, as well. Keeping this in mind would provide us with the possibility of inducing flow experiences, particularly when the person is not familiar with the activity or intrinsic reasons alone would not justify engagement. Intervention programs to foster flow also rely on this. On the other hand, the assumed reluctance to accept the possibility that flow could be triggered by extrinsic reasons may (implicitly) reflect a hesitation to manipulate individuals. To do so, we could set up extrinsic reasons and implement flow antecedents that make the experience of flow very likely. This is, incidentally, what we do in experiments on flow: participants take part for course credits, and we implement flow-promoting factors in a video game (e.g., Engeser & Rheinberg, 2008; Rheinberg & Vollmeyer, 2003). When the person experiences flow, we expect the person to subsequently carry out the activity for intrinsic reasons alone. Thus, we not only set up extrinsic rewards (e.g. like in instrumental conditioning) but also build our intervention on the positive experience of the activity itself. Therefore, the power to manipulate individuals has advanced through our deeper understanding of what makes an activity a rewarding experience. Fostering flow influences an individual’s behavior to repeat the respective activity for its own sake. This principle can be used in many application contexts, for example in prevention and rehabilitation settings (see e.g. Stoll & Ufer, Chap. 13 and Triberti et al., Chap. 15). It can also be used in schools to foster learning and in organizational settings to foster well-being and performance at work (cf. Peifer & Wolters, Chap. 11). It could even be used in creative activities—because how should an individual know that an activity is rewarding, before he/she tried? In a way, an initial extrinsic reward will in many cases be the door-opener to the experience of flow. And, in general, the flow-principle can be applied to almost all contexts within the spectrum of human development (cf. Freire et al., Chap. 12). At the same time, the shaping of behavior also applies to undesired contexts, as described by Zimanyi and Schüler (Chap. 7). This becomes evident in the use of flow to make persons become attracted or addicted to video games or for marketing purposes, or in the case of combat flow as experienced by hooligans in a soccer stadium. Accordingly, flow itself is a rewarding experience, but the activity can be associated with desired or undesired outcomes for the person or for society. Flow per se is not good or bad, it is a rewarding experience irrespective of its outcomes. We should recognize this and keep this in mind in order to be sensitive in this respect, and we should use the power for influencing individuals with considerable caution.

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Development of an Autotelic Personality Nakamura and Csikszentmihalyi (2005) urged research into early childhood in order to understand the development of autotelic personality in general and to investigate adolescents who seek challenging activities and who do not “prefer states of control, relaxation, and even apathy” (p. 101). Understanding the development of an autotelic personality is still an important endeavor (cf. Freire et al., Chap. 12), although we do not have to start from scratch. The personality dispositions outlined by Baumann (Chap. 9) as characteristic of an autotelic personality have already been investigated with relevant results for flow research. We do, for example, have some solid empirical data on the development of the need for achievement. A relevant work in this respect is a study by Trudewind & Husarek (1979; cf. Brunstein & Heckhausen, 2018). He observed the interaction of mothers and second-grade children while doing homework. Half of the children had shown a positive approach to achievement situations in the first year of school, while the other half had developed a strong fear of failure (thus avoiding achievement situations). As expected, the mothers of children with a negatively developed approach praised success less and punished failures more strongly, attributing failure to the child’s lack of ability and success to good luck. Related to this, they compared the children’s achievement with other children instead of taking the individual ability of their own child as a reference, thereby hindering the child’s setting standards based on his or her own ability (i.e. preventing balance of demands and skills). Thus, the knowledge concerning the development of the achievement motive provides information about the development of an autotelic personality (cf. Brunstein & Heckhausen, 2018). The same holds true for other personality dimensions presented by Baumann in Chap. 9. We recommend that future research on the development of an autotelic personality should also rely on existing studies of related personality dimensions, especially when keeping in mind that research to understand the development of personality is not as easy to accomplish when longitudinal data have to be collected for a fairly long period of time.

Consequences of Flow The consequences of flow have been addressed by many chapters of this book, and there is large agreement that flow leads to increased performance and well-being. However, evidence still shows inconsistencies, and experimental and longitudinal studies are needed to shed more light on these widely accepted relationships—on moderating factors, on their potential bi-directionality and on the mediating mechanisms. To determine the causal link of performance and flow, Barthelmäs and Keller (Chap. 3) pointed to methodological and conceptual problems in empirically testing this link. They outlined how many studies rely on correlational data in

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cross-sectional designs. Even experimental designs pose fundamental problems in studying the causal link of flow and performance. There are experimental ways to manipulate flow, such as video games that realize a fit of skills and demands versus conditions of boredom or overload. In those video games, performance can be measured easily. But whether flow itself is the mediating variable is not that clear, as both flow and performance occur at the same time. Thus, the causality between the two is not rigorously tested. One other way would be to carry out cross-lagged analyses on the basis of longitudinal data. As discussed above, the ESM provides such data, but does not yet include performance measures. However, this is principally not a problem, and a suitable design could find ways to measure performance in a meaningful way. One could think about self-reported performance being included in the ESF, more objective measures such as the performance indicators of a run (e.g. distance and time), or the performance evaluation of colleagues or supervisors. Although not conclusive, there is conceptual and empirical evidence that flow fosters performance in causal ways (for some activities). But it could be that flow is (for some activities) merely an indicator of high performance, thus not causally influencing performance. We search for causality, but having a good indicator of high performance is valuable, too. On a subjective level, flow provides the individual with the feedback that performance is, or has been, good, and scientists may be interested in having another indicator when measuring performance itself is not possible or is inappropriate. We should also keep in mind that the core of the flow concept is motivation rather than performance. It is critical that flow predicts engagement in an activity (or predicts other closely related variables of motivation) which very likely impact long-term performance. In addition, there are more specific open questions with regard to performance. Barthelmäs and Keller (Chap. 3) argue that flow influences the kind of processing style and the allocation of cognitive resources. Data indicates that flow fosters a bottom-up processing style. We were initially surprised that flow was found to be accompanied by bottom-up processing, as flow represents a highly ordered state. It should at least implicitly be guided by top-down processes that provide the structure for action. A potential explanation could be that the structure of the activity is already so clear during flow that top-down processing can be reduced to a minimum, and instead, it becomes more important to be “open-minded”, as implied by a bottom-up processing style (cf. Baumann, Chap. 9). This might also help us to understand why flow is sometimes related to better performance and sometimes not. In some instances, top-down, and in others, bottom-up processes may lead to better performance. Similarly, the allocation of cognitive resources is central for flow and will shed further light on the flowperformance relationship, while more research is clearly needed (cf. Bruya, 2010; Nakamura & Csikszentmihalyi, 2005). In the case of a marathon run, Engeser, and colleagues (Chap. 1) speculated that a key demand is a rigorous self-control (i.e. top down control) that is not compatible with the experience of flow. In swimming, as in many other sports, the demand lies in the perfect execution of movements learned over years of intensive training. Flow might be an optimal state that allows these highly practiced abilities to be performed to their full extent. Albeit highly

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speculative at this point, a bottom-up processing style makes the individual attentive to minor discrepancies of movements or situational changes, allowing him or her to intuitively adapt to this appropriately (cf. Baumann, Chap. 9). Besides this possible link between cognitive mechanisms and performance, the suggested cognitive mechanisms are a good theoretical basis to investigate brain functions or other psychophysiological correlates for a deeper understanding of the experience of flow.

Practical Relevance of Flow In this second edition of Advances in Flow Research, all chapters of the first edition were revised and new chapters were added. The new chapters primarily discuss flow in applied contexts such as in the context of work, arts, sports, development, and human-machine interaction. The new selection of chapters underlines that flow is of relevance in many different fields. This can be explained by the positive consequences of flow (compare Barthelmäs & Keller, Chap. 3; Peifer & Wolters, Chap. 11; Stoll & Ufer, Chap. 13; Harmat et al., Chap. 14). Due to the rewarding character of flow, it leads to increased motivation to perform the flow-eliciting activity again, thereby increasing skills and future performance (compare Moneta, Chap. 2). Accordingly, flow leads to an upward spiral of skills and resources that can foster positive development in general (compare Freire et al., Chap. 12). Fostering flow can thus be used to trigger life-long positive development from childhood to the elderly, be it at work, in sports, or in the arts. While there is no chapter on the role of flow in education in this book, schools and other educational settings are further interesting application contexts. And: flow does not only contribute to individual development but also to societal development: flow affects the selection, replication, and transmission of activities within communities and societies, thereby contributing to the shaping of culture (Delle Fave & Bassi, 2017). The importance of flow for positive development becomes even clearer when imagining flow deprivation. Already Csikszentmihalyi (1975), in the first version of his flow model, postulated that when skills are much higher than challenge/demands, the person will experience anxiety (see Engeser et al., Chap. 1, Box 1.2). This seems a little surprising and was not further explicated by Csikszentmihalyi. In the first chapter, we read from Csikszentmihalyi’s other works that humans need structure, because otherwise, a state of disorientation, chaos and anxiety will result when there are no opportunities to act out skills. This could best be understood when imagining extreme examples of sensory deprivation or being imprisoned in isolation. Considering this aspect, we asked ourselves what could happen when individuals (habitually) avoid situations where demands match or exceed skills. Csikszentmihalyi’s (1975) flow deprivation experiment may guide this research question. He studied what happened when persons voluntarily skip all activities in which they normally engage just for “play” and for non-instrumental reasons. Although the sample was small, effects on concentration, sleepiness, health, and basic functioning were found.

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Extrapolating the results, one could even expect people to lose normal functioning, developing clinical symptoms of mental illness. Coming almost to the end, we would like to illustrate the importance of flow with a personal story of two friends who found themselves in comparably unfavorable life situations. After disruptions of structure in their lives (breakdown of a relationship, unemployment), one found a way to structure his life again (at least partially): he was able to build on his interest in the biology of ants. He described losing track of time and space (i.e., indicators of flow) when being out looking for ants of a certain subfamily, watching their behavior or taking pictures. He actually carried a GPS device with him to find his way back! Another friend could not rely on such an experience and started to think over and over about the same things, feeling like he was imprisoned in isolation. In the past, he had also avoided challenges (e.g. he looked for jobs that were well below his qualification level; he did not engage in achievement-oriented leisure activities like sports) as they posed a threat to him – i.e. a sign of a low pronounced autotelic personality. It would be of interest whether the negative development of the second friend could have been expected long before he actually encountered the serious problems. Was it due to his avoidance of challenges as such and/or that he had not found an activity providing flow to trigger positive development again? Both potential answers underline the importance of flow experience in development processes throughout one’s lifespan and for healthy development.

References Aellig, S. (2004) Über den Sinn des Unsinns: Flow-Erleben und Wohlbefinden als Anreize für autotelische Tätigkeiten: Eine Untersuchung mit der Experience Sampling Method (ESM) am Beispiel des Felsklettern [On the sense of nonsense. Flow experiences and well-being as incentives of autotelic activities]. Münster: Waxmann (1937). Allport, G. W. (1937). The functional autonomy of motives. American Journal of Psychology, 50, 141–156. Bakker, A. (2005). Flow among music teachers and their students: The crossover of peak experiences. Journal of Vocational Behavior, 66, 26–44. Brunstein, J. C., & Heckhausen, H. (2018). Leistungsmotivation. In J. Heckhausen & H. Heckhausen (Eds.), Motivation und Handeln (pp. 163–221). Berlin: Springer. Bruya, B. (2010). Introduction. In B. Bruya (Ed.), Effortless attention. A new perspective in the cognitive science of attention and action (pp. 1–28). Cambridge: MIT Press. Caplan, R. D. (1987). Person-environment fit: Commensurate dimensions, time perspectives, and mechanisms. Journal of Vocational Behavior, 31, 248–267. Ceja, L., & Navarro, J. (2011). Dynamic patterns of flow in the workplace: Characterizing within individual variability using a complexity science approach. Journal of Organizational Behavior, 32, 627–651. Ceja, L., & Navarro, J. (2012). ‘Suddenly I get into the zone’: Examining discontinuities and nonlinear changes in flow experiences at work. Human Relations, 65(9), 1101–1127. Christandl, F., Mierke, K., & Peifer, C. (2018). Time flows: Manipulations of subjective time progression affect recalled flow and performance in a subsequent task. Journal of Experimental Social Psychology, 74, 246–256.

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Csikszentmihalyi, M. (1975). Beyond boredom and anxiety: Experiencing flow in work and play. San Francisco: Jossey-Bass. Csikszentmihalyi, M., & Csikszentmihalyi, I. (1988). Optimal experience: Psychological studies of flow in consciousness. Cambridge: Cambridge University Press. Csikszentmihalyi, M., & Rathunde, K. (1993). The measurement of flow in everyday life. Toward a theory of emergent motivation. In J. E. Jacobs (Ed.), Current theory and research in motivation, Vol. 40. Nebraska Symposium on motivation, 1992: Developmental perspectives on motivation (pp. 57–97). Lincoln, NE: University of Nebraska Press. Debus, M. E., Sonnentag, S., Deutsch, W., & Nussbeck, F. W. (2014). Making flow happen: The effects of being recovered on work-related flow between and within days. Journal of Applied Psychology, 99(4), 713. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour. New York: Plenum. Delle Fave, A., & Bassi, M. (2017). Work, cultures, and the culture of work. Flow across countries and professions. In C. Fullagar & A. Delle Fave (Eds.), Flow at work: Measurement and implications (pp. 157–175). New York: Taylor & Francis. Delle Fave, A., Bassi, M., & Massimini, F. (2003). Quality of experience and risk perception in high-altitude rock climbing. Journal of Applied Sport Psychology, 15, 82–98. Edwards, J. R., Caplan, R. D., & Harrison, R. V. (1998). Person-environment fit theory: Conceptual foundations, empirical evidence, and directions for future research. In C. L. Cooper (Ed.), Theories of organizational stress (pp. 28–67). Oxford: Oxford University Press. Edwards, J. R., & Cooper, C. L. (1990). The person-environment fit approach to stress: Recurring problems and some suggested solutions. Journal of Organizational Behavior, 11, 293–307. Engeser, S., & Baumann, N. (2016). Fluctuation of flow and affect in everyday life: A second look at the paradox of work. Journal of Happiness Studies, 17, 105–124. Engeser, S., & Rheinberg, F. (2008). Flow, moderators of challenge-skill-balance and performance. Motivation and Emotion, 32, 158–172. Jackson, S. A. (1995). Factors influencing the occurrence of flow state in elite athletes. Journal of Applied Sport Psychology, 7, 138–166. Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport and Experience Psychology, 18, 17–35. Jackson, S. A., & Roberts, G. C. (1992). Positive performance states of athletes: Toward a conceptual understanding of peak performance. The Sport Psychologist, 6, 156–171. Massimini, F., Csikszentmihalyi, M., & Delle Fave, A. (1988). Flow and biocultural evolution. In M. Csikszentmihalyi & I. S. Csikszentmihalyi (Eds.), Optimal experience: Psychological studies of flow in consciousness (pp. 60–81). New York: Cambridge University Press. Massimini, F., & Delle Fave, A. (2000). Individual development in a bio-cultural perspective. American Psychologist, 55, 24–33. McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114, 376–390. Nakamura, J., & Csikszentmihalyi, M. (2005). The concept of flow. In C. R. Snyder & S. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). New York: Oxford University press. Oud, J. H. L., & Delsing, M. J. M. H. (2010). Continuous time modeling of panel data by means of SEM. In K. van Montfort, J. H. L. Oud, & A. Satorra (Eds.), Longitudinal research with latent variables. New York: Springer. Peifer, C. & Syrek, C. (2015). How unfinished tasks impact flow-experience. Talk at the 8th SELF Biennial International Conference, Kiel. Peifer, C., Syrek, C., Ostwald, V., Schuh, E., & Antoni, C. (2020). Thieves of flow: How unfinished tasks at work are related to flow experience and wellbeing. Journal of Happiness Studies, 21, 1641–1660. Rheinberg, F., & Vollmeyer, R. (2003). Flow-Erleben in einem Computerspiel unter experimentell variierten Bedingungen [Flow experience in a computer game under experimentally controlled conditions]. Zeitschrift für Psychologie, 211, 161–170.

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Rheinberg, F., Vollmeyer, R., & Engeser, S. (2003). Die Erfassung des Flow-Erlebens [The assessment of flow experience]. In J. Stiensmeier-Pelster & F. Rheinberg (Eds.), Diagnostik von Motivation und Selbstkonzept [Diagnostic of motivation and self concept] (pp. 261–279). Göttingen: Hogrefe. Schüler, J., Sheldon, K. M., Prentice, M., & Halusic, M. (2016). Do some people need autonomy more than others? Implicit dispositions toward autonomy moderate the effects of felt autonomy on well-being. Journal of Personality, 84(1), 5–20. Trudewind, C. & Husarek, B. (1979). Mutter-Kind-Interaktion bei der Hausaufgabenanfertigung und die Leistungsmotiventwicklung im Grundschulalter. Analyse einer ökologischen Schlüsselsituation [Mother-child interaction in homework preparation and achievement motivation development at primary school age. Analysis of an ecological key situation]. In H. Walter & R. Oerter (Eds.): Ökologie und Entwicklung [Ecology and development]. (pp. 229–246). Donauwörth: Auer.

Index

A Ability/abilities, xiv, 17, 57, 77, 86, 93, 101, 111, 116, 122, 147, 173, 174, 181, 186, 192, 204–207, 213, 216–218, 232, 236–238, 240, 243, 245, 247, 248, 251, 252 254, 255, 290, 291, 293, 297, 301, 303, 308, 309, 332, 335, 354, 361, 366, 379, 386, 394, 395, 397, 399–401, 403, 410, 411, 420, 422, 431, 434, 435 Above average, 44, 76–78, 87, 89, 94, 97, 99, 100 Absenteeism, 288, 314 Absolute difference model, 48 Absorption, 2, 4, 18, 22, 23, 36, 54, 55, 57, 58, 61, 146, 160, 196, 265, 275, 324, 353, 360, 381, 394, 395, 423, 424 Academic performance, 94–95 Achievement, 10, 11, 24, 115, 270, 273, 276, 327, 338, 340, 342, 343, 378, 381, 385–387, 411, 418, 420, 430, 431, 434 flow, xiv, 61, 82, 84, 109, 110, 234, 236–238, 240, 243–247, 250, 252–255, 295, 299, 304 flow motive, xiv, 237, 238, 240, 241, 251 motivation, 49, 82, 235, 237, 239, 247, 249, 295 motive, xiv, 82, 112–115, 126, 129, 130, 235–239, 242, 250, 252, 295, 304 orientation, 82, 236, 240 situations, xiv, 109–111, 249 Action, 173, 181, 184, 185, 188, 195, 202, 209, 216, 218, 221, 265, 326, 328, 357, 362, 369, 370, 378, 382, 383, 394, 397–401, 405, 407, 410, 419, 423, 430, 432, 433, 435

opportunities, 10, 16, 17, 111–118, 120, 123–128, 130, 131, 250, 305 orientation, 82, 84, 85, 98, 100, 185, 236, 245, 248, 250, 252, 253, 295, 304 Action-awareness merging, 352, 355, 379, 380, 382 Activity oriented approach, 236 Activity-specific incentives, 13 Addiction, xiv, 173–179, 184, 207, 273, 335, 338, 339, 419 Adolescents/adults, 90, 180, 185, 247, 248, 255, 325, 330–339, 341, 343, 360, 434 Affective, 8, 18, 23, 57, 72, 87–91, 98, 99, 110, 117, 181, 197, 202, 217, 238–241, 243, 246, 247, 327, 334, 341 Affiliation-intimacy motive, 113, 115–118, 120–124, 130, 131 Affiliative situations, 118, 120, 121, 131 Age, xiv, xv, 35, 44, 331–333, 335, 336, 339–341, 343, 366, 421 Agreeableness, 385 Ambient intelligence, 407, 409, 411 Animals, 11, 24, 92, 163, 164, 277–279, 281 ANS, see Autonomous nervous system (ANS) Antecedents, xiv, 52, 53, 60, 61, 63, 64, 71–101, 158, 192, 252, 288, 294–309, 311, 313, 327, 357, 359, 418, 420, 422, 428, 430–433 Anxiety, 2, 4, 10, 11, 17, 34, 36, 38, 42–44, 144, 156, 173, 198, 199, 222, 265, 268, 274, 275, 278, 290, 300, 326, 336, 354, 359, 379, 387, 397, 425, 436 Apathy, 42–45, 47, 65, 77, 265, 268, 274, 333, 334, 359, 434

© The Author(s) 2021 C. Peifer, S. Engeser (eds.), Advances in Flow Research, https://doi.org/10.1007/978-3-030-53468-4

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442 Appraisal interviews, 310 Appraisals, 160, 161, 199–201, 210, 222, 295, 296, 338, 366, 367 Approach orientations, 248 Arousal, 8, 44, 117, 118, 124, 177, 194, 197, 198, 201, 203, 215, 216, 219, 222, 252, 300, 305, 308, 309, 313, 353, 354, 356, 369, 395, 397, 400, 408, 410, 420 Artificial intelligence, 312 Artistic creation, 378, 381–384, 387, 388 Arts, xiii, xv, 9, 17, 19, 25, 57, 95, 183, 272, 276, 279, 377–388, 421, 436 Athletes, 19, 75, 123, 124, 138, 146, 148, 162, 176, 268, 270–272, 279, 291, 351–359, 361–366, 370, 421 Attention, xiii, 2–5, 8, 18, 20, 35, 53, 54, 92, 112, 113, 117, 118, 122, 124, 130, 146, 160, 163, 164, 173, 175, 180, 186, 192, 195, 197, 202–206, 208, 211, 212, 215–218, 221, 233, 242, 250, 265, 267, 268, 279, 280, 298, 307, 331, 351, 359, 360, 363, 382, 388, 395, 400, 403, 419, 423 Attentional involvement, 146, 151, 158, 164 Attentional networks, 355 Attentional processes, 18, 146, 355, 378 Attentional resources, 160, 161, 164, 383 Audience, 265, 268, 270–273, 278, 279, 338, 379 Augmented reality, 400 Automaticity, 197, 216, 382, 388 Automation/automatization, 161, 312 Autonomous nervous system (ANS), 210, 213, 214, 219 Autonomous regulation, 334 Autonomy, 18, 75, 82, 138, 140, 145, 148–150, 183, 238, 240, 250–251, 290, 297, 298, 303, 304, 310, 311, 314, 336, 338, 343, 431, 433 Autopilot, 312, 351 Autotelic, 192, 203, 207, 217, 272, 274, 276, 280, 294, 330, 332, 333, 336, 338, 343, 352, 363, 379–381, 385, 420, 423, 424, 430, 434, 437 experience, 3, 6, 8, 9, 20, 22, 50, 72, 98, 126, 139, 148, 150, 156, 158, 177, 233–235, 242, 243, 249, 250, 253, 254, 297, 298 personality, 17, 82, 83, 98, 100, 231–255, 294 quality, 83, 233, 238 Avatars, 398

Index Avoidance orientations, 237 Awareness, 3, 61, 162, 164, 165, 180–182, 203, 205, 220, 245, 275, 332, 352, 359, 394, 400

B Behaviorism, 9 Big five, 235, 242, 247, 294, 295 Bio-behavioral theory of flow, 193 Bioecological model of human development, 325, 342 Boredom, 2, 10, 34, 36, 38, 42, 43, 90, 92, 118, 144, 156, 173, 178, 194, 196, 198, 199, 204, 211–213, 215, 217, 219, 222, 234, 237, 265, 268, 270, 273, 274, 300, 326, 359, 397, 425, 435 Bottom-up, 92, 388, 435, 436 Boundary conditions, xiv, 71–101, 235, 251, 254, 418, 420 Brain, 18, 193–195, 197, 203–207, 210, 214, 216, 218, 220–222, 382, 386, 387, 420, 436 Brain metabolism, 193 Brain networks, 197, 381, 382 Breaks, 84, 117, 162, 236, 271, 301, 302, 310, 311, 383 Breath/breathing, 33, 35, 37, 160, 195, 214, 219, 309, 365, 370, 378 Broaden-and-build theory of positive emotions, 293 Burnout, 116, 288, 310, 314 Business, 57, 242, 267, 271, 276, 279–281, 291

C Cardiovascular measures, 213, 214, 216 Causal effects, 98, 295 CET, see Cognitive evaluation theory (CET) Challenges, xiv, 5, 10, 12, 15, 21, 24, 33, 34, 36, 38–45, 47–50, 53, 55–65, 72, 77, 78, 84, 87, 89, 90, 93, 94, 99, 101, 110–113, 117, 121, 123, 127–130, 137–140, 143–146, 149, 150, 155, 163, 174, 179, 186, 194, 198–200, 204, 205, 211, 217, 222, 232–234, 236, 237, 240, 254, 255, 263–266, 268, 270, 275, 276, 281, 291, 294, 295, 297, 326, 327, 330–332, 334–337, 339, 342, 343, 356, 360, 361, 368, 378, 383, 394–399, 401–404, 410, 411, 418, 420, 421, 426, 428, 430, 436, 437

Index Challenge-skill balance, 4, 5, 7, 18, 113, 117, 118, 187, 199, 222, 235–237, 253, 297, 298, 304, 326, 352, 354, 380, 422 Channel models, 42–45, 48, 49 Character strengths, 311 Children, 13, 122, 145, 150, 162, 165, 235, 250, 325, 331–333, 343, 344, 434 Choreography, 272, 379 Classical test theory, 51 Clinical, 19, 25, 178, 278, 279, 330, 437 Clustering, 92 Clutch, 353, 356, 357, 364, 365, 370 Coaching, 271, 272, 302, 309, 311, 313 Co-active/coactive, 264, 277, 279 Co-active flow, 264, 420 Cognition, 161, 199, 216, 246, 378, 381, 382, 384, 386–388, 394, 409 Cognitive, 91, 178, 184, 193–195, 197, 199, 202, 204, 206, 207, 211, 217, 218, 220–222, 293, 296, 308, 313, 325, 327, 329, 334–336, 341, 356, 366, 368, 381–383, 385, 388, 395, 399, 401, 402, 404, 435, 436 capacity, 91 control, 178 flexibility, 193, 381, 383, 388 processes, 57, 58, 88, 91, 92, 296 Cognitive evaluation theory (CET), 11, 137–151, 419 Cognitive-motivational-relational theory of emotions, 200 Cohesiveness, 265, 268, 270, 275 Collaboration, 276, 380, 384 Collative properties, 158, 164 Colleagues, 8, 34, 42, 43, 49, 54, 59, 81, 82, 94, 96, 111, 115, 123, 130, 156, 158, 159, 192, 193, 195–197, 199, 201, 203–215, 220–222, 232, 234, 237, 247, 249, 250, 289–305, 310, 333, 337, 339, 340, 363, 380, 382–384, 395–397, 399, 401, 405, 410, 421, 424, 429, 435 Collective efficacy/collective self-efficacy, 274, 291, 302 Collective flow, 19, 291, 302, 378–380, 386–388 Combat flow, 181–182, 433 Combined flow, 380 Commitment, 95, 248, 252, 289, 291–295, 336, 337 Communications, 34, 36, 127, 156, 264–266, 268, 275, 277, 303, 400 Communities, 270, 436

443 Compatibility effect, 91 Compensatory model of work motivation and volition, 117 Competence, 11, 82, 93, 116, 128, 130, 131, 139–142, 144–148, 150, 151, 163, 164, 174, 180, 275, 290, 359, 394 Competition, 12, 95, 113, 123, 126–127, 272, 279, 290, 306, 310, 357, 361, 362, 366 Competitive motivation, 306 Competitive sports, xv, 421 Complexity, 34, 55, 59, 63, 233, 264, 326, 327, 330–332, 334, 337, 338, 341, 342, 399, 409, 418, 420, 425 Componential approach, 36, 50–55, 59, 61–64, 426, 428 Components, 2–8, 10–13, 15, 20–22, 50, 51, 53–55, 64, 84, 87–89, 94, 96, 97, 101, 111, 115, 116, 122, 128, 158, 159, 172, 176, 180, 185, 202, 214, 217, 236–242, 246, 251–254, 274, 280, 281, 294, 298, 303, 313, 329, 336, 342, 363, 364, 378, 383, 386, 395, 405, 417, 421–428 Compositions, 94–96, 378, 384, 421 Computer games, 60, 78, 84, 85, 94, 97, 173, 175, 194, 196, 202, 205, 206, 211, 301, 304, 305, 361, 366, 401 Concentration, 3, 20, 23, 33, 35–37, 40, 41, 44, 48, 50, 54, 55, 57, 58, 93, 101, 121, 146, 159, 173, 175, 177, 181–183, 186, 200, 202, 207, 217, 231, 232, 234, 236, 240, 251, 302, 324, 334–336, 338, 341, 343, 352, 355, 356, 359, 369, 380, 400, 410, 436 Conditions, xiii, xiv, 4–6, 19–22, 37, 43, 44, 47, 48, 58, 60, 65, 72, 74–79, 81, 84, 85, 88, 90, 92, 96, 100, 116, 138, 140, 142, 145, 150, 160, 179, 180, 182, 183, 185, 192, 196, 202, 203, 206, 209–217, 221, 236, 237, 245, 250–254, 264, 266, 267, 272, 273, 275, 276, 278, 280, 281, 294, 295, 298, 301, 305, 311–314, 327, 329, 331, 333, 341, 343, 353, 356, 360, 362, 363, 368, 369, 377, 382, 396, 401, 405, 435 Confidence, 143, 245, 255, 291, 308, 313, 353, 358, 359, 369, 379 Confirmatory factor analysis (CFA), 49, 51 Conflicts, 125, 126, 173, 175, 176, 178, 184, 236, 276, 299, 334, 336, 387 Connection, 129, 177, 265, 268, 271, 275, 298, 302, 359, 388, 403 Conscientiousness, 94, 235, 242, 294, 333, 385, 386

444 Consciousness, 7, 12, 15, 17, 23, 24, 55, 59, 91, 159, 160, 163, 164, 175, 203, 382, 394 Consequences, xiii, xiv, 4, 6, 7, 12, 17–22, 71–101, 125, 146, 161, 174, 177, 179, 180, 182, 184, 185, 187, 192, 253, 268, 274, 277, 288–295, 302, 305, 312, 327, 331, 333, 337, 360, 362, 408, 418–420, 422, 434–436 Construct validity, 49–51, 62, 63 Consumer behavior, 19, 340 Contagion/contagious, 75, 122, 265, 268, 275, 277, 280, 292, 293, 302, 312, 367, 369, 370, 380, 381, 386, 421 Continuous phenomenon, 425, 426 Control, 2, 4, 5, 12, 14, 15, 40, 44, 45, 50, 72, 73, 75, 93, 96, 101, 116, 121, 124, 125, 145, 150, 158, 161, 163, 173–175, 177, 179, 180, 186, 187, 192–194, 197, 201, 204–206, 211, 218, 220, 221, 231, 234, 240, 242, 244–246, 249, 250, 269, 278, 290, 295, 298, 333, 334, 338, 352–357, 359, 369, 380–383, 388, 395, 396, 399, 400, 410, 426, 434, 435 Coordination, 264–266, 276, 380 Coping, 161, 199–202, 210, 212, 215, 217, 222, 239, 240, 253, 289, 291, 293, 336, 359 Core components, 54, 422–424 Core job dimensions, 297, 298, 309, 313 Correlational, 8, 21, 72, 74, 89, 93, 94, 96–98, 101, 274, 279, 434 Corrugator supercilii, 194, 209 Cortisol, 195–197, 210–214, 218, 219, 221, 300, 305 Creative/creativity, 6, 8, 16–18, 24, 57, 58, 95, 142, 156, 172, 193, 241, 272, 273, 278, 279, 289, 293, 332, 333, 337, 338, 359, 378, 380–388, 395, 398, 401, 418, 421, 433 Cross-over/crossover, 122, 123, 292, 312 Cross-sectional, 21, 96, 98, 289, 293, 296, 299, 302, 311, 341, 369, 434 Crowd, 266, 277, 280 Cultivation, 327, 401, 410, 421 Cultures, 17, 35, 44, 49, 60, 61, 63, 183, 249, 254, 310, 333, 436 Customer, 293 Customer orientation, 249

D Daily experience, xiii, 15–16, 18, 21, 24, 36, 38–50, 58, 60, 62, 63, 110, 327, 328, 418

Index Daily life, 15, 50, 63, 122, 250, 327, 330, 331, 336, 340, 342, 343, 384 Dancers, 9, 156, 272, 278, 377, 379, 421 Day reconstruction method (DRM), 60, 73, 429 Deep concentration, 91, 204, 394 Deep flow, 37, 57, 60, 79, 80, 364, 425, 429 Default mode, 197, 204, 205, 207, 360 Default network activation, 206, 218, 221 Definition, xiii, 2–8, 20, 23, 31, 32, 35, 61, 157, 172, 195, 198, 201–203, 205, 217, 218, 232, 237, 246, 247, 253, 268, 279, 280, 289, 324, 367, 384, 399, 400 Deindividuation, 277 Deliberate focus, 353 Demand-ability-fit, 366 Demands, 2, 3, 5, 6, 10, 20, 36, 49, 72, 74–86, 88, 90, 93, 97–101, 115, 118, 125, 144, 161, 193, 195, 198–203, 206, 210, 213, 214, 216, 217, 219, 220, 222, 237, 245, 246, 253, 288, 290, 291, 295, 299–301, 303–305, 308–310, 313, 337, 338, 353, 360, 366, 401, 410, 418, 419, 422–425, 430–432, 434–436 Demand-skill balance, 199, 302, 304, 305, 364, 422–424, 430, 431 Depletion, 236 Depression, 75, 175–177, 359, 360 Deprivation, 10, 183, 436 Detachment, 300, 309 Development, xiii–xv, 2, 9–12, 16–22, 45, 49, 53, 59–61, 63, 88, 142, 149, 164, 233, 234, 249, 252, 265, 266, 270, 273–279, 281, 290, 293, 300, 302, 306, 308, 312, 313, 324–334, 336, 339, 341–343, 364, 368, 408, 421, 434, 436, 437 Developmental stages, 344 DFS, see Dispositional flow scale (DFS) Dialectical principle, 247–249, 251, 252 Difficulty, xiv, 40, 49, 95–97, 113, 174, 177, 204–207, 210, 212, 215, 219, 233, 235–238, 240, 243–248, 252, 254, 255, 295, 305, 312, 366, 401, 411, 420 Disengagement, 239, 334, 336 Dispositional flow, 51–53, 235, 354, 378, 421 Dispositional flow scale (DFS), 234, 242, 291, 363 Dispositions, 58, 236, 237, 242, 251, 274, 278, 280, 294, 420, 430, 434 Distress, 58, 201, 269 Divergent thinking, 386 Dopamine, 159, 193, 195–197, 207, 208, 219, 220 Dopaminergic systems, 195, 196, 208

Index DRM, see Day Reconstruction Method (DRM) Duration, 10, 185, 193, 266, 366, 424–429 Dynamical system theory, 401, 411 Dynamic balance, 50, 304 Dynamics, xiv, 38, 55, 58, 61, 85, 197, 233, 237, 246, 248, 251–253, 266, 329, 399, 420, 424–428, 430

E Ecological perspective, 325, 326, 330, 341, 342 Ecstasy, 157 EDA, see Electrodermal activity (EDA) Education/educational, xiii, xv, 25, 122, 173, 249, 265, 276, 292, 331–334, 336–338, 381, 387, 401, 436 EEG, see Electroencephalography (EEG) Effectance motivation, 24, 164 Effortless attention, 18, 195, 196, 202, 204, 205, 220, 221, 335, 336, 353, 355, 379, 382 Effortlessness, 193, 195, 200, 202, 203, 205, 209, 213, 214, 220, 221, 388 Efforts, 3, 84, 90, 111, 113, 116, 131, 145, 179, 192, 195, 196, 200, 202, 203, 205, 209, 211, 213, 214, 216, 217, 221, 222, 236, 270, 273, 275, 280, 295–297, 352, 353, 382, 408, 427 Egoistic motivation, 312 Elation, 182, 265, 269 Elderly, 331, 339, 340, 342–344, 401, 436 E-learning, 54, 172, 395, 400 Electrodermal activity (EDA), 194, 215–217, 219 Electroencephalography (EEG), 192, 197, 206, 219, 368, 370 Electromyography (EMG), 194, 209, 210 EMG, see Electromyography (EMG) Emotional, 4, 8, 18, 57, 58, 61, 116, 122, 125, 131, 157, 158, 160, 176, 194, 195, 197, 204, 205, 207, 209, 219, 220, 247, 248, 251, 265, 266, 268, 274, 275, 277, 292, 302, 329, 335, 367, 369, 370, 379–383, 386, 388, 397, 403, 405, 408, 421 Emotionless state, 160 Emotions, 8, 118, 125, 157, 158, 160–163, 165, 197, 200, 201, 204, 205, 209, 211, 222, 247, 265, 268, 269, 274, 275, 353, 354, 360, 367, 369, 382, 383, 400, 401, 408, 409 Empathy, 131, 289, 380, 386, 388 Energy, 9, 17, 172, 193, 202, 203, 207, 210, 211, 220, 291, 293, 296, 300, 352, 385

445 Engagement, xv, 5–7, 14, 24, 57, 72, 80, 95, 100, 138, 141, 144, 145, 150, 156, 161, 164, 194, 204, 207, 211, 220, 233, 265, 267, 271, 279, 289–292, 301, 330, 332, 334, 336, 341, 343, 380, 381, 385, 387, 388, 395–398, 401, 403, 404, 407, 409, 424, 432, 433, 435 Enjoyment, xiii, xiv, 2, 7, 9, 11–13, 16, 17, 23, 24, 48, 54, 60, 72, 75, 81, 84–86, 89, 91, 96, 138–151, 155–165, 192, 203, 209, 221, 232, 234, 270, 324, 332, 339, 340, 353, 359, 377, 381, 384, 387, 394, 395, 419, 423–425, 427 Enthusiasm, 164 Environment, 3, 11, 13, 19, 24, 37, 38, 61, 81, 161, 163, 172, 177, 183, 199, 201, 204, 233, 235, 254, 266, 293, 302, 303, 307, 308, 311, 312, 326, 328, 329, 332, 333, 335, 337, 341, 359, 395, 398–401, 407, 408, 410, 420, 432 Environmental characteristics, 80, 181 ESF, see Experience Sampling Form (ESF) ESM, see Experience sampling method (ESM) Ethic, 171, 184–185, 188, 419 Eudaimonic technologies, 404 Eustress, 201, 420 Excitement, 335 Exercising, 97, 163, 175, 184, 185, 358, 360, 402 Experience fluctuation models, 41–45, 64, 65 Experience of time, 3, 72 Experience sampling form (ESF), 15, 16, 20, 21, 39–41, 121, 428, 435 Experience sampling method (ESM), 8, 15, 16, 21, 24, 32, 38–41, 43, 45, 49, 50, 52, 53, 55, 59–63, 73, 89, 110, 111, 121, 122, 138, 150, 233, 242, 327, 330, 334–336, 339–342, 344, 362, 363, 365, 368–370, 408, 427–429, 435 Experimental, 21, 22, 60, 74, 76, 78, 84, 90, 92, 96–99, 101, 194, 198, 206, 208–210, 212, 214–216, 221, 247, 279, 295, 301, 356, 361, 378, 382, 408, 434, 435 Expertise, xiii, 5, 17, 195, 304, 378, 382, 383, 385, 422 Explicit motives, 82, 110, 114–118, 120, 130 Exploratory behavior, 54, 177, 335 Extension memory, 244–246 Extra-role performance, 94, 289 Extraversion, 294, 306, 385 Extrinsic rewards, 2, 76, 140, 148, 424, 433 Extrospective networks, 382 Eye blink rate, 208

446 F Facial expressions, 122, 162, 292 Family, 33, 34, 41, 184, 233, 278, 330, 335, 343, 379 Fear, 182, 235, 240, 241, 268, 400 Fear of failure, 84, 98, 100, 235, 240, 247, 253, 434 Feedback, xv, 5, 17, 18, 36, 50, 57, 58, 74, 111, 120, 125, 131, 141–145, 150, 161, 177, 180, 183, 200, 249, 265–269, 271–273, 275, 276, 294, 297–299, 302, 308, 310, 311, 313, 314, 353, 369, 378, 381, 396, 399, 422, 429, 435 Feeling of control, 2–5, 11, 13, 20, 22, 35, 72, 173, 180, 207, 291, 297, 303, 401, 411, 422 Fit, 47, 51, 54, 72, 74–80, 82–86, 88, 90, 93, 97, 98, 100, 128, 212, 268, 303–308, 311, 326, 331, 357, 368, 418, 421, 431, 435 Flow-affect-relationship, 98 Flow channel, 44, 45, 47, 77 Flow channel model, 76–78, 83, 198–200, 221, 294, 300, 303, 304 Flow complexity, 34 Flow contagion, 381 Flow hypothesis of motivational competence, 114, 116–118, 128, 130 Flow indicators, 192, 208, 312, 332 Flow in groups, 20, 110, 123–124, 129, 387 Flow intensity, 38, 59, 63, 78, 79, 82, 97, 98, 305, 427 Flow model, 10, 11, 20, 21, 58, 60, 65, 75–80, 82, 97, 98, 100, 146, 232, 235, 297, 326, 342, 353, 380, 418, 425, 430, 436 Flow personality, xiv, 235 Flow prevalence, 35, 36, 38, 52, 55, 62, 63 Flow proneness, 58, 159, 195, 196, 208, 234, 235, 242, 378, 381, 384–388 Flow questionnaire, 32, 33, 36, 57, 58, 62, 63, 234, 426, 428 Flow short scale (FKS), 4, 96, 363, 365, 366, 423, 426, 428 Flow state scale (FSS), 177, 273, 363, 381 Flow theory, xiv, 9–12, 32, 33, 38, 41, 43–45, 49, 61, 74, 79, 88, 92, 99, 110, 128, 137–151, 183, 198, 219, 222, 235, 237, 245, 251, 270, 271, 297, 326, 330, 341, 357, 359, 397, 398, 419 Flow Therapy, 278, 330 Fluid interaction, 394, 397, 404, 405 Focus, 2, 9, 12, 14, 24, 25, 81–84, 92, 98, 122, 123, 138, 139, 142, 147, 156, 160, 164, 173, 175, 181, 186, 202, 204, 205, 207,

Index 211, 214, 217, 221, 265, 273, 290, 324, 327, 329, 336, 340–343, 351, 353, 355–359, 365, 367, 369, 379, 382, 395, 400, 403, 410, 418, 419, 421, 426 Focused attention, 53, 164, 202, 205 Focused concentration, 50, 72, 76, 158, 354 Free-choice task, 80 Frequency, 38, 83, 88, 89, 176, 196, 213, 233–235, 241, 242, 290, 291, 301, 330, 334, 335, 340, 355, 384, 385, 424–428 Fun, 139, 143, 144, 232, 239, 240, 245, 267, 269, 302, 310 Functional analysis, 246 Functional approach, 243–246, 420 Functional magnetic resonance imaging (fMRI), 195, 196, 208, 219, 382 Functional near infrared spectroscopy (fNIRS), 197, 204 Funktionslust, 11

G Gambling, 176, 177, 179, 338, 339 Game addiction, 177–179 Games/gaming, 3, 17, 21, 22, 54, 72, 74, 84, 85, 90, 95, 96, 100, 125, 126, 129, 139, 140, 143, 145–147, 149, 150, 164, 177, 178, 184, 185, 194, 195, 197, 208, 209, 214, 216, 240, 250, 271, 272, 278, 281, 301, 311, 312, 332, 333, 335, 338, 339, 361, 366, 369, 395, 396, 398, 401, 402, 410 General Adaptation Syndrome (GAS), 201 Genetic, 58, 197, 208, 219, 242, 386 Goal, 3, 5, 6, 9, 12, 17, 18, 22, 23, 38, 40, 41, 49, 50, 61, 64, 74, 75, 80, 81, 95, 99, 111, 120, 139, 150, 156, 158, 160, 172, 173, 177, 180, 183, 184, 186, 199, 206, 232, 233, 235–237, 245, 246, 252, 255, 266–269, 273, 275–277, 293–299, 303, 306, 310, 311, 324, 326, 334, 335, 340, 341, 352, 354, 356, 357, 359, 362–364, 379, 396, 397, 405, 419, 422, 426, 431 Goal-directed activities, 140, 147, 148, 158 Goal-setting, 308, 356 Group-centered, 265, 267 Group cohesion, 386, 387 Group flow, 19, 264, 266, 378, 380, 381, 384, 386, 387, 398 Group identity, 267, 277 Group level, 95, 123, 266, 274, 278, 401 Group polarization, 367

Index Group processes, 265, 267, 268, 274, 278, 280, 281

H Happiness, 7–8, 16, 23, 24, 41, 48, 49, 88–90, 160, 162, 163, 178, 184, 192, 265, 269, 330, 335, 403 Health, 19, 174, 179, 180, 184, 187, 212, 213, 288, 314, 329, 333, 334, 339, 341, 358, 360, 363, 398, 403, 436 Heart rate, 213, 214 Heart rate variability (HRV), 195, 196, 213–215, 368, 427 Hedonic balance, 335, 336 Hedonic technologies, 404 Hierarchical linear modeling, 45 Hindrances, 334 Hope, xv, 9, 22, 92, 98, 110, 128, 155, 235, 236, 239–241, 247, 272, 296, 308, 313, 428 Human complexity, 324, 326–329, 331 Human-Computer Confluence (HCC), 407–409 Human-computer interaction (HCI), xiii, 19, 25, 54, 110, 178, 194, 393–411, 421 Human development, xiv, 324–344, 421, 433 Human-technology interaction, 406, 407 Hypnosis, 356 Hypofrontality, 193, 197, 203–205, 219, 220, 355, 361, 368 Hypothalamus–pituitary–adrenal (HPA) axis, 210, 397

I Immersion, 2, 160, 335, 338, 395, 396, 401 Immune systems, 210, 335, 336 Implicit motives, 82, 83, 112, 114–118, 129, 130, 185, 243, 250, 252 Improvisation, 264, 378, 382, 383, 421 Incentives, xiv, 2, 6, 12–16, 19, 24, 81–83, 110, 112–118, 120, 121, 123–126, 128, 130, 131, 233, 236, 241, 250, 431 Individual identity, 267 Individualistic culture, 267 Individual level, 274 Individual sphere, 288–291, 293–296, 307–311, 314 Infancy/infants, xiv, 164, 165, 331–333, 344, 421 Information technology, 395 In-role performance, 289

447 Intensity, 33, 37–39, 50, 51, 55, 60, 62–64, 78–80, 82, 83, 88, 89, 100, 157, 158, 193, 201, 210, 233, 234, 242, 269, 353, 355, 361–364, 369, 424–428 Intention, 138, 340, 380, 400, 401, 405, 406, 408, 410, 411 enaction, 405 memory, 244–246, 249, 250 Interactive flow, xiv, 123, 420 Interdependence/interdependency, 265–267, 273, 301, 380 Interest, xv, 2, 8, 12, 17–19, 21, 23–25, 39, 48, 49, 75, 125, 144, 149, 150, 158, 161, 164, 172, 175, 178, 183, 184, 186, 192, 194, 198, 204, 216, 219, 233, 238, 239, 243, 248, 252, 274, 278, 303–305, 310–312, 327, 331–334, 340, 343, 352, 368, 377, 379, 380, 385–388, 408, 420, 429–431, 437 Internal locus of control, 82, 84, 98, 100, 236, 237, 248, 296 Internet, 139, 146, 149, 177–179, 335, 395 Internet addiction, 177–179 Interpersonal relationship, 120, 122, 273 Interruptions, 76, 264, 302, 362, 365, 395, 428 Interview, 9, 13, 14, 20, 21, 24, 32, 33, 60, 121, 138, 146, 162, 176, 180, 185, 191, 222, 310, 333, 336, 344, 358, 359, 361, 362, 365, 368, 370, 379, 380, 382, 395, 424 Intrinsically rewarding, 6, 12, 93, 146, 156, 158, 163, 164, 183, 201, 206, 207, 220, 404, 432 Intrinsic motivation, xiii, xiv, 24, 49, 76, 89, 137, 140–142, 144, 145, 147–149, 158, 159, 183, 231, 234, 297, 302, 324, 332, 334, 336, 340, 341, 343, 380, 384, 385, 387, 388, 394, 401, 411, 419, 423, 424, 432 Introspection/introspective, 202, 204, 205, 383, 386, 388 Introspective network, 382, 383 Involvement, 17, 41, 48, 72, 75, 84, 86, 96, 121, 146, 147, 173, 214, 337, 382, 383, 388, 394, 400, 401, 409, 410

J JCM, see Job Characteristics Model (JCM) JD-R, see Job Demands-Resources Model (JD-R) Job Characteristics Model (JCM), 297–299, 309, 313

448 Job crafting, 293, 294 Job Demands-Resources Model (JD-R), 301, 302 Job satisfaction, 211, 290–291, 293, 338 Joy, 123, 157, 164, 175, 184, 265, 267–269, 274, 275, 278, 368

K Kinesthetic perceptions, 353

L Latent construct, 51, 59, 60 Leadership, 127–129, 266, 273, 280, 302, 310, 311, 314 Learning, 6, 12, 14, 16, 18, 19, 22, 25, 57, 94, 96, 97, 110, 112, 115, 116, 140, 141, 172, 179, 184, 186, 193, 208, 238–240, 247, 272, 278, 299, 303, 310, 332–334, 336, 337, 341, 364, 401, 433 Learning environments, 97, 337, 359 Leisure, 36, 41, 43, 51, 58, 62, 172, 179, 183, 234, 242, 291, 293, 297, 309, 312, 330, 335–337, 339, 341, 343, 358, 429 Leisure activities, 13, 15, 16, 18, 24, 36, 41, 140, 288, 291, 296, 297, 300, 310, 334, 339, 340, 428, 437 Life satisfaction, 88–90, 290, 330, 340, 429 Lifespan, 269, 323–344, 421, 437 Locus of control, 84 Longitudinal, 21, 90, 96–99, 290, 291, 293, 295, 298, 311, 342, 363, 369, 428, 429, 434, 435 Loss of self-consciousness, 2, 3, 12, 15, 35, 50, 73, 158, 177, 231, 234, 338, 352, 379, 380, 423

M Massive multiplayer games, 398 Mastery, 233, 237, 245, 249, 291, 293, 295, 308, 313, 360, 396, 420 Meaning, 3, 22, 23, 77, 127, 163, 214, 216, 265, 269, 280, 328, 334, 336, 339, 340, 403, 408, 421 Measurement, xiii, 20, 21, 31–65, 72, 83, 86, 96, 100, 185, 192, 233–235, 238, 274, 281, 295, 327, 328, 344, 358, 361, 364, 365, 368, 379, 418, 420, 428 Media use, 19, 25, 178, 407 Meditation, 12, 160, 192, 204, 205, 218–221, 309

Index Memory distortion, 365 Mental health, 330, 343 Merging of action and awareness, 2–4, 35, 50, 55, 57, 58, 64, 158, 159, 176, 202, 231 Metacognitions, 61 Metaskills, 233 Micro flow, 79, 280, 339, 364 Mimicry, 367 Mindfulness, 218, 356 Mood, 88, 92, 172, 178, 238, 290, 293, 304, 340, 360, 362, 384, 422 Motivation, xiii, 6, 7, 11–14, 16, 19, 23, 24, 39, 49, 54, 76, 82, 88, 95–97, 127, 143, 144, 149, 156, 159, 184, 235, 237, 239, 241, 247, 249, 253, 290, 295, 297, 301, 312, 335, 336, 338, 340, 401 Motivational orientation, 139, 140, 143–145, 149, 428, 430 Motives, xiv, 82, 83, 112–115, 117, 118, 121, 123–131, 178, 235–242, 250–253, 295, 304, 311, 312, 419, 420, 431, 434 Multilevel modelling, 45 Multitasking, 300, 304, 305, 312 Music, xiii, xv, 13, 16, 19, 25, 75, 94–98, 121, 122, 183, 185, 209, 214, 267, 272, 281, 289, 292, 332, 334, 335, 339, 356, 377–388, 420, 421 Musicians, 5, 13, 127, 200, 267, 268, 272, 279, 378, 380 Mutual support, 293, 301 Mutual trust, 380, 388

N Naches, 157, 236, 242, 243 Near-infrared spectroscopy, 355 Need for achievement (nAch), 236, 434 Need for autonomy, 148, 150, 290, 304 Needs, 3, 8–10, 12, 40, 43, 45, 49, 52, 53, 59–61, 65, 80, 81, 110, 112, 113, 116, 122–124, 127, 129, 130, 140, 145, 148, 150, 156, 161, 163, 172, 178, 183–186, 199, 204, 205, 207, 208, 213, 215, 219, 221, 222, 232, 236–238, 240, 241, 243, 247, 250, 251, 253–255, 266, 269, 274, 276, 278, 280, 281, 290, 293, 302–304, 306, 311, 312, 325, 326, 328, 329, 342, 356, 367, 369, 403, 406, 407, 418, 420, 424, 426–431, 436 Negative affect, 57, 115, 209, 239, 241, 243, 244, 253, 297, 335, 336, 340

Index Negative consequences, xiv, 19, 40, 85, 140, 175, 177, 179, 180, 183–185, 187, 192, 273 Negotiation, 306, 312 Networked flow, 398, 401 Network synchronization, 194 Neuro-imaging, 368 Neurophysiology, 193, 197, 204 Neuroticism, 235, 242, 251, 294, 333, 385 Neurotransmitter, 193, 207, 208, 220, 367, 370, 421

O Octant model, 42, 59, 77, 78, 97, 99 Ontogeny, 326 Openness/openness to new experience, 84, 185, 232–234, 251, 303, 333, 335–338, 341, 343, 385–387 Operant motive test (OMT), 83, 112, 238–243, 250–254 Opioids, 367, 370, 421 Opportunities, 5, 10, 16, 17, 21, 49, 111–118, 121–131, 163, 233, 250, 253, 305, 306, 312 Optimal activation, 207 Optimal arousal, 247, 354, 356 Optimal challenge, 45, 48, 49, 60, 138, 140, 143–149, 151, 202, 217, 254, 419 Optimal experience, 2, 6–7, 18, 23, 60, 138, 139, 149, 150, 159, 215, 232, 279, 324, 326–335, 341, 342, 394, 396, 397, 399–401, 404, 405, 408, 410, 411 Optimal experiential states, 327 Optimal functioning, 202–207, 219, 324–344, 354 Optimal Functioning Therapy for Adolescents (OFTA), 330 Optimal motivation, xiii Optimism, 173, 186, 248, 296, 302, 308, 313 Optimized physiological activation, 202, 203, 217, 218 Orbicularis oculi, 194, 209 Organizational climate, 18, 303 Organizational commitment, 289, 293 Organizational culture, 273, 276, 303, 432 Organizational sphere, 293 Organizations, 117, 174, 265, 266, 270, 273, 276, 279, 280, 288, 289, 292, 294, 299, 303, 306, 307, 309, 310, 312, 313, 326, 399, 420, 431 Outcomes, 18, 19, 32, 55, 56, 81, 84, 111, 120, 121, 125, 126, 131, 138, 140–145, 147, 148, 150, 173, 177, 183–185, 214, 234, 236, 243, 249, 253, 254, 265, 268–271,

449 274, 280, 281, 293, 297, 298, 303, 308, 312, 327, 329, 330, 343, 356, 380, 383, 401, 407, 430, 432, 433 Overload, 60, 84, 90, 92, 212, 213, 215, 237, 435

P Pain, 13, 80, 81, 100, 180, 359, 367 Parasympathetic activation, 196, 214, 215, 218, 219, 221, 309 Parents, 235, 254, 333–336, 343 Peak experiences, 12, 24, 60, 156, 175, 216 Peak performance, 60, 95, 175, 183, 351, 352, 357 Pedagogy, 332 Peers, 150, 211, 306, 307, 330, 332, 343, 432 Perceived abilities, 117, 118, 354 Perceived autonomy, 138, 148–149, 419 Perceived balance, 394, 422 Perceived competence, 138, 141–145, 147, 149, 150, 163, 384, 419 Perception, 20, 63, 64, 139, 145–147, 149–151, 163, 164, 179–181, 187, 235, 237, 239, 254, 269, 274, 333, 335, 353, 366, 368, 394, 399, 400, 407, 410, 418, 422, 430 The Perfect Interaction Model (PIM), 405, 406, 411 Performance, xiv, 2, 6, 7, 18, 19, 21, 23, 76, 87, 93–98, 101, 109–111, 113, 118, 140–145, 147, 148, 164, 171, 172, 174, 178, 184, 185, 195, 199–204, 206, 207, 211, 213, 214, 216, 217, 233, 237, 249, 253, 264–268, 270, 271, 273, 275, 276, 278, 281, 288–296, 298, 299, 304, 308, 310, 313, 314, 327, 330, 351–354, 356–359, 364–366, 368, 369, 378–384, 386–388, 398, 410, 411, 418, 420, 421, 433–436 Performance feedback, 95, 113, 141, 144, 150, 158, 161, 264, 299, 308 PERMA, 290, 403 Personal growth, 7, 93, 101, 183, 330, 331 Personal identity, 13, 23, 265, 268, 337 Personality, xiv, 8, 13, 14, 17–19, 25, 49, 63, 72, 81–83, 85–87, 90, 94, 98, 100, 115, 131, 185, 192, 196, 203, 207, 217, 231–255, 272, 274, 276, 280, 294–295, 304–306, 311, 312, 326, 331, 333, 335, 337, 338, 341, 343, 355, 356, 363 Personality systems interaction (PSI), 243–246, 250–253 Personalized power motive, 125, 126 Person characteristics, 80 Person-environment fit, 303, 327, 420, 431

450 Phenomenological state, 160 Physical activity, 19, 163, 234, 309, 339, 355, 360, 361 Physical health, 172, 201, 339, 340 Physiological activation, 202, 204, 215, 300, 309, 408, 410 Physiological arousal, 196, 201, 212, 213, 219, 305, 309, 397 Physiological correlates, 18, 99, 159, 192 Physiological indicators, 194, 203, 209, 214, 216, 219, 420 Physiology, 197, 198, 202, 204, 208, 210, 220, 420 Play, 8, 10, 11, 13, 24, 79, 85, 96, 110, 112, 141, 147, 149, 150, 156, 163, 201, 205, 208, 212, 220, 232, 233, 235, 246, 250, 254, 264, 271, 277, 278, 281, 295, 298, 299, 301, 310, 312, 330, 355, 359, 360, 378, 381, 382, 396, 397, 399, 407, 419, 436 Playfulness, 335, 395 Pleasure, 9, 11, 75, 80, 81, 100, 116, 157, 159, 160, 162, 163, 165, 179, 180, 183, 232, 290, 337, 424 Pleasure centres, 207 Pleiotropy, 386 Polychronicity, 304 Positive affect, 6, 54, 57, 60, 88–90, 98, 101, 125, 126, 177, 195, 209, 220, 239, 240, 243–250, 252–255, 291, 302, 330, 334–336, 340, 381, 384, 388, 418, 420 Positive emotions, 158, 200, 201, 215, 269, 278, 281, 293, 302, 360, 381, 400, 403, 404 Positive mental health, 265, 269 Positive psychotherapy, 330 Positive technology, 403–406, 408 Posttraumatic stress disorder (PTSD), 359 Power, xiv, 3, 9, 82, 90, 110, 112, 115, 124–128, 131, 161, 215, 238, 240, 248, 250–251, 253–255, 267, 269, 419, 433 Power motive, 82, 113–117, 124–131, 250, 419 Practice, 7, 50, 97, 161, 204, 205, 218, 252, 272, 278, 290, 292, 308–311, 314, 328, 330, 332, 333, 343, 356, 357, 369, 378, 385–388 Precondition, 87, 93, 101, 265, 274, 280, 281, 352, 354, 364, 366 Precursor, 11–12, 24, 332 Predisposition, 183, 278, 327 Prefrontal cortex, 197, 204, 206, 221, 355, 360, 368, 383, 386

Index Presence, 118, 123, 124, 127, 131, 138, 160, 233, 263, 264, 273, 274, 359, 379, 382, 395, 399–402, 405, 408, 410, 411 Prevention, 312, 358–360, 433 Pride, 157, 162, 239 Problem-solving, 57, 58, 61, 244, 254, 255, 299, 302, 312, 337, 338, 383, 388 Processing styles, 91, 92, 435, 436 Productivity, 178, 201, 288, 313 Pro-social motive, 306 PsyCap, see Psychological capital (PsyCap) Psychological capital (PsyCap), 296, 308, 313 Psychological development, 326 Psychological interventions, 328–331, 343, 356 Psychological selection, 327, 395, 396 Psychological states, 6, 201, 298, 308, 324, 335, 358, 384, 388, 399 Psychopathology, xiv, 329, 330, 343, 421 Psychophysiological measures, 378, 421, 426 Psychophysiology, xiv, 191–223, 419 Purpose, 3, 19, 20, 22, 32, 50, 52, 60, 88, 112, 115, 184, 233, 264, 265, 268, 269, 275, 277, 401, 405, 410, 422, 433

Q Quadrant model, 41–45, 64, 65, 77, 111 Quality of life, 88, 183, 234, 330, 337, 338

R Reappraisal, 199, 222 Reasons for action, 430, 432–433 Recovery, 91, 98, 300, 301, 309 Recreation, xv, 176, 359, 421 Reduced positive affect, 244, 247, 248 Regression modeling approach, 45–49, 53, 55, 418 Regulatory fit, 80, 81, 100, 430 Rehabilitation, 19, 338, 339, 368, 401, 402, 410, 411, 422, 433 Relaxation, 7, 34, 44, 198, 213, 216, 219, 222, 253, 300, 301, 308, 309, 313, 354, 378, 427, 429, 434 Relevance, xiv, 74, 139, 143, 161, 199, 212, 219, 288, 294, 305, 310, 324, 325, 341, 343, 429, 436, 437 Reliability, 279, 289, 293 Relief, 17, 157, 239 Religious experiences, 12 Resilience, 296, 308, 313, 333 Resources, 91, 160, 173, 198–202, 204, 210, 222, 249, 265, 270, 275, 290, 292–294,

Index 298, 299, 301–303, 331, 341, 383, 394, 398, 421, 429, 432, 435, 436 Restored positive affect, 248, 252 Retirement, 339, 340 Revised model, 35, 77–80, 232 Reward networks, 194 Reward/rewarding, xiv, 3, 6, 7, 9, 11–13, 16, 17, 19, 22–24, 72, 76, 80, 110, 112, 122, 126, 128, 130, 138, 148, 150, 156, 164, 174–175, 179, 181, 182, 184, 186, 187, 195, 197, 204–208, 213, 218, 220, 221, 232, 247, 265, 276, 290, 301, 307, 352, 358, 360, 368, 379, 419, 427, 433, 436 Risk-taking, 125, 173, 179–181, 235, 419 Risky behaviors, 179, 181, 184, 333 Risky-shift-phenomenon, 367, 369, 370 Rumination, 75, 360

S School, 9, 14, 41, 97, 177, 185, 233, 254, 276, 277, 291, 296, 303, 312, 330, 332–336, 341, 343, 381, 383, 433, 434, 436 Self-actualization, 385 Self-awareness, 162, 173, 205, 360, 383, 387, 388 Self-centered, 265, 267 Self-consciousness, 2, 20, 50, 72, 75, 91, 121, 164, 165, 176, 394 Self-control, 172, 178, 185, 337, 338, 435 Self-determination, xiv, 11, 24, 140, 141, 163, 183, 236, 240, 241, 255, 290, 334, 335, 385, 419, 430 Self-efficacy, 88, 149, 181, 291, 293, 295, 296, 308, 309, 313, 314, 340, 430 Self-esteem, 175, 234, 330, 336, 343 Self-evaluation, 164 Self-expression, 13, 337, 341 Self-referential processing, 197, 207, 355 Self-reflection, 75, 173, 176, 179–182, 184, 186, 187, 192, 202, 205, 361, 365 Self-regulation, 57, 61, 236, 237, 251, 327, 420 Self-regulatory abilities, 237, 250 Sensory pleasure, 157 Shared flow, 19, 366, 380 Short Flow Scale (FSS-2), 52, 53, 60, 63, 363, 364, 366, 379 Signature strengths, 330 Skills, xiv, 5, 7, 9, 10, 12, 14, 15, 17, 20, 21, 23, 24, 33, 34, 36, 38–45, 47–50, 53, 55–65, 72, 74–87, 89, 90, 93, 94, 97–101, 109–114, 118, 121–123, 128–131, 137–140, 143, 146, 161, 163, 164, 174, 176, 179, 187, 193, 194, 197, 198, 202–204, 216, 217, 232, 233, 236, 237,

451 254, 263, 266, 270, 272, 275, 290, 291, 294, 297, 301, 303–306, 310–312, 326, 327, 330–339, 343, 354–356, 378, 383, 394, 395, 397, 399, 401, 402, 404, 410, 411, 418, 419, 421–425, 430, 431, 434–436 Skills-demands-compatibility, 72–74, 82, 87–93, 98–101, 195, 213 Skill variety, 297–299, 303, 309, 313 Skin conductance (SC), 177, 216 Social capital, 298, 301 Social context, 25, 235, 255, 274, 301, 306, 310, 380, 420 Social flow, xiv, 19, 263–281, 380, 420, 431 Social identity, 265, 268, 337 Social inclusion, 290 Social/interpersonal technologies, 173, 175–178 Socialized power motive, 125–128 Social media, 178, 207, 277, 335 Social networks, 274, 277, 332, 340 Social presence, 380, 400, 401 Social situations, xiv, 19, 118–124, 128, 129, 131, 264, 272, 278, 279, 337, 419–421, 431 Social sphere, 288, 292–293, 306–307, 310–311, 314 Social support, 95, 301, 302, 310 Socio-cognitive processing, 328 Software, 91, 289, 296, 299, 302, 394, 405 Solitary flow, xiv, 263, 264, 267, 268, 270–272, 281, 420 Sports, xiii–xv, 3, 4, 7, 13, 18–20, 22, 23, 25, 50, 94–98, 110, 122–124, 140, 147, 172, 176, 179, 181, 183, 187, 266, 268, 270–272, 276, 278–281, 299, 301, 306, 337, 351–370, 401, 420, 421, 435–437 Standardized questionnaires, 21, 40, 51 State flow, 378, 379, 381–384, 386–388 State oriented, 236, 237 Strength, 16, 32, 35–38, 43–45, 48–49, 52–54, 58–59, 62–65, 80, 100, 113, 125, 302, 305, 311, 326, 329–331, 333, 341, 343, 358, 403, 418 Stress, 7, 14, 61, 91, 116, 138, 192, 196, 198–202, 205, 210–212, 215–217, 219, 221, 222, 288, 291, 293, 295, 300, 303, 310, 312, 314, 329, 397, 419, 420, 427, 429, 430 Structure, xiv, 5, 10, 23, 40, 55, 60, 63, 74, 92, 118, 122, 123, 126, 127, 129, 130, 193, 195, 206, 274, 277, 280, 288, 293, 294, 329–331, 366, 382, 383, 405, 406, 411, 419, 428, 431, 435–437 Swedish Flow Proneness Questionnaire, 242, 385

452 Sympathetic activation, 194–197, 214–216, 218–221, 300 Sympathetic arousal, 194, 196, 378 Synchronization, 197, 218, 220, 221, 264, 367, 386 Synchroniziation-theory of flow, 194, 195 Synchrony, 367, 380 Syntelic, 272, 274, 276, 280

T Task characteristics, 90, 299, 304–305, 400, 431 Task identity, 297, 298, 309, 314 Task significance, 297–299, 314 Task spheres, 288, 293, 297, 304–305, 307, 309–310, 314 Teaching, 60, 113, 126, 128, 198, 250, 292, 299, 300, 332 Team-efficacy, 291 Team flow, 19, 264, 306, 365–367, 369, 370, 380 Team play, 123, 271, 272, 353, 369 Technological support, 293, 294 Technology, xv, 266, 277, 332, 394–411 Telepresence, 338, 395, 399 Temporal dissociation, 395 Thalamic gateway hypothesis, 205, 220 Therapeutic applications, 19 Therapy, 178, 330, 331, 359, 360, 368 Thought control, 354 Thoughtless state, 160 Three Spheres Framework of Flow Antecedents, 288, 307, 431, 432 Time distortion, 338, 355, 400, 410, 423 Time transformation, 3, 354 Toddlers, 331–333, 344, 394 Top-down, 92, 250, 383, 388, 435 Training, 7, 75, 95, 96, 218, 273, 288, 290, 308–311, 313, 314, 356–358, 360, 367, 401, 435 Trait, 4, 49–51, 59, 63, 73, 81, 86, 98, 185, 192, 217, 232–235, 237, 242, 243, 247–252, 254, 255, 291, 294, 295, 303–306, 332–335, 337, 338, 341, 343, 378, 381, 384–388, 404, 431 Transactional Model of Stress and Flow, 200

Index Transactional Stress Model, 198–200, 210, 221, 222 Transformation of time, 177, 231, 234, 338, 352, 380 Transformative Experience Design, 407–409 Trust, 123, 207, 270, 301, 310

U Unambiguous feedback, 3, 50, 64, 74, 75, 99, 109, 298, 352, 354, 364, 394 Unfinished tasks, 300, 312, 337, 338 Unity, 24, 268, 379 Upward spiral, 207, 220, 267, 291, 292, 295, 300, 302, 436 Users, 19, 127, 177, 194, 335, 394–400, 403–411, 421, 428

V Valence, 8, 89, 194, 197, 209 Validity, 20, 38, 50, 54, 55, 60, 63, 83, 86, 242, 243, 279, 364, 365, 370, 420 Values, 17, 40–42, 78–83, 98, 100, 115, 120, 127, 173, 174, 181, 183, 184, 186, 194, 206, 211–213, 215, 216, 248, 300–306, 354, 361, 364, 383, 397, 401, 408, 418, 419, 428, 430, 431 Video games, 144, 147, 150, 178, 185, 207–209, 211, 396–399, 401, 407, 409, 410, 422, 433, 435 Vigilance, 81, 82, 164, 216 Virtual reality, 359, 398–403, 410, 422 Volition, 249 Volitional depletion, 236

W Web surfing, 395 Well-being, xiv, 2, 16–18, 24, 88, 89, 116, 126, 160, 176, 183–185, 236, 248, 253, 269, 274, 288, 290–291, 293, 308, 313, 314, 330, 335–338, 340, 341, 351, 358, 360, 398, 403, 418, 433, 434 Wonder, 157, 408 Work, xiii–xv, 2, 6–9, 11–15, 17–20, 23–25, 33, 34, 36–38, 41, 43, 51, 54, 55, 57, 58, 62,

Index 72, 75, 76, 81, 82, 84, 87, 91, 94, 110, 115, 116, 122, 127, 138, 139, 144, 148, 150, 155, 156, 158, 160, 161, 172, 173, 177–179, 187, 198, 200, 219, 232–234, 236, 240, 242, 249, 254, 264–268, 270, 272, 273, 275, 276, 278, 279, 287–314, 325, 330, 334, 336–338, 341, 343, 352, 357, 360, 361, 364, 367, 381, 386, 394, 411, 418–421, 428, 429, 432–434, 436 Work engagement, 54, 60, 293

453 Y Yes-or-no continuous phenomenon, 425, 427, 428 Yes-or-no phenomenon, 424, 425

Z Zygomaticus major, 194, 209, 427