Future Perspectives for Higher Education: A Delphi-based Scenario Study with Special Regard to Elite Higher Education Institutions and Leadership Education 3658407115, 9783658407117

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Nick Lange

Future Perspectives for Higher Education A Delphi-based Scenario Study with Special Regard to Elite Higher Education Institutions and Leadership Education

Future Perspectives for Higher Education

Nick Lange

Future Perspectives for Higher Education A Delphi-based Scenario Study with Special Regard to Elite Higher Education Institutions and Leadership Education

Nick Lange Herrenberg, Germany Nick Lange Future Perspectives for Higher Education A Delphi-based Scenario Study with Special Regard to Elite Higher Education Institutions and Leadership Education Zugl. Inaugural-Dissertation zur Erlangung des Doktorgrades der Philosophie an der Ludwig-Maximilians-Universität München Referent: Prof. Dr. Rudolf Tippelt, Lehrstuhl für Allgemeine Pädagogik und Bildungsforschung Korreferent: Prof. Dr. Bernhard Schmidt-Hertha, Lehrstuhl für Allgemeine Pädagogik und Bildungsforschung Tag der mündlichen Prüfung: 07.11.2022

ISBN 978-3-658-40711-7 ISBN 978-3-658-40712-4 (eBook) https://doi.org/10.1007/978-3-658-40712-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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 VS imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH, part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Foreword by Rudolf Tippelt

Based on a detailed review of international literature, Nick Lange’s introductory remarks describe four features that characterize excellent institutions of higher education: reputation, academic excellence, selection, and the formation of networks. The author emphasizes his concern with analyzing institutions of higher education that are considered to be elite. In doing so, he links the formation of these institutions to the challenges of “leadership education”. The author classifies his analysis both as exploratory as well as deductive because it starts from clear theoretical definitions and hypotheses. He examines the perceptions of various groups of experts on higher education institutions and the future of higher education. He formulates no predictions, however, but describes scenarios that can be derived from empirical Delphi analyses. The theoretical foundations of the concept of elite education are meaningfully explained with the classical elite and differentiation theories. Here, the phenomenon of elite education is deduced through five mechanisms: selection, choice, coherence, distinction, and valorization. This framing through these mechanisms and concepts refers to socio-educational and to explicitly pedagogical perspectives. Nick Lange’s aim, following sociological and pedagogical reflections, is to further sharpen the system-oriented perspective on elite education. In his multi-level analytical reflections on elite education, he distinguishes between the system, the institution, and the individual. For him, elites are not socially aloof. Instead, elites achieve their status by fulfilling their responsibility to society and thus contributing to the common good. On a higher level, the institutions of higher education are assigned central tasks, namely education, research, and innovation. The first task focuses on personal development through education. The second task is characterized by research, whose aim is to create an environment for achieving a high quantity

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of high-quality research activities. Higher education institutions, if they have elite character, must succeed not only in producing new and relevant knowledge, but also in aggregating, processing, distributing, and promoting it. This thesis posits that a consistently important task of higher education institutions is to advance innovation. By analyzing the academic literature, the author identifies four complementary characteristics of elite education in higher education institutions: reputation, academic excellence, selection, and the network of institutions. For the author, leadership education is integrated into the curriculum of elite education institutions, because it is a matter of this subject penetrating the curriculum, with the goal of fostering creative personalities. These personalities, in turn, should be leaders in society and thus contribute to positive change. In the empirical analyses, the author aims to clarify constructions of future circumstances in more detail, focusing on the subjective perceptions of various stakeholder groups and thus analyzing the possible “futures” of this sector from the perspective of the experts interviewed. The data was collected using a sophisticated real-time Delphi survey. The findings are not trivial. In the long term, the experts believe it is likely that higher education institutions will develop into lifelong and lifewide learning companions, with the use of virtual education platforms becoming increasingly important. The lowest long-term probability of occurrence is attributed to purely individual research by outstanding individuals independent of institutions, and it is considered equally unlikely that Education Cities will establish themselves, i.e., barely networked elite education institutions. The experts evidently believe that the time when such institutions were poorly networked is over. Impacts on higher education institutions are also historically characterized by the construct of lifelong and lifewide learning. In this context, intelligent digital systems and also alternative funding sources are perceived as having a strong impact. In terms of desirability, lifelong and lifewide learning in such institutions of elite education is also emphasized, while institution-independent research and the separation of elite education does not appear desirable. It is substantial for the study that a total of 939 qualitative text contributions accompany the expert assessments. They evaluate particular social trends and developments—such as individualization, digitalization, and demography—in greater detail. The author meaningfully incorporates these qualitative text passages into his Delphi analysis. It is interesting that the perceived desirability of a development is closely related to the attributed probability of its occurrence. Three outstanding scenarios became visible: first, the return of higher education institutions to their educational task in the future; second, the importance of higher education institutions for economic development, education as a supplier,

Foreword by Rudolf Tippelt

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so to speak; and third, the ever-increasing importance of higher education institutions as learning companions across life phases and places. Thus, there is not one future of higher education and elite education; instead, the future must be plural. There are alternative possibilities, which this analysis shows. The author suggests that in the future, the more precise meaning of an institution and also of a subject area should be considered even more. It would certainly be interesting to work out connections between how public higher education institutions and private institutions differ in their understanding of elite education. This work contains valuable impulses for educational practice and policy. Based on the intensive and meticulous theoretical preparation as well as the astutely conducted Delphi survey, this study adds value to the view of higher education institutions and elite education. In this study, elite education is the subject of a rational analysis—it is neither normatively exaggerated nor discredited. The literature used is highly international as well as brilliantly focused. December 2022

Rudolf Tippelt Ludwig-Maximilians-Universität München (LMU) Munich, Germany

Foreword by Stefanie Kisgen & Werner G. Faix

We are in the midst of turbulent and uncertain times. We are experiencing crisis after crisis at the level of the national economy(s) as well as the global economy, from the pandemic to the Russian war in Ukraine, the energy crisis, and rising costs and inflation. Further issues show us that we are facing a fundamental change, namely overpopulation, the climate catastrophe with its dramatic consequences, and another driver of uncertainty that at the same time offers many opportunities: digitization. The fundamental change that lies ahead of us is not new to human history. What is new, however, is the immense speed of change, which is being triggered above all by the turbo accelerator for change: digitization. Digitization is changing all spheres of our world, our (social) lives, our economy and much more. These are changes that leave traditional thinking far behind. We can call them uncertainty. We can also call them innovations. According to the innovation researchers Zillner and Krusche, innovation requires leadership. Leadership, as Plato’s Politeia underlines, means personality. According to Faix and Mergenthaler, creative personalities, who are so urgently and desperately needed in politics, business and society, contribute with their actions to the well-being of the community in which they are located. Precisely because of this immense and urgent responsibility for society, the question is more burning than ever: What must education and what must educational institutions be like today and in the future to foster leaders who contribute to the well-being of society and communities to a high and excellent degree? It is against this background that the present work of Nick Lange is to be appreciated.

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Foreword by Stefanie Kisgen & Werner G. Faix

In his work, Nick Lange illuminates the concept of elite education from the sociological, pedagogical and system-oriented perspectives within the framework of a broad and well-founded literature analysis. Based on this interdisciplinary approach, he focuses particularly on the pedagogical-institutional perspective on elite higher education institutions. An elite higher education institution in the context of this thesis is described based on the concept of the responsibility elite. According to this, an institution achieves its elite status primarily when it assumes a certain responsibility towards the society in which it is located. It consequently contributes to the common good. The responsibility of higher education institutions in society is constructed primarily from the central tasks originally attributed to them, namely education—understood in the Humboldtian sense as personality development (Persönlichkeitsbildung) -, research, and innovation. Nick Lange elaborates these central tasks as well as further characteristics of elite higher education institutions. He also pays special attention to leadership education, which higher education institutions offer through excellent frameworks for personality development. In such a framework, leaders can develop who in turn contribute to the common good. Based on the substantiated, critical, and at the same time elaborate theoretical foundation, Nick Lange uses a qualitative research design to empirically investigate how higher education will change in the future. He successfully merges the research process of the social sciences with the generic foresight process of futures research. In a real-time Delphi survey, more than 100 experts from various stakeholder groups evaluated and discussed ten projections for the future. These projections represent a broad spectrum of topics, ranging, for example, from funding, to institutions as lifelong and lifewide learning companions, to the entry of virtual education platforms, to a research system based on individual researchers independent of institutions. Through his systematic and structured quantitative as well as qualitative data analysis, Nick Lange scientifically elaborates a wideranging dissent within the expert panel discussions. In a subsequent multistage process, Nick Lange synthesized the data analysis and interpretation to derive three exploratory scenarios for the future development of higher education and elite institutions. These possible futures range from elite higher education institutions returning to their educational mission, the elite higher education institution as a ‘supplier’ to the business sector, and the elite higher education institution as a learning facilitator across life stages and locations. Elite higher education institutions, which assume a certain responsibility towards the society in which they are located and thus contribute to the common good, must critically examine the question of future orientation and (further) development in a responsible and conscientious manner—especially in times of

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rapid and disruptive change—from the perspective of various stakeholder groups. With his thesis, Nick Lange initiates this discourse and provides much more than just a readable contribution. We wish a lot of profit with this read. December 2022

Stefanie Kisgen School of International Business and Entrepreneurship (SIBE) Steinbeis University Herrenberg, Germany Werner G. Faix School of International Business and Entrepreneurship (SIBE) Steinbeis University Herrenberg, Germany

Acknowledgements

After completing my master’s studies, the logical consequence was to continue my formal education. I thus began my doctorate to acquire the skills that enable independent scientific work. Although this is an individual process, it is always supported by a multitude of people, whom I now wish to thank. To begin with, I would like to express my heartfelt gratitude to my first supervisor, Prof. Dr. Rudolf Tippelt, for his pioneering guidance during this central phase of my life. Prof. Tippelt’s well-founded and open-minded guidance enabled me to work on my topic with true independence. His critical feedback enriched this dissertation and contributed significantly to its success. Secondly, I would like to express sincere gratitude to my second supervisor, Prof. Dr. Bernhard Schmidt-Hertha, for his willingness to review my dissertation and his openness to the rather unconventional approach I took in investigating elite education and higher education institutions. I would also like to thank my third supervisor, Prof. Dr. Frank Fischer, for his openness and willingness to participate in and evaluate my disputation. Beyond this, I wish to express special gratitude to my supporters Prof. Dr. Stefanie Kisgen and Prof. Dr. Werner G. Faix. The valuable discussions and impulses from our conversations contributed significantly to the successful completion of this thesis. Likewise, I would like to express my deepest gratitude to Prof. Dr. Rixa Georgi-Kroehl and Prof. Dr. Heiko von der Gracht for the valuable impulses and their willingness to proofread my dissertation in part or in whole. Many others, not listed here by name, have also contributed to the successful completion of my doctoral studies, including all of the experts who enabled my empirical work and enriched it with their time and expertise. From the bottom of my heart, I thank the people to whom I dedicate this work: my family, especially my parents, my brothers, and my grandmother. You

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have contributed to the drive that has made the successful completion of this doctorate possible. Thank you for your support, and for being encouraging as much as demanding, over the course of the past years and decades. Finally, I would like to thank my “Partner in Crime”, Marie, from the bottom of my heart. Your support in the past years, the fact that you never got tired of motivating me, and all our conversations about this book, have meant everything to me. November 2022

Nick Lange Herrenberg, Germany

Contents

Part I

Introduction

1

Frame of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Research Interest and Research Gaps . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Questions and Objectives . . . . . . . . . . . . . . . . . . . . . . .

3 3 6

2

Approach and Structure of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

Part II 3

4

Terminology, Framework, and Conceptualization

Terminology: Elite Higher Education Institutions and Leadership Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Framing Elite Education and Elite Higher Education . . . . . . . . 3.1.1 Mechanisms of Elite Education . . . . . . . . . . . . . . . . . . . 3.1.2 Perspectives on Elite Higher Education . . . . . . . . . . . . 3.2 Leadership and Leadership Education . . . . . . . . . . . . . . . . . . . . . 3.2.1 Defining Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Leadership Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Constitution of Contemporary Elite Higher Education Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 The Tasks of Higher Education Institutions In Society . . . . . . . 4.2 Education in Elite Higher Education Institutions . . . . . . . . . . . . 4.2.1 The Concept of Education in Higher Education Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Education in the Practice of Elite Higher Education Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Research in Elite Higher Education Institutions . . . . . . . . . . . . .

15 15 16 21 32 33 44 49 49 53 53 60 69

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Contents

4.4

Innovation in Elite Higher Education Institutions . . . . . . . . . . . 4.4.1 The Concept of Innovation . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Elite Higher Education Institutions and Innovation in Practice . . . . . . . . . . . . . . . . . . . . . . . . Complementary Characteristics of Elite Higher Education Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Reputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Academic Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.4 Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85 86 91 94 98

Conclusions from Part II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4.5

5

Part III 6

7

75 76 82

Research Process, Methodology, and Results

Empirical Approach and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Overarching Empirical Approach and Process . . . . . . . . . . . . . . 6.2 Data Collection: Real-Time Delphi Survey . . . . . . . . . . . . . . . . . 6.2.1 The Delphi Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1.1 Methodological Foundations . . . . . . . . . . . . . . 6.2.1.2 Types and Variants of the Delphi Survey . . . 6.2.2 Design of the Real-time Delphi Survey in this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2.1 Expert Selection . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2.2 Development of Delphi Projections . . . . . . . .

113 113 118 118 118 121

Results of the Real-time Delphi Survey . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Results for Each Delphi Projection . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 The Real-time Delphi Survey in Detail . . . . . . . . . . . . . 7.1.2 Data Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Projection 1—Real Output Evaluation . . . . . . . . . . . . . 7.1.4 Projection 2—Focus on Personality Development . . . 7.1.5 Projection 3—Institution-Independent Researchers . . . 7.1.6 Projection 4—Virtual Educational Platforms . . . . . . . . 7.1.7 Projection 5—Lifelong and Lifewide Education . . . . . 7.1.8 Projection 6—Education Cities . . . . . . . . . . . . . . . . . . . . 7.1.9 Projection 7—Intelligent Digital Systems . . . . . . . . . . 7.1.10 Projection 8—New Funding Models . . . . . . . . . . . . . . . 7.1.11 Projection 9—Performance Certificates . . . . . . . . . . . . 7.1.12 Projection 10—Consortial Degrees . . . . . . . . . . . . . . . .

137 137 138 140 144 148 152 156 160 165 169 173 176 180

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Contents

Concluding Remarks on the Results . . . . . . . . . . . . . . . Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Personal Dimensions for Group Analysis . . . . . . . . . . . Procedure for Group Comparisons . . . . . . . . . . . . . . . . . Group Analysis for Projection 1 . . . . . . . . . . . . . . . . . . Group Analysis for Projection 2 . . . . . . . . . . . . . . . . . . Group Analysis for Projection 3 . . . . . . . . . . . . . . . . . . Group Analysis for Projection 4 . . . . . . . . . . . . . . . . . . Group Analysis for Projection 6 . . . . . . . . . . . . . . . . . . Group Analysis for Projection 7 . . . . . . . . . . . . . . . . . . Group Analysis for Projection 8 . . . . . . . . . . . . . . . . . . Overarching Remarks on Dissent Analysis . . . . . . . . . Supplementary Dissent Analysis: Desirability Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

184 188 189 193 194 196 198 200 201 203 203 204

Scenario Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Scenario Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Prescenario Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Portfolio Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Cross-impact Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.3 Concluding Remarks on Portfolio Analysis and Cross-impact Analysis . . . . . . . . . . . . . . . . . . . . . . . 8.3 Three Scenarios for Higher Education in 2040 . . . . . . . . . . . . . 8.3.1 Scenario I—Renaissance of the University . . . . . . . . . 8.3.2 Scenario II—The Commercial Higher Education Institution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Scenario III—Lifelong and Lifewide Learning Companions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

209 209 214 214 217

235

Conclusions from Part III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

241

7.2

8

9

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Part IV

7.1.13 Dissent 7.2.1 7.2.2 7.2.3 7.2.4 7.2.5 7.2.6 7.2.7 7.2.8 7.2.9 7.2.10 7.2.11

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223 224 228 231

Conclusions

10 Findings and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Main Findings of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Implications for Higher Education . . . . . . . . . . . . . . . . . . . . . . . .

251 251 258

11 Contributions of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Scientific Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Practical Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12 Limitations and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Limitations of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Outlook on Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

265 265 267

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviations

A A&I C CBL CI CIA CONTRA D EC ECI EHEI EP EP5 EP10 EP15 EP20 I IBL IC ICI IQR LMX LoC MOOC n NCI

Intensity of activity (cross-impact analysis) Intensity of activity and interconnectedness (cross-impact analysis) Self-assessed confidence in own assessment (Delphi survey) Competency-based learning Control indication Cross-impact analysis Nonsupportive arguments Desirability of the development (Delphi survey) External control of reinforcement External control indication Elite higher education institution Expected probability of the development (Delphi survey) Expected probability of the development in 5 years (Delphi survey) Expected probability of the development in 10 years (Delphi survey) Expected probability of the development in 15 years (Delphi survey) Expected probability of the development in 20 years (Delphi survey) Impact on higher education institutions (Delphi survey) Inquiry-based learning Internal control of reinforcement Internal control indication Interquartile range Leader-member exchange Locus of control Massive open online course Number Neutral control indication

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NEG NEU OECD POS PRO QCA SD STEEP SVE TA TP VUCA

Abbreviations

Negative general opinion Neutral general opinion Organisation for Economic Co-operation and Development Positive general opinion Supportive arguments Qualitative content analysis Standard deviation Social, technological, economical, ecological, and political Sociology of valuation and evaluation Total active (cross-impact analysis) Total passive (cross-impact analysis) Volatile, uncertain, complex, and ambiguous

List of Figures

Figure 3.1 Figure 3.2 Figure 4.1

Figure 4.2 Figure 4.3

Figure 4.4 Figure 4.5 Figure 4.6 Figure 4.7 Figure 5.1 Figure 6.1 Figure 6.2 Figure 6.3 Figure 6.4 Figure 7.1

Stratification of contemporary higher education systems as ideal types, according to Trow (1973) . . . . . . . . . . . . . . . . Foci of research on elite (higher) education . . . . . . . . . . . . . . Relationship between process perspective and impact perspective on performance of higher education institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Five core principles of didactics, according to Klafki (2007) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seven principles of curriculum design for business leadership education in higher education, according to Kisgen (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Areas impacted by societal innovation . . . . . . . . . . . . . . . . . . Complementary institutional characteristics and subfactors of elite higher education institutions . . . . . . . Types of networks in elite higher education institutions . . . . Types and areas of networks regarding elite higher education institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heuristic theoretical concept of contemporary elite higher education institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration of foresight process and process of research in social sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distinction between the process of Delphi and real-time Delphi, according to Gnatzy et al. (2011) . . . . . . . . . . . . . . . . Process of expert selection in this thesis . . . . . . . . . . . . . . . . . Process of projection development . . . . . . . . . . . . . . . . . . . . . Professions within the expert panel . . . . . . . . . . . . . . . . . . . . .

29 32

52 62

62 80 86 100 101 107 118 123 129 132 190

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Figure 8.1 Figure 8.2 Figure 8.3 Figure 8.4 Figure 8.5

List of Figures

Portfolio analysis for expected probability and impact of the development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Portfolio analysis for expected probability and desirability of the development . . . . . . . . . . . . . . . . . . . . . Total active and total passive for each projection . . . . . . . . . Intensity of activity and level of interconnectedness for each projection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of scenario content with the five characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

215 216 221 222 226

List of Tables

Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 4.1

Table 4.2 Table 6.1 Table 6.2 Table Table Table Table Table

6.3 6.4 7.1 7.2 7.3

Table 7.4 Table 7.5

Overview on definitions of elite higher education from a system-oriented perspective . . . . . . . . . . . . . . . . . . . . . Comparison—tasks of leaders and managers (Kotter, 2001) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different contexts of leadership and management (Bass & Bass, 2008) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition of leadership education, according to Mergenthaler (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principles of business leadership education and underlying guiding questions, according to Kisgen (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Knowledge production Mode 1 and Mode 2 in comparison, adapted from Gibbons et al. (2010) . . . . . . . Central distinguishing feature of Delphi, group Delphi, and e-Delphi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Differences of different approaches of real-time Delphi surveys, according to Gnatzy et al. (2011) . . . . . . . . . . . . . . . Overview of expert groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview set of projections . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics regarding projection 1 . . . . . . . . . . . . . . Details of qualitative arguments for Projection 1 . . . . . . . . . Contentual structure of qualitative arguments for Projection 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics regarding Projection 2 . . . . . . . . . . . . . . Details of qualitative arguments for Projection 2 . . . . . . . . .

31 42 42 47

63 75 122 126 130 134 145 146 146 149 149

xxiii

xxiv

List of Tables

Table 7.6 Table 7.7 Table 7.8 Table 7.9 Table 7.10 Table 7.11 Table 7.12 Table 7.13 Table 7.14 Table 7.15 Table 7.16 Table 7.17 Table 7.18 Table 7.19 Table 7.20 Table 7.21 Table 7.22 Table 7.23 Table 7.24 Table 7.25 Table 7.26 Table 7.27 Table 7.28 Table 7.29 Table 7.30 Table Table Table Table Table

7.31 7.32 7.33 7.34 7.35

Contentual structure of qualitative arguments for Projection 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 3 . . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 3 . . . . . . . . . Contentual structure of qualitative arguments for Projection 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 4 . . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 4 . . . . . . . . . Contentual structure of qualitative arguments for Projection 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 5 . . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 5 . . . . . . . . . Contentual structure of qualitative arguments for Projection 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 6 . . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 6 . . . . . . . . . Contentual structure of qualitative arguments for Projection 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 7 . . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 7 . . . . . . . . . Contentual structure of qualitative arguments for Projection 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 8 . . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 8 . . . . . . . . . Contentual structure of qualitative arguments for Projection 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 9 . . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 9 . . . . . . . . . Contentual structure of qualitative arguments for Projection 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for Projection 10 . . . . . . . . . . . . . . . . . . Details of qualitative arguments for Projection 10 . . . . . . . . Contentual structure of qualitative arguments for Projection 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of descriptive statistics for all projections . . . . . . . Overview of group analysis by region . . . . . . . . . . . . . . . . . . Results of group analysis for Projection 1 . . . . . . . . . . . . . . . Results of group analysis for Projection 2 . . . . . . . . . . . . . . . Results of group analysis for Projection 3 . . . . . . . . . . . . . . .

150 154 154 155 157 158 159 162 162 163 166 166 167 170 170 171 174 174 175 177 178 179 181 182 182 185 191 195 197 199

List of Tables

Table Table Table Table Table Table

7.36 7.37 7.38 7.39 7.40 8.1

Table 8.2 Table 8.3 Table 9.1

xxv

Results of group analysis for Projection 4 . . . . . . . . . . . . . . . Results of group analysis for Projection 6 . . . . . . . . . . . . . . . Results of group analysis for Projection 7 . . . . . . . . . . . . . . . Results of group analysis for Projection 8 . . . . . . . . . . . . . . . Overview of quantity of significant differences . . . . . . . . . . . Indicators within cross-impact analysis, according to Kisgen (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of cross-impact analysis . . . . . . . . . . . . . . . . . . . . . . . Overview of scenario dimensions . . . . . . . . . . . . . . . . . . . . . . Comparative overview of scenarios . . . . . . . . . . . . . . . . . . . . .

200 202 203 205 206 219 220 227 246

Part I Introduction

1

Frame of Research

This chapter serves as an introduction. It provides an overview of the frame of the research in this thesis. It illustrates the underlying research interests and their respective research gaps. This chapter presents research questions and objectives derived from the gaps in the previous research. Finally, it explicates the concrete structure of this thesis.

1.1

Research Interest and Research Gaps

In recent decades, trends, such as educational expansion and globalization, have caused structural changes in higher education worldwide. Simultaneously, policy makers have adopted political programs that introduce neoliberal ideology into higher education (Rustin, 2016). These trends and political programs have resulted in a transformation of higher education institutions into businesses (Pucciarelli & Kaplan, 2016; Rustin, 2016). Consequently, contemporary higher education institutions face global competition for human resources, financial resources, and quality (Musselin, 2018). To successfully place higher education institutions in an environment characterized by global competition, various nations have expressed aspirations for excellence for their higher education sectors (Salmi, 2016b). Within this frame, they have launched excellence initiatives. Examples include the Excellence Initiative and Excellence Strategy in Germany, Project 5–100 in the Russian

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_1

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Frame of Research

Federation, or Projects 211 and 985 in the People’s Republic of China. Denmark, France, Japan, South Korea, and Spain can also be mentioned in relation to excellence initiatives (Salmi, 2016a, p. 17).1 Excellence initiatives pursue the goal of producing excellent universities for the various nations. Within this frame, they aim at accelerating a transformational process of higher education institutions. The focus here is on two approaches: establishing new universities that become world-class universities and improving established universities (Salmi, 2016b, p. 40). This is because higher education institutions support “globally competitive economies by developing a skilled, productive, and flexible labor force and by creating, applying, and disseminating new ideas and technologies” (Salmi, 2016b, p. 15). Beyond the aspirations described by Salmi, elite higher education institutions (EHEIs)2 , e.g., world-class universities, aim to develop future leaders who foster positive change in society (Howard & Maxwell, 2021). Therefore, EHEIs can be viewed as central actors in shaping the future of society. First aspect of research interest and research gap Excellence initiatives mainly concentrate on promoting higher education institutions financially. However, higher education institutions do not benefit solely from monetary subsidies. Rather, excellence initiatives also create status differences between institutions. Promoted institutions receive an attribution as elite, excellent, or worldclass (Bruckmeier et al., 2017). This status should be viewed critically, as “most existing definitions of a world-class university rely on either scholars’ subjective experiences or global ranking systems” (Wei & Johnstone, 2020, p. 555). The status of a higher education institution as excellent, world-class, or elite is created by subjective criteria and rankings. Subjective criteria are, as suggested by their name, designed by individuals. Rankings are instruments that attempt to quantify the quality of educational institutions (Gioia & Corley, 2002). Their focus is on research in higher education institutions (Ordorika & Lloyd, 2015; Pusser & Marginson, 2013). Excellence initiatives also adopt this factor of research as their central criterion for promotions (Salmi, 2016a). Against this background, research is the central element of attribution as an EHEI through rankings and excellence initiatives, in addition to subjective perception. However, according to Schneevoigt (2004, p. 46), the use of the term elite 1

For an overview of excellence initiatives worldwide and their geographical distribution, see Salmi (2016b, p. 17). 2 In the context of this dissertation, the concepts of world-class university, excellence university, or elite university are subsumed under the term elite higher education institution (EHEI).

1.1 Research Interest and Research Gaps

5

should depend on measurable, objectively observable performance. A foundation for assessment centered on the factor of research cannot confer a vertically differentiating status on educational institutions or justify special financial support. This argument is supported by the position of elite institutions as actors in shaping society and its future. Excellence initiatives and rankings should consider institutions holistically. They must integrate further aspects of higher education institutions beyond research. What those elements are is investigated in this study. This leads to the first aspect of the research interest of this thesis: the question of how EHEIs are constituted and a description of characteristics that determine an elite status for these institutions. The constitution of higher education institutions in the context of the elite concept has only been marginally studied. The focus has been on the concept of the world-class university, which concentrates, in particular, on the creation of internationally recognized, excellent research universities (e.g., Altbach, 2004, 2012; Salmi, 2016a, 2016b). Theorists have mainly investigated educational inequality in elite education (e.g., Helsper et al., 2019b; Helsper & Krüger, 2021). This thesis supplements literature on the topic by addressing this research interest and its related research gap comprising an outline of the constitution of contemporary EHEIs. Second aspect of research interest and research gap There is consensus in the academic literature that EHEIs are closely intertwined with the notion of leaders. The institutions are credited with producing future leaders (Howard & Maxwell, 2021; Koh & Kenway, 2012). This idea is reflected, for example, in the institutions’ self-images (Zymek, 2014, p. 73). Regarding learning and the educational processes from which leaders emerge, leadership education has been established in the research literature, particularly in the field of higher education (e.g., Bloomquist et al., 2018; Kisgen, 2017; Lumpkin & Achen, 2019). Against the background of these rather superficial statements on the connection between EHEIs and leadership education, it seems reasonable to examine how leadership education is embedded in EHEIs. Leadership education is integrated into describing the constitution of contemporary EHEIs in this thesis. These remarks comprise the second aspect of the research interest of this thesis, which is considering the concepts of leadership education and EHEIs as interconnected by embedding the former into the latter. It is precisely this research gap that this thesis addresses to expand the body of scientific research on the topic.

6

1

Frame of Research

Third aspect of research interest and research gap Considering the position of EHEIs as actors developing future leaders and shaping the future of society, it is reasonable to investigate how higher education and especially elite institutions may develop. This thesis adopts a future-oriented perspective in addition to examining the constitution of contemporary EHEIs and leadership education. The future is not researchable, because it does not exist yet. Its exploration is, therefore, in this sense not falsifiable and not scientific either (Popp & Schüll, 2009, p. 26). The well-founded theories and premises of futures research are applied in this thesis. Future is not examined in the sense of the future present, but as present futures. Present futures are present constructions of the future (Grunwald, 2009, p. 26). The third aspect of the research interests aims to examine contemporary perceptions about the future development of higher education and, especially, EHEIs. Scientific studies that apply futures research methods to educational research issues are scarce. When investigating higher education institutions, publications have concentrated on developing future skills (Ehlers, 2020) or outlining concrete designs for future study programs (Kisgen, 2017). Learners in the institutions, their competencies, and the educational environment in higher education institutions are the focal points. Thus, competence-theoretical or didactic-theoretical perspectives have been adopted. Research within this frame has not paid attention to institutional characteristics and status and, therefore, elite institutions have not been integrated. In this thesis, the focus is not on competence-theories and learners. Rather, practical theories and institutional structures in higher education and changes over time are highlighted. The research gap of applying methods of futures research in educational research concentrating on institutions and their characteristics is addressed. Elite institutions are integrated within this frame.

1.2

Research Questions and Objectives

The previous subchapter provides insight into the research interests and research gaps addressed by this thesis. Combining their three underlying aspects results in the central research questions for this thesis: In what respect will higher education change, how are elite higher education institutions constituted in this context, and how is leadership education embedded within this nexus?

1.2 Research Questions and Objectives

7

To answer this research question, it is useful to derive subordinate research questions: RQ 1: RQ 2: RQ 3: RQ 4:

Which characteristics determine an elite status for contemporary higher education institutions? How is leadership education embedded in elite higher education institutions? How are higher education institutions likely to change and which factors determine these changes? What do possible future images of higher education, and especially elite higher education institutions up to the year 2040, look like?

These questions each aim to address one aspect of the research interests. Only Research Questions 3 and 4 jointly focus on one aspect of the research interests. Beyond addressing the research interests and related gaps, these research questions and this thesis pursue further research objectives. On a less abstract level, specific research objectives can be identified from a scientific and (educational) practical perspective. This thesis aims to contribute to the scientific literature in various ways. On the one hand, it aims to contribute to the study of the phenomenon of EHEIs. On the other hand, this thesis aims to consider the phenomena of elite education and leadership education at the level of higher education institutions, and in connection with each other. This thesis pursues the objective of contributing to linking educational research and futures research. Within this frame, the futureoriented consideration of higher education, especially of elite institutions, is in the foreground. Regarding methodology, this thesis aims to contribute to applying methods of futures research in educational research. From a practical perspective, this thesis pursues three objectives. First, the future-oriented consideration of higher education aims at laying the groundwork for proactive treatment of future developments in the field. Second, this thesis aims at informing action by higher education institutions concerning the establishment of future-oriented and future-robust structures. Finally, addressing contemporary EHEIs aims at providing recommendations for educational policy. The thesis aims to provide descriptive impulses for developing holistic political measures in the context of elite, excellent, or world-class institutions in higher education.

2

Approach and Structure of Thesis

The research gaps addressed by this thesis illustrate the absence of scientific research on the intersecting topics of elite higher education, leadership education, and futures research. Thus, there is no basis for hypothesizing at the outset of this thesis. Therefore, an exploratory approach was adopted to investigate the subject. Within this frame, the overarching procedure was designed deductively. The starting point is the existing literature on the three topics. This thesis is structured according to this exploratory-deductive research approach. Following Part I, the introduction, the thesis is divided into two main parts and a concluding part. Part II addresses Research Questions 1 and 2, which are investigating contemporary EHEIs and leadership education. Part III aims to answer Research Questions 3 and 4. The focus is on a future-oriented perspective on higher education and, particularly, on the topics presented in Part II. Part III represents the empirical work of this thesis. The foundation of scientific research is describing phenomena relevant to the research questions and objectives (Wichmann, 2019, p. 19). Accordingly, Part II discusses the conceptual foundations of this thesis through a literature review. The central concepts and their underlying theoretical frameworks are described and definitions are established. First, the concept of elite education is examined. This chapter considers underlying theories and definitions of the term from a range of disciplinary perspectives. Subsequently, the concept of EHEIs is derived. Both subchapters highlight concrete definitions for the terms used in this thesis (Part II, Subchapter 3.1). Second, the concept of leadership is described and classified. The historical background of the term is presented and an overview of perspectives, theories, and definitions of the phenomenon are discussed. The definition of the term as © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_2

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10

2

Approach and Structure of Thesis

used in this thesis is explicated. The following subchapter presents a discussion of the concept of leadership education and this is considered in contrast to other related concepts. The focus is on a comparison with the concept of leadership development. Finally, the definition of leadership education in this thesis is presented (Part II, Subchapter 3.2). These conceptual and theoretical foundations aim to address Research Question 1. Subsequently, a heuristic theoretical conceptualization of the constitution of contemporary EHEIs is presented. This is deductively derived from the body of academic literature on the complex of topics under consideration. Research Question 2 is also addressed within this frame. The conceptualization illustrates how leadership education is embedded in EHEIs (Part II, Chapter 4). Finally, a brief conclusion for Part II of this thesis is presented (Part II, Chapter 5). At the beginning of Part III, the overarching research approach and process of the empirical work in this thesis are presented. Within this frame, a methodological positioning of the research is provided. Considering the exploratory nature of this thesis, a qualitative approach is applied. The steps of the research process are explained. Against the background of the combination of futures research and educational research, the process of foresight and the research process of social science are considered in an integrated manner. The underlying quality criteria, to ensure the research quality, are presented (Part III, Subchapter 6.1). Following the consideration of the overarching research approach and process, the specific empirical procedure is described. In the first step, the method of the Delphi survey is explained. Within this frame, types of method are delimited and quality criteria to be considered are presented. In the second step, the implementation of the method in this thesis, in the format of the real-time Delphi survey (Part III, Subchapter 6.2), and the linkage to Part II of this thesis are described. Part III, Chapter 1 provides the foundation for answering Research Questions 3 and 4. Part III, Chapter 2 presents the results of the real-time Delphi survey. First, results are described for the individual aspects of the survey (projections). Subsequently, results across these aspects are presented (Part III, Subchapter 7.1). This is followed by a description of the results of further analyses (Part III, Subchapter 7.2). Both subchapters conclude with a brief classification of the results. Chapter 2 aims to answer Research Question 3. Based on the results, scenarios are developed in Part III, Chapter 3. The scenarios answer Research Question 4. The starting point for scenario development is defining the term scenario and summarizing approaches for the development of scenarios. Subsequently, how scenarios were developed in this thesis is described (Part III, Subchapter 8.1).

2

Approach and Structure of Thesis

11

Methods for prescenario analysis and their results are described (Part III, Subchapter 8.2). The developed scenarios are presented. The scenario chapters (Part III, Subchapter 8.3) conclude with a classification of the respective scenarios in the theoretical-conceptual context (Part II) of this thesis. First impulses of action for higher education institutions derived from the scenarios are illustrated. Finally, a brief conclusion for Part III is presented (Part III, Chapter 9). Part IV includes the overall conclusion of this thesis and answers the research questions. In the beginning, it gives an overview on the extent to which the research objectives have been accomplished. Implications for higher education are summarized (Part IV, Chapter 10). The contributions of this thesis to practice and science are presented (Part IV, Chapter 11). Building on the preceding conclusion, the limitations of the research are presented. This thesis concludes with an outlook on future lines of research that have emerged from the research process (Part IV, Chapter 12).

Part II Terminology, Framework, and Conceptualization

3

Terminology: Elite Higher Education Institutions and Leadership Education

The following chapter introduces the central terminology of this thesis. Within this frame, theories underlying the phenomena of elite education and elite higher education are presented. Definitions of these terms, from their central perspectives in research, are addressed and the concept of leadership is explicated. On this foundation, the final subchapter illustrates the understanding of leadership education in this thesis.

3.1

Framing Elite Education and Elite Higher Education

How the term elite education is defined depends on the perspective from which it is viewed. H.-H. Krüger et al. (2012, p. 332) view elite education as a phenomenon in the tension between the meritocratic idea of achievement and the elitist idea of hierarchy. This tension is described by the authors on a theoretical level. Its foundations are traditional elite theories1 and differentiation theories.2 Democratic theoretical views are also integrated in this term (H.-H. Krüger et al., 2012). Various authors address the ambiguity of elite education. For example, Khan (2016, p. 174) describes elite education as referring to the status of an educational institution and to the social backgrounds of its students. The author outlines two meanings, which can be distinguished from each other. On the one hand, elite education is viewed as education of social groups that are elite. On the other hand, elite education is an education that holds an elite status. Trow (1974a) discussed this distinction in the 1970 s and described elite education: 1 2

For classical elite theories, also see Michels (1910); Mosca (1939); Pareto (1935). For theories of differentiation, also see Daloz (2007); S. Keller (1963); Parsons (1951).

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_3

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3 Terminology: Elite Higher Education Institutions … in terms of the social origins of the student body or of the substance of what is studied and taught, ... by reference to the mode of education and the level of intensity and complexity at which the subject is pursued. (p. 355)

A special attribute of the term elite education can be found in German literature. Here, elite education can be understood in different ways, depending on how the concept of Bildung (education) is interpreted. On the one hand, elite education refers to the formal education of elite social groups. On the other hand, the term is understood as an education that holds an elite status. A further distinction can be made, which describes elite education as the formation of elites (Deppe, 2016, p. 808). Against this background of ambiguity in defining elite education, the following subchapter concentrates on framing the phenomenon. It presents the theoretical foundations on which elite education is built. The concept of mechanisms of elite education and its underlying theories are described. This subchapter focuses on clarifying and distinguishing between three definitions of elite education on the foundation of their respective underlying perspectives. The understanding of elite education in this research is elaborated.

3.1.1

Mechanisms of Elite Education

A concept illustrating the phenomenon of elite education was developed at the beginning of the last decade by H.-H. Krüger et al. (2012). The authors addressed the interdisciplinary lines of discourse and research for the terms elite and education. They developed the concept of mechanisms of elite education. This concept aims at establishing theoretical and empirical approaches concerning the interaction of processes of elite education (H.-H. Krüger et al., 2012, pp. 334–335). Its development was embedded in the research project of the DFG research group 1612 (Helsper et al., 2019b). Within that framework, the authors investigated the extent to which the increasing importance of exclusive educational institutions results in a vertical differentiation of various sectors of the education system in Germany (Helsper et al., 2019b). This subchapter includes an explanation of the overarching concept of mechanisms of elite education, and an overview of the individual mechanisms and their underlying theories. The subchapter discusses why this concept is suitable for framing elite education within this research.

3.1 Framing Elite Education and Elite Higher Education

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The concept of mechanisms of elite education includes four mechanisms: applicants’ choice, institutional selection, distinction, and production of coherence (H.-H. Krüger et al., 2012, p. 334). Sackmann (2019, p. 45) in a later work adds a fifth mechanism which underlies all other mechanisms. This fifth mechanism is valorization (Sackmann, 2019). Following Sackmann (2019, p. 43) and H.-H. Krüger et al. (2012, p. 332), the term mechanism is to be understood as a causal subprocess that contributes to the understanding of the higher-level processes of a phenomenon. Accordingly, the concept integrates a social constructivist and praxeological reformulation of the term (Helsper & Krüger, 2021, p. 3).This is based on a definition retrieved from the context of sociological theory development (Hedström, 2008, p. 43). The mechanisms of elite education are based on a broad range of theories. According to Helsper et al. (2019a, p. 11), the theoretical basis includes system-theoretical and neoinstitutionalist lenses, discourse and power-theoretical perspectives and various approaches regarding research on educational inequality. It integrates cross-disciplinary theoretical references and concepts, such as the innovation concept, status preservation concepts (e.g., human capital theory), Bourdieusian habit theory, and discourse-analytical research regarding educational competition (H.-H. Krüger et al., 2012, p. 331). Furthermore, the concept includes social structure analysis to investigate the interaction of all mechanisms (Sackmann, 2019, p. 43). Within this frame, the research project followed Diewald and Faist’s (2011) approach, highlighting inequalities and factors fostering inequalities. The first mechanism of elite education is choice. This mechanism refers to decisions made by individuals for or against a particular educational institution (H.-H. Krüger et al., 2012, p. 332; Sackmann, 2019, p. 43). The theoretical frame for this mechanism consists of theories of action developed by Coleman (1991, p. 16), for example, rational choice theory. Coleman uses this theory following Max Weber, who states that actions are carried out on purpose. Therefore, decisions are based on specific, rational motives (Coleman, 1991, p. 17). The author incorporates the theory of choice by elimination as an alternative to the Weberian rational choice theory (Coleman, 1991, p. 17). This theory assumes decisionmaking on the foundation of alternatives. The objective here is selecting the best alternative available (Tversky, 1972). Complementing these individual-centered theories, the mechanism of choice draws on findings that show an individual’s social context influences their actions and decisions. One factor from the social environment can be an individual’s family. Although families can influence choices concerning education, Mare (1980) shows that this influence decreases with the increasing age of the individual.

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Regarding higher education, it can be assumed that families have a low influence on students’ choices. The mechanism choice in the theoretical matter mainly picks up sociologically oriented theories, which arises from the context of the development of the concept. For further development of the concept, specific reference to psychological theories of action could be suitable. For example, links could be made to Maslow’s theory of human motivation (1978). Self-determination theory of motivation (Deci & Ryan, 1993) could also be established to enrich the sociologically anchored concept in an interdisciplinary manner. The perspective of educational institutions is diametrically opposed to the perspective of students. This perspective includes the second mechanism of selection. H.-H. Krüger et al. (2012, p. 332) and Sackmann (2019, p. 44) have described selection as specific, often institutionalized, procedures of single or multiple selections of educational participants. It includes processes, factors, and motives, which are integrated in the selection of students. The theoretical foundation of the mechanism selection is French sociologist Pierre Bourdieu’s practice theory (Sackmann, 2019, p. 44). Bourdieu (1996) examined elites and elite institutions in France’s higher education, the grandes écoles. He described the production and reproduction of various mechanisms of ruling in a system theoretically oriented toward meritocracy (Bourdieu, 1996). He illustrated how the selection of such elite institutions was shaped in practice at the time (Bourdieu, 1996, pp. 57–60). An extension of the theoretical basis could be undertaken based on theories mentioned for the first mechanism. The reason for this is that selection, like choice, has a common basis in actions and decisions. Sackmann (2019, p. 44) has pointed out that both mechanisms in everyday life can be summarized under the term selection. Nevertheless, a distinction in the context of elite education is useful as the previous arguments show. Beyond selection and choice, H.-H. Krüger et al. (2012) have argued that distinction is a third relevant mechanism of elite education. Distinction on the one hand is perceiving educational institutions and practices as vertically differentiated. On the other hand, it is setting also vertically understood distinctions in educational acts (Sackmann, 2019, p. 44). Thus, the focus of this mechanism is on the conscious and unconscious actions through which educational institutions and their clientele distinguish themselves from others or attempt to do so. As a theoretical foundation for this mechanism, H.-H. Krüger et al. (2012) and Sackmann (2019) have described the practice theory of differentiation (Bourdieu, 1982, 1984). Holt (1997) has referred to this theory as the theory of tastes. It is based on three interconnected theories developed or influenced by Bourdieu:

3.1 Framing Elite Education and Elite Higher Education

19

• capital theory • habit theory • field theory. Capital theory assumes that social actors can possess different types of capital. According to Bourdieu (1984), there are at least three such types, which are economic, social, and cultural capital. Habitus theory considers the concept of habitus as “systems of dispositions” (Bourdieu, 1984, p. 6). It thus represents the entirety of a person’s way of thinking and behaving. Strongly associated with habitus and the different forms of capital, is the concept of field (Bourdieu, 1982, 1984). Fields are social systems in which different actors operate. Across fields, these actors display different forms of capital and distinct habitus (Bourdieu, 1984, p. 94). Distinction regarding elite education refers to capital, habitus, and field. They all represent characteristics of distinction, which are based on the “logic of competition” (Bourdieu, 1984, p. 231) concerning individuals and groups. In American literature, Bourdieu’s theory of distinction has been viewed ambivalently. Holt (1997, p. 94) has provided an overview of the initially positive response in the U.S. literature and the subsequent criticism of Bourdieu’s theory. The author has undertaken a critical reflection on the literature and has pointed out the sustained prevalence of Bourdieu’s theory as he applies it to the U.S. context in the last century (Holt, 1997). The fourth mechanism in H.-H. Krüger et al.’s (2012) concept is production of coherence. This is understood as processes that contribute to and lead to the formation of a collective identity (Sackmann, 2019, p. 44). On a theoretical level, it refers to Elias and Scotson’s (1994) concept of figurations. In their work, the authors build on a foundation of observations to describe reciprocal dependencies (figurations) of different individuals in a group. They explain power imbalances within social groups and processes for the formation of a collective identity (Elias & Scotson, 1994). Snow (2001) has described the formation of a collective identity in his review of the literature on collective identity on the disciplinary frontier between psychology and sociology. In psychological literature, the theory of identity development within the life cycle, according to Erikson (1994),3 is commonly used to describe the concept of identity. Sociological literature primarily draws on the understanding of collective identity in terms of social movements (Polletta & 3

For the concept of identity from a psychological perspective, see Erikson (1968); Erikson (1994).

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Jasper, 2001; Rucht, 1995). Bourdieu (1996, p. 180) has described collective identity as a “Esprit de corps”. Applied to the context of elite education, this means: that one is able to see the different schools for what they truly are, miniature closed societies that ... share a single lifestyle, visible not only in coherent and distinctive systems of cultural references and ethical or political values, but also in bodily hexis, clothing, ways of speaking, and even sexual habits – all of which have the effect of encouraging, and outwardly legitimating, individual monographs. (Bourdieu, 1996, p. 180)

While the original concept of mechanisms of elite education initially consisted of the four mechanisms described, a fifth mechanism was added in later works. According to Sackmann (2019, p. 45), the reasons for this addition lie in observations made by the research group over the course of their investigations. It is thus not surprising that the fifth mechanism, valorization, is transversal to the other mechanisms. Valorization in this concept is defined as the intersubjective production of values through categories, legitimations, and their ordering in more or less formalized heterarchies and hierarchies (Sackmann, 2019, p. 45). This fifth mechanism is based on theories from the sociology of valuation and evaluation (SVE). Cefaï et al. (2015, p. 2) have shown that the SVE can be understood both as a subdiscipline of pedagogy and as the focus of a perspective underlying all social sciences. Lamont (2012) has provided an overview of the state of research with regard to the SVE in a literature review. Lamont (2012, p. 214) has seen his own work as a first step in bringing together different theoretical strands of the SVE, to enable building a cumulative theory based on it. In this first step, the identified subprocesses of valuation and evaluation, such as categorization and legitimation, the implementation of valuation and evaluation in practice, and the hierarchies and heterarchies that emerge from these processes, are key (Lamont, 2012). Within this frame, Lamont (2012) has distinguished between the intersubjective process of value creation (valuation) and the subjective process of evaluation (evaluation). This terminological differentiation is a crucial factor while elaborating the mechanism of valorization (Sackmann, 2019, p. 45). With regard to a specific definition of the term, Kjellberg and Mallard (2013, p. 17) have shown its underlying plurality. A. K. Krüger and Reinhart (2016) have investigated the underlying terms of valuation from a conceptual-theoretical and process-theoretical perspective. In terms of conceptual theory, the authors have shown that a distinction can be made between value and values. The singular denotes a categorization in the area of tension between valuable and worthless,

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while the plural describes orders that are used to evaluate human actions (A. K. Krüger & Reinhart, 2016, p. 497). The process-theoretical level comprises the processes of valuing and evaluating. Valuing provides the basis for evaluation because, in the context of this process, it is decided to what extent something is valuable. If the status valuable is ascribed to an object, a classification in the context of evaluating this object in comparison to a reference group follows (A. K. Krüger & Reinhart, 2016, p. 497). The two processes differ in nature in that valuing is a process of mere attribution, while evaluating is a comparative process. Due to the position of the mechanism of valuation, transversal to the further mechanisms, beyond theories of the SVE, all theories mentioned so far in this chapter can be used for further theoretical embedding. In this research, the concept of mechanisms of elite education is not used to theorize a phenomenon or explore social inequalities (e.g., Diewald & Faist, 2011). Rather, it provides a framework that describes the boundary lines of the phenomenon of elite education and allows for a clear localization of the phenomenon in different scientific disciplines. In the context of these disciplines, the different perspectives on the phenomenon and related definitions of elite education can thus be identified. These perspectives and definitions are described in the following subchapter.

3.1.2

Perspectives on Elite Higher Education

Research associated with the five mechanisms presented in the preceding subchapter is based on various distinct understandings of the term elite education. Different perspectives on the phenomenon can be identified within this frame. Perspectives identified in the literature are presented in the following subchapter. Research from these perspectives is illustrated and respective definitions of elite education are highlighted. The focus here explicitly lies with the field of higher education. Higher education is also referred to as tertiary education. It is a sector of the education system, which students are eligible to undergo after finishing secondary education. “Tertiary education includes what is commonly understood as academic education but also includes advanced vocational or professional education” (UNESCO, 2012, p. 46). According to the Organisation for Economic Co-operation and Development (OECD) (2015, p. 260), tertiary education includes all universities, technical colleges, and other institutions that offer formal higher education programs. In addition, it also includes research institutes, clinics,

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and experimental stations that are administered by or affiliated with the aforementioned tertiary education institutions (OECD, 2015, p. 260). In this research, tertiary education is understood broadly as higher education. It includes institutions that offer formal educational programs that can only be attended by individuals who have completed secondary education. These institutions are to be distinguished from institutions of continuing education.4 Elite education and thus elite higher education are investigated from an overarching perspective of the sociology of education. The sociology of education is described as a subfield of sociology (e.g., R. Becker, 2009, 2019), as its name already indicates. Furthermore, it can be described as a subfield of pedagogy and the interdisciplinary subdiscipline of educational research (Zedler, 2018). Krais (2003, p. 82) has described the sociology of education as interdisciplinary and multiperspective educational research. These factors indicate two central disciplines of reference for the sociology of education: sociology and pedagogy. Two perspectives can thus be highlighted that frame the topic of elite education and elite higher education in the scientific literature: a sociological perspective and a pedagogical perspective. These perspectives center on a “social and institutional approach to the study of elite education” (van Zanten et al., 2015, p. 57). Both can be distinguished by their respective focus. Karen (1991, p. 349), in the context of access to elite positions in society, has described two foci that can be adapted to the two perspectives on elite education and elite higher education: • Class-reproduction: focus on inheritance—sociological focus • Structural-functional: focus on ability—pedagogical focus. These foci include sociological aspects of education (Maxwell & Aggleton, 2016, p. 1), and subtopics related to educational research (Bloch & Mitterle, 2017, p. 930; Deppe & Kastner, 2014, p. 274; Maxwell & Aggleton, 2016, p. 1). Both disciplinary perspectives integrate an overarching, system-oriented focus, which indicates the interconnectedness of both perspectives. This focus is addressed as the interdisciplinary system-oriented perspective on elite education. The two central perspectives and their interdisciplinary perspective cannot be considered completely separately from one another. Nevertheless, different approaches to and 4

Continuing education can be viewed as synonymous with adult and further education. It consists of formal and nonformal education beyond the scope of education at secondary or higher education institutions. For continuing, adult, and further education, see Tippelt and Hippel (2018).

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definitions of elite education within the respective perspectives of research can be highlighted. The sociological perspective Howard and Kenway (2015, p. 1005) have stated that elite education refers to a group of people who are elite and their education, connecting elite education strongly to the scientific discussion on elites.5 Within this frame, elite education research focuses on the clients of educational institutions. Croxford and Raffe (2015, p. 1626) have indicated that one result of education expansion is that attending university does not ensure access to elite groups, but that, now, special elite institutions have to be attended. Following this argument, the “primary function of elite schools is to protect the exclusivity of elites” (Ayling, 2019, p. 45). These institutions have to ensure that elite standards regarding demands and choices are met (Maxwell & Aggleton, 2016, p. 1). According to Karabel (2005, p. 4), elite educational institutions protect and foster exclusivity to help maintain a social order characterized by inequalities between distinct groups of people. Various authors have supported this view by describing elite educational institutions as vehicles for elite reproduction (Feeney et al., 2017; Gaztambide-Fernández, 2009). In a broader sense, Melldahl (2018) has described higher education as a strategy of class reproduction. According to these authors, elite educational institutions aim at educating new generations of elites, including the offspring of past and present elites (Bloch & Mitterle, 2017, p. 930; Börjesson & Broady, 2016, p. 118; Trow, 1974a, p. 355). Regarding this role of elite educational institutions, Kingston and Lewis (1990, p. 12) have highlighted that elite institutions entertain close relationships to contemporary social elites. While the authors discussed mainly focus on the reproduction of a social elite, elite education “is concerned primarily with shaping the mind and character of the ruling class, as it prepares students for broad elite roles in government and the learned professions” (Trow, 1973, pp. 7–8). Elite education includes the education of economic and political elites. Deppe and Kastner (2014, p. 274) have added the formation of an educational elite as part of elite education. On which foundation institutions educate elites has been investigated by Lee and Brinton (1996, p. 177) among others. These authors have stated that “in sociology, much debate has revolved around how higher education at elite institutions contributes to or reflects individuals’ human capital, social capital, and cultural capital” (Lee & Brinton, 1996, p. 177). Therefore, elite education is connected to the theories 5

For the various facets of elite theory, see Bohlken (2011); Michels (1910); Mosca (1939); Pareto (1935); Mills and Wolfe (2000).

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of capital (G. S. Becker, 1962; Bourdieu, 2002). Investigating how cultural, social, symbolic, and economic capital are connected to elite education has subsequently received more attention in research (Binder & Abel, 2019a; Börjesson et al., 2016; Lee & Brinton, 1996; Noble & Davies, 2009). Börjesson et al. (2016, p. 32) have highlighted that higher education contributes to the formation of general cultural capital and further specific forms of capital. The authors have also referred to field theory, indicating that the specific forms of capital usually correspond to “the social fields that together constitute the field of power” (Börjesson et al., 2016, p. 32), once again illustrating the connection between elite groups and elite education. Finn (2012) has used a practice-oriented starting point to investigate the foundation of educating elites. The author has examined matching processes of educational content and social class of students, illustrating that elite institutions focus on teaching elites what is of interest for them, while standard institutions focus on more general, less critical content for mass education. In addition to the reproduction of elites and capital theories, the study of elite education from a sociological perspective focuses on elite educational institutions and their effects on social closure, equality, and inequality (Liu et al., 2014; van Zanten, 2009; Warikoo & Fuhr, 2014; Zhang & Wang, 2020). In one of his works Bourdieu (1996) has provided profound insights into the social origins of former students in French EHEIs, highlighting that the social closure of such institutions was predominant in the late 20th century: Our survey … reveals that the structure of the distribution of prizewinners according to social origin … has remained largely stable. Contrary to received wisdom about “democratization”, we even observe a slight increase … in the proportion of the social categories that were already the most highly represented in the initial years of the study. (Bourdieu, 1996, p. 54)

Various authors have demonstrated that such social closure of EHEIs is still intact. Buisson-Fenet and Draelants (2013) have identified school-linking processes as one mechanism that amplifies social closure. The authors have stated that social privileges are created through attending specific educational institutions within secondary education (Buisson-Fenet & Draelants, 2013). Amsler and Bolsmann (2012, p. 284) have investigated university rankings as another method for creating social exclusion in EHEIs. Other authors have focused on factors that unconsciously foster social exclusion, by examining the selection processes of EHEIs (Bastedo et al., 2018).

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In addition to methods, indicators for social closure in EHEIs, such as diversity in different areas, have been investigated. With a focus on the United States, Hillman (2013) has examined diversity concerning the economic capital of students in EHEIs. The author has shown that a positive development toward social inclusion is happening. Gasman et al. (2015) have concentrated on the diversity of staff at elite institutions. They identified a lack of diversity in all eight Ivy League institutions, regarding senior leadership positions. Harrison (2011) has examined accepted students in EHEIs in the UK and the diversity of their social backgrounds. He found that “students from minority ethnic communities tend to prefer institutions or locations which already contain a significant proportion of students from their cultural background” (Harrison, 2011, p. 463). Most studies on social closure, inequality, and equality concentrate on access to elite institutions. Nevertheless, some authors have focused on equality in regard to accepted students at EHEIs. Espenshade and Radford (2009) have investigated how ethnicity and social class are related to admissions and to campus life in EHEIs. The authors have found that EHEIs contribute to reproducing inequalities (Espenshade & Radford, 2009, p. 381). Nevertheless, those institutions take steps toward increasing equality, such as racial affirmative action, class-based affirmative action, and financial aid for students (Espenshade & Radford, 2009, pp. 381–382). Terms associated with elite education in sociological research are reproduction of social class, class-matching, exclusivity, and inequality. Those processes provide the foundation for social hierarchization. Stratification is a field investigated within the frame of this perspective (Bloch et al., 2014; Bloch & Mitterle, 2017; Kwiek, 2019). In this context, stratification does not concern institutional stratification but social stratification that is fostered by elite institutions (Bloch & Mitterle, 2017; Gerber & Cheung, 2008). In this broad context of sociological research, elite education can be defined as the formation of elites and the production and reproduction of elites. Elite here concerns status attainment in society through specific forms of capital and the consequences of this specific education on social structures. The pedagogical perspective Gaztambide-Fernández (2009, p. 1091) has stated that elite is a relative term, always viewed in comparison to a certain reference group. “The term elite is typically mobilised in the sociology of education to describe individuals and institutions occupying positions of privilege and power in particular countries and/or fields of power” (Ayling, 2019, p. 45). Following this classification of the term elite, elite education describes actors within the educational field who inherit a position on top

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of a system of institutions. What this classification does not outline is how those on top of the hierarchy attain their position. A possible explanation can be that society ascribes an elite status to some institutions. These institutions are part of the best regarding various characteristics. “These collective evaluations in large part rest on an institution’s ability to generate favorable life chances for its graduates” (Kingston & Lewis, 1990, p. 17). From this point of view, an elite institution is elite because of external attributions that derive from produced societal outcomes. This statement connects the sociological and pedagogical perspectives on elite education, highlighting the relevance of institutions’ actions and their social implications. The latter was presented in the previous subchapter. Institutional factors are included in the pedagogical perspective on elite education. A keyword in educational research on elite education is excellence. Extensive debate revolves around the term elite institutions in contrast to excellent institutions. According to H.-H. Krüger and Helsper (2014, pp. 2–3), the terms, excellent and elite are often used synonymously in the research literature. Nevertheless, in contemporary literature the notion of excellence is preferred, because it is more closely associated with meritocratic ideals, such as the performance of individuals or institutions (Ricken, 2009, p. 194 f.). Excellence in educational institutions is used to refer to “high-impact research and high-quality teaching” (Friedman, 2018, p. 249). Börzel et al. (2006, p. 17) have supported this approach by describing elite educational institutions in higher education as elite research institutions through prizes and excellent researchers and due to an elite teaching framework. Within the described frame, the adjectives high-impact and high-quality regarding teaching and research are used but not defined. According to Krull (2016, p. 119), high-impact research refers to one of the original tasks of a university: strengthening society through the production of new knowledge and conveying this knowledge to leaders in society. Quality can be associated with various components of education and educational institutions. It can be defined as characteristics of input, output, and outcome of educational processes (Klieme & Tippelt, 2008, p. 8). Some authors have described quality as “teaching excellence and student experience” (Stevenson et al., 2014, p. 4 f.). Other authors have associated quality with the future earnings of students, their learning, research, and productivity (James et al., 1989, p. 252). Another component of quality mentioned by James et al. (1989, p. 252) has been value formation of students and institutions. The “preparation of faculty, selectivity, graduation rate, placement of graduates, and so on” (Henderson et al., 2012, p. 113) can be added as indicators of quality in higher education institutions. Beyond quality of institutional components, such as teaching and impact of an institution’s research, selectivity is emphasized as a key differentiating factor between institutions from

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the elite subfield and the mass subfield of higher education (Brezis & Hellier, 2018, p. 36). Research on educational contents in elite institutions is limited. Trow (1974a) has stated that elite education “has also been used to refer to a traditional humanistic education centering on the study of the classics, which included ancient philosophy and history and mathematics” (p. 355). Elite education investigated from a pedagogical perspective includes institutional characteristics that lead to a position on top of a hierarchy for an institution or individual and thus an elite status. Status is often viewed as equivalent to reputation (Sutton & Hargadon, 1996). For this reason, a differentiation between the two terms is useful and necessary for this research. Here status is understood in the sense of positional status, as described by Elsbach and Kramer (1996). Status thus is “a rank ordering of reputation” (Washington & Zajac, 2005, p. 283). It indicates the position of an institution within a hierarchy of reputation. The concept of reputation is considered in specific terms later in this thesis (see Part II, Subchapter 4.5.1). The interdisciplinary, system-oriented perspective The system-oriented perspective incorporates whole educational systems and their structural changes over time (e.g., Trow, 1973). Elite education is investigated, regarding higher education as a subsystem of the education system, higher education as a system in itself, and distinct subsystems of higher education. Within this frame, characteristics of Luhmann’s and Parsons system theory6 can be recognized. As the overarching point of view the interdisciplinary, system-oriented perspective concentrates on structural changes that occur in higher education systems over time (Trow, 1973). Within this perspective different national systems of higher education are compared (Maxwell & Aggleton, 2016). The system-oriented perspective comprises investigating the stratification of educational systems and educational subsystems (Börjesson & Broady, 2016). Considerable research from a system-oriented point of view has been conducted by Trow (P. Scott, 2019; Trow, 1973, 1974a, 1974b). Trow (1973; Trow, 1974a, 1974b) has investigated problems that expansion poses for higher education systems. He has described two phases of transition in higher education that include three ideal types of higher education systems:7

6

For more on particular modules of (social) system theory and subsystems, see Parsons (1951); J.F.K. Schmidt and Kieserling (2017, p. 86). 7 Ideal types, according to German philosopher Max Weber. For more on Weberian ideal types, see Käsler (2014, pp. 242–247).

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• Transition from elite education (1) to mass education (2) • Transition from mass education to universal education (3). Scott has described Trow’s work as a “conceptualization of the development of Higher Education into three stages—elite, mass and universal systems” (P. Scott, 2019, p. 496). Beyond that, Trow himself has aimed at identifying problems and posing questions on the transition of higher education systems in industrial countries (Trow, 1973, p. 55). The three stages of the higher education system are defined by distinct factors associated with growth: the size of the student body, the size of the system, and the proportion of the age grade enrolled in institutions (Trow, 1973, pp. 2–5). Trow (1973, p. 7) has used the latter to clearly separate the (sub)systems as follows: • Elite higher education: up to 15% of the age group • Mass higher education: up to 50% of the age group • Universal higher education: access to education for everyone D. Baker (2014, pp. 23–40) has picked up Trow’s ideas on the transitions of education while not referring to or connecting his own thoughts to Trow. He has described a gradual education revolution that results in widened access to education within society, which leads to a “schooled society” (D. Baker, 2014). The education revolution can be viewed as a description of the transitions already defined by Trow (1973) regarding higher education. The concept of the schooled society (D. Baker, 2014) can be considered an outcome of universal access to education, which was described as the final stage of structural change within higher education by Trow (1973). While developing these concepts Baker (2014, p. 23), in contrast to Trow, has concentrated on factors that lead to the expansion of education, for example, industrialization and globalization, rather than expansion of education itself and educational institutions (Trow, 1973, 1974a, 1974b). The transitions described do not imply that the forms and patterns of the prior phase or phases disappear or are transformed. On the contrary, the evidence suggests that each phase survives in some institutions and in parts of others while the system as a whole evolves to carry the larger numbers of students and the broader more diverse functions of the next phase. (Trow, 1973, p. 19)

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In his later work Trow (2007, p. 243) has indicated that contemporary higher education is situated within the transition from mass to universal education. Altbach et al. (2009, p. 166) have described how some countries are already approaching universal education since their enrolment rates were at around 80% in 2009. Goglio and Regini (2017) have additionally described three stages of historic transitions of higher education on an institutional rather than a system level: • Horizontal differentiation: addition of vocational tracks to classical academic tracks (1960s–1970s) • Vertical differentiation: competition-based differentiation between more and less competitive institutions (1990s–2000s) • Internal and functional differentiation: differentiation between segments of one institution “In research on higher education, the term ‘stratification’ is generally understood as a metaphor that describes a stable vertical order” (Bloch & Mitterle, 2017, p. 929). Following this argument, the vertical differentiation described can be understood as hierarchization of institutions within the educational system and, thus, as stratification of the system (see Fig. 3.1). Contemporary higher education systems Elite institutions Stratification

Mass institutions

Figure 3.1 Stratification of contemporary higher education systems as ideal types, according to Trow (1973). (Own illustration)

This basic illustration (Fig. 3.1) of contemporary higher education systems displays a stratified educational system. This system resulted from the past expansion of education (Deppe et al., 2015). It consists of two prevailing subsystems: an elite subsystem and a mass subsystem. Brezis and Hellier (2018, p. 38) have described these subsystems as two sectors of higher education consisting of elite institutions

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and standard institutions. Gallacher (2006, p. 355) has shown that differentiation between institutions results from competition, which itself is a consequence of educational expansion. Deppe and Kastner (2014, p. 267) have illustrated various factors that lead to a vertical stratification of educational systems, for example, institutions as a whole, educational contents, organizational arrangements, selection procedures, and the production of excellent junior scientists. Gibson (2017, p. 15) has added changes in societal or educational policies and strategies to this list of stratifying factors. Some policies foster the notion of elite education, promoting processes of hierarchization and stratification in higher education (Maxwell & Aggleton, 2016, pp. 6–7). While examining elite education and stratification of higher education Börjesson and Broady (2016) and Börjesson et al. (2016) have referred to Bourdieu (1983, 1984, 2002) and the theories of capital and field shaped by him. According to these authors, the elite subsystem of higher education can be defined as the “subspace with the highest density of cultural capital (both acquired and inherited) and a strong concentration of other species of capital that constitute the … field of power” (Börjesson & Broady, 2016, p. 123). Production of (cultural) capital is thus a factor fostering stratification of higher education. To describe system-oriented and institution-oriented stratification, Kwiek (2019, p. 420 f.) has investigated stratification on the level of individual researchers. The author has outlined three types of individual stratification in higher education, academic performance stratification, academic salary stratification, and international research stratification (Kwiek, 2019, p. 420 f.). On the foundation of these three types, it can be observed that “at the micro-level of the individual scientist, research and the increasing competition for research funding is the single most stratifying factor in Higher Education today” (Kwiek, 2019, p. 421). As Börjesson and Broady et al. (2016, p. 135) have argued, higher education always functions at two levels: national and international. Some authors have compared global systems of higher education and their respective characteristics. For example, Palfreyman and Tapper (2009, pp. 11–12) have examined structural changes in higher education systems in different countries. Maxwell and Aggleton (2016) have compared geographically distinct higher education systems in regard to their subsystem of elite education. The authors have used an “historical approach, examining how the construction of an elite education has changed over time” (Maxwell & Aggleton, 2016, p. 13 f.). For their research, the authors acknowledged that development of the global system and national systems do not advance at a steady speed (Trow, 1973, p. 19) and that the stratification of higher

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education differs in national contexts (Börjesson & Broady, 2016). Therefore, they selected four geographical areas that are at different stages of development in their national higher education system. For example, they used the Anglophone world as an example of a higher education system with a well-established subsystem of elite education (Maxwell & Aggleton, 2016). Following Trow’s research and the research of other authors influenced by Trow, elite (higher) education from an interdisciplinary, system-oriented perspective is investigated as three distinct but interconnected research objects (see Table 3.1). Table 3.1 Overview on definitions of elite higher education from a system-oriented perspective. (Own illustration) Object of investigation

Definition

Exemplary sources

Elite higher education as a system

The subsystem on top of a stratified higher education system.

Trow (1973, 1974a)

Elite higher education as an institution

Institutions within this subsystem, thus being institutions on top of a hierarchy of institutions within the contemporary higher education system.

Brezis and Hellier (2018)

Elite higher education as an individual

Individual researchers on top of a hierarchized research system in higher education.

Kwiek (2016, 2019)

Concluding remarks The two presented perspectives and their interdisciplinary, system-oriented perspective investigate elite education and elite higher education with different foci and thus on different levels (see Fig. 3.2).

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System-oriented focus Past higher education systems

Elite institutions

Pedagogical focus

Contemporary higher education systems Elite institutions Sociological focus

Mass institutions

Pedagogical focus

Stratification

Structural changes over time

Sociological focus

Future higher education systems

Sociological focus

…

Pedagogical focus

Figure 3.2 Foci of research on elite (higher) education. (Own illustration)

This thesis concurs with previous research on the pedagogical and the interdisciplinary, system-oriented perspective on elite (higher) education, placing institutions at the center of attention (see Fig. 3.2). This study focuses on characteristics that justify an elite status for institutions, the excellence of institutions (pedagogical perspective), and their position on top of the hierarchy of higher education institutions (system-oriented perspective). However, it also addresses factors included in the sociological perspective to meet the aspiration of completeness in framing the phenomenon elite (higher) education.

3.2

Leadership and Leadership Education

The following subchapters focus on leadership and leadership education. Subchapter 3.2.1 presents the theoretical foundations and approaches to defining the term leadership. The subchapter ends with the definition of the term in the context of this research. Subchapter 3.2.2 shows theoretical approaches and definitions in the context of leadership education. The interplay between the terms leadership development and leadership education is explained and provides the basis for delimiting and defining the latter in the context of this thesis.

3.2 Leadership and Leadership Education

3.2.1

33

Defining Leadership

As a first step when writing about leadership, the term itself must be established and defined. A brief introduction on historical definitions and the different contexts of leadership is the guide through this subchapter. Approaches to leadership and underlying perspectives are illustrated. A definition for the use of the term leadership in the context of this thesis is presented. An early review of literature on leadership was conducted by Stogdill (1948) in the middle of the 20th century. The author investigated more than 100 studies on leadership. Regarding a clear definition he states “in many of the studies surveyed leadership was not defined” (Stogdill, 1948, p. 35). While examining further past and contemporary literature a different picture emerges. Many definitions of leadership exist. Dugan and Humbles (2018, p. 11) argue that understandings of leadership differ in regard to individuals’ subjective perspectives, which is a point supported by Yukl (1989, p. 251) and Stogdill (1974, p. 259). The latter states in one of his later works that “there are almost as many definitions of leadership as there are persons who have attempted to define the concept” (Stogdill, 1974, p. 7). Within scientific and professional literature definitions of leadership largely depend on the authors. Furthermore, the context of definition and thus the scientific field it is defined in assumes a substantial role. Yukl (1989, p. 251) illustrates the interdisciplinary nature of the phenomenon of leadership by listing various professional and scientific disciplines in which leadership is investigated, for example, management, psychology, political science, and educational science. Rost (1993, p. 45) adds further fields, such as theology, history, and philosophy, to this list, highlighting leadership’s interdisciplinary character. Subjective perspectives and interdisciplinarity raise various difficulties when trying to find a concise and widely agreed on definition of the term leadership. Rost (1993, pp. 35–97) approaches leadership by creating an overview on definitions of leadership, illustrating historical definitions from different fields and decades of the 20th century. In the early decades (1900–1929), leadership in scholarly literature was described as a social process that focuses on controlling and managing other people (Rost, 1993, p. 45). This narrow definition was gradually extended over the course of the century to include other perspectives. The focus of scholarly works moved from a social process targeting control over people’s behavior to traits and behaviors leaders inherit. The central point of this behavioral research was the influence of leaders on individuals, groups, and whole organizations toward achieving common goals (Rost, 1993, p. 45). Another factor

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research concentrated on in these decades was effectiveness within this influential process (Rost, 1993, pp. 50–65). Bass and Bass (2008, pp. 41–49) illustrate those historical definitions of leadership and add more up to the 21st century. The authors summarize different perspectives that have been used to define the term leadership. They distinguish three types of foci regarding leadership studies: 1. People perspective: focus on the leader 2. Effect perspective: focus on leadership as an effect 3. Interaction perspective: focus on interactions. Different approaches to the term leadership can be identified for these perspectives. The first perspective of leadership studies Bass and Bass (2008, p. 41) identify is people-centric. It focuses on the person who is a leader. Scholarly literature here describes leadership as a leader’s personality and attributes leaders inherit. It identifies and investigates leaders as the drivers for change within group processes (Bass & Bass, 2008, p. 42). Most people-centered theories focus on individuals who are perceived as leaders. Those leader-centered theories have traits, skills, behavior, appearance, and characteristics of leaders as their central theme. In addition to approaches that investigate which traits and behaviors define a leader, Schyns et al. (2011, pp. 398–399) describe implicit leadership theories that concentrate on examining behaviors and traits that are perceived as those of a leader by the majority of people. Stogdill (1948) describes leadership as traits of leaders and published an earlier work on a leader-centered approach to leadership. The author investigated various studies on leadership to define which traits a leader should inherit. In addition to these traits, Furtner and Baldegger (2016, pp. 63–74) describe leaders as able to lead themselves before leading others. The authors construct a model for self-leadership on the foundation of theories on the two factors of motivation and self-regulation of leaders (Furtner & Baldegger, 2016, pp. 63–74). Kotter (2001, p. 25) describes leadership as leaders’ behavior. This behavior is characterized as two things. First, as visionary, meaning leaders must set a direction for their followers and develop a vision of the future. They should develop strategies that produce change to achieve that vision. Second, leaders’ behavior should cohere, motivate, and inspire groups of people (Kotter, 2001, p. 25). In their work, Barendsen and Gardner (2009) make use of the term “good leadership” implicating that a right way for leaders to behave exists within the

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frame of behavioral attributes assigned to leaders. Elements of good leadership are “an excellent technical and professional quality and competence, an ethical orientation and a completely engaged sense of fulfilment and meaningfulness” (Barendsen & Gardner, 2009, p. 245). Schmidt-Huber and Tippelt (2014, p. 6) describe good leadership as effective and efficient leadership, which leads to achieving performance objectives and human objectives. Various authors describe leader behavior as authentic (Avolio, Walumbwa, & Weber, 2009; Demont-Biaggi, 2020). Authentic leadership is conceptualized with the help of four components: “balanced processing, internalized moral perspective, relational transparency and self-awareness” (Avolio, Walumbwa, & Weber, 2009, p. 424). Authentic leaders enact leadership in a transparent way, are balanced in regard to their decision-making, build trust, and energize people (Avolio, Griffith, et al., 2009, p. 4). The authors add that authentic leaders are genuine and optimistic with the objective of developing leaders’ and followers’ selfawareness (Avolio, Griffith, et al., 2009, p. 4). Schmidt-Huber and Tippelt (2014, p. 9) outline that authentic leadership is the foundation for ethical leadership. The authors add that ethical leadership is characterized by personal attributes, attitudes, motives, and values on the basis of which a leader operates (SchmidtHuber & Tippelt, 2014, p. 9). This conceptualization is supported by the following assertion: “the notion of authenticity—if suitably construed—lies at the heart of ethical leadership” (Demont-Biaggi, 2020, p. 2). Demont-Biaggi (2020) describes authentic leadership as a type of ethical leadership. In this context, leadership in its foundation must be ethical. Therefore, leaders have to put “considerable emphasis on the capacity to recognize the moral dimension of a situation” (Demont-Biaggi, 2020, p. 7). Authentic leadership and ethical leadership constitute the basis for charismatic leadership.8 In addition to ethical, authentic, and charismatic leadership, servant leadership is an approach that focuses on leader behavior. According to Hinterhuber (2009, p. 22), leadership is a service from one individual to others. A leader in this model serves followers. Northouse (2016, p. 226), on the basis of various authors, describes three approaches constituting servant leadership: 1. Leaders focus on followers and their development rather than self-interests 2. Leaders emphasize (strong) moral behavior 3. Leadership as a service can be learned.

8

For the process of charismatic leadership, see House and Howell (1992); Jacobsen and House (2001).

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Beyond leader-centered approaches, people-centered leadership includes a focus on followers. A widely recognized follower-centered approach is shared leadership. Avolio, Walumbwa, and Weber (2009, p. 431) define shared leadership as an emergent state where followers collectively lead each other. “Shared approaches to leadership question this individual level perspective, arguing that it focuses excessively on top leaders and says little about informal leadership or larger situational factors” (Pearce & Conger, 2003, p. 18). The focus of shared leadership thus is not only formal leadership but informal leadership. As a second perspective on leadership, Bass and Bass (2008, p. 46) describe an effect-oriented focus. Within this frame leadership is observed as a mechanism to achieve goals. It is not viewed as a cause for specific interactions within a group but rather as a result of those interactions. Leadership here is an effect caused by the verification of individuals as leaders by other group members. That is because “leadership truly exists only when it is acknowledged and conferred by other members of the group” (Bass & Bass, 2008). The third perspective described by Bass and Bass (2008, p. 47) focuses on interactions within a group. Interaction-centered definitions see leadership as different processes. Leadership in those definitions can occur through a relationship in which one has power over others. It can be a relationship in which interactions define individuals’ roles in a group, where leadership is associated with a position (Bass & Bass, 2008). The literature on this definition describes leadership as a differentiated role within a group and recognition by group members. Within interaction-centered definitions, Bass and Bass (2008, p. 49) demonstrate that some scholars combine various definitions of leadership, creating a field of definitions differentiated by narrow and broad definitions. Those narrow and broad definitions are characterized by the scope of elements they include. An interaction-centered approach in leadership studies is leader-member exchange (LMX). It focuses on the relationship and interactions between leaders and followers (Kerschreiter & Frey, 2014, p. 132; Northouse, 2016, p. 137). “According to LMX, the quality of the relationship that develops between a leader and a follower is predictive of outcomes at the individual, group, and organizational level” (Gerstner & Day, 1997, p. 827). The importance of leadership on a relational level is highlighted in this approach. In further interaction-centered approaches to leadership, scholars focus on leadership as a transformational process (Rost, 1993, p. 79). This transformational leadership approach has interactions between leaders and followers and leader behavior in such interactions as focal points. Leader behavior here should inspire followers. The objective is to lead followers to perform beyond

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expectations (Avolio, Walumbwa, & Weber, 2009, p. 423). Transformational leadership suggests that “leaders raise followers aspirations and activate their higher-order values (e.g., altruism)” (Avolio, Walumbwa, & Weber, 2009, p. 428). This approach emphasizes charismatic and affective elements of leadership (Northouse, 2016, p. 161). The outline above along with Rost’s (1993) and Bass and Bass’s (2008) overviews on the scholarship on leadership demonstrate the complexity of the phenomenon. As a foundation for leadership, most definitions describe how “leadership … is a continuous social process” (Barker, 2001, p. 472). Northouse (2016, p. 1) adds that leadership, as a complex process, includes multiple different dimensions. Burns (1996, p. 155) describes leadership more decidedly as a process in which a leader identifies problems and supports followers in solving those problems. Heifetz and Laurie (2001, p. 36) add that a leader’s task is fostering the collective intelligence of followers. Within the scope of the leadership process, leaders must acknowledge the needs of followers to exercise leadership. Leadership in this definition “can be exercised by a person (or group) with an established formal or informal position in an organization” (Burns, 1996, p. 155). Burns’s (1996) definition illustrates various dimensions of leadership. The author describes its nature (process), its objective (solving problems), its elements (leaders, followers and behavior), its appearance (formal and informal positions), and its frame (organization). Nevertheless, the author fails to capture the phenomenon as a whole. This definition and the explanatory approaches outlined above confine the concept of leadership to an organizational context. Rost (1993, p. 100) describes them as having an industrial focus, referring to the era in which those definitions emerged. Hunt (1993) develops an extended model for leadership confined to an industrial focus. The author distinguishes between three levels of leadership: • Systems leadership (top level) • Organizational leadership (intermediate level) • Direct leadership (bottom level). The central point here is not the process of leadership itself but rather the context of this process. Consequently, context-centered approaches can be added to the tripartite of people-centered, interaction-centered, and effect-centered approaches. These context-centered approaches to leadership can be separated into two categories: situational and system-oriented.

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Many researchers investigating context-centered leadership assume that leadership depends on individual situational contexts (Blanchard et al., 1993; Hersey & Blanchard, 1996; Lewin et al., 1939). Some of the first scholars to observe situational leadership were Lewin et al. (1939, p. 273). In their experimental research on group behavior the authors noticed different styles of leadership used by leaders participating in their study. Therefore, leadership styles must be considered while discussing situational leadership. Lewin et al. (1939, p. 273) describe three leadership styles: authoritarian, democratic, and laissez-faire. Blake and Mouton (1994, p. 75) conceptualize leadership styles of managers with the help of the two dimensions of task- and relations-orientation. They describe five different leadership styles. Reddin (1977) adds three further styles to this concept and determines all styles’ effectiveness on the basis of their task- and relationship-orientation. The eight styles incorporated in this concept are deserter, autocrat, missionary, compromiser, bureaucrat, benevolent autocrat, developer, and executive (Northouse, 2016, p. 75; Reddin, 1977, pp. 286–293). A different, nonmanagerial, perspective is used by Blanchard et al. (1993). The authors develop a model of leadership styles based on the life cycle theory of leadership. Life cycle theory in situational leadership approaches is oriented to the different styles of leadership that parents need to apply over the course of their children’s lives (Blanchard et al., 1993, p. 22). The authors identify two patterns of leadership styles, which are directive and supportive. On this basis they devise a matrix consisting of four leadership styles that are applied in different situations (Blanchard et al., 1993, p. 26; Northouse, 2016, p. 94): • • • •

directing (highly directive / less supportive) coaching (highly directive / highly supportive) supporting (highly supportive / less directive) delegating (less supportive / less directive).

Separated from leadership styles, emerging leadership is discussed within the literature on situational leadership (Pearce & Conger, 2003, p. 8). Emerging leadership “refers to the phenomenon of leader selection by the members of a leaderless group” (Pearce & Conger, 2003, p. 8) depending on the need for leadership within different situations. Strongly connected to emerging leadership is instant leadership. Bass and Bass (2008, p. 1619) describe instant leadership as leadership that emerges from a (critical) situation and disappears immediately after that situation. Instant leadership is a form of emerging leadership that is temporary.

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Another context-centered, situational approach to leadership is adaptive leadership. “Unlike the trait approach or authentic leadership, which focus predominantly on the characteristics of the leader, adaptive leadership stresses the activities of the leader in relation to the work of followers in the contexts in which they find themselves” (Northouse, 2016, p. 257). Heifetz and Laurie (2001, p. 41) describe how coping with adaptive situations is a key challenge for leaders. The authors compare leader’s responsibilities in technical and adaptive situations, illustrating as a main difference that adaptive leadership focuses on and tries to actively promote change (Heifetz & Laurie, 2001, p. 47). An approach that views leadership in the context of a system rather than a situation is complexity leadership. As a system-oriented approach complexity leadership applies complexity theory to leadership studies. It understands leadership as “an interactive system of dynamic, unpredictable agents that interact with each other” (Avolio, Walumbwa, & Weber, 2009, p. 430). Hazy et al. (2007, p. 2) state that leadership is emergent within complex systems, connecting complexity leadership to the previously mentioned concept of emerging leadership. This statement illustrates the interconnectedness of distinct approaches to leadership. According to Rost (1993, p. 100), civilizations are in the midst of a transformation from industrial to postindustrial societies. Therefore, the industrial model and comprehension of leadership do not serve the needs of contemporary leaders and followers. This paradigm shift calls for new perspectives on various studies, including leadership studies. Rost (1993, pp. 100–101) and Barker (2001) express a need for leadership theories that go beyond the industrial scope of definitions described above. In the postindustrial era, leadership can be defined as “an influence relationship among leaders and followers who intend real changes that reflect their mutual purposes” (Rost, 1993). This leadership definition includes four essential elements directly related to a postindustrial context, which are discussed below. These elements aim to distinguish leadership from other social relationships. 1. The leadership relationship is based on influence Leadership is an influence relationship (Comelli et al., 2014, p. 83; Rosenstiel, 2011, p. 27). It is characterized by persuasion from one individual toward others. In contrast to industrial leadership theories, influence does not originate in authority. It is not coercive. Influence in leadership relationships does not only come from the leader. It operates multidirectionally (Rost, 1993, pp. 105–107).

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2. People are leaders and followers in this relationship Within the leadership relationship, people have clear roles. There are leaders and followers. In a postindustrial era, those roles do not change. They alter toward other meanings. Followers in this relationship are not subordinates to leaders. They are actively involved in the leadership process, which often results in more than one leader within a relationship. These leaders possess an advantage on a scale of influence. They exercise more influence than followers do. People’s roles (as leader or follower) depend on context. Anyone can be a leader in one situation and a follower in another (Rost, 1993, pp. 108–112). 3. Leaders and followers intend real change The objective of leadership in its industrial definitions is to achieve change. In a postindustrial era, this output-oriented approach is neglected. Part of the foundation for a leadership relationship is that real change is intended. Leadership happens, even if the change is not achieved in the future. The leaders’ and followers’ contemporary intentions to achieve change in the future are relevant. Real change in this definition includes only substantial changes that are transforming in an area (Rost, 1993, pp. 113–118). Aiming for transformational change implies the relevance of defining objectives within the leadership process since objectives function as indicators for directions of action (Welge et al., 2017, p. 208). Objectives defined within the leadership process can be described as innovation objectives (W. G. Faix et al., 2015). Defining these objectives is a central ability of leaders (Kisgen, 2017). 4. Leaders and followers develop mutual purposes A significant difference between industrial and postindustrial leadership is not only visible when examining output-orientation. A difference can be observed within the foundation of the leadership process. Some definitions of leadership describe its foundation as the common goals of leaders and followers. In a postindustrial era, the foundation consists of mutual purposes. Purposes are distinguishable from goals. The scope of purposes is more holistic and they are not oriented toward quantification and are, therefore, less settled. Within this leadership definition leaders and followers create mutual purposes so that all individuals involved are able to create their own visions and missions regarding the purpose of the relationship. Those purposes are not realized in the future. Instead, they are reflected continuously by leaders and followers. Mutual purposes develop toward common purposes as the result of the leadership process where leaders and followers engage in leadership together (Rost, 1993, pp. 118–123).

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“Industrial leadership serves institutional need” (Barker, 2001). This statement clarifies the narrow nature of industrial leadership theories (Rost, 1993). Postindustrial definitions offer a new, broadened perspective by detaching the focus of leadership from its previous organizational point of view. Complementing Rost’s (1993) postindustrial definition and Hunt’s (1993) extended concept of leadership, Barker (2001, p. 471) stresses the need for a metaphysical foundation for the phenomenon of leadership as an approach to cope with its complexity. Within the spectrum of industrial leadership theories, Barker (2001, p. 477) identifies leader-centered and context-centered theories. Fundamentally, those are “the man in charge, and the outcomes of the social milieu which he appears and operates in” (Barker, 2001). According to the author, the two facets of leader persona and outcome in the leadership context form the foundation of the leadership process. Beyond this physical foundation, Barker (2001) devises a first concept for a metaphysical foundation of leadership. This foundation consists of three elements that characterize the leadership process: 1. Leadership is a process that considers the wills and serves the needs of individuals and the collective. Therefore, it converges individual and collective wills and needs. 2. Leadership is a process that focuses on exchanging values. 3. Leadership is a process that aims for and promotes change. In summary, leadership on a metaphysical level is “a process of transformative change where the ethics of individuals are integrated into the mores of a community as a means of evolutionary social development” (Barker, 2001). In most industrial leadership definitions, Barker (2001, pp. 469–470) criticizes how leadership is often used solely for the purpose of describing various activities in the context of people on top of a hierarchical system. Barker (2001, pp. 480– 483) highlights that the consequence of this is that scholars tend to examine leadership but are actually describing management activities. Acknowledging this conceptual difficulty, a need for distinguishing between management and leadership becomes apparent. Leadership and management differ in various regards. “Management is the execution of technical/transactional solutions to technical problems where no adaption/transformation is necessary” (Burns, 1996, p. 155). Leadership is transformational. It intends substantial change (Barker, 2001, p. 491; Kotter, 2001, p. 26; Rost, 1993, p. 116). Regarding the roles of leaders and managers “the essential distinction appears to be

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that leaders influence commitment whereas managers merely carry out position responsibilities and exercise authority” (Yukl, 1989, p. 253). Table 3.2 illustrates the distinguishing characteristics between the tasks of leaders and managers, as described by Kotter (2001). Table 3.2 Comparison—tasks of leaders and managers. (Kotter, 2001, pp. 24–26)

Leaders

Managers

Setting direction

Planning, budgeting, staffing, organizing

Aligning people

Controlling

Providing motivation

Problem solving

Bass and Bass (2008, p. 50) add two further factors to this framework of tasks for leaders, which are enabling performance improvement for individuals and empowering group members. The authors extend the comparison of leadership and management concerning their respective contexts. Table 3.3 illustrates the different contexts of both terms as described by Bass and Bass (2008, p. 50) Table 3.3 Different contexts of leadership and management. (Bass & Bass, 2008, p. 50)

Leadership

Management

Accorded by the group

Imposed on the group through position

Can be established through spontaneous recognition

Maintained through an organized system

Joint objectives & joint purposes

Top-down objectives

The main differences between management and leadership are nature, tasks, context, and sphere of action. Management’s sphere of action is confined to an organizational status. It is based on hierarchy and authority. Leadership can occur outside of an organizational context. It is not established by means of hierarchy or authority but rather through recognition and common purposes. Considering the postindustrial leadership definition and its integrated relevance of defining innovation objectives within the leadership process presented, the latter must be emphasized as a central distinctive criterion of the two phenomena since it represents the foundation for achieving transformational change. In addition to this comparison of management and leadership, CzarniawskaJoerges and Wolff (1991) describe entrepreneurship as part of a triad that needs to

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be distinguished. The authors distinguish leadership and management by nature, outlining that management is a function focusing on objectives and order while leadership is a process that establishes social events (Czarniawska-Joerges & Wolff, 1991, pp. 532–536). Entrepreneurship, as a third facet of this triad, is described as a social process that is based on creation. It can be defined as leadership in, and leadership that creates, certain contexts (Czarniawska-Joerges & Wolff, 1991, p. 533). When defining leadership within the frame of this thesis different aspects and approaches should be considered. It is widely agreed that “there are many possible ways to define leadership. However, the definition of leadership should depend on the purposes to be served” (Bass & Bass, 2008, p. 52). Yukl (1989, p. 253) adds, that it is not viable nor preferable to attempt to define leadership as one thing. The author states that “it is better to use the various conceptions of leadership as a source of different perspectives on a complex, multifaced phenomenon” (Yukl, 1989, p. 253). Acknowledging these authors’ remarks, leadership is not defined conclusively within this thesis. However, to construct a solid foundation for further remarks it is desirable to frame the phenomenon. According to A.-V. Faix (2020), leadership is the process of shaping a creative and innovative future in complex and open situations. In this context, “a leader is someone who shapes an innovative future in complex situations” (A.-V. Faix, 2020, p. 43). A more specific definition concerning this line of thought was developed by W. G. Faix et al. (2020): Leadership means to lead oneself and human communities with personality—reasonably, responsibly, and ethically into an innovative and creative future in open and complex situations under unclearly defined and dynamic conditions while always considering the framework conditions and collective rationality. (p. 3)

Based on the various aspects and definitions of leadership presented in this subchapter, leadership in this thesis can be defined as two things. First, it is a social process between people who aim for (substantial) positive change within society. Second, it is the characteristics, skills, and behavior of the people who shape this social process and attain the label of leader. Both process and process shaping must be based on an ethical foundation. Following this line of thought, the definition of leadership stated by W. G. Faix et al. (2020) is well suited as a practical understanding of the term within the context of this thesis.

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Leadership Education

The preceding subchapter presents a discussion of the term leadership and the underlying theories and concepts. In the following paragraphs, leadership is placed in the educational context, according to the definition given. This subchapter focuses on leadership education. Theoretical approaches and definitions of the term are presented. Leadership education within the frame of this work is defined. At the beginning of the 20th century, Anglo-American authors wrote scientifically about leadership education. Harper (1936, p. 92) shows that the terms leadership education and leadership training were used synonymously for a long time, although they have different meanings. The author describes how education has been viewed historically as teacher centered. However, in the context of leadership education, it is an active process steered by the learner (Harper, 1936, p. 93). The basic idea is that teachers advise learners while learners educate themselves. Harper (1936, p. 95) describes how such an approach to education cannot happen theoretically. Rather, the focus must be on learning through experience and creating a link between learning and practice. In the current literature on leadership in an educational context, different terms are used to elaborate the phenomenon. These include leadership development, leader development, leadership learning, leadership training, teaching leadership, and leadership education (Jenkins & Andenoro, 2016; Lumpkin & Achen, 2019; Owen, 2012; Schellhammer, 2016). In this thesis the term leadership education is used. To define it, the two terms, leadership development and leadership education, are described comparatively. Leadership education can then be delineated. Other terms applied in the literature are also mentioned but they are not the focus of this work. Leadership development refers to “the expansion of the organization’s capacity to enact the basic leadership tasks needed for collective work: setting direction, creating alignment, and maintaining commitment” (van Velsor & McCauley, 2004, p. 18). The authors derive this definition from their own definition of leader development. This refers to the individual, whereas leadership development is seen in the context of an entire organization (van Velsor & McCauley, 2004, p. 2). This organizational focus of leadership development stems from the organizational focus of research on the term, as Subchapter 3.2.1 (Part II) has already shown. On a theoretical and empirical level, leadership development has been sparsely studied (Subramony et al., 2018). In their work, Subramony et al. (2018) show that leadership development is closely related to the concepts of human

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and social capital. Various practical implementations of leadership development are positively associated with an individual’s human capital (practices with an intrapersonal focus), and social capital (practices with an interpersonal focus). To embed the term into a theoretical frame, it can be referred to human capital theory according to G. S. Becker (1962) and capital theory according to Bourdieu (1984). Human capital theory originates from the field of economics. G. S. Becker (1962) describes the concept of human capital as the resources that individuals hold. With reference to the abovementioned definition of leadership development, it can be said that intrapersonal leadership development has a focus on the formation of the human capital of individuals whereas interpersonal leadership development promotes the formation of social capital. The term leadership development is often used synonymously with leadership education in the literature. There is no clear delineation between the two terms, as many overlapping characteristics can be observed (Goertzen & Whitaker, 2015; Guthrie & Jones, 2012; Jenkins & Andenoro, 2016; Owen, 2012, p. 24). From an integrated perspective, the term leadership development initiatives used by Subramony et al. (2018) can distinguish between leadership development and leadership education. Several other authors also describe leadership development initiatives. Gigliotti (2017), for example, refers to them as formal programs designed to (further) develop leaders in academia. Barry et al. (2018) categorize leadership development initiatives as formal leadership education programs. To further delineate the concepts, a definition of leadership education is necessary. Leadership education is defined differently in different contexts. Caza and Rosch (2014, p. 1586) describe leadership education as the development of better leaders. Schellhammer (2016) views leadership education as the teaching of leadership and leadership models. Barry et al. (2018) describe the term as preparing individuals for leadership roles. A.-V. Faix (2020) goes beyond these basic definitions to show the specific components of leadership education. The basic elements of the term, according to A.-V. Faix, are the development of creative personalities, the integration of theoretical, practical, and reflective educational areas, and the preparation of individuals to lead a fast-paced, global, and digital world into a sustainable future, against the backdrop of an ethical framework (2020, p. 45). The process of leadership education is described by A.-V. Faix (2020, p. 45) as a lifelong development, which is carried out autonomously, by the individuals, on themselves. This definition shows similarities and differences between leadership education and leadership development. Both terms include proportions of the traditional German concept of Bildung (education). This understanding of the term is influenced by humanism. Education (in this context of leadership education) happens

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through the active, critical discussion of oneself, in interaction with one’s own environment (Humboldt, 1903 / 1968). Learners lead the educational process on themselves, while they are accompanied by teachers. A notable difference between the two terms is that leadership development encompasses only the organizational context, while leadership education focuses on society. In this context, Wren et al. (2009) point out a connection between leadership education and the liberal arts. According to the authors, the liberal arts enable individuals to look beyond their own horizons and commit to a higher purpose (Wren et al., 2009, p. 21). Beyond context, format is also a distinguishing characteristic of the two terms. Leadership development is often applied in the context of formal leadership education programs (Barry et al., 2018; Gigliotti, 2017; Stephenson, 2011). Leadership education on the other hand, following A.-V. Faix (2020, p. 43), aims to go beyond such programs to enable individuals to engage in leadership education on their own, beyond the period of formal programs. These differences show that leadership education and leadership development are closely linked, but that there are certain points that need to be distinguished. For this reason, leadership development is considered in this paper as an integral part of an overarching leadership education framework. The leadership education process is unlimited in time and space and focuses on (the creation of) added value in society. Such a constellation of the two concepts is also taken up by Stephenson (2011), at a low-threshold level. In the context of the formal educational programs of a single educational institution, the author describes leadership development initiatives as part of an overarching leadership education framework (Stephenson, 2011, p. 321). Following the definition discussed above, formal phases of the leadership education process (structured educational programs) should meet several requirements to be considered leadership education (W. G. Faix et al., 2020). W. G. Faix et al. (2020) illustrate such criteria based on an in-depth literature review. The authors describe three categories of criteria, which are objectives, content, and methods. The objectives of formal leadership education programs are on (further) developing personality, competencies, and the performance of individuals. Additionally, the aim is to create confidence in the individual’s own scientific judgment (W. G. Faix et al., 2020). On the content level, the authors address various aspects of leadership, such as leadership theory, the framework in which leadership takes place, ethical aspects of leadership, innovation and added value, entrepreneurship and intrapreneurship, focus on objectives, and international and intercultural skills (W. G. Faix et al., 2020). Methodologically, the focus should be on reflection.

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Active and digital learning, along with collaboration and practical application in real-life situations, should be encouraged in such programs (W. G. Faix et al., 2020). Complementary to these practical criteria, leadership education can be described on a theoretical basis. Mergenthaler (2017) derives theoretical assumptions about the term through an abductive process, which can help classify it in the context of related terms. Holistically, leadership education is understood and defined within the frame of educational science and educational practice (see Table 3.4). Table 3.4 Definition of leadership education, according to Mergenthaler. (2017, p. 447) Leadership Education (educational science)

Leadership Education (educational practice)

Systematic creation of scientific knowledge regarding a pedagogical concept of leadership (this, and the underlying educational concept, should be shared with educational practice).

Theoretical and empirical engagement with the practice of education of individuals who should and want to take a leading role in society. Systematic thinking through and elaboration of the practice of education of individuals who should and want to take a leading role in society in the form of educational models, and concrete teaching and learning offers.

Mergenthaler’s (2017) definition illustrates the connection between the theoretical and practical levels of leadership and leadership education. The author brings up the aspects of should and want. Leadership and leadership education are based on an individual’s motivation (want), and on others ascribing a leadership role to the individual (should) (Mergenthaler, 2017, p. 447). In this thesis, leadership education can be considered to be educating individuals who shape the social process of leadership. Educational institutions support learners with the development of their personalities. Leadership education means two things. On the one hand, it is providing a frame for the development of personality. On the other hand, it enables the self-education of these individuals regarding the leadership process and underlying leadership behaviors, competencies, and characteristics beyond an institutional scope.

4

The Constitution of Contemporary Elite Higher Education Institutions

The previous chapter includes an examination of central theories and definitions within this thesis. On this foundation, the constitution of the phenomenon of EHEIs is conceptualized in the following chapter. Within this frame, leadership education is embedded in the context of EHEIs. This chapter represents a synthesis of previous literature but specifically focuses on higher education institutions.

4.1

The Tasks of Higher Education Institutions In Society

The starting point for the conceptualization of EHEIs is the definition of elite education from a pedagogical and interdisciplinary, system-oriented perspective, which was discussed in Subchapter 3.1. According to this definition, an educational institution is elite because it is located in the elite subfield of the higher education sector. Therefore, it is at the top of the hierarchy of similar institutions. This position provides an elite status. However, both factors, position and status, are mutually dependent in the understanding of this thesis. Thus, a higher education institution can possess an elite status because it is at the top of the hierarchy or it is at the top of the hierarchy because it possesses an elite status. To answer the question of how an institution achieves such a top position and an elite status, Bohlken (2011) provided a basis. In his work, the author describes the responsibilities regarding public welfare, and the moral responsibility to society, that elite individuals or groups of individuals, and in this sense institutions, have for the society in which they operate. Responsibility elites occupy a position on top of a hierarchy, which they gained through merit or selection. In this position, they possess the power to change social structures and underlying norms. Therefore, they are responsible for society as a whole (Bohlken, 2011, p. 77). © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_4

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In this context, Markl (1990, p. 222) stated that an elite group earns its status through transferring its own success toward the lasting success of the community from which it emerged. Considering EHEIs as responsibility elites responds to the critique of the term elite from Helsper and Krüger (2021). The term is precariously situated in relation to education since the former is based on aristocratic principles while the latter is based on democratic principles. Consequently, elite in education should only be applied in the context of further terms such as responsibility or merit (Helsper & Krüger, 2021, p. 3). An educational institution achieves an elite status and a top position in the field through their responsibility and a disproportionately positive influence on the society in which it operates. This influence is viewed in comparison to other higher education institutions. Following these remarks, EHEIs possess certain excellent characteristics to generate the highest possible benefits for society. These characteristics are treated in this thesis as the factors that constitute an EHEI. Considering that higher education institutions achieve a top position and an elite status via a disproportionate positive impact on society the questions arise of what positive impact a higher education institution has on society and how it benefits society. To answer these questions, the role of higher education institutions in society and their central tasks must be considered. In the context of current research on the role and tasks of higher education institutions, vision and mission are two central concepts. Both are borrowed from the discipline of strategic management (Bryson, 2018; Mintzberg, 1994). A vision is the target image of an institution, which determines the path it sets for itself (Mintzberg, 1994, p. 107). This path can be seen as the institutional mission. Transferred to the context of this thesis, the vision is understood as the objective and the mission as the role of an institution in society. The factors of objective and role constitute the tasks of institutions. Institutions often define their mission and vision in statements. These act as a sense-making tool on an institution’s path toward a target image for all the people involved (Bryson, 2018; Gurley et al., 2015, p. 218). Citing Babnik et al. (2014), Kopaneva and Sias (2015) describe how mission statement and vision statement are connected: “An organization’s mission statement defines what the organization is today—its purpose or reason for existence …. A vision statement defines what the organization wants to be in the future—its ideal future goal” (p. 359). In the context of (elite) educational institutions, researchers investigate two types of vision and mission: the self-defined and the externally attributed. The

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self-defined vision and mission are the objective and path to achieving this objective, which are defined by the institutions themselves. However, in view of the social responsibility of higher education institutions, binding and nonbinding missions and visions are also defined from outside the organization, for example from politics. The degree of binding depends on how much influence the external stakeholder group has on an institution, for example, regarding its sponsorship. In the European context, Gornitzka and Maassen (2017, p. 231) describe different facets of the externally attributed missions of higher education institutions. The central issue here is the expectations the European Commission has of those institutions. The different facets of externally attributed missions include the transfer of research achievements into commercial technologies and jobs, the enhancement of economic growth, and the reduction of social inequality. Regarding the self-defined perspective, various content analyses of vision and mission statements have already been conducted (Gurley et al., 2015). These analyses have been critiqued for focusing primarily on national contexts, rather than overarching international perspectives (Cortés-Sánchez, 2017). Cortés-Sánchez (2017) conducted such a comparative, international analysis. The results show that in the international context, the visions and missions of higher education institutions address different core aspects. In the context of vision statements, the focus of institutions is their own global presence, or their own global influence. Mission statements focus on the factors of society and students, and research and teaching (Cortés-Sánchez, 2017, p. 34). These findings show that higher education institutions carry out externally attributed and self-defined tasks. Within this frame, they pursue certain objectives (vision) and assume a certain role (mission). This role is manifested in the institutions’ central tasks. Having a positive impact on society and creating progress are key, as Spiel et al. (2018) have shown. These authors describe the central tasks of higher education institutions as producing new knowledge, educating citizens, and generating economic benefit (Spiel et al., 2018). Sam and van der Sijde (2014, p. 891) refer to teaching, research, and economic development. Other authors include these three functions in the concept of the performance of higher education institutions, described as, how well, by how much, and at what cost higher education is contributing to addressing the needs of society in terms of human capital formation, knowledge creation, and wider economic, social, cultural, and environmental development. (Sarrico, 2018, p. 1)

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Cloete et al. (2018) write of bringing forth highly qualified graduates, producing new knowledge which is relevant for growth and understanding societal challenges, and developing solutions to address these challenges. The key characteristics of world-class universities are described as graduates, research output, and technology transfer (Salmi, 2009, p. 8). From these descriptions, it becomes evident that a variety of terms are used in the academic literature to describe the central tasks of higher education institutions. In this thesis, they are addressed as education, research, and innovation. The different terminologies for higher education institutions’ tasks show general accordance. This common ground suggests the relevance of a differentiated and systematic consideration of education, research, and innovation. A fundamental differentiation between process and impact can be derived from the distinction picked up between mission and vision, and the roles and objectives of institutions. Pinheiro and Benneworth (2018) derived this differentiation from an extensive literature review in their work on the regional role of higher education institutions. Within this frame, they illustrate what impact higher education institutions achieve (impact) and how they achieve it (process). This pattern can also be applied to the context of this research (see Fig. 4.1).

Responsibilities

Tasks

Process view

Impact view

Mission

Vision

Role

Objective

Figure 4.1 Relationship between process perspective and impact perspective on performance of higher education institutions. (Own illustration)

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A distinction is made between the process perspective and the impact perspective. The process perspective looks at measures taken to achieve an impact, that is, the role of institutions in society and their associated missions. The impact perspective focuses on institutions’ objectives, achieving those objectives, and their vision, which is the result of the measures taken from the process perspective. This structure underlies the detailed explanations of the three essential tasks of higher education institutions in the following subchapters.

4.2

Education in Elite Higher Education Institutions

The previous subchapter illustrates the fundamental tasks of higher education institutions in society. In the following discussion, the task of education is the main topic. A definition for the term and its specific implementation in EHEIs is presented. For this purpose, the concept of education is considered. Ideological and philosophical perspectives on the term are explored. A transfer takes place of the points discussed to educational practice in higher education institutions, which hold an elite status.

4.2.1

The Concept of Education in Higher Education Institutions

In this conceptualization, the term education is considered. A delimitation of this term is necessary. Education encompasses various facets. To illustrate these, reference can be made to a distinction in the German language, which is not found in the English language. The German Bildung (education) contains the two elements of upbringing and education. Following Menck (2015, p. 70), both terms can be distinguished on several levels. Three central aspects should be mentioned here, which are the process, the purpose, and the end (Menck, 2015). Upbringing is understood as a process of development toward independence. This process is partly externally controlled, for example by parents or educators (Tenorth & Tippelt, 2007, p. 204). According to Menck (2015, p. 70), the purpose of upbringing is attaining maturity. This step ideally occurs during young adulthood. Education, on the other hand, is the process of acquiring what is humanly possible (Menck, 2015, p. 70). It serves the purpose of perfection and ends only with the end of human life (Menck, 2015, p. 70). Following this line of argument, education in comparison to upbringing is a lifelong and self-controlled process.

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Regarding the end of both processes it can be said that upbringing happens up to the point where a person is enabled to educate themself. The focus of this subchapter is the concept of education, distinct from the concept of upbringing.1 The former is described in more detail below and considered in the context of higher education institutions. W. G. Faix and Mergenthaler (2015, p. 100) describe education as a concept with two levels, education as a process and education as an end product. It can be understood as the process of education, and the result of being educated. This fact has already been pointed out by Klafki (1964, p. 298) in his theory of categorical education. The author describes education as a concept of processes in which the contents of a certain (spiritual and material) reality open up to a person. This process is a person’s opening or becoming open to those contents and their context as reality. Education is this process and its result (Klafki, 1964, p. 298). These statements illustrate the relevance of differentiating between process and impact in the context of the educational element of a higher education institutions’ tasks. The process of education can be understood here as the appropriation of and engagement with culture. The result and thus impact of this process is being human, or as Menck (2015, p. 58) describes it, education is what people make of themselves. The shape of the educational process, and defining being human, have a long tradition of discussion in educational science. Klafki (1964) tried to bring together the object-focused theory of material education with the subject-focused theory of formal education with the help of his theory of categorical education.2 On this foundation, he further developed his concept of education in the following decades, resulting in the concept of general education. The term general education is often used in everyday language as a synonym for general knowledge. It describes a minimum level of knowledge, skills, and competencies a person should acquire and develop which enables participating in advanced educational programs (UNESCO, 2011). Klafki’s definition is not limited to these remarks, but rather incorporates them. It is based on the basic features of general education and the process that leads to the educated person, focusing especially on education in schools (Klafki, 1986).

1

Regarding the concept of upbringing, reference should be made to the extensive body of pedagogical literature on the topic. For further information, see, for example, Menck (2015). 2 For a distinction between formal and material education and their integration in the concept of categorical education, see Klafki (1964).

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In his framework, Klafki (2007b, p. 52) outlines education as the interrelationship of the three abilities of self-determination, codetermination, and solidarity. These are described in three dimensions or moments of meaning (German original: Bedeutungsmomente) (Klafki, 2007b, p. 52): 1. Education for whom? (clientele) 2. Education with what content? (content) 3. Education with what ideal? (ideal).3 According to Klafki (1986, pp. 474–475, 2007b, p. 52), the clientele of a general education are all people. General education is not limited to certain groups of people. The content of general education focuses on critically examining the general (Klafki, 1986, p. 474; 2007b, p. 53). The general refers to problematic developments on a global scale, which are either currently happening or are foreseeable in the future. Klafki writes of key issues typical for a certain epoch (German original: epochaltypische Schlüsselprobleme) (Klafki, 2007b, p. 56). These key issues are defined by Klafki (2007b, p. 60) as global challenges, which have an impact on society as a whole, and on individuals. On this basis, at any time in past, present, and future a finite sum of key issues typical for a given epoch prevails (Klafki, 2007b, p. 60). Considering the contents of general education, this statement can be transferred unchanged. Beyond the finite number of contentual elements, the reference to present and future shows that the content of general education is not so much a predefined set of contents as it is a set of contents that must be redefined continuously and adaptively developed. Klafki (2007b, p. 62) emphasizes that within this focus on key issues typical for an epoch, contents of general education should not be limited to the scope of respective key issues. Rather, attitudes and capabilities must be developed that go beyond the individual issues.4 The ideal of general education is the development of all levels of human ability. This means education in all basic dimensions of human interests and abilities (Klafki, 2007b, p. 54). In summary, Klafki states that general education must 3

The term “ideal” (noun) is adopted from the discipline of philosophy. It describes an objective that embodies perfection. In education, an ideal is a state that should be reached. However, since it embodies perfection an ideal cannot truly be achieved. For the term ideal and ideals in education, see Kisgen (2017, p. 99) and Tenorth (2013). 4 Klafki (2007b, p. 63) describes four specific contentual elements that go beyond the scope of individual key issues, which are willingness and ability for critical thinking, willingness and ability to argue, willingness and ability to be emphatic, willingness and ability for contextual thinking.

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be understood as appropriating past, present, and future questions and problems that all people face together and discussing these collective tasks, problems, dangers (Klafki, 2007b, p. 53). General education here also includes the historical derivation of key issues typical for an epoch in the present and future, and the development of approaches to solving them. Overall, this concept of general education is intended to serve as an orientation framework for the further development of the educational system (Klafki, 2007b, p. 53). The presented definition of the term ‘general education’ was developed in the context of critical-constructive didactics. Within this frame, the focus was on secondary education (e.g., Klafki, 2007b). Klafki’s (2007b) definition does not explicitly address higher education. However, its generalist nature enables assigning a high-level function to it. It can be viewed as a point of orientation for pedagogical measures on all levels of the educational system. The specific design of measures based on general education thus depends on the level of the education system that is investigated. For example, clientele, contents, and ideal differ when comparing secondary education and higher education. The definition of general education presented is used as a basis to describe education in higher education institutions in this thesis. Focusing on the tasks of higher education institutions, Temple (2018) describes liberal education as a historical focal point. Liberal education may be viewed as an ideological manifestation of the concept of (general) education. Following Kuhn (1963, p. 23), ideology can be compared to the concept of worldview. The term is mainly used in the context of political movements and the related abuse of power.5,6 In relation to the concept of education, this negatively connoted understanding of the term should not be used. Rather, the understanding, originating from the word’s nature, is used. Consequently, an ideology is to be understood neutrally, as a system of world views, basic attitudes and valuations attached to a social group, culture, or the like (Duden, 2021). With regard to this research, an educational ideology is defined as the totality of ideas of different (groups of) people toward the various facets of education (Prisching, 2008, p. 9). Guerra (2013) defines liberal education as education toward being free. Its objective is the “liberation of the mind” (Agresto, 1990, p. 70). Strauss (2004), 5

For the concept of ideology with a focus on the political context, and the abuse of power that takes place here, see Kuhn (1963). 6 For a philosophical discussion of the concept of ideology in the context of society, see Habermas (1968).

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in his commentary on the definition of liberal education, also speaks of educating cultured people. Specifically, liberal education aims “to enable students to make sense of the world and their place in it, preparing them to use knowledge and skills as means toward responsible engagement with the life of their times” (Colby et al., 2011, p. 53). In discussions on higher education, the terms liberal education and general education are often used interchangeably (Xin, 2004, p. 1). Chandler and Teckchandani (2015, p. 330) describe liberal education as concentrating on the formation of creative and critical thinking. Moreover, this educational ideology integrates a mix of fundamental (general) education of people and more specialized education beyond that (Guerra, 2013, p. 252). The rationale given is that “expertise in a single field … does not constitute a liberally educated mind” (Carson, 1997, p. 230). Liberal education can thus be understood as a manifestation of general education, with an emphasis on concrete aspects of specialized education. The liberal character of education in higher education institutions is based on various complementary philosophical directions. However, in the first step, before considering these directions, the concept of philosophy of education must be clarified. The specific meaning of philosophy of education is controversially discussed in the literature (Siegel, 2017, p. 21). Underlying educational research is a pursuit of practical implications (Tippelt & Schmidt-Hertha, 2018, p. 6). Therefore, a definition of the term philosophy of education should also address the practical implications of education. In this work, Siegel’s (2017, p. 21) definition is used. This definition occupies a dual perspective, consisting of the view on the superordinate discipline of philosophy and the view on educational practice. Accordingly, philosophy of education is a segment of philosophy that reflects on the nature, goals, and problems of education (Siegel, 2017, p. 21). With reference to liberal education in higher education institutions, two complementary, educational philosophical directions can be identified. The theory of general education can be found within the idea of liberal education. This theory is based on a (German new) humanistic understanding of education (Sander, 2017). This understanding of education, in turn, is partly influenced by social constructivist approaches. Educational philosophical directions underlying liberal education, thus, are (German new) humanism7 and social constructivism.

7

For a detailed description of the development of the concept of humanism and its evolution into (German) new humanism by the end of the 19th century, see F. Paulsen (1885).

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The characteristics of education described above illustrate the concept as an interactional development process. Education based on interaction is the fundamental premise of a social constructivist understanding of education. Following Chandler and Teckchandani (2015, p. 331), this statement has to be limited to the fact that the term interaction in this context, describes the social interaction between individuals. The reason for this limitation is that social constructivist pedagogy understands knowledge as a social product (Prawat & Floden, 1994, p. 37). Social constructivist approaches to education stem from psychological research in cognitive development. In Piaget’s (1932) work on constructivist processes in the context of children’s moral development, he describes the concept of cognitive constructivism. On this foundation, Vygotsky (1978) analyzes the development of higher psychological processes in school children, and their implications for educational practice. In his work, the author outlines the two directions of learning and development. He developed a third theoretical approach that connects the two terms. This third theory describes learning and development as interdependent interactive processes (Vygotsky, 1978, p. 81). Interaction is integrated here as a new element in the process of development. Social constructivism is defined as a “constructivist perspective emphasizing the importance of the individual’s social interactions in the acquisition of skills and knowledge” (Schunk, 2012, p. 498). Humboldt (1903 / 1968, p. 283) points out that the mutual interaction of the individual with their environment is an integral part of humanistic education. In the (German new) humanistic tradition, the education of individuals toward humanity is at the center of the concept of education (van Bommel, 2015, p. 23). Klafki (1986, pp. 458–459) speaks of empowerment to reasonable self-expression and subject development in the medium of objective-general content. Education integrates the concepts of self-expression, freedom, emancipation, autonomy, maturity, reason, independence, humanity, world, objectivity, and generality (Klafki, 1986, pp. 458–459). With reference to education on the individual level, Humboldt (1903 / 1968, p. 286) describes the concept of inner education. This concept aims at further developing one’s own personality and abilities (Humboldt, 1903 / 1968, p. 286). Considering this background, Horlacher (2004, p. 421) describes education as a process of inner self-development, unfolding, and cultivation based on a shared understanding of nature and natural development. Biesta (2002) supports this understanding of the term. This author argues that education is cultivating the inner life, that is, the human mind and soul (Biesta, 2002, p. 345). Higher education institutions, by aiming at the cultivation of their students, focus on

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their holistic education. According to Raithel et al. (2009, p. 36), that is promoting a person’s independence and self-determination, which arises from intensive appropriation of and intellectual engagement with the living world. Cultivation, in the sense described, can be understood as a component of (new) humanistic education. The term cultivation, often applied in older educational definitions, refers to the development of various facets of a person to enable that person to live in a society (Raithel et al., 2009, p. 36). In such a broad definition, it fits into the tradition of true education described by an anonymous author in the 19th century ([S.N.], 1878). This author argues that true education possesses a firm center in that it unites knowledge and skills with a certain view of life and a recognized purpose in life. Every person who freely develops, pays attention to, and understands the surrounding life and takes up a secure standpoint in it, is educated in their own manner ([S.N.], 1878, p. 1). Humboldt’s (1809) central view is that education should be understood as the formation of character rather than the formation of knowledge. Character has been widely studied, especially in the psychological literature. It often occurs in the context of personality. Banicki (2017) Illustrates the basic features of both character and personality in terms of their historical, conceptual, and functional dimensions. The author shows that a conceptual distinction is only possible when personality-related facts are distinguished from character-related values (Banicki, 2017, p. 59). In this regard, there are several conceptual challenges8 that prevent a clear distinction between the two concepts (Banicki, 2017, p. 60). The psychological debate on personality and character focuses on the extent to which individuals’ actions are a consequence of character traits or personality traits (Lamiell, 2018). A clear distinction between the terms and thus an answer to this question from a psychological perspective has yet to be found. W. G. Faix and Mergenthaler (2015, p. 100 f.) adopt the two terms character and personality in the context of education. Personality is to be understood in two forms as being a personality and as having a personality. Having a personality describes “a combination of elements that gives a person unique and distinctive individuality” (W. G. Faix & Mergenthaler, 2015, p. 112). It consists of an individual’s knowledge, competencies, identity, values, virtues, temperament, and character. Therefore, it includes the ability and the willingness to take actions (W. G. Faix & Mergenthaler, 2015).

8

For the conceptual challenges in distinguishing the concepts of personality and character, see Banicki (2017).

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In comparison, being a personality is ascribed to a person by others. This ascription results from an individual’s charisma, respect, and authority. These attributes are reflected in an individual’s actions, specifically “in the fact that he takes action, how he takes this action, and the action itself” (W. G. Faix & Mergenthaler, 2015, p. 120).9 In this concept of personality, character is described as part of a personality, specifically having a personality (W. G. Faix & Mergenthaler, 2015, p. 116). It is treated as such within the frame of this thesis. Following the explanations of this subchapter, it is a higher education institution’s pivotal task to enable people to develop their own personality. The central point here is enabling Bildung, that is self-governed development toward mature, autonomous, and free persons. Implementing such an education happens through a focus on (social) interaction and the connection of general and special education or, as Faix and Mergenthaler (2015) put it: Education ... means more than the mere accumulation of knowledge; it is selfrealization and the development of all of one’s congenital and acquired traits through the active engagement with everything in one’s world. (p. 106)

With reference to this definition, the use of the concept of education in this work follows the humanistic tradition. It incorporates the definition of general education presented by Klafki (1986, 2007b) as a general point of orientation for pedagogical measures on all levels of the educational system and, thus, also applying to the field of higher education. Understanding education in higher education in term of personality development is limited. This is because higher education institutions enter individual’s lives at a late stage. In this stage, only a few aspects of a personality can be (further) developed (see, e.g., W. G. Faix & Mergenthaler, 2015, p. 123).Therefore, education in higher education institutions generally focuses on individuals further developing their personalities.

4.2.2

Education in the Practice of Elite Higher Education Institutions

Education in higher education, as already described, can be understood as enabling people’s personality development. Following the logic of argumentation in this research, an institution stands at the top of an educational hierarchy if 9

For the concepts of being a personality and having a personality, see W. G. Faix and Mergenthaler (2015, pp. 109–123).

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it offers an excellent education. Arranging an excellent education in an institution is problematic because, according to the described concept, education understood in terms of the German concept of Bildung can only be performed by oneself. Therefore, institutions do not educate individuals. Instead, they offer a frame for education and development, so that individual personalities emerge. Such a framework refers to the educational practice and the practical design of education. The following subchapter focuses on educational practice with special consideration for EHEIs and personalities emerging from them. In the German tradition, especially in the context of secondary education, the frame in educational practice is called didactics. According to Klafki (2007a, p. 158), didactics are the theory of teaching and learning in the classroom. In the Anglo-American area the terms curriculum and curriculum development are used analogously to didactics (Klafki, 2007a, p. 158).10 In this subchapter, the focus is not on didactic theories, but on the practical conception of didactics. In view of the common use of the term didactics in the context of school education and the international focus of this research, the term curriculum is used in the following discussion. Curriculum is understood synonymously with didactics, in the sense of the practical design of teaching and learning in educational institutions. To illustrate educational practice in EHEIs, the term leadership education can be adopted. As Subchapter 3.2.2 (Part II) illustrates, the term is understood as enabling the development of individual personalities who want to assume leading roles in all areas of society. A suitable concept for explaining the curriculum at EHEIs is the holistic approach to business leadership education in tertiary education developed by Kisgen (2017). This approach integrates the philosophical and ideological factors of education in higher education institutions described in the previous subchapter and places them in the frame of a specific curriculum. In addition, the concept holistically considers factors described by W. G. Faix et al. (2020) as criteria for formal leadership education programs. Kisgen (2017), in her research, adapts the didactical concept according to Klafki (2007a, p. 160). This comprises five core principles of didactics regarding secondary education. These principles refer to didactics in the sense of general didactics, subject didactics and area didactics.11 The five core principles are the educational goal, the educational content, the educational methods, the media 10

For a comparative description of U.S. curriculum research and the Northern and Central European didactic tradition, see Riquarts and Hopmann (1995) and Friesen (2018). 11 For the distinction between general didactics, subject didactics, and area didactics, see Klafki (2007a, p. 159).

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used, and the control of the educational quality (see Fig. 4.2) (Klafki, 2007a, p. 160). The two aspects of contents and methods have already been described in detail by Klafki in his earlier work on didactics in school (Klafki, 1958).

Educational objectives

Educational content

Educational methods

Educational quality

Media

Figure 4.2 Five core principles of didactics, according to Klafki (2007)

Kisgen adopts these five principles to describe the curriculum design of business leadership education. In her work, the author emphasizes the freedom and individuality of educational institutions in developing a curriculum design. The following curriculum design does not represent an ideal representation and is also not to be understood as a detailed description (Kisgen, 2017, p. 96). In her adaptation of Klafki’s didactic principles, Kisgen (2017, p. 96) adds two further core aspects of curriculum design regarding business leadership education, which are the educational ideal and funding. The principle of media is rephrased as educational setting and the principle of quality is rephrased as educational evaluation (see Fig. 4.3).

Educational ideal

Educational objectives

Educational setting

Educational content

Educational evaluation

Educational methods

Funding

Figure 4.3 Seven principles of curriculum design for business leadership education in higher education, according to Kisgen (2017). (Own illustration)

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These seven principles of curriculum design in business leadership education in higher education are based on a number of guiding questions (see Table 4.1). Table 4.1 Principles of business leadership education and underlying guiding questions, according to Kisgen (2017, pp. 97–126) Principle

Guiding question

1. Educational ideal

How can an educational framework for business leadership education in tertiary education be facilitated that enables students to develop the educational ideal that is (creative) personality?

2. Educational objectives

What are the educational goals of business leadership education in tertiary education?

3. Educational contents

Which educational contents have to be chosen for business leadership education in tertiary education, based on educational goals?

4. Educational methodologies

Which methodologies and organizational forms are best suited in business leadership education in tertiary education, based on defined educational goals and contents of education?

5. Educational settings

Which educational settings are best suited for business leadership education in tertiary education?

6. Educational evaluation

How is business leadership education in tertiary education controlled and evaluated?

7. Funding

How can business leadership education in tertiary education be funded?

It is these guiding questions that Kisgen (2017) answers in her work. Designing those core principles in the context of business leadership education practice is outlined in the following discussion. 1. Educational ideal An educational ideal is the foundation of all principles of curriculum design in business leadership education (Kisgen, 2017, p. 99). It influences all further principles. Kisgen (2017) describes the educational ideal of leadership education against the background of an education based on (new) humanistic thoughts. This educational philosophy understands self-education as the goal of education and focuses on the individual and challenges in modern society (van Bommel, 2015, p. 4). Developing autonomous people is to be considered its educational ideal (Levinson, 1999, p. 64).

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Following W. G. Faix and Mergenthaler (2015), Kisgen (2017, p. 99) describes the creative personality as the educational ideal of (business) leadership education. Such a personality is manifested through the interaction of various elements, which are: a qualifications profile characterized primarily by general knowledge, intercultural knowledge and expertise, a competence profile characterized primarily by a pronounced ability to make decisions and take action, a character that above all explores the world and seizes opportunities, an identity characterized primarily by selfconfidence, maturity and self-determination, a set of virtues and values characterized primarily by reliability, prudence and awareness and trust, tolerance, sustainability (“consciousness of one’s responsibility”), consistency and respect. (W. G. Faix & Mergenthaler, 2015, p. 133)

This definition includes the principles of leadership education, and the basic character traits of (humanistic) education in higher education institutions. It is based on an understanding of the concept of education in higher education institutions as individuals further developing their personality. This developmental process consists of both, developing toward having a (creative) personality and toward being a (creative) personality (W. G. Faix & Mergenthaler, 2015). These two facets of the concept of personality are interconnected (see Subchapter 4.2.1). Becoming a creative personality is achieved through creating value and consequently benefiting society (W. G. Faix & Mergenthaler, 2015). Therefore, this developmental process is based on an individual’s actions. These actions manifest the concept of having a personality. Through creating benefit via one’s actions, a person can achieve having a creative personality. This can lead to an ascription of being a creative personality, by an individual’s social environment. However, achieving this ascription depends on how one’s community perceives the created value (W. G. Faix & Mergenthaler, 2015, p. 217). 2. Educational objectives Within the frame of a curriculum for business leadership education at least five educational objectives can be identified: First, it is the task of business leadership education to enable learners to further develop their personality, toward the educational ideal of a creative personality. Second, business leadership education aims at providing a framework in which learners can develop their competencies. Third, its objective is enabling learners to make a valuable contribution to their associated institution or community. Fourth, business leadership education aims to enable learners to set personal and

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professional goals upon completion of such an educational program. The educational institution should enable learners to realize these goals. Finally, another goal of business leadership education is to enable learners to create and leverage a valuable network (Kisgen, 2017, pp. 97–99). These goals are supplemented in later works with reference to leadership education in general. Curricula for leadership education aim at developing people who are confident in their scientific judgement. They aim at enabling individuals to develop their performance, as a step beyond developing their competencies (W. G. Faix et al., 2020). 3. Educational content Educational content within a business leadership education curriculum can be divided into three sections: theories of leadership, formation of leadership competencies, and reflection (Kisgen, 2017, p. 103). A balanced integration of these three aspects into the curriculum is highly relevant (Kisgen, 2017, p. 104). Beyond Kisgen’s (2017) work, Clapp-Smith et al. (2019) regard reflection as an essential component of a leadership education curriculum. According to W. G. Faix and Mergenthaler (2015, p. 168), the relevance of reflection in the context of leadership education can be found in the fact that, in addition to practical experience, it enables the development of individuals’ skills. Reflection is, therefore, a prerequisite for the second core aspect of educational content in business leadership education, which is the formation of leadership competencies and subsequently the formation of performance. 4. Educational methodologies Educational methodologies refer to “how teaching and learning could be carried out” (Kisgen, 2017, p. 107). In the context of business leadership education, Kisgen (2017, p. 108) focuses on two methodological components, which are inquiry-based learning (IBL) and competency-based learning (CBL). Both forms of learning move away from the model of memorizing knowledge. Regarding this model, Zorek et al. (2010) use the term bulimic learning. There is no long-term retention of knowledge and skills for practice in this approach (Zorek et al., 2010, p. 1). A clearer criticism of the model of memorization is provided by Freire (1993) in his work, Pedagogy of the Oppressed, with the words, “apart from the praxis, individuals cannot be truly human. Knowledge emerges only through invention and re-invention, through the restless, impatient, continuing, hopeful inquiry human beings pursue in the world, with the world, and with each other” (p. 72). According to this author, education happens within the confrontation of individuals with their natural and social environment. Moreover, such education is a

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prerequisite for being truly human (Freire, 1993). Beyond moving away from this criticized form of learning, IBL and CBL have in common that learning should take place on the foundation of and linked to scientific research results. Wildt (2009) uses the term research-based learning. Research-based learning connects the two core elements of higher education institutions, teaching, and research (Wildt, 2009, p. 6). To explain IBL, Kisgen (2017, p. 110) uses the experiential learning model. This combines the four elements of experience, reflection, conception, and experimentation (Kolb, 1984, p. 21). The basis for this is the concept of experiential learning according to Dewey (1910). The foci of IBL and business leadership education are, therefore, learning through testing and experience. According to Kisgen (2017, p. 111), CBL as a second component of business leadership education must fulfill nine criteria. The author adopts six criteria from one of Tippelt’s (1979) earlier works on project-based studies, which is one form of CBL. In their generalized form and supplemented by three additional criteria, they illustrate the final nine criteria12 of competency-based learning (Kisgen, 2017, p. 112). These criteria are also integral components of business leadership education as an experiential education model. In summary, the criteria focus on higher appreciation and preferential integration of scientific characteristics compared to urgencies and desires from practice, the consideration of the individuality of learners and teachers, and the integration of real-world problems into the curriculum (Kisgen, 2017, p. 112). The latter includes problems that are characterized by complexity, uncertainty, and openness in the outcome and, thus, participate in the initiation of the competence development process (Kisgen, 2017, p. 113). It can be added that leadership education integrates group work and online learning (W. G. Faix et al., 2020). 5. Educational setting The educational setting in business leadership education, following Kisgen (2017, p. 119), is based on a social constructivist approach to education (see Subchapter 4.2). The goal is to develop the learning individuals from passive listeners to active learners (Chandler & Teckchandani, 2015). Kisgen (2017, p. 119) is drawing here on Huang’s (2002) research in which the author describes six basic, social constructivist-oriented principles for shaping educational settings in distance education for adults (Huang, 2002, pp. 32–34). Following this approach, the educational setting in business leadership education should be interactive and 12

For a detailed look at the nine criteria of competency-based learning, see Kisgen (2017, p. 112).

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collaborative. Teachers should provide guidance and orientation and focus on the real problems of the learners. In addition, the setting of business leadership education is characterized by the fact that the learners are at the center and a high quality of education is guaranteed (Huang, 2002, pp. 32–34). According to Kisgen (2017, p. 119), the educational setting refers to the environment in which education takes place. The previous explanations refer primarily to the design of education by teachers and institutions. Beyond the social environment, the environment can also be understood as the actual physical or digital environment. For this reason, the place of learning should be added at this point. In leadership education, education should be provided in a flexible form. Guidance and support from teachers should therefore take place both digitally and physically. Learners can then proactively shape the place of education according to their own needs. In this form, the educational venue can be seen as conforming to the presented principles of the educational setting (e.g., learner centered and facilitating learning). 6. Educational evaluation Evaluation in education tries to answer certain questions about various educational instruments, such as processes, personnel, procedures, and programs (Scriven, 1972, p. 61). With reference to formal educational programs, the goal of evaluations can be described as ensuring the programs’ quality (Ditton, 2018). For the design of evaluations in the context of educational programs in higher education, B. Schmidt et al. (2010) developed a three-level model consisting of the macro, meso, and micro levels. The micro level of evaluation focuses on teaching and learning processes, that is, on courses and individuals. The meso level includes institutional resources, issues related to curriculum and faculty, and personnel. Finally, the macro level represents the transition of graduates leaving the institution (B. Schmidt et al., 2010, p. 100). Kisgen (2017) includes these three levels in her concept of curriculum design in business leadership education. The author points out that the three-level evaluation of business leadership education is a continuous process that focuses on the improvement of the curriculum. Therefore, the evaluation of a business leadership education curriculum is formative (Dolin et al., 2018, p. 55; Scriven, 1972). 7. Funding The seventh core aspect of the concept for designing business leadership education is funding (Kisgen, 2017, p. 125). Here, a central component is public funding of educational institutions (Altbach et al., 2009; Altbach & Teichler, 2001). Due to a variety of developments, such as the expansion of education

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(Altbach et al., 2009), Kisgen (2017, p. 126) points to the need for alternative ways of financing (business) leadership education in higher education. Warren and Bell (2014), in the context of EHEIs in the United Kingdom, describe a development toward increased funding by private philanthropists as a result of declining public funding. Orkodashvili (2007) conducted a comparative analysis of revenue sources in the elite higher education sector of the United Kingdom and the United States. The author describes the following revenue sources as examples: government funding, tuition, loans, endowments, donations, revenue from real estate investments and land development, research funding and entrepreneurial initiatives, and trademark and patent rights (Orkodashvili, 2007, p. 1). The sources of funding mentioned may vary depending on the nation and the sponsorship of an institution. H.-D. Meyer and Zhou (2017, p. 836) show that European institutions increasingly rely on government funding. A special feature of elite institutions in the United States is a high proportion of revenue they generate by reinvesting their endowment assets (H.-D. Meyer & Zhou, 2017, p. 836). In the People’s Republic of China, on the other hand, the core funding of elite institutions is provided by the state (C. Xie & Teo, 2020). Kisgen’s concept focuses on business leadership education as a sub-area of the overarching concept of leadership education. This sub-area focuses on the education of leaders in business and also on educational institutions and programs specifically designed for this purpose in tertiary education, for example, business schools. However, the seven principles of curriculum design can also be applied to general leadership education, as W. G. Faix et al. (2020) illustrate in their research. Other authors have developed their own concepts for leadership education. Bloomquist et al. (2018), for example, present a concept of interdisciplinary leadership education. This adopts various aspects that Kisgen already presents, such as real-world problems, the relevance of dealing with ambiguity and uncertainty, and the readiness for lifelong learning (Bloomquist et al., 2018, p. 60). ClappSmith et al. (2019) in their model refer to the aspect of identity. They describe a leadership education concept that focuses on the identity of leaders. Reflection is a key element of this concept (Clapp-Smith et al., 2019, p. 28). Both identity and reflection are considered integral in Kisgen’s (2017) concept. As part of the practical application of leadership education, Grunberg et al. (2019) present their own educational framework with regard to leadership education in the health sector. The focus here is on the interaction of various elements at four different psychosocial levels of interaction. This “FourCe-PITO concept” (Grunberg et al., 2019, p. 646) includes the elements of character, competence,

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context, and communication, which interact at the four levels of personal, interpersonal, team, and organizational (Grunberg et al., 2019, pp. 646–648). The goal of this framework is to support the design and evaluation of formal leadership education programs (Grunberg et al., 2019, p. 648). Compared to Kisgen’s (2017) concept, which targets the pedagogical perspective of education, this framework focuses on a psychosocial point of view (Grunberg et al., 2019). Kisgen (2017) draws on a broad theoretical and conceptual foundation in her work, while Grunberg et al. (2019) incorporate practical insights into their framework. Due to this theoretical basis, the holistic approach, and the pedagogical focus, this research refers to Kisgen (2017) while conceptualizing education in EHEIs.

4.3

Research in Elite Higher Education Institutions

In addition to higher education institutions’ task of education, a second central task of research should be considered. It represents another factor that enables the elite status of institutions. The following subchapter deals with research. At the beginning, a general definition of the term is provided. Subsequently, research is applied to the context of higher education institutions. The impact of research is explained with reference to these institutions. Finally, the exemplary practical design of research with reference to the elite status of higher education institutions is considered. The following subchapter does not explicitly deal with research from a perspective of philosophy of science.13 The perception of research by researchers14 and a differentiation according to disciplines and their distinct characteristics15 are not addressed. The focus is on the concept of research in general and how it is shaped in the practice of higher education institutions. Research is a central element of every scientific discipline. It describes the process of elaboration and accumulation of new knowledge. “Research is a process of steps used to collect and analyze information to increase our understanding of a topic of issue” (Creswell, 2012, p. 3). While this author understands research as 13

For a discussion of the concept of research in terms of the construction of scientific facts, see Latour and Woolgar (1986). 14 For a comprehensive discussion of the concept of research and how it is viewed by active researchers, see Åkerlind (2008). 15 For an elaboration of the distinct characteristics of research in different disciplines, see Becher (1994).

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a process, Brew (2002, p. 3) extends this definition with the factor of systematization. According to the author, research is “a systematic process for understanding aspects of our [humans’] experience” (Brew, 2002, p. 3). This broad definition of the term underlies almost every activity that focuses on exploring. For a clear definition in the context of this thesis, the term research must be further narrowed down. The OECD (2015, p. 44) views research in the context of classifying terms for official statistics as research and experimental development. This is defined as the systematic and creative development of knowledge. The organization thus adds another facet to research’s objective as stated by Creswell (2012) and Brew (2002). The focus is on expanding the knowledge base with respect to people, culture, and society. In addition, the novel application of existing knowledge is also considered part of research (OECD, 2015, p. 44). Based on both conceptual elements of knowledge production and application, the concept of research from this perspective is understood as research activities in general. This refers to the activities of an individual and the activities of an institution or several institutions. To identify research in the context of all activities of an entity, the OECD proposes five central criteria. Activities are considered research, if they are new, creative, uncertain, systematic, and transferable and reproducible (OECD, 2015, p. 45). Regarding research activities, the adjective new describes the goal of creating new knowledge. In this framework, knowledge is understood as new depending on the context of the research activity. Thus, knowledge can be completely new to society. However, it can be equally new to the organization carrying out the research activity (OECD, 2015, p. 46). The creativity aspect of a research activity focuses on its originality. Originality presupposes a human input to the activity. Thus, a research activity is creative if a researching person contributes original impulses to it (OECD, 2015, p. 47). The uncertainty of a research activity does not refer to the uncertainty of its execution. Rather, other aspects of the activity are in the foreground. First, during an activity that can be called research, there is uncertainty about its outcome. Second, other factors, such as the time and cost required to achieve the expected research objectives, are uncertain (OECD, 2015, p. 47). An activity is systematic in that it proceeds as a planned process. In this context, planning refers to the documentation of all steps. Evidence is provided on both the structure of the process and the results of that process (OECD, 2015, p. 47). The final criterion for classifying an activity as research is transferability or reproducibility. The results of the activity (new knowledge) should subsequently be made usable and find application in practical situations. It is relevant that they

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can be reproduced by other researchers in their own activities. Fulfilling both subcriteria is not a necessity. Nevertheless, activities should comprise at least one or both to be eligible for classification as research (OECD, 2015, p. 48). After classifying an activity as research, the OECD (2015, p. 50) distinguishes between different types of research activities as basic research, applied research, and experimental development. Basic and applied research differ in relation to the transferability of research activities. The former represents theoretical research. These activities do not focus on the transferability of research results, but on their creation and reproducibility. The latter is carried out with a clear practical goal. Applied research creates new knowledge with this goal in mind. Its focus lies primarily on transferability of research results into practical application (OECD, 2015, pp. 50–51). Experimental development differs from these types of research in that it does not focus on the production of completely new knowledge, but rather on supplementing the knowledge gained. The goal here is, for example, the improvement of processes through systematically, experimentally developed supplementary knowledge (OECD, 2015, p. 51). Beyond the classification of activities as research and the different types of research, the organizational context of research needs to be considered. One reason for this is that organizational contexts can influence the criteria for a research activity. Amabile et al. (1996), for example, show that a person’s work environment, or the person’s perception of the work environment, has a direct influence on this person’s creativity. Bazeley’s (2010) research supports this statement. In his work on the research performance16 of individual researchers, the author shows that a supportive institutional environment can foster such performance (Bazeley, 2010, p. 899). Regarding research activities in the context of higher education institutions, the term academic research is used in the literature. The edited volume by Brew and Lucas (2009, p. 4) illustrates that this field is only sporadically studied and existing publications are often not considered in relation to each other. The authors’ work attempts to systematically identify existing knowledge on the concept of academic research. They show that the focus of academic literature on academic research is on politics and culture that influence academic research, and on the perception of the concept by individual researchers (Brew & Lucas, 2009, p. 4). The authors perceive academic research as “what is understood by research, how research 16

The term “research performance” describes the output of individual researchers. This includes, as Falola et al. (2020, p. 5) illustrate, research productivity, collaboration, citations, industry partnership, teaching, transfer of knowledge, mentoring, and community service. For research performance, see Avital and Collopy (2008); Bazeley (2010).

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operates, how it is communicated and how it is enacted and experienced within the higher education context” (Brew & Lucas, 2009b, p. 4). Academic research in comparison to general research is, therefore, research within the frame of higher education institutions, with the help of scientific methods. It should be noted that, following Neumann (1993), there is no consensus in the relevant scientific circles as to what research and, thus, also what academic research means. The understanding of the term is strongly dependent on the underlying research context (e.g., the discipline) and the philosophical and political views of the various stakeholders of higher education institutions (Neumann, 1993, p. 97). Following the previous paragraphs on defining the term research, two central factors are relevant for this thesis. These factors are research activities that can be integrated into the term academic research and institutional structures that promote academic research. Against this background, it is again useful to draw on the perspective of the OECD (2015). Here, activities classified as research in higher education institutions are illustrated. According to this classification framework, research activities in higher education institutions can be divided into three classes concerned with the execution of research activities, the supervision of research activities, and the continuing education of researchers. The former refers to all research activities of the academic staff of higher education institutions (OECD, 2015, pp. 266–267). The OECD (2015, p. 275) divides academic staff of a higher education institution into four levels (A to D), with the highest level A, covering professors and chairs, and the lowest level D, describing junior researchers. These are either conducting a doctorate at the time of the research or have not started one (OECD, 2015, p. 275). Activities are, thus, considered research if they are conducted at doctoral level or above, and in some cases at master’s level as well (OECD, 2015, p. 266). The second class of research activities comprises the supervision of research projects by researchers at the higher levels. This includes supervision of doctoral projects and other nonstudy research projects (OECD, 2015, p. 266). In the third class, the OECD (2015, p. 267) illustrates scientific, researchrelated continuing education of academic staff as research activities in higher education institutions. These include both self-study by staff and research-related training, and conference and seminar attendances (OECD, 2015, p. 267). Prerequisite in all cases is that the above-described criteria for determining research activity are fulfilled. Regarding the promotion of academic research, the environment that higher education institutions offer to researchers should be examined. This can be done at the level of research in general or at the level of its components. For example,

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Amabile et al. (1996) illustrate an approach to promoting the component creativity. In this research, the focus is on research in general. It does not explicitly address the promotion of all components of research, but rather current research findings on an institutional environment that promotes research. The literature speaks of institutional support strategies. Objective of such strategies is, “institutional support on the effectiveness of job performance of faculty in universities” (Falola et al., 2020, p. 5). Various measures are used to support and improve research performance. In this context, Bazeley (2010, p. 899) describes three general environmental factors that support researchers in higher education institutions, which are time, funding, and resources. Institutions should provide researchers with freedom concerning time, financial security, and other necessary resources to support research. More specifically, Falola et al. (2020, p. 5) use the example of public universities in Nigeria to show that institutions should provide research grants to support research. In addition, they should provide support for the distribution of research results, in terms of conferences and publications. Other infrastructural features mentioned by the authors are effective e-learning platforms and support for researchers in teaching (Falola et al., 2020, p. 5). Concerning the institutional research infrastructure, Huenneke et al. (2017, p. 423) add two approaches for supporting research. They describe the conventional approach of funding research through expanding research capacities, that is, hiring new academic staff. The authors propose an alternative approach of establishing multidisciplinary research clusters (Huenneke et al., 2017, p. 423). Torres Zapata (2019, p. 39) supports this approach, which focuses on a high diversity of researchers. According to this author, improved research performance can be created through involving researchers with diverse characteristics into research activities. These characteristics are, for example, age, gender, or educational level (Torres Zapata, 2019, p. 39). Reference can be made to the levels of academic staff defined by the OECD (2015). Research teams should thus be assembled across various levels of academic staff (A to D). Lucas (2009) links the operational and infrastructural aspects of supporting research with the socially oriented aspect. The author summarizes the former under the term research management and describes how institutional research management should be set up to promote a certain research culture (Lucas, 2009). As Lucas (2009, p. 68) elaborates, the concept of culture is ambivalently discussed, especially in the context of higher education institutions. For this reason, the author defines the concept of research culture through a framework established by Tierney (2008, p. 28), which aims to describe organizational culture.

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This framework consists of the key elements of mission, environment, leadership, strategy, information, and socialization (Tierney, 2008, p. 28). According to Lucas (2009, p. 72), an enabling and supportive research culture, in contrast to a power-centered one, promotes the performance of researchers in higher education institutions. To promote research, the key elements of a research culture should be aligned with the two characteristics of supportive and enabling. Research activities in higher education institutions and the promotion of these activities achieve an impact on different levels of society. In his work on the impact of educational research in South Africa, Wolhuter (2018, p. 5) conceptualizes this impact alongside the categories of scholarly impact and practical impact. Scholarly impact describes the influence of research results in science, for example, in terms of citation frequency or the creation of a foundation for further research. Practical impact includes the effect of research results to improve practice (Wolhuter, 2018). This categorization illustrates how the general impact of research includes research results and their use in science and practice. The impact of research can be understood first as the production of knowledge and second as its aggregation and processing, as Temple (2018) describes. Beyond these three impacts, Torres Zapata (2019, p. 30) adds knowledge dissemination and promotion as part of the impact of research in higher education institutions. In this thesis, aggregation, processing, dissemination, and promotion of knowledge are understood as substructures of the central impact of research, which is knowledge production. For this reason, production of knowledge is placed in the foreground in the following discussion to specify the research impact of higher education institutions. Knowledge production describes the creation of new knowledge. This can be achieved with the help of various approaches. Gibbons et al. (2010) describe two central approaches to knowledge production, which are the traditional and the modern approaches. They denominate these approaches as Mode 1 and Mode 2 knowledge production (Gibbons et al., 2010). According to the authors, Mode 1 knowledge production is characterized by disciplinary thinking. In contrast, Mode 2 knowledge production takes place in a broad, transdisciplinary context (Gibbons et al., 2010, p. 1). Table 4.2 illustrates the key distinctions between the two approaches. In the context of higher education institutions, Mode 1 knowledge production is the predominant approach. However, various social developments necessitate

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Table 4.2 Knowledge production Mode 1 and Mode 2 in comparison, adapted from Gibbons et al. (2010, p. 3) Mode 1—traditional

Mode 2—modern

Context

Academic interests of specific communities

Application of knowledge

Disciplinarity

Disciplinary

Transdisciplinary

Character

Homogenous

Heterogenous

Organization

Hierarchical

Heterarchical

the increased use of the modern approach and a general development toward Mode 2 knowledge production (Gibbons et al., 2010, p. 11).17 Following this subchapter’s discussion, deductions concerning an elite status can be made from the general research task of higher education institutions. An institution can attain this status through conducting excellent research and achieving a stronger effect for its research in society in comparison to other institutions. Excellent research within this frame is understood as implementing the described research activities. The creation of excellent framework conditions and an excellent research environment determine excellent research. Torres Zapata (2019, p. 29) refers to promoting scientific excellence. Beyond such excellent research, an institution can achieve an elite status by producing, aggregating, processing, disseminating, and promoting knowledge in an excellent manner. It, thus, achieves an excellent impact through its research. This excellent impact must be viewed as superior to that of comparable institutions. Knowledge is produced both traditionally, in Mode 1, and in modern fashion, in Mode 2.

4.4

Innovation in Elite Higher Education Institutions

Beyond education and research, society ascribes a third central task to higher education institutions. In the literature on the missions of higher education institutions, the “third mission” (Berghaeuser & Hoelscher, 2020; Stolze & Sailer, 2021) describes their contribution to the social, economic, and cultural development of the region in which they operate (Compagnucci & Spigarelli, 2020, p. 1). Enders et al. (2011, p. 82) outline a deliberate involvement of higher education institutions in society through various contributions. 17

One approach to integrating a combination of Mode 1 knowledge and Mode 2 knowledge into the practice of higher education institutions can be found in Fischer et al. (2011).

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Higher education’s third mission originates from the ongoing change of society from an industrial to a knowledge society and, subsequently, as Audretsch (2014) illustrates, to an entrepreneurial society. During this development, this further central task was formed on the foundation of the changed expectations of society and politics toward higher education institutions (Stolze & Sailer, 2021). As a result of these changes, the role of higher education institutions in society changed. Consequently, the scope of contributions within the third mission exceeds those of the two traditional tasks of education and research. Specific contributions, within the frame of the third mission, are described by various authors. Berghaeuser and Hoelscher (2020) refer to transfer of technology, promotion of lifelong learning, and social engagement as specific forms of contribution. Compagnucci and Spigarelli (2020) also highlight these three forms. However, the design of the third mission of higher education institutions cannot be comprehensively described since it depends on the regional context in which the institution operates (Berghaeuser & Hoelscher, 2020, p. 58). The design of the third central task of higher education institutions is not addressed in this research. This study focuses on a description on a more abstract level, especially referring to the objective of the third mission, of contributing to the development of regions in different areas (Compagnucci & Spigarelli, 2020). The concept of innovation is used to explain the third central task of higher education institutions. This functions as an overarching sphere that includes the aspects of the third mission of higher education institutions. In the literature, it is already referred to as their “innovation mission” (Etzkowitz, 2014, p. 224). In this subchapter, the concept of innovation is considered in further detail. An exemplary design for this task in an EHEI is described.

4.4.1

The Concept of Innovation

Following the structure of the previous subchapters, this third central task of higher education institutions is also described alongside the two factors of process and impact. At the beginning, a definition of the process of innovation is presented, followed by a discussion of the impact of this process. The term innovation originated in the field of economics. It was coined by Schumpeter’s (1961) work on innovation in economic organizations. In that context, an innovation is understood as creating something new, which, for example, leads to higher production yields or lower costs in an economic organization.

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According to that author, this is achieved through various forms of innovation. The focus is on innovation regarding products, processes, and organizational structures (Schumpeter, 1961). Literature on the concept of innovation in the context of organizations from this millennium include other forms of innovation. Johannessen et al. (2001), for example, developed six levels on which innovative activities can be measured. These levels imply different forms of innovation. These authors, like Schumpeter (1961), describe product innovation as one such form. They go on to describe innovation of services, production methods, market entries, sources of supply, and organizational methods (Johannessen et al., 2001). Governmental institutions, such as the OECD (2005), refer to comparable forms of innovation in their elaborations. Here, innovation is understood as: “implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations” (OECD, 2005, p. 46). This definition reflects three of the forms of innovation mentioned above, which are innovation in products, processes, and organizational methods. An addition here is the concept of innovation in marketing methods. The preceding descriptions show that the concept of innovation in an organizational context is based on a similar, but not a uniform understanding. Against this background, W. G. Faix et al. (2015) point out that no generally applicable understanding of the term innovation exists. The authors state that instead of delivering a generalizable definition of the term, the constituent elements of an innovation can be described. The three key elements of an innovation in this framework are the nature, the social context, and the type of innovation (W. G. Faix et al., 2015, p. 45). The latter describes the forms of innovation already pointed out. The nature of an innovation is explained by W. G. Faix et al. (2015) and Johannessen et al. (2001) on the basis of the concept of novelty. The authors draw on central guiding questions. W. G. Faix et al. (2015) show for whom the new is new and how new the new is. Johannessen et al. (2001) add the question of what is new. This research seeks to answer these three questions but to detach them from the context of economic organizations in which they were originally defined. For this reason, they are described below on a more abstract level. This step follows Rammert’s (2010, p. 24) notion that a certain degree of abstraction is useful when defining the concept of innovation to do justice to its many facets. To answer the question of what is new, a distinction between the terms invention and innovation is useful. This distinction can already be found in Schumpeter (1961). It is also adopted by Horn (2005). A central, distinctive element of

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both terms is that innovation is implemented, while inventions are not necessarily implemented. Therefore, inventions are a mental construct, while innovation involves the practical step of creating something new. In this sense, an invention can function as the basis of an innovation. However, it does not represent a precondition for innovation, as Schumpeter (1961, p. 91) already highlighted. W. G. Faix et al. (2015, pp. 45–46) answer the question for whom the newness of an innovation is new from two perspectives, the producer’s perspective and the recipient’s perspective. The authors further discuss which central criterion of innovation is perceived as new by these two groups. Following W. G. Faix et al. (2015, pp. 47–49), producers perceive the newness of an innovation as its dissemination and potential for dissemination (e.g., global or regional). From the perspective of recipients, the newness of an innovation is viewed as the degree to which it represents an improved alternative to the old. W. G. Faix et al. (2015) conceptualize how new something is with the help of the degree of change that an innovative activity entails. According to the authors, innovations can create something radically new or they can change the existing situation (W. G. Faix et al., 2015, p. 49). They describe the concepts of radical and incremental innovation, whereby “radical innovations come across as creative destruction; incremental innovations come across as creative … change” (W. G. Faix et al., 2015, p. 49). Within the frame of their conceptualization of the nature of an innovation, the authors also point out the difficulty of measuring the degree of change (radical or incremental) of an innovation. The reason for this difficulty is that its perception is influenced by the (social) context of an innovation. Regarding higher education institutions, the social context is not to be understood exclusively as part of innovation as a process. Rather, it is an overarching element that is located between the process of innovation and its impact. The characteristics and definitions of innovation described above illustrate the quintessence of the process of innovation in higher education institutions. Following these explanations, innovation is to be understood as creating something new or significantly improving something old. The impact of innovation in higher education institutions can be discussed by first considering the general impact of innovation. According to C. M. Christensen (1997) and C. M. Christensen and Overdorf (2000), innovation can result in disruptive or evolutionary change. It creates new things by destruction or improves old things by evolution. Reference can again be made to the concepts of radical and incremental innovation presented by W. G. Faix et al. (2015). Resulting from these two types of the innovation process is positive change. However, the change itself is again a process that has an impact.

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The impact of innovation should, therefore, not be considered in relation to the initiated change process, but to the result that emerges from it. Schumpeter (1961) mentions economic progress as the result of innovation. In the broader context of innovation this research focuses on, the term is investigated beyond this economic focus. Frank and Meyer (2020), for example, speak of innovation as an integral component for progress in general. While the aforementioned authors indicate that innovation is a cause for progress and change, Gopalakrishnan and Damanpour (1997) point out that innovation can also act as an instrument for dealing with progress and change. In this thesis, the former understanding of innovation as a process and progress as its impact is used. Both concepts encompass several facets of society. For this reason, the overarching concept of societal innovation as described by Rammert (2010, pp. 40–41) is used to make innovation tangible as a task for higher education institutions. Societal innovation addresses innovation within the diverse facets of society (Rammert, 2010, p. 21). Analogously, the impact of innovation consists of progress in different areas of society thus it is considered under the umbrella term of societal progress. The different areas that societal progress covers can be described as the areas in which innovation unfolds its effects. From an organizational perspective, macroeconomic environmental analysis can be used to illustrate these areas. Macroeconomic environmental analysis originates in business administration, specifically from its subarea of strategy development (Schönert, 1997, pp. 25– 58). Such an analysis investigates factors within the external environment of an organization to develop strategies for coping with such factors. According to Kotler (2002, p. 46), factors within the external environment of an organization can be classified in the four dimensions of demographiceconomic, technological, political-legal, and socio-cultural. These dimensions are also adopted by Ginter and Duncan (1990). These dimensions of an organization’s macroeconomic environment are combined within the concept of STEEP analysis, where the acronym stands for social, technological, economical, ecological, and political (STEEP). This concept is also used under different names, for example PEST, PESTLE, STEP or PESTE analysis (Brown, 2007, p. 211). In relation to the impact of innovation, these areas can be considered because innovations in all those societal areas potentially result in progress (see Fig. 4.4). Regarding the STEEP impact fields of innovation, related innovation terms can be found in the scientific literature. These are social, technological, economic, ecological and political innovation, which are directly related to each other. In addition to the understanding of innovation from an economic perspective, in which economic innovation is already described, social innovation is a widely

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Social progress

Technological progress

Political progress

Innovation

Ecological progress

Economical progress

Figure 4.4 Areas impacted by societal innovation. (Own illustration)

considered concept (Howaldt & Jacobsen, 2010a, 2010b; Nicholls et al., 2015). In their work, Howaldt and Jacobsen (2010b) describe social innovation as a postindustrial evolution of the concept of innovation, which is traditionally viewed as economic. Focus lies on the idea that beyond this type of innovation, specific social innovation also promotes social progress. Especially in social scientific innovation research, the concept of social innovation has emerged (Howaldt & Jacobsen, 2010a, pp. 9–10). A differentiated conceptual understanding of social innovation is provided by Howaldt and Schwarz (2010):

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A social innovation is an intentional, purposeful reconfiguration of social practices in specific fields of action or social contexts, emanating from specific actors or actor constellations, with the aim of solving or satisfying problems or needs better than is possible on the basis of established practices. (p. 89)

This definition emphasizes that social innovation is distinct from the traditional understanding of innovation. In contrast, social innovation focuses on a social field of activity. Here, both the object of social innovation (social practices18 ) and its objectives (to better satisfy needs or to better solve problems) are specified. Beyond social innovation, technological innovation has been a central concept in innovation research for several decades (e.g., Utterback, 1971). Like economic innovation, it is often considered in the context of innovation in the traditional sense. Howaldt and Jacobsen (2010a, pp. 9–10) state that technological innovation was originally understood as a driver of economic dynamics. Beyond that, technological innovation can also underlie innovation in other STEEP impact fields since novel technologies usually have an impact in various areas. Political innovation can be understood as innovation promoted by politics. The term puts innovation policy in the center of attention. Additionally, political innovation can be understood as innovation in the field of politics. It is, therefore, the innovation in the politics field of action. Considering both facets, the term political innovation describes the promotion and implementation of innovation by and in politics (Mai, 2014). It is to be understood as a cross-sectional area of social, technological, and economic innovation, with a special focus on the area of politics. Another cross-sectional area in this sense is ecological innovation. This is also referred to as sustainable innovation (G. Gordon & Nelke, 2017) because ecological innovation generates progress with the objective of environmental sustainability. According to the European Commission (2019), the focus of environmental innovation lies in reducing environmental pollution and strengthening the regenerative capacity of the planetary ecosystem. Ecological innovation can be understood, like political innovation, as innovation with a special focus. In this case, the focus is on the area of sustainability and environmental issues.

18

The term social practices is used by the authors to refer to a tendency in sociological theory to develop toward practice orientation in the 20th and 21st centuries. For the concept of social practices, see Reckwitz (2003).

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Elite Higher Education Institutions and Innovation in Practice

The previous subchapter shows that one of the central tasks of higher education institutions is to promote and shape the process of societal innovation. The objective is the creation of societal progress. Like Subchapter 4.2.2 does for education, the following subchapter shows how the third task is exemplified in the current practice of higher education institutions. Further reference is made to the elite status and elite position of institutions. Tierney and Lanford (2016) describe three aspects that frame innovation in the practice of higher education institutions: 1. Innovative research 2. Innovative pedagogical interventions 3. Innovative organizational structures. On a more abstract level, as considered in this research, these three aspects can be summarized within the frame of two concepts. First, institutions can be innovative, that is, they can shape and execute innovation themselves. Second, they can promote innovation. For the former, the term institutional innovativeness is used in the following discussion. The latter is understood by the term promoting innovation. The practical design of institutional innovativeness includes innovative research, innovative organizational structures and innovative pedagogical approaches (Tierney & Langford, 2016, p. 31). At its core, it consists of institutionally created innovations which are based on new knowledge and its transfer into practice. Promoting innovation strongly refers to the structures and environments of higher education institutions. Utterback (1971) describes these two factors and their interaction playing a decisive role in the promotion of innovation in (economic) organizations. Damanpour and Schneider (2006) complement this statement by describing organizational characteristics that also have a strong influence on the execution of innovation in (public) institutions. These complementary statements show that within the frame of organizational structures, a smooth transition between institutional innovativeness of higher education institutions and promoting innovation in higher education institutions can be observed. When considering the organizational structures of higher education institutions in relation to innovative and innovation-promoting institutions the concept of

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the entrepreneurial university can be used. This concept has been discussed in the scientific literature for more than 40 years (Etzkowitz, 1983). As the name suggests, the focus is not on innovation itself, but on entrepreneurship. Thus, the literature draws a link between the terms innovation and entrepreneurship. Schumpeter (1911, p. 172) has described this link by stating that an entrepreneur implements new combinations. This connection between innovation, understood here as the creation of something new, and entrepreneurship is shown again by Schumpeter in one of his later works, when he describes how it is not the inventor who is necessarily innovative, but the entrepreneur who transforms the invention into an innovation (Schumpeter, 1961, p. 93). Entrepreneurship, understood as described in Part II, Subchapter 3.2.1, can thus be understood as a practical vehicle for shaping innovation. Entrepreneurship in the context of higher education institutions refers to a strongly market-oriented and economics-oriented viewpoint on innovative activities. This is stressed by Mars and Rios-Aguilar (2010) in their content-analytical study on the use of the term entrepreneurship in academic journals on higher education. Despite this limitation, the entrepreneurial university-concept can be used to illustrate the exemplary practical design of an innovative higher education institution. Etzkowitz et al. (2000) place the concept of the entrepreneurial university within the frame of their triple helix model.19 In this framework, societal developments cause changes in society’s demands for higher education institutions. These, in turn, command an evolution of the traditional research-based institution (Etzkowitz & Zhou, 2008, p. 627). For Etzkowitz (2013, p. 507), the entrepreneurial university model represents this evolution. The starting point while explaining the model is the author’s understanding of the institution as an entrepreneur. He describes three phases of institutional development from research university to entrepreneurial university. In the first phase, the institution must adjust its strategic direction. It begins to diversify its sources of income, for example, by expanding on existing government funding to include private donors. In the second phase, the institution takes an active role in the commercialization of its intellectual output. It creates its own technology transfer capacities for this purpose (Etzkowitz, 2013, p. 488). This statement can be 19

The triple helix model describes the frame that is relevant for higher education institutions in the present and in the future to innovate and generate social progress. The focus is on the relationships among institutions, industry, and government. For a comprehensive description of the model, see Etzkowitz and Leydesdorff (1998).

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extended to include capacities for knowledge transfer. In the third phase, the institution aims to improve its regional innovation ecosystem. This is usually achieved through cooperation with local industry and the regional government. According to the author, these three phases are prerequisites for the emergence of an entrepreneurial university, but are not fixed to the described sequence (Etzkowitz, 2013, p. 488). The objective of these three phases is creating an institution that possess four central, interrelated characteristics. These are to be understood as constituent elements of the entrepreneurial university model (Etzkowitz, 2013, pp. 491– 492). These elements are interaction, independence, hybridization, and reciprocity (Etzkowitz, 2013, p. 491). Interaction refers to embedding the higher education institution into its social framework. The output of the institution should, therefore, achieve an effect in society. According to Etzkowitz (2013, p. 491), this embedding is achieved through cooperation with industry and governments. The element of independence refers to the relative independence of an institution from its stakeholders. This is created, for example, through diversified funding sources. Both elements, interaction and independence, are combined in the element of hybridization. Here an institution creates hybrid organizational structures to be able to revert to strong cooperation with industry and governments despite secured relative independence. In such an organization, industry and government can act as cooperation partners and potential providers of funding. The model’s final element refers to continuous adaptation of internal structures, both on the side of industry and government, and in the educational institution itself (Etzkowitz, 2013, p. 492). Compared to the research university, the focus of the entrepreneurial university lies on embedding the institution in its social context through directly connecting it with regional industry and government. Etzkowitz and Zhou (2008, p. 630) illustrate exemplary measures for embedding, for example, through technology patents, consulting services of the educational institution for economic organizations, spin-offs, or including entrepreneurship education20 within their own structures. This model, as mentioned above, relies heavily on an economic perspective. In view of the overarching definition of the concept of innovation in this thesis,

20

Following Fellnhofer (2019, p. 29) entrepreneurship education can be understood in a wide sense as developing personal qualities, attitudes, and skills relevant for entrepreneurship or in a narrow sense as specific training to practically execute an entrepreneurial project. For entrepreneurship education, see Fayolle and Gailly (2008); Fellnhofer (2019); Zaring et al. (2019).

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the model can be enriched by means of further elements and further developed in the direction of a general model of an innovative higher education institution. For example, the integration of the institutions into society through cooperation in the social sector can be added. The measures mentioned can be implemented with a view to social organizations, such as associations or nongovernmental organizations. In addition, the promotion of social entrepreneurship21 can be integrated. With reference to the elite concept within this thesis, this subchapter illustrates a third form of elite higher elite education institutions, in addition to excellent education and excellent research. Institutions can perform the task of innovation excellently to achieve a top position. This is achieved through an excellent design of the framework conditions for innovation (promoting innovation) and their own institutional innovativeness. Such a design can be observed in contemporary educational practice, for example, within the framework of the entrepreneurial university model.

4.5

Complementary Characteristics of Elite Higher Education Institutions

In the previous subchapters, the constitution of EHEIs was described based on the central tasks of higher education institutions. In the following discussion, complementary institutional characteristics of these EHEIs (see Fig. 4.5) are described. This is based on extensive research on the literature regarding elite education institutions of different nations. In their original context, these characteristics were examined considering distinct contexts and understandings of the term elite education institution. Therefore, they were investigated detached from the definition of the term used in this thesis. For application in this research, they are considered in the following subchapter in a generalized form.

21

The relationship between social entrepreneurship and entrepreneurship can be considered as analogous to the distinction between economic and social innovation in this thesis. For a critical review of the concept, see Peredo and McLean (2006).

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Image

Communications

Rankings

Reputation

Quality

Merit

Research

Academic Excellence

Institution

Selectivity

Teaching

Character

Corporate

Exclusivity

Schoollinking

Network

International

Place-making (Passung)

Academic

Figure 4.5 Complementary institutional characteristics and subfactors of elite higher education institutions. (Own illustration)

4.5.1

Reputation

Contemporary elite educational institutions have excellent reputations. According to Washington and Zajac (2005, p. 283) the term reputation is defined as intersubjective differences in perceptions of the quality of different institutions. Piazza and Castellucci (2014, p. 292) add that the merits of an institution are elements of reputation. Reputation can be understood as a construct of “performance-based rewards” (Piazza & Castellucci, 2014, p. 292), which result from the outstanding quality and merit of an institution. Bloch et al. (2014) describe the emergence of a certain reputation alongside three types of positioning, which are self-positioning, politically induced positioning, and further external positioning. These three types of positioning are used as a basis in this subchapter to categorize elements of an EHEI’s reputation. Respective passages highlight the underlying type of positioning. An institution’s reputation impacts on various other characteristics that ensure an elite status. For example, reputation is relevant in the decision-making processes of potential scientific employees (faculty) (Barrett & Smith, 2008, p. 951; Tafertshofer et al., 2018). Brooks et al. (2012) argue that this is the case because individuals, in their case, students, distinguish themselves from others through university reputation to increase their chances on the labor market.

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Strathdee (2009) criticizes the concept of reputation for this very reason. The author describes how the reputation of an institution provides advantages for students on the labor market and that, on this basis, social inequalities arise. He describes the need for an approach to constructing a sociology of reputation (Strathdee, 2009, pp. 91–94). Opposed to this critique is a conceptual distinction between reputation and status illustrated by Washington and Zajac (2005, p. 283). According to these authors, reputation, unlike status, does not contain references to privileges and potential inequalities (Washington & Zajac, 2005, p. 283). In this research work, reputation is to be understood as such a characteristic, without implications regarding social inequality. In the context of reputation, several subordinate elements can be identified, which are relevant to the elite status of a higher education institution. Following Draelants (2012), a prestigious image is one such factor. In marketing literature, image is a frequently studied phenomenon (Gardner & Levy, 1955; K. L. Keller, 1993). An image is the picture that an institution’s various stakeholders have of it. According to Lievens and Highhouse (2003, p. 77) the concept of image in the context of employers, can be divided into two components. On the one hand, the instrumental attributes and, on the other hand, the symbolic attributes. Instrumental attributes are functions that an institution fulfills from the point of view of recipients. Symbolic attributes include symbols that recipients associate with the institution. An image is created through subjective evaluation of both factors, functions and symbols, in the individual context of the recipients (Lievens & Highhouse, 2003). Concerning the three types of positioning developed by Bloch et al. (2014) an institution’s image can be classified as self-positioning and external positioning. In the context of EHEIs, Draelants (2012) empirically investigated the concept of image. Using the French grandes écoles as an example, the author examined decision-making processes of potential students. The focal point was the image of these institutions and its impact on the attractiveness of an institution. In his work, the author used the framework of instrumental and symbolic attributes. Draelants (2012, p. 575) describes the symbolic importance of an EHEI within society as the main reason for its prestigious image. This importance is not further elaborated by the author. However, it can be viewed in the context of the definition of the term elite within this research work. The symbolic significance can be interpreted in terms of the positive influence of educational institutions on society. Further symbolic attributes in the context of the image of an elite educational institution are the (academic) selectivity of an institution and the identification of stakeholders with the institution (Draelants, 2012). For the latter, however, the author describes that relevance exists only in a few cases (Draelants,

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2012, p. 575). In addition, the historical heritage of an educational institution, or in other words the institution as a tradition, can also be considered as a symbolic attribute. Regarding this symbolic attribute the process of formation of a tradition can be viewed. Hobsbawm and Ranger (1983) describe the phenomenon of invented traditions. Traditions often emerge close to the present, but are ascribed to the more distant past, to increase their perceived value in society (Hobsbawm, 1983, pp. 1–14). Following this argument, the historical heritage of an educational institution as a symbolic attribute should be viewed cautiously. Instrumental attributes of the image of an elite educational institution focus on the “value of their programs in the job market” (Draelants, 2012, p. 575). The prestigious image that contributes to reputation is also based on the outlook that educational institutions offer to their (potential) students. This view is gaining ground in the literature (Brooks et al., 2012; Winkler, 2014, p. 280). At this point, a paradoxical fact should be pointed out. On the one hand, an image that promises privileges (in the perception of the relevant recipients and clientele) contributes to the reputation of an institution. On the other hand, the concept of reputation is defined as detached from any (social) privileges and inequalities. Beyond image, the (perceived) employability of graduates and their subsequent economic earnings shape the reputation of an institution. Following the definition of reputation, this refers to meritocratic factors. The term employability is defined distinctly in the literature. According to Holmes (2013, p. 540), it is understood in everyday language as employment outcomes of individuals. Harvey (2001) views employability as the ability of students to achieve and maintain a position in the labor market. Following the author, this definition lies at the core of the term’s individual level (Harvey, 2001, p. 98). In the context of employability, educational institutions are highly relevant. For this reason, Harvey (2001), beyond the aforementioned individual level, describes the institutional level of employability. In this framework, the author critically points out that employability is often understood as the employment rates of graduates of institutions (Harvey, 2001, p. 98). Deviating from this understanding, Boden and Nedeva (2010) write that employability at the institutional level has historically been viewed as an aspect of the link between the institution and the labor market. In modern times, the term and its classification in this context have changed. According to the authors, employability currently describes the performative function of an educational institution, which is desired or prescribed by the respective government (Boden & Nedeva, 2010). This statement is to be regarded as a controversial view, since here the social legitimation of an elite educational institution is not considered. Only

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the political legitimation and, thus, politically-induced positioning according to Bloch et al. (2014) are included here. Holmes (2013) makes a divergent distinction beyond the individual and institutional levels of employability. The author describes three perspectives and their underlying definition of the term. These perspectives understand employability as: 1. Cognitive property of persons (possessional) 2. Positions of persons (positional) 3. Processes (processual) The possessional perspective understands employability as certain characteristics and skills of college graduates (Holmes, 2013, p. 542). The positional perspective describes employability as a social position. This perspective focuses on Bourdieu’s concepts of cultural capital and habitus (Holmes, 2013, p. 548). D. Bennett (2018, p. 49) explicitly adds the social capital of individuals to this perspective. The processual perspective views employability as a process. Here, the focus is on the process of developing a graduate identity, which results in employment outcomes. Specifically, graduates should not only become formal graduates, but also graduates in a social and biographical sense, “whereby they act in ways that lead others to ascribe to them the identity of being a person worthy of being employed” (Holmes, 2013, p. 549). Graduate identity and its implications for employability have been investigated by Hinchliffe and Jolly (2011). They describe the concept as including the four elements of values, intellect (critical thinking), performance, and commitment (Hinchliffe & Jolly, 2011, p. 575). According to these authors, employability is based on graduate identity, which, in turn, consists of the interplay of these four components (Hinchliffe & Jolly, 2011). Based on Useem and Karabel’s (1986) results, elite educational institutions enable their graduates to access high-level positions in the labor market. This statement, focusing on U.S. higher education, is also supported with respect to U.K. institutions. Wakeling and Savage (2015) describe how attending an educational institution that belongs to the elite Russell Group has an impact on students’ outcomes in the labor market. Zimmerman (2019) adds, using Chile as an example, that these outcomes are namely employability and future earnings. Moreover, he describes how this positive influence appeared in his research only in the context of male subjects. The author explains these results with the help of five distinguishing factors between gender groups in his work, which are academic preparation, geographic preferences and constraints, interest in business

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careers, academic success in college, and success in forming valuable relationships in college (Zimmerman, 2019, p. 21). From these results it can be deduced that elite educational institutions support their students in different ways and to different degrees. The aforementioned remarks illustrate that in elite educational institutions, the focus is on the formation of the components of employability and graduate identity. For this reason, employability in this research is understood in a broad sense including all its conceptual components. Other potential tools for building and solidifying the reputation of an elite educational institution are rankings (Stensaker et al., 2019). According to Amsler and Bolsmann (2012, p. 284), rankings are competitive tools that promote the distinction of institutions and are hierarchical. Ishikawa (2009) adds that this hierarchy refers to a global context and not to a national one, as was common in the past. Kauppi (2018) describes rankings as creating a global hierarchy of values with a focus on (institutional) performance. As a result of perceived competition between educational institutions, the reputation of an institution at the top of such a hierarchy is perceived to be more excellent. Goglio and Regini (2017) state that rankings are a reason for hierarchization and also a consequence of it. They create reputation as much as they are promoted by reputation. According to Ishikawa (2009, p. 170), rankings are objectifications of academic excellence.22 They can be viewed as an attempt to quantify quality in educational institutions. They can be classified as both external positionings and politically-induced positionings in the sense of Bloch et al.’s (2014) description. Regarding quantification of educational quality, rankings are discussed critically in the literature (David, 2016; Dill & Soo, 2005; Kroth & Daniel, 2008; Pusser & Marginson, 2013). Dill and Soo (2005, p. 496), for example, criticize the statistical inaccuracies of rankings and the quality indicators that are used within such frameworks. Urdari et al. (2017), in their study, investigate the extent to which the impact of higher education institutions in different areas of society is adequately represented in institutional rankings. The authors conclude that rankings as measures of public perception do not capture the multifaceted impact that higher education institutions achieve (Urdari et al., 2017). Despite these criticisms on the validity and reliability of rankings, the literature recognizes that these instruments occupy a relevant position within various stratification processes in higher education. Beyond external positioning and politically-induced positioning described above, Bloch et al. (2014, 245 f.) describe the self-directed communication of

22

The concept of academic excellence is discussed in more depth later in this chapter.

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educational institutions as a form of self-positioning. The objective of this communication is creating a reputation with the help of emphasizing “differences from fellow competitors using comparative and superlative rhetoric” (Bloch et al., 2014, p. 246). In terms of practical tools in this context, the authors highlight mission statements, websites, and descriptions of study programs as examples (Bloch et al., 2014, p. 246). McDonald et al. (2012) investigated such instruments in the form of institution-specific brochures in school education in Australia. They found that elite educational institutions employ various communication strategies toward their recipients.23 In doing so, institutions draw on a broad cultural policy to create specific meanings and shape interactions with their own stakeholders, and to enhance their reputation (McDonald et al., 2012). This can be applied to the context of EHEIs but has not yet been concretely investigated in this context. L. T. Christensen and Askegaard (2001, p. 292) show that communication strategies are part of the symbolic dimension of an organization’s activities. Symbols are another factor that should be considered in the context of an institution’s reputation. They can be viewed as a single factor transverse to other described elements of reputation, as the symbolic dimension of the concept of image already highlights. According to Lamont and Molnár (2002), symbols are an instrument for constructing boundaries. With regard to EHEIs, Binder and Abel (2019a, 2019b) suggest that these organizations do not only support their students in terms of employability. They also provide them with a sense of specialness through symbols and associated symbolic boundaries (Binder & Abel, 2019b). Combining this idea with Bourdieu’s capital theory, elite educational institutions foster their reputation and that of their students and graduates through the accumulation of symbolic capital. This represents the totality of symbols that influence an institution’s reputation (Bourdieu, 1984, p. 291).

4.5.2

Academic Excellence

Beyond reputation a second characteristic that enables educational institutions to achieve an elite status is academic excellence. This is closely intertwined with reputation. The concept of academic excellence is frequently addressed in the literature, but is rarely defined (Yogev, 2000). In the context of higher education, literature on excellence is dominated by a discourse on the concepts of excellence

23

The authors identified six communication strategies in the context of their study. For these strategies, see McDonald et al (2012, p. 9).

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and elite.24 This is not explored in detail in this research work. The reason for this is that the concept of elite in this thesis is understood as the status of an institution and its position at the top of a hierarchy and not elite concerning the term’s often criticized social implications. In this framework, excellence is a factor that an elite status is based on. The concept of academic excellence is criticized, for example, by Gruschka (2015). Based on his own biographical experiences, the scholar critically describes that excellence in universities is currently measured on the basis of financial grants through research, instead of focusing on factors such as knowledge and truth (Gruschka, 2015, p. 167). According to Bröckling and Peter (2017) and Peter (2019, p. 26), in the context of higher education, a dispositive of excellence is prevalent. This encompasses different aspects of higher education within which excellence and aspirations for excellence are found. Peter (2019, p. 26) mentions political excellence initiatives, institutional mission statements, academic support programs for junior faculty, and the processes of hiring academic staff. Achieving excellence in higher education means providing cutting-edge standout academic education (Peter, 2019). Standout education or excellent education is determined according to Bröckling and Peter (2017, p. 293) by the two components of research and teaching, and their quality. However, there is no clear understanding of how academic quality can be measured. Instead, different nations use different criteria to measure it, for example, in rankings (Dill & Soo, 2005). Commonly, within this frame, a high degree of relevance is accorded to the quality of research (Goglio & Regini, 2017; King, 2018, p. 325; Kreckel, 2018, p. 39). Indicators of research performance can be criticized as not providing a reliable picture of whether higher education institutions are having a positive impact on society (David, 2016, p. 170; Goglio & Regini, 2017, p. 326). There is dissent in the scientific literature about the quality of research. The same applies to the quality of teaching. A reason for this, according to Skelton (2004, p. 452), is that excellent teaching is anchored in a constantly changing social, economic, and political context. In one of his later works, the author describes teaching excellence on the basis of different characteristics at the individual and institutional level (Skelton, 2009, p. 109) as follows:

24

For the elite excellence discourse in science, see Helsper et al. (2014); H.-H. Krüger et al. (2014); H.-H. Krüger and Helsper (2014); Ricken (2009).

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Individual: • Teachers act according to a personal teaching philosophy. • Teachers exemplify certain educational values. • Teaching should be established as a moral category (teaching what is good). Institutional: • Teaching encompasses pluralistic cultures of consultation in which not only teaching methods but also pedagogical theories, values, and guidelines are shared. • Teaching does not consist of heroic individuals, but excellent frameworks for teaching. • Various aspects of academic practice are integrated into teaching On the individual level of this construct of factors that determine high quality teaching, Saunders and Blanco Ramírez’s (2017) ideas can be incorporated. These authors view teaching excellence as prioritizing and orienting with students’ ideas, beliefs, understandings, and aspirations (Saunders & Blanco Ramírez, 2017, p. 404). Excellence, following the argument of this subchapter, should be seen as a performative characteristic of higher education institutions. Ab Hamid (2015), in the context of evaluating (performance) excellence, states that not only the factors of teaching and research are relevant components of excellence. Rather, various value-based (performance) criteria must also be considered. These include leadership values and cultural values in an institution, and various types of performance-related values, including productivity-focused, employee-focused, and stakeholder-focused values of the educational institution and its overall performance. Salmi (2009, p. 8) also outlines a culture of excellence that underlies the actions in educational institutions. Beyond the factors mentioned—research, teaching, and values—James et al. (1989, p. 252) describe study experience at and selectivity of educational institutions as factors influencing quality and excellence. The latter was also observed by Mitterle and Stock (2015, 187) in their empirical study of exclusive educational institutions in the German higher education system.

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Selection

Another factor regarding excellence and the elite status of an educational institution is the selection process. The reason for this, according to Bloch et al. (2015), is that the selection process creates an exclusive positioning for and by the institution. This is characterized by the fact that not everyone can become a member and those selected are distinguished by special features and characteristics (Bloch et al., 2015, p. 190). In their research, the authors examined the selection processes of EHEIs in Germany and revealed their underlying structures. The results of this research are presented below to illustrate the characteristic selection process. The selectivity of educational institutions, according to Gutmann (1987, pp. 194–196), is necessary because there are more qualified potential students than there are available study places. Therefore, in the process of selecting the “right” students, a wide variety of qualitative and quantitative data are collected from applicants and evaluated. According to Bloch et al. (2015), two logical directions of selection can be distinguished, which are ranking logic and threshold logic. Ranking logic is characterized by a direct comparison of applicants on the basis of a fixed scale. Focus here is on metric data and a clear number of maximum places to be awarded. Within the frame of threshold logic, no upper limit on possible successful applicants exists. Applicants are not compared directly. Rather, they are considered individually and individual selection decisions are made based on the metric and qualitative data collected (Bloch et al., 2015, p. 190). Overall, the selection of students at elite educational institutions is closely linked to the process of commensuration. Following Espeland and Stevens (1998, p. 315) this process attempts to transform qualitative-individualizing data into quantitative, metric-unifying data to enable comparison of diverse data on different subjects. Selection processes can be analyzed as a commensuration and reduction process of diverse qualities into uniform quantities (Bloch et al, 2015, p. 191). The objective is to create a ranking in the circles of applicants through complexity reduction, based on which a selection of the intersubjectively comprehensible “best” is possible. Selection processes in elite educational institution’s practice include four stages, invoking the best, classification, evaluation, and actual selection (Bloch et al., (2015, p. 192). Within this frame, both the described directions of selection logic are applied in alternating frequency (Bloch et al., 2015, p. 206).

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Invoking the best refers to an elite educational institution’s communication with its potential applicants. In this first step of selection, the best potential students are to be addressed, with the adjective best being defined by institutions based on their own aspirations. In this stage, institutions indicate what expectations they hold regarding personality, skills, and performance of applicants. These aspects are examined in the process, for example, with the help of letters of motivation or interviews (Bloch et al., 2015, p. 193). After the application has been received, classification takes place. This stage includes requesting certificates, curriculum vitae, letters of motivation, language tests, subject-related tests, letters of recommendation, application forms, transcripts of records, and exposés for submission (Bloch et al., 2015, p. 196). These documents are then checked for compliance with formal requirements. The objective of this stage is preselecting applicants based on classifying applications as proper (Bloch et al., 2015, p. 198). Bloch et al. (2015) relate the stage of evaluation to Lamont’s (2012) work on the SVE. According to this, an evaluation must show different characteristics to be legitimate. On the one hand, intersubjectively agreed reference values are necessary for the evaluation of a subject or object. On the other hand, evaluation criteria and persons, who are authorized to make an evaluation decision, must be defined and illustrated (Lamont, 2012, p. 205). In practice, Bloch et al. (2015, pp. 198–202) describe the process of evaluation on the basis of different steps, which the authors could observe in their investigation. At the beginning, the submitted qualitative documents (exposé, motivation letter, etc.) are anonymized and numerically evaluated by experts. Depending on the institution, different criteria are particularly relevant here to achieve a high score and a higher place in the applicant ranking. The focus of evaluations carried out within institutions were examined by Bloch et al. (2015) and were the factors of commitment and motivation. In the next step, applicants in the upper range of the quantitative ranking are invited to interviews. In these, the underlying selection motives are examined. According to Bloch et al. (2015, p. 201) questions about applicants’ motives aim at examining the fit between applicants and study programs. Evaluating applicants in a multistage process targets identifying the best applicants based on their previous quantitative educational achievements and the qualitative factors of commitment and motivation. Finally, the fit between applicant profile and institutional profile represents the threshold to the last stage, which is the actual selection decision. Within this frame, purely quantitative and commensurate qualitative data, and the weighting of different steps in the process, are compiled. These form the basis for preparing a final ranking of applicants,

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according to which the persons are admitted as students (Bloch et al., 2015, p. 202). This practical design of selection in elite educational institutions refers to the example of Germany. However, elite educational institutions’ selection of students can vary depending on the national context.25 For example, Schippling (2015) describes the selection processes in the French system of elite grandes écoles. As a distinct procedural feature compared to other nations, the author highlights systematic preselection within the two-year preparatory program concours (Schippling, 2015, 255). Liu et al. (2014) point out the two selection process types in the People’s Republic of China, which are modern, independent, freshman admissions and the traditional gaoko. Here a gap exists between the former multistage selection process, which focuses on the competencies of individual applicants, and the standardized gaoko testing process, which focuses specifically on quantitative data (Liu et al., 2014, p. 46). An element of the characteristic selection that has not been mentioned so far is the character of potential students. Character traits considered as the right traits by institutions are the focus of attention here (Karabel, 1984, p. 12). Following Karabel’s (1984) work, Khan (2010, p. 102) describes how the character of potential students is the most important selection criterion in elite educational institutions. However, the author shows that instead of a predefined “right” character, institutions mainly admit interesting characters to their programs (Khan, 2010, p. 102). In the literature on elite education and elite educational institutions, no clear definition of the term character is described (Khan, 2010). Rather, understanding of the term is adopted from everyday language. Elite educational institutions select individuals with character. Following the explanations of Banicki (2017, p. 60), described in Subchapter 4.2.1, an unambiguous definition of the term character in the context of elite educational institutions is not possible. For this reason, the term is understood in this research, according to Faix and Mergenthaler (2015, p. 116), as part of a person’s personality. Elite educational institutions thus select individuals who are and have a certain type of personality. A person’s personality results in his or her actions. These actions and their results can be described in the context of EHEIs by means of the term merit (Karen, 1991). Merit is hardly quantifiable as a term, so that elite educational institutions define it themselves. 25

In the context of various national selection processes in elite higher education, Zymek (2014) conducted a historical-comparative analysis illustrating historical developments of selection processes and similarities and differences in Germany, the United Kingdom, and France.

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Karabel (2005) examines the different definitions of the term applied by elite educational institutions within their selection processes. Using three institutions in the United States as examples, the author describes four different approaches to how merit is understood. In the early years of admissions processes, merit in EHEIs was defined as individual academic excellence (Karabel, 2005). Over time, the definition of merit changed various times. With the first definition being academic excellence of students, a radical change came about when merit was redefined as character. Admissions shifted from academic skills to personal traits, which usually influenced academic skills. Merit as character thus broadened the diversity of accepted students in EHEIs. Within the frame of the third definition of merit, intellectuality was added to character and academic excellence. The fourth definition of merit was focusing on diversity and inclusion, having the social mission of EHEIs, reducing social inequality, as a focal point. Swartz (2008), on the basis of Karabel’s work, describes these four definitions as gatekeeping tools used by EHEIs. While Karabel (1984) offers these four different definitions of merit, it can be said that today merit in EHEI’s is defined as a mixture of all four. Overall, academic excellence, character, and intellectuality play an important role in contemporary selection processes, as the description above illustrates. All elements described show another fundamental element of selection in EHEIs, which is fit.26 The concept of fit refers to the degree of harmony between individuals’ real-world and institutionalized educational processes (lebensweltliche und institutionalisierte Bildungsprozesse) (Grundmann et al., 2016). The concept of fit in this form is modeled on Robert Merton’s theory of anomie27 (Grundmann et al., 2003). In this context, fit is the result of an individual’s adaptation to an educational institution, in the sense of conformity28 . This describes the conforming behavior of members of a social order with the cultural patterns established in that order (Merton, 1968, 1995). Regarding elite educational institutions, A. Xie and Reay (2019) distinguish between social and academic fit in the context of institutions in the People’s Republic of China. Carter et al. (2018) describe fit alongside the concept of place.29 Here, the authors show how individual students in Australia create fit with their respective elite educational institutions. 26

The term fit in this work is understood as synonymous with the German concept of Passung. 27 For Robert Merton’s theory of anomie, see Merton (1968, p. 198 f.) and Merton (1995, p. 127 f.). 28 For the types of individual adaptation in the context of social structure, see Merton (1995, p. 135 f.). 29 For the concept of space and place, see Tuan (1977).

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According to these authors, academic fit can be caused by individuals themselves. Therefore, it is to be understood as an active process performed by individuals. Beyond this, fit can also be seen as an active process performed by institutions. Kramer and Helsper (2011, p. 104) describe how educational institutions appeal to a particular habitus in potential students. In addition, they look for a particular expression of cultural capital. This links to Bourdieu’s (1984, 2002) theories of capital and habitus as providing the basis for a fit, actively created by institutions. This is supported by representative results of quantitative analyses in an EHEI in the United Kingdom. These show that a relationship between cultural knowledge and admissions decisions at this institution exists (Zimdars et al., 2009). As a consequence of this habitual and cultural selection, achieving a social fit, as defined by A. Xie and Reay (2019), can be viewed as an institutionally controlled process. Selection processes at higher education institutions can be criticized as having a discriminatory effect.30 From an idealistic perspective, the selection process at selective educational institutions, and thus also elite educational institutions, should be oriented entirely along the principle of nondiscrimination. Gutmann (1987, p. 196) describes this in terms of two guiding principles. Applicants should be admitted on the maxim that their skills and qualifications advance the institution’s purpose and that all applicants who meet this maxim should be granted access to the institution.

4.5.4

Networks

Another characteristic of EHEIs that has been receiving significant attention in research literature is the institutions’ networks. Networks in the context of social sciences are usually called social networks (e.g., Tippelt et al., 2009). Social networks are systems of social entities in the sense of systems theory according to Luhmann.31 Social entities, in the social ontology of philosophy, include individuals and pluralistic organizations and institutions (L. R. Baker, 2015, p. 77). Elite higher education institutions investigated in this thesis belong to the latter category.

30

For a discussion of selection processes in (elite) higher education institutions and inequalities that arise from them, see Liu et al. (2014); Zhang and Wang (2020). 31 For social systems according to Luhmann, see J. F. K. Schmidt and Kieserling (2017, p. 100).

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Granovetter (2005) describes the relevance of networks for institutions based on the economic outcome created through them. Three causes for economic implications prevail, which are information sharing, reward and punishment mechanisms, and trust of network partners. In the context of elite educational institutions, the relevance of networks has so far mainly been studied regarding the former cause. Thus, it is described that the exchange of information and knowledge is the central task of a network (Maesse, 2017). It has been empirically shown that the exchange of knowledge in networks increases innovative capacity of institutions, which results in competitive advantages for them (Lo & Tian, 2019). In view of the resulting relevance of networks, the following subchapter presents examples of how networks are currently set up in elite educational institutions and what factors support successful cooperation in these networks. Ellis et al. (1971) were among the first to investigate this characteristic, describing different subcultures in higher education institutions that foster different kinds of networks. The authors stress that in EHEIs students are guided toward two kinds of professional relationships, which are collegiate, focusing on ties to economic leaders within the community of alumni, and academic, focusing on ties to academic leaders within the organization. These two subcultures and associated networks, the corporate network and the academic network, can be used as starting points to describe networks in the context of contemporary EHEIs. Without referring specifically to Ellis et al.’s (1971) work, Bloch and Mitterle (2019) observe these same two types of networks in the context of elite institutions in their research. The corporate network is composed of various links between educational institutions and firms in the labor market. These links are both institutional in nature (e.g., career services and practice collaborations) and individual in nature (e.g., professor relationships and networks) (Bloch & Mitterle, 2019, p. 198). The authors observed institutional promotion of self-active networking by students and alumni in the form of alumni and student associations. These organize a wide variety of events and consulting projects under the umbrella of the educational institution to establish points of contact with companies (Bloch & Mitterle, 2019, p. 199). The reason for an extensive, multifaceted, and strong network between educational institutions and the labor market is that educational institutions can enable their students and graduates to enter further influential networks (Gioia & Corley, 2002). This statement is consistent with the view of Bloch and Mitterle (2019) that educational institutions are moving away from their role as passive distributors of educational certificates to active organizations that create career advantages for their students.

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The academic network of elite educational institutions can be divided into an institutional level and an individual level. As an institution, for example, networks focused on research are maintained with other prestigious educational institutions.32 Elite educational institutions also maintain exchange programs for students and scholars among themselves (Bloch & Mitterle, 2019, p. 202). Additionally, scientific events (e.g., workshops, meetings, and conferences) are attended and self-organized to interact with respected scientists from around the world (Bloch & Mitterle, 2019, p. 202). A newer development are study programrelated networks in teaching. For example, in the German state of Bavaria, an elite network of study programs was launched a few years ago (Deppe et al., 2015, p. 90; Mitterle & Stock, 2015, p. 185). On the individual level, the personal contacts of professors, lecturers, and working group leaders are emphasized (Bloch & Mitterle, 2019, p. 202). According to Podolny and Baron (1997), these individual networks can have a major influence on the productivity of an institution. Corporate and academic networks, as described here, largely refer to the German context. Nevertheless, the basic characteristics of such networks (see Fig. 4.6) can also be transferred to an international context. For example, results presented by Bloch and Mitterle (2019) are supported by a study in the context of elite educational institutions in France and the United Kingdom (Tholen et al., 2013).

Corporate Network

Institutional

Individual

Academic Network

Institutional

Individual

Figure 4.6 Types of networks in elite higher education institutions. (Own illustration)

Regarding the foregoing types of networks, several areas of networks of elite educational institutions can be highlighted (see Fig. 4.7). One of these is the social network of institutions. This refers not, as the initial use of the term implies, to the nature of the network, but to its purpose. 32

Two of these networks are the International Alliance of Research Universities in the international context and the League of European Research Universities in the European context. For more information, see International Alliance of Research Universities (2020); League of European Research Universities (2020).

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Within this frame, Taylor et al. (2018) include collaborations of higher education institutions with social institutions, such as nonprofit organizations.

Corporate Network

Institutional

Academic Network

Individual

Institutional

Individual

Regional

Social

Network areas Political

Educational

Figure 4.7 Types and areas of networks regarding elite higher education institutions. (Own illustration)

Another network area is education. The educational network is understood as connections to institutions of secondary education. Such connections are investigated in the literature in relation to individual networks. In an analysis of Chinese EHEIs, A. Xie and Reay (2019) found that one element in such individual educational networks is the habitus of teachers. This creates attachment from students to institutions. This is because teachers and their habitus significantly influence how students think about their educational institutions. They are part of the foundation for school-linking processes on an individual level. Oliver and Kettley (2010) support this statement in the context of the United Kingdom. Zimmerman (2019), on the contrary, argues that school-linking processes in regard to elite educational institutions are merely as relevant today as they were in the past. On an institutional level, Buisson-Fenet and Draelants (2013) observed schoollinking processes in elite education in France. They describe how secondary education institutions and higher education institutions generate ties to each other. According to the authors those school-linking processes are used to prepare students not only on a technical level, for elite higher education, but on a social and cultural level. The authors hint toward another area of network for elite institutions, which is a regional network.

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Usually, EHEIs work together with institutions near their locations to position themselves within the region (Stensaker et al., 2019). Another network area of institutions is the political network. This includes both a network of politically active individuals and a network through political activities. The former is described in the United Kingdom as “political ties long institutionalized between elite universities and the nation” (Friedman, 2018, p. 247). Political activities of EHEIs are described by Barringer et al. (2019) in the United States as networking through trustees. Transversal to the types and areas of networks described, institutions maintain an intra-institutional network. This primarily includes a strong alumni network (Rothblatt, 2006). Various tools are used to create such a network between students, alumni, and faculty. Karabel (1984) highlights alumni parentage as a measure. This includes mentoring of students by alumni to create so-called “peer ties” (Shiner & Noden, 2015). Zimmerman (2019) shows that institutions promote connections and active networking among their students and alumni. All types of networks are characterized by cooperation between EHEIs and further actors of various types and from various societal spheres. Focusing on cooperation in education, Tippelt (2021) proposes eight success factors for cooperation: • • • • • •

Cooperation must focus on actual problems. Resources must be combined. Social cohesion must be ensured. Actors from politics and civil society must be engaged. Institutions must be open to new and additional cooperation. People coordinating the project must possess adequate leadership competencies33 . • Cooperation must consist of proper partners. • An open work culture, free of rivalry, must be established. In the general form presented, these success factors can be adapted to cooperation in networks concerning EHEIs. This is implied by an elite institution’s status. The institutions must conduct successful cooperation in their various networks as part of the foundation for their status since failed cooperation can impact, for example, an institutions reputation. 33

While not specifying the leadership competencies, Tippelt (2021) indicates that participative and transformational leadership is suitable for cooperation. These aspects are incorporated in the leadership definition within this thesis. See Part II, Subchapter 3.2.

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Concluding remarks The previous paragraph already indicates the interconnectedness of different complementing characteristics of EHEIs. Kreckel (2011) exemplarily illustrates the interconnectedness of all characteristics mentioned in this subchapter. In the context of stratification and compartmentalization processes, the author describes excellent research as a prerequisite for economic success in international competition. The research successes of universities serve to politically legitimize state funding and an ethos of research shapes the traditional self-perception and reputation of university teachers (Kreckel, 2011, p. 245). These remarks highlight the interactions of the characteristics of research, legitimation through rankings and initiatives, funding, and reputation. Beyond the central tasks of education, research, and innovation, complementary institutional characteristics can be viewed as reasons for the elite status of a higher education institution. This subchapter demonstrates that an institution’s reputation, and its network, academic performance, and selection procedures, are highly relevant when considering position and status.

5

Conclusions from Part II

Part II constituted the theoretical and conceptual basis of this research. It illustrated theories and concepts that underlie the research process and further parts of this thesis. It aimed at answering Research Questions 1 and 2, and approximating an answer to the overarching research question of this thesis. This part was based on a traditional literature review. In the first chapter, the concept of mechanisms of elite education (H.-H. Krüger et al., 2012; Sackmann, 2019) was described. It includes the mechanisms of choice, selection, distinction, coherence, and valuation. These five mechanisms represent a heuristic model for framing the phenomenon of elite education. The concept offers a sound theoretical foundation for understanding the phenomenon since it is based on a broad spectrum of theories. Central theories within this frame are theories of action (Coleman, 1986, 1991), practice, capital, habitus and field theory (Bourdieu, 1984, 1996), theory of figurations (Elias & Scotson, 1994), and theories from the SVE (A. K. Krüger & Reinhart, 2016; Lamont, 2012). Within the frame of literature concerning the five mechanisms, different perspectives and underlying definitions of the terms elite education and elite higher education were identified. The phenomena are investigated overarchingly from a perspective of the sociology of education. A distinction between a sociological and pedagogical perspective on elite (higher) education can thus be made (R. Becker, 2009; Zedler, 2018). Both perspectives are interconnected. This culminates in a third, interdisciplinary perspective which was identified in the literature as the system-oriented perspective (Trow, 1974a). Terms associated with elite education in sociological research are reproduction of social class, class-matching, exclusivity, and inequality (Bloch et al., 2014; Bloch & Mitterle, 2017). Elite education can be defined broadly as the formation, production, and reproduction of elites. Elite education, investigated from a © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_5

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pedagogical perspective, mainly focuses on meritocratic ideals and the concept of excellence (Friedman, 2018; H.-H. Krüger & Helsper, 2014). Consequently, elite education can be defined as institutions or individuals that possess an elite status. Therefore, they possess characteristics that lead to a position on top of a hierarchy. Elite (higher) education from an interdisciplinary, system-oriented perspective was investigated as three distinct but interconnected research objects, as a system, an institution, and an individual. The phenomenon was investigated as the subsystem on top of a stratified higher education system (Trow, 1973), institutions within this subsystem, being institutions on top of a hierarchy, of institutions within the contemporary higher education system (Brezis & Hellier, 2018), and individual researchers on top of a hierarchized research system in higher education (Kwiek, 2019). In this thesis, the focus is placed on the pedagogical and system-oriented perspective, with institutions at the center of attention. After framing elite education, the phenomena of leadership and leadership education were described. The starting point here was defining leadership. Theories and approaches to leadership from the research literature were presented. These were structured according to their respective underlying perspectives of people, effect, and interaction (Bass & Bass, 2008) as well as context. Leadership was distinguished from the terms management and entrepreneurship (Czarniawska-Joerges & Wolff, 1991; Kotter, 2001). Leadership, in this thesis, is defined as a social process between people that aims for (substantial) positive change within society and the characteristics, skills, and behavior of people who shape this social process (W. G. Faix et al., 2020; A.-V. Faix, 2020). On this basis, leadership education was defined. The focus was on delineating leadership education from the concept of leadership development. Leadership development describes formal programs that aim at developing leadership competencies (Barry et al., 2018). Leadership education incorporates this aspect and expands on it (Stevenson et al., 2014). Therefore, leadership education was defined as providing a frame for the development of personality. It includes enabling the self-education of individuals with regard to the leadership process and underlying leadership behaviors, competencies, and characteristics beyond the scope of formal programs (W. G. Faix et al., 2020). In the second chapter, a conceptualization of EHEIs was presented. Leadership education was embedded in this context. An elite higher education institution was defined as an institution of higher education that is at the top of an institutional hierarchy and holds an elite status. Attribution as an elite institution results from a position within the field of other higher education institutions and status in terms of a position in a reputation

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hierarchy. Figure 5.1 illustrates the heuristic theoretical concept of contemporary EHEIs developed on the foundation of the literature review.

Elite Position (institutional hierarchy)

Status (reputational hierarchy) Responsibili ties

Impact

New, relevant knowledge

Educated people

Societal progress Central tasks in society

Process

Research

Education

Innovation

Excellent execution of tasks

Network

Academic Excellence

Elite Institution

Reputation

Selection

Figure 5.1 Heuristic theoretical concept of contemporary elite higher education institutions. (Own illustration)

The basis for attaining elite status and a position at the top of the institutional hierarchy employed in this thesis is the notion of the responsibility elite (Bohlken, 2011). Elites obtain their status by assuming a certain responsibility toward the society in which they are located. The responsibility of higher education institutions in society can be described by their central institutional tasks. These were also derived from the literature and form the basis for describing the constitution of contemporary EHEIs (see Fig. 5.1). The main tasks of contemporary higher education institutions can be summarized in the three concepts of education, research, and innovation (e.g., Cloete et al., 2018; Sam & van der Sijde, 2014; Spiel et al., 2018).

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These tasks can be broken down into impact and the process leading to impact (Pinheiro & Benneworth, 2018). The central tasks were, therefore, considered from process and impact perspectives. Education at higher education institutions includes the process of personality development. The focus is not on accumulating knowledge, but on people who work on holistically developing themselves in an interactional process with their environment (e.g., see W. G. Faix & Mergenthaler, 2015; Klafki, 2007b). Higher education institutions have the task of enabling this process. The resulting impact in the task of education is the production of educated persons. Elite higher education institutions stand out from others regarding their educational task because they execute it excellently. Excellent execution is synonymous with providing an excellent framework for personality development. In such a framework, special persons, for example, leaders, develop. Leadership education can thus be seen as part of the educational task in EHEIs. This comprises seven curricular elements that pursue the goal of producing creative personalities (Kisgen, 2017). These personalities, in turn, are capable of leading (A.-V. Faix, 2020; W. G. Faix & Mergenthaler, 2015) From the perspective of the institutions, research involves two parts of a process. One is research activities and the other is the creation of an adequate environment for research. The former refers to the activities of an individual and the activities of an institution or several institutions in different types of research (Falola et al., 2020; OECD, 2015). Impact of research at higher education institutions is newly generated, relevant knowledge. However, this knowledge is not only produced. It is also aggregated, processed, distributed, and promoted by institutions (Gibbons et al., 2010; Wolhuter, 2018). The final main task of innovation involves the process of creating societal innovations (Rammert, 2010). Institutions focus on innovations that cover all areas of the STEEP field (Kotler, 2002). The process perspective here includes promoting innovation and creating institutional innovativeness (Etzkowitz, 2013; Tierney & Lanford, 2016). The impact of innovation is progress in different spheres of society.

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Beyond the central tasks of higher education institutions and their excellent execution, further characteristics of EHEIs were identified in the literature on elite education institutions. These possess both overlaps and complementary aspects to the three central tasks. The four central further characteristics are reputation, academic excellence, selection, and networks. In summary, the main results from Part II of this thesis are the definition of EHEIs as responsibility elites, embedding leadership education into the concept of EHEIs, and the heuristic theoretical concept of EHEIs itself. These results form the foundation for the empirical work in the third part of this thesis.

Part III Research Process, Methodology, and Results

6

Empirical Approach and Methodology

Part II included a heuristic theoretical concept of the constitution of contemporary EHEIs. This framework comprised embedding leadership education in the context of EHEIs. It offered first answers to Research Questions 1 and 2: “Which characteristics determine an elite status for contemporary higher education institutions?” and “How is leadership education embedded in the context of EHEIs?” To approach answering Research Question 3, “How do higher education institutions change in the future and which factors determine these changes?”, data must be collected. The following chapter focuses on the empirical research process applied in this thesis. It incorporates relevant methodological remarks and descriptions of the steps taken to collect and analyze data.

6.1

Overarching Empirical Approach and Process

The research questions of this thesis are based on an interest in investigating the subjective perception of a specific group of people. The key point is understanding these perceptions, locating this thesis in the context of qualitativeinterpretive social research as opposed to standardized social research (Strübing, 2018). Concerning empirical studies in the context of an approach that focuses on understanding subjective perceptions, a qualitative research design is to be preferred to a quantitative design (Wichmann, 2019, p. 25). Qualitative research approaches are often used in social sciences to investigate, describe, or explain social phenomena (Leavy, 2020, p. 2). Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-3-658-40712-4_6. © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_6

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Qualitative research can be viewed as reconstructive research (Przyborski & Wohlrab-Sahr, 2019, p. 106). The term reconstructive includes two facets. A reconstruction takes place in the context of the object of investigation. Something that is already meaningful in itself and whose meaning needs to be deduced is reconstructed and transferred into scientific concepts (Przyborski & WohlrabSahr, 2019, p. 106). The research project itself is reconstructed by documenting the procedure in detail within the frame of the qualitative study (Przyborski & Wohlrab-Sahr, 2019, p. 106). Quality of research is usually ensured with the help of quality criteria. In quantitative research, the criteria of validity, reliability, and objectivity are used for this purpose (Flick, 2019, 2020; Krebs & Menold, 2019; Kuckartz, 2018; Kuckartz & Rädiker, 2022). Representativeness can be added to these criteria (Strübing, 2018). However, these criteria cannot be transferred unchanged to the context of qualitative research. This is because the traditional quality criteria are not compatible with the specifications of qualitative research (Flick, 2019, p. 474). The research literature tries to formulate suitable quality criteria for qualitative research. Two schools of thought can be identified on the topic. There is an attempt to adapt the quantitative quality criteria to the context of qualitative research or research focuses on developing methodologically appropriate criteria, which replace the traditional quality criteria in the context of qualitative research (Flick, 2019, p. 475). However, a conclusive understanding of generally valid quality criteria for qualitative research has not been agreed upon. Quality criteria used in qualitative research are generally internally consistent, but not applicable beyond the context in which they were developed (Flick, 2020, p. 14). This is partly due to the diversity of qualitative research backgrounds. Against this background, and in view of the claim of qualitative research not to resort to standardized procedures, Flick (2019, p. 485) has raised the question of to what extent standardized criteria are reasonable for qualitative research in general. This question illustrates a need to define a nonstandardized approach to quality assurance in qualitative research. Flick (2019) in this context, has described a specific quality framework for qualitative research. This framework consists of factors individually editable to respective research projects. The choice of method must be justified and the specific approach must be made explicit. The goal and quality claims underlying the project must be named and the procedures must be presented in such a transparent way that readers can form their own picture of the project’s claim and reality (Flick, 2019, p. 485). Therefore, for quality assurance in qualitative research a quality framework, which focuses on complete

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transparency of the research process should be implemented. The central components of this framework are the justification of the method, the explication of the procedure, and the description of research objectives and quality requirements. This nonstandardized framework for qualitative research was applied in the context of this research. This is because it meets the individual nature of qualitative research thus ensuring its quality. The qualitative research of this thesis was located within the discipline of educational research. Within this frame, methods of futures research, specifically (strategic) foresight, were utilized. Futures research is often considered a metadiscipline (R. A. Slaughter, 2002). This is rooted in the fact that futures research “makes use of theories, methods, and epistemologies from all the other disciplines that are relevant to the future study at hand” (von der Gracht, 2020, p. 3). Applying methods of futures research is especially suitable for investigating process theories. These theories consider the process of phenomena and thus their emergence and further development (Gray & Hovav, 2011, p. 306). Against the background of the process-oriented research questions of this thesis, methods of futures research were suitable for investigating the underlying phenomenon. Futures research as a metadiscipline encounters various criticisms, which usually point out that the future cannot be researched because it does not exist yet. In this sense, research on the future is not falsifiable and thus not scientific (Popp & Schüll, 2009, p. 26). It is reasonable to clarify the concept of “future” on which research is based as a starting point to counter this criticism. The literature has distinguished between two concepts of future: the future present and the present futures (Grunwald, 2009). The future present refers to what is understood by the term future in everyday language. Therefore, it is a state that corresponds to the experience of the present, but is provided with a different time index (Grunwald, 2009, p. 27). The term assumes that a future reality can be described. Present futures consider the future as a concept of reflection, which changes on the basis of present developments. The term understands the future as possibilities of how the future present is perceived today (Grunwald, 2009, p. 27). This definition is based on the assumption of an open future, according to which the future is not a pure extrapolation of past and present, but is characterized by uncertainty (Grunwald, 2009, p. 26). The open future includes the premise that not everything that is imaginable is also feasible and that not everything that is feasible will also be implemented (Opaschowski, 2009, p. 17). Following the line of argumentation presented above, futures research is not a science of the future, but of its respective present constructions (Grunwald, 2009, p. 26). Future-related statements in the context of futures research are to

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be understood as constructed images of a contingent future. They do not claim to represent future facts (Neuhaus & Steinmüller, 2015, p. 18). These constructed images of the future are referred to as futures in the literature and consequently also in this research. The previous remarks show that future cannot be studied in its everyday language understanding. Instead, present constructions of the future become the focus of investigation. Against this background, special quality criteria for futures research have to be applied to guarantee the quality of the investigation. Gerhold et al. (2015) present a collection of quality criteria, which can and should be applied to futures research. The aim here is to ensure good futures research. The authors understand good futures research as a well-founded examination of the future, which meets scientific requirements and does justice to its subject. Futures research is considered good if it effectively supports its addressees (Gerhold et al., 2015, p. 9). These remarks result in three guiding criteria for assessing the quality of futures research (Gerhold et al., 2015, p. 13):1 – Future adequacy – Scientific nature – Effectiveness concerning achieving its goals and fulfilling its tasks. In the context of this thesis, these guiding criteria for the quality of futures research were applied as a framework for maintaining the quality of research. The empirical research process One of the most comprehensively investigated subfields of futures research in the literature is (strategic) foresight (Fergnani, 2019, 2020). In particular, the underlying process of foresight is one focus in the research literature. A first variant of the process of foresight was developed at the end of the last century (Horton, 1999). Based on this variant, Voros (2003) developed a generic framework for the process of strategic foresight. This comprises four basic phases of input, foresight, output, and strategy (Voros, 2003, p. 14).

1

On this basis, Gerhold et al. (2015) describe three groups of quality criteria. First, criteria that consider futures research in comparison to other disciplines. Second, standards that perceive futures research in comparison to other forms of engagement with the future. Third, standards that result from the tasks of futures research. For a comprehensive description of the quality criteria and standards of futures research underlying these groups, see Gerhold et al. (2015).

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The input phase includes data collection using different methods. In the foresight phase, three sequential steps are applied, which are analysis, interpretation, and exploration. Analysis refers to the analysis of the data obtained from the input phase. Here, the focus is on structuring the data. Building on this, the data are interpreted to provide a basis for exploration (Voros, 2003). Exploration refers to “the activity of purposefully looking forward to create forward views” (Voros, 2003, p. 15). It is the step in which data are used to create forward views. The third phase of the foresight process is the output phase. On the one hand, this includes tangible results, such as interpreted data and future images derived from them. On the other hand, nontangible results are included at this point. These include, for example, raising awareness for the relevance of foresight and the need for a sound strategy (Voros, 2003, p. 15). The final step of the foresight process involves integrating the lessons learned from previous phases into strategy development of the entity that conducted the foresight process. In this framework, the persons conducting the foresight process hand over the results to decision makers, who carry out this integration (Voros, 2003, p. 16). Complementing these remarks, alternative foresight processes can be found in the literature. An example is provided by Popper (2008, p. 67), who describes the foresight process on a practical level in five central phases. Here, the preparation for data collection is followed by the recruitment of key persons for strategy development. Subsequently, data are collected, and the key persons are integrated into political or economic practice during the implementation of derived measures. Finally, the results are classified in order to identify opportunities and risks (Popper, 2008, pp. 67–68). Both described foresight processes focus on foresight as a tool in the context of strategy development. In this research work, the generic framework according to Voros (2003) was used to structure the empirical research process. This is justified by the fact that its generic nature is suitable for adaptation to different situations. Popper’s approach, on the other hand, is practically oriented and already structured in detail. The approach according to Voros (2003) is easily adaptable beyond its original business context. It can also be effectively used for futures research in social sciences. Beyond the generic foresight process, the process of research in social sciences was included in this thesis. Both processes can be viewed in an integrated manner. The process of research in social sciences comprises the central phases of preparing the research design, collecting data, analyzing data, and documenting results (Häder, 2019, p. 75; Hussy et al., 2013, p. 26). Figure 6.1 illustrates the integration of the research process of social science into the foresight process.

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Input = Data collection

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Foresight (incl. data analyses)

Outputs (incl. documentation)

Figure 6.1 Integration of foresight process and process of research in social sciences. (Own illustration)

Collecting data is equivalent to the input phase of the foresight process. The foresight phase includes analyzing data. The documentation of results can be integrated into the output phase. These three phases are combined in the preparation of the research design. This integrated research process (see Fig. 6.1) illustrates the empirical research approach of this thesis. The strategy phase was removed from the process since strategy must be included in the transfer of the scientifically generated results into practice. Therefore, it follows in a next step, after completing this scientific work, which itself provides impetus for strategy development.

6.2

Data Collection: Real-Time Delphi Survey

6.2.1

The Delphi Survey

A wide variety of methods are available in scientific disciplines to collect data. An established method in the context of futures research is the Delphi survey (Cuhls & Johnston, 2008, p. 110; T. J. Gordon, 1994b; Gray & Hovav, 2011, p. 301; Voros, 2003, p. 14). This subchapter addresses methodological foundations of the Delphi survey. It includes a description of types and variants, and strengths and weaknesses of the method.

6.2.1.1 Methodological Foundations The Delphi survey is an explorative research instrument (Linstone & Turroff, 1975; Mietzner, 2009, p. 42; Steinert, 2009). It was originally developed in the military context in the United States (Dalkey & Helmer, 1963). The initial Delphi survey focused on long-term foresight concerning new technologies (Rowe et al., 1991, p. 236).

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Today, Delphi surveys are used in a wide variety of disciplines, from health science and medicine, to engineering and (information) technology, to the social sciences (Beiderbeck et al., 2021b, p. 2). Delphi represents a specific form of group communication (Häder, 2014, p. 19). Specific in the sense that the group communication is designed as an iterative process. Moreover, the communicating groups are usually experts regarding the underlying object of investigation (Häder, 2014; Niederberger & Renn, 2019). Within the iterative Delphi process experts assess a certain number of theses concerning specific facts or developments. After an initial period, data are gathered, structured, and analyzed. The subsequent results represent the aggregated group opinion for round one of the traditional Delphi survey. The aggregated group opinion is reflected to the expert panel. On this foundation, experts can adjust their own assessments in a second round. This process is usually repeated in up to three rounds (Brady, 2015; Chalmers & Armour, 2019; Dalkey & Helmer, 1963; Häder & Häder, 1995; Scheibe et al., 1975). The objective of the traditional Delphi survey is to “obtain the most reliable consensus of opinion of a group of experts” (Dalkey & Helmer, 1963, p. 458). However, considering the diverse use of the method over the past decades, its objective has changed. The main objective of a Delphi survey now depends on the background of its application. At present, two general fields of application for Delphi surveys can be identified. On the one hand, Delphi is used as a method to navigate group communications.2 On the other hand, it offers a foundation for investigating (especially uncertain) developments (Häder, 2014, pp. 19–20). The former application of Delphi is used, for example, in the health sciences, while the latter is applied, among others, in futures research (Niederberger & Renn, 2019). In the context of these applications, at least six different objectives of the method can be described (Häder, 2014, pp. 69–78). First, Delphi surveys can pursue a prospective goal. Here, the method aims at aggregating insights on future developments. Second, the inverted design of this goal can be considered. Consequently, Delphi surveys serve the retrospective investigation of an issue. Third, Delphi aims at mapping the current state of research on a subject with the help of experts. Fourth, the method is used to evaluate certain issues. The focus here is on finding consensus within the expert panel to map evaluations that are as accurate as possible. Fifth, the Delphi method can be used to identify research needs on 2

For the use of the Delphi method as an instrument to navigate group communications, see selected articles in Niederberger and Renn (2019).

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a subject, in which experts jointly identify research gaps. Finally, Delphi can pursue the goal of solving specific technical problems. In this case, designated experts work together to develop solutions (Häder, 2014, pp. 76–77). Regardless of its objective, Delphi is characterized by some basic features. The central characteristics of traditional Delphi surveys are anonymity, iteration, controlled feedback, and statistical aggregation of group feedback (Niederberger & Renn, 2019; Rowe et al., 1991, p. 237). Anonymity refers to the experts who participate in the survey. They are unknown to each other, and their identities are anonymized throughout the course of a Delphi study. The reason for this anonymization is the attempt to allow participants to answer questions without social pressure (Rowe et al., 1991, p. 237). Iteration refers to the process of interviewing in several rounds. Here, the experts receive the questionnaire at the beginning of each new round. The researcher gives controlled feedback to the experts after the completion of such a round. Iteration is used in Delphi since prior research shows that it supports changes in experts’ opinions (Gnatzy et al., 2011, p. 1684). Before starting the next iteration, the aggregated group opinion is reflected to the group. On this foundation, experts can adjust their opinions (Rowe et al., 1991, p. 237). At the end of the Delphi process, the median of the group opinion regarding every aspect of the survey is presented. This statistical aggregation of total group feedback is used to gather insights, for example, on the consensus within responses of the expert panel (Rowe et al., 1991, p. 237). Beyond these four central characteristics, Delphi surveys are based on certain premises. These can also be seen as characterizing elements of the method. First, the facts on which a Delphi survey is based are uncertain. Therefore, knowledge on the subject is incomplete. Second, the processes of evaluation within the framework of the survey are also characterized by uncertainty. The experts’ contributions do not represent facts, but assessments (Cuhls, 2009, p. 209; 2019, p. 6). A third premise of Delphi relates to the participating experts. They are to be identified and integrated according to their knowledge of the underlying issue and their experience with it. In addition, Delphi studies consider the psychological processes of the experts. Here, the focus is on communication. Finally, Delphi works with the concept of self-fulfilling prophecy (Cuhls, 2009, p. 209; 2019, p. 6). By working proactively with the future, experts are made aware of developments that they may then work on based on their assessments in the method. In addition to the selffulfilling prophecy, Delphi also uses its opposite, the self-destructive prophecy (Cuhls, 2019, p. 6).

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6.2.1.2 Types and Variants of the Delphi Survey Delphi is a structured group communication process during which facts are assessed by experts. These facts are uncertain and only incomplete knowledge exists about them (Häder & Häder, 1995, p. 12). Against the background of this basic definition and the described objectives of the method, and on the basis of the described characteristics and premises, four types of Delphi survey can be distinguished. These types are the qualitative Delphi for aggregating ideas, the mixed-method Delphi determining an issue, the mixed-method Delphi determining expert opinions, and the quantitative Delphi for building consensus on an issue (Häder, 2014, p. 37; Häder & Häder, 2019, pp. 703–704). The first type of Delphi focuses on aggregating ideas (Type-1). This is done in a qualitatively oriented process in which open-ended questions are used to accumulate knowledge in an unstructured way. Quantitative elements do not play a role here (Häder & Häder, 2019, p. 703). The second type of Delphi pursues the purpose of the traditional Delphi method according to Dalkey and Helmer (1963) (Type-2). The focus is on predicting or determining an uncertain state of affairs (Häder & Häder, 2019, p. 703). This type of Delphi is designed as a mixed-method study. It uses qualitative elements and quantitative elements. Both open and closed questions are integrated into the questionnaire (Häder, 2014, p. 37). The third type of Delphi is also designed as a mixed-method study (Type3). Its objective is to determine the opinion of a specific group of experts. This serves, for example, to identify necessary interventions to an identified problem. This type of Delphi survey can raise awareness for undesirable developments (Häder & Häder, 2019, p. 704). It mainly includes quantitative elements and closed questions. Qualitative elements stay in the background (Häder, 2014, p. 37). The purpose of the fourth type of Delphi is to create the highest possible degree of consensus among participants (Type-4). Within this frame, recommendations for normative actions, for example, political guidelines, are developed (Häder & Häder, 2019, p. 704). This type of Delphi has a purely quantitative focus and is, therefore, designed as a standardized survey (Häder, 2014, p. 37). Over the past decades, the format of Delphi has evolved simultaneously with the development of different Delphi types. In the early days of the method, questionnaires and aggregated results were collected and sent by mail. This approach has changed. An example of a contemporary Delphi format is the group Delphi.3 3

For a detailed description of the group Delphi, also as distinct from other Delphi formats, see Niederberger and Renn (2018).

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It represents a Delphi survey in in-person form. On one or two days, experts are invited to discuss issues in workshops. This form of Delphi survey dispenses with the characteristic of anonymity and shifts the character of the survey from an anonymous, passive discussion to an active discussion in which the experts meet (Niederberger & Renn, 2018, p. 27). The aim of the group Delphi is to capture the substantive reasons for expert judgments to a greater extent than is possible in written questionnaires. Within the frame of the group Delphi, the process of the traditional Delphi is used despite its deviation concerning the central characteristic of anonymity (Niederberger & Renn, 2018, p. 27). Beyond postal Delphi and group Delphi as a face-to-face format, a digitized form of the method has also been in use for more than a decade. The traditional Delphi, moved from its original analog form into a digital environment is referred to as e-Delphi (Hall et al., 2018). Just as in group Delphi, e-Delphi mirrors the process of traditional Delphi. Table 6.1 illustrates the central distinguishing feature of the three Delphi formats: Table 6.1 Central distinguishing feature of Delphi, group Delphi, and e-Delphi. (Own illustration)

Delphi format

Distinguishing feature

Delphi

Postal implementation

Group Delphi

Implementation in-person

e-Delphi

Implementation on online platform

Another digital form of Delphi was developed and introduced by T. Gordon and Pease (2006) as the real-time Delphi. In contrast to e-Delphi, real-time Delphi not only differs from the traditional Delphi regarding its place of execution. The entire Delphi process is applied in an altered form. Real-time Delphi focuses on a modified feedback and iteration procedure (Gnatzy et al., 2011, p. 1686). Real-time Delphi dispenses with the iterative process in the form of clearly delimitable rounds. Instead, participants can edit their own assessments at any time in an online portal. In this process, the aggregated group opinion is not reflected back to the respective participant at fixed points in time, but immediately and, thus, in quasi-real-time (Gerhold, 2019; Gnatzy et al., 2011; T. Gordon & Pease, 2006). In T. Gordon and Pease’s (2006) concept, participants should already receive feedback before answering a question. Gnatzy et al. (2011) oppose this, because receiving the group’s opinion in advance of one’s own answer does not allow for unbiased assessments. To resolve this problem, the

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authors suggest reflecting feedback only after a participant has answered a question (Gnatzy et al., 2011, p. 1686). Figure 6.2 illustrates the basic differences between the process of traditional Delphi and real-time Delphi.

Survey process of conventional Delphi

Survey process of real-time Delphi Re-assessments Option to re-access the survey

Expert 1

First round estimates

Feedback Expert 2

Expert 2 Delphi Moderator

Expert 3

Expert n

(1) Assessment (2) Realtime Feedback (3) Possibility to re-assess

Expert 1

First round estimates

Decision to stop survey

Assessment of projections by small experts group to deliver starting values

Expert 3 Feedback Expert n

Re-assessments

Closure of Delphi survey t

Expert 1

Official start of Delphi survey

Expert 2

Expert 3

Expert n

Closure of Delphi survey t

Figure 6.2 Distinction between the process of Delphi and real-time Delphi, according to Gnatzy et al. (2011, p. 1686)

The use of a real-time Delphi is particularly useful in two cases. First, when the rapid execution of a Delphi study with a few participants is necessary. Second, when an asynchronous Delphi with more time and more participants is planned (T. Gordon & Pease, 2006, p. 322). The former case arises, for example, in organizations where expert opinions are aggregated for decision-making. In such a scenario, real-time Delphi can also be conducted in presence (T. Gordon & Pease, 2006). The second case is used, among others, in the field of futures research, as Gnatzy et al. (2011, p. 1682) have shown. The main advantage of real-time Delphi compared to traditional Delphi is saving resources. The process is flexible and becomes less time-consuming for participants due to its asynchronous nature and location-independence. This leads to lower drop-out rates in the survey. Thus, both efficiency and effectiveness of the whole process are improved without influencing the results of the Delphi survey (Gnatzy et al., 2011). Beyond the peculiarities and advantages of Delphi and real-time Delphi, the weaknesses of the method and criticism of it should not be disregarded. Delphi, as an explorative research instrument, has been criticized with reference to quantitative applications (Woudenberg, 1991). Critique has focused on the weaknesses of the method concerning statistical and sampling methods. For example, there is no consensus on the procedure for recruiting experts for Delphi surveys and the replication of the same results at a later point in time is not possible (Steinert, 2009, p. 293).

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Further weaknesses of the Delphi method relate to cognitive biases in expert panels. For example, normative or informational pressures can lead experts to subscribe to a majority opinion (Bolger & Wright, 2011, p. 1511). In the literature, this bias has been referred to for several decades as the bandwagon effect (Leibenstein, 1950; Majumdar, 1996; Steinert, 2009). Delphi surveys are also prone to desirability bias. In this case, experts evaluate developments described in the survey more positively or more negatively, depending on the extent to which they consider the respective development to be desirable (Ecken et al., 2011). Beyond statistical challenges and cognitive biases, Häder and Häder (1995) have described three basic problems of the Delphi method with respect to its application. First, the prerequisites and limitations for the successful implementation of Delphi have not been systematically clarified. Second, cognitive processes are in the background in Delphi surveys. However, an explanation is needed for which processes can lead to experts estimating uncertain facts. The rationale for using a particular variant of Delphi is sometimes inadequate (Häder & Häder, 1995, p. 11). In summary, these criticisms have described the lack of insight into certain aspects of how Delphi surveys work. Despite its aforementioned weaknesses, Delphi was suitable as an instrument for data collection in the context of this research. This is because the underlying research question here explored subjective perceptions. According to Linstone and Turroff (1975, p. 4), this is one of the central criteria that justify the application of Delphi. Another reason for using Delphi in this thesis was the prospective nature of the overarching research question. Resulting from this prospective nature was the fact that no historical data on the specific subject of the study exists. Conducting a Delphi survey is particularly suitable for research for which evaluations become necessary due to a lack of background information (Rowe et al., 1991, p. 236). The context of foresight, in which collecting data for this research was integrated, justified the choice of method. Compared to other methods of foresight, for example, the conference method, the Delphi study shows a higher accuracy. Therefore, it was preferable in the context of data collection (Riggs, 1983). Beyond the reasons mentioned above, the described weaknesses of the Delphi method can be described as usual weaknesses of scientific methods (Geist, 2010, p. 148). Nevertheless, they should be explicitly considered since this allows for transparency in the research process. Basic problems of the Delphi method described by Häder and Häder (1995) were consciously included and considered within this thesis. For this reason, prerequisites and limitations for the successful implementation of Delphi have already been presented in this subchapter.

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Disclosing cognitive processes were carried out in the form of analyses, which are discussed in more detail in Chapter 7. The justification of the variant of the method is presented in the following subchapter.

6.2.2

Design of the Real-time Delphi Survey in this Thesis

The previous subchapter describes the methodological foundations of the Delphi survey, its variations, strengths, and weaknesses. Reasons for applying Delphi within this thesis were presented. On this foundation the following subchapter delineates and justifies how the method was consequently implemented. In this thesis, Delphi represented the empirical foundation for an overarching foresight process. Therefore, it was the central element for research in the context of futures research. Within this frame, Delphi was a subjective-intuitive method of foresight as the intuitive assessments of participating experts provided quantitative and qualitative data, which were of both explorative and normative character (Cuhls, 2009, p. 209). Concerning the type of Delphi survey, a mixed-methods approach (Type-2) as described in Subchapter 6.2.1 was preferred because it is particularly suitable for conducting prospective Delphi surveys (Häder, 2014, p. 69). Due to the methodological advantages regarding time and cost efficiency, real-time Delphi was used as a variation of the traditional Delphi method. The specific design of real-time Delphi surveys depends on the underlying research project. Gnatzy et al. (2011) have illustrated a comparison of two commonly used approaches for real-time Delphi. These approaches are distinct concerning some basic elements of real-time Delphi, such as survey design, layout, presentation of questions, scales used, type of feedback, role of the researcher, and point in time at which initial feedback is reflected back to the experts (Gnatzy et al., 2011, p. 1687). Table 6.2 summarizes the differences between the two approaches. Real-time Delphi in this thesis was structured according to Gnatzy et al.’s (2011) approach. However, interquartile range (IQR) was not fed back to the experts as suggested by Gnatzy et al. (2011). This element was not included because the pretest of the survey showed that it could irritate experts. According to feedback from experts who took part in the pretest, some experts possessed little expertise concerning the Delphi method, which was not a prerequisite for participation. Consequently, they could be unable to interpret the IQR as part of the feedback presented to them.

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Table 6.2 Differences of different approaches of real-time Delphi surveys, according to Gnatzy et al. (2011, p. 1687) Element

Gordon & Pease (2006)

Gnatzy et al. (2011)

Survey design

Assessment of alternative strategies and criteria

Assessment of projections concerning the future

Layout

Matrix format (two-dimensional)

One page—One question (one-dimensional)

Presentation of questions

Overarching

One question per page

Scales

Weighting of criteria (1–10)

Percentage for probability of occurrence (0–100%) and 5-point Likert scales

Feedback type

Average group opinion

Average group opinion, median, interquartile range

Role of the researcher

Determines the end of the survey

Determines the end of the survey, controls qualitative feedback

Time for the initial feedback

At survey entry

After initially assessing the respective projection

6.2.2.1 Expert Selection Selecting suitable experts is a central challenge of expert-based research methods and this also holds true for Delphi. Delphi studies should not be based on the assumption that experts, by virtue of being attributed the label, automatically contribute largely to the data collection process (Mauksch et al., 2020). Rather, a justification of the procedure of selecting experts that meets scientific criteria must be provided. A basis for this is defining criteria for the inclusion of experts in the expert panel of a Delphi survey (Chalmers & Armour, 2019, p. 721). Therefore, the process of selecting experts must be explained in detail. The first step of this process is defining the term expert. According to Mauksch et al. (2020, p. 2) and J. Baker (2006, p. 61), the term expert describes persons who are capable and well informed in a specific field. However, this broad definition does not allow any conclusions to be drawn about why people are selected as experts for scientific studies. This difficulty arises from the low tangibility of the concept of expertise: Expertise is an elusive concept. It is not well understood and consequently, it is difficult to define and operationalize. Depending on the approach used, expertise may be defined in terms of knowledge, decision process, and quality of judgment as measured

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by consensus. Because of these ambiguities in the concept of expertise, it is difficult to identify who is an expert or who is more expert. (Bédard, 1989, p. 116)

In one of her later works, Bédard (1992) has designed a concept for operationalizing the notion of expert. This is based on the premise that the attribution of expert is done by viewing a person in relation to people who are novices in a field (Bédard, 1992). Experts possess different characteristics compared to novices. First, they possess more knowledge in and about their field than novices. Second, they can organize this knowledge better. Third, they are better at performing tasks in their field than novices (Bédard, 1992, pp. 138–139). These three core elements are limited by two further factors. First, expertise is always field specific. A transfer to other fields rarely takes place. Second, experts are generally experts for one field. They do not stand out in many situations (Bédard, 1992, p. 139). Consequently, an expert in one specific field may be considered a novice in another field. Attributing the label expert to a person is related to the level of expertise a person displays as the remarks above illustrate. Expertise is understood as the volume of knowledge about and in a particular field.4 This core characteristic for selecting a person as an expert provides several challenges. Central to this designation is determining the amount of knowledge necessary to include persons as experts in a scientific study (Chalmers & Armour, 2019, p. 721). Various indicators are used to determine this. These are, for example, the formal qualifications, reference lists, professional positions, or professional experience of a person (Burgman et al., 2011). However, these indicators are controversial. Qualifications as one example do not always reflect the volume of knowledge a person possesses (Chalmers & Armour, 2019, p. 720). Another approach does not focus on the volume of knowledge that a person possesses, but rather on the exclusivity of this knowledge. Experts are not experts because of who they are. As participants in a survey, however, they are only replaceable to a limited extent because they possess relevant special knowledge (Meuser & Nagel, 2002, p. 264). Therefore, the main criterion for selecting experts for scientific studies is not only their access to more knowledge than novices, and its better organization and retrievability in the implementation of tasks, but rather access, organization, and retrievability of exclusive knowledge. 4

Expertise is described through further indicators beyond pure knowledge, depending on the perspective one possesses. In the context of this research, the definition presented is considered sufficient since the experts are sought for a study that focuses on exclusive knowledge. For supplementary definitions of the term ’expertise’ see Grenier and Germain (2014); Tynjälä et al. (1997).

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Over the past decades, a collection of procedures for identifying and selecting experts for scientific studies has been developed. A summary can be found in Mauksch et al. (2020). Here, the authors particularly focus on the selection of experts for prospective studies. Selection procedures can be categorized with nine attributes, which are informal, sociological, political, personal involvement, performance-related, knowledge-related, external cues, self-assessment, and psychological (Mauksch et al., 2020, pp. 6–10). These categories differ in relation to basic characteristics according to which experts are to be considered experts and in their procedures for selecting these experts. For example, informal expert selection procedures are characterized by the fact that experts are selected according to intuition. Self-assessment-based procedures, on the other hand, emphasize the self-assessment of individuals’ expertise. Knowledge-based procedures are further based on the prior inquiry of knowledge from persons in order to classify their expertise (Mauksch et al., 2020, p. 4). In this research, a combination of different expert selection procedures was used to assemble an expert panel for the real-time Delphi survey. The combined use of selection procedures was often preferred because it partially compensated for the various weaknesses of individual measures (Mauksch et al., 2020, p. 9). The focus of expert selection procedures in this thesis was selecting experts by external cues and by personal involvement. As part of the external cuesprocedure, experts were selected based on three indicators. First, they were defined as individuals who demonstrate expertise in the field of higher education through their experience. The operationalization of the indicator experience was based on the factor of years in the profession (Mauksch et al., 2020, p. 7). Second, they were identified in terms of their awards and publications. Finally, their professional position was used as an indicator for being an expert (Mauksch et al., 2020, p. 7). The selection by personal involvement does not, as the name implies, refer to the selection of individuals in the personal environment of the author of this research. Instead, the focus was on the selection of individuals who show a personal interest in the research topic (Mauksch et al., 2020, p. 7). Against the background of a difficult identifiability of the factor of interest, it was assumed that persons who are involved in the field of the research object have a personal interest in it. Transferred to this thesis, persons who were part of the field of higher education were selected.

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As a result, this research also included its target groups as experts. This presented an exception in expert-based scientific studies which, according to Bogner and Menz (2002, p. 37), is generally accepted in the context of explorative research. The previous explanations show the structural framework and first inclusion criteria of expert selection in this thesis. Using the two selection procedures described above, further inclusion criteria of expert selection were established at two levels, which were surface and deep-level (Spickermann, Zimmermann, & von der Gracht, 2014). Deep-level criteria refer, for example, to research contributions, educational level, or the academic background of the experts. Surface criteria include position in the profession, age, or occupational field (Spickermann, Zimmermann, & von der Gracht, 2014, p. 108). Following the definition of the expert selection process and inclusion criteria, the implementation of expert identification and selection took place. Figure 6.3 provides an overview of this process:

Selection by external cues • Experience • Qualifications • Position Explicating expert selection process and inclusion criteria

Identification of higher education institutions’ main stakeholder groups

Definition of quality criteria for expert selection

Selection by personal involvement

Figure 6.3 Process of expert selection in this thesis. (Own illustration)

After defining the selection process and inclusion criteria, the space of potential experts was narrowed down to the different stakeholder groups of higher education institutions. This was done by drawing on stakeholder groups from five different clusters, which were internal, academic and research, regional, student, and other direct, national stakeholder groups (Chapleo & Simms, 2010, p. 16). Within these clusters, institutions were identified, and groups of people were defined that were eligible for the Delphi expert panel based on previously described inclusion criteria. Table 6.3 provides an overview of the final groups of people included, and associated institutions. The groups of people depicted were not narrowed down nationally. The goal of this choice was to create a high degree of heterogeneity in the expert panel to reduce cognitive biases in the subsequent discussions (Ecken et al., 2011, p. 1655; Förster & von der Gracht, 2014, pp. 215–216; Tichy, 2004, p. 360). Furthermore,

130 Table 6.3 Overview of expert groups

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Empirical Approach and Methodology

Institutions

Individuals

Universities & colleges

• • • • • •

Nonuniversity research institutes

• Managers • Employees

Scientific networks (institutional associations)

• Managers • Employees

Companies (partners, recruiters, consultancies)

• Managers for (junior) management programs • Responsible for university cooperation

Presidents Deans Professors Lecturers Scientific staff Nonscientific employees

Political entities (ministries, • Education policy makers working groups, etc.) (international) Sponsoring associations

• Leadership of sponsoring associations at elite universities

Foundations & funding associations

• Managers • Employees

Press

• Journalists (higher education)

Further individuals

• Education researchers • Educational influencers • Alumni from elite universities • Students from elite universities • Critics of elite educational institutions

the international focus of the underlying Delphi survey was a reason for not limiting stakeholder groups to national contexts. Beyond the composition of the expert panel, its size should also be considered. This varies greatly in Delphi surveys. Thangaratinam and Redman (2005, p. 120) have presented a range of four to 3,000 participants as acceptable for Delphi surveys. Other authors have highlighted 15 to 20 participants as a sufficient panel size. However, there is no consensus on the size of an expert panel in Delphi

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surveys (Hsu & Sandford, 2010, p. 344). In many cases, the panel size is chosen for pragmatic reasons (Chalmers & Armour, 2019, p. 721). In this thesis, a high degree of heterogeneity was aimed for. Consequently, defining a target for the panel size was guided by various prospective Delphi studies conducted over the past decade. These show a panel size within the range of 60 to 142 experts (Beiderbeck et al., 2021b; Gary & von der Gracht, 2015; Kisgen, 2017; Markmann et al., 2013; von der Gracht et al., 2021). For this thesis the targeted panel size was set at a minimum of 80 participants and aimed at an ideal of 100 participants. Individuals from the mapped groups (see Table 6.3) were identified through online searches and the author’s personal network. The final set of experts for invitation included 1,196 contacts. These were invited to participate in the Delphi survey via the online tool Surveylet. This tool was chosen because it offers a wide range of options in the design of Delphi studies (Aengenheyster et al., 2017).

6.2.2.2 Development of Delphi Projections The foundation for selecting experts is the specific design of a Delphi survey. Beyond a focus topic, questions that are to be answered are particularly relevant. In the context of this study, the questions were designed as statements, which should be discussed by the experts. These statements described possible future developments concerning various aspects of higher education. In the context of Delphi surveys, statements that describe future developments are called projections (Gnatzy et al., 2011). In some Delphi studies, projections are supplemented by additional questions (Beiderbeck et al., 2021b). The process of developing projections for Delphi can be designed in various forms. Within this thesis, projections were developed according to the procedure proposed by Beiderbeck et al. (2021b). These authors have developed projections based on various measures, including conducting desk research on the state of research and key challenges in the field of the study or formulating overarching themes in creative workshops with the help of experts, and subsequently developing specific projections. Finally, the projections are refined based on further workshops (Beiderbeck et al., 2021b, p. 5). Figure 6.4 gives an overview of the process of projection development within this thesis, in line with Beiderbeck et al. (2021b). The starting point for developing projections was desk research. It included academic and practical publications that focus on future developments and trends in higher education. A total of 16 publications were included in the final projection development.. Since the number of publications was small, a complementary

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Identification of relevant, global developments in higher education

•

•

•

literature review on the future of and trends in higher education supplementary bibliometric analysis: Web of Science & Google Scholar preparation of an overview of relevant developments

Empirical Approach and Methodology

Preparation of a shortened list of projections: 21 projections

Preparation of a long list of projections: 34 projections

•

•

foundation: overview of relevant developments & conceptualization projections connect elements of conceptualzation and trends in higher education

• • •

expert assessments four experts two experts who have a background in educational science and two experts who have a background in futures studies

Preparation of a (final) list of projections: 10 projections

• •

expert workshop two experts who have a background in educational science

Figure 6.4 Process of projection development. (Own illustration)

bibliometric analysis of scientific publication databases was conducted. This analysis aimed at identifying lines of research and, thus, trends in higher education. It was used as a tool to supplement and validate the desk research conducted in advance (see Fig. 6.4). The bibliometric analysis focused on the Web of Science—Core Collection database. Additionally, the Google Scholar database was included in the analysis via Publish or Perish. “Publish or Perish provides data statistics to Google Scholar, which collects the citations of all papers, independently of the final source the paper was taken from” (Müßigmann et al., 2020, p. 993). Within these databases various search strings were searched for. These search strings resulted from the research interest of this study. They were searched using the Boolean operators in the categories of publication title (TITLE) and publication topic (TOPIC). The following search strings were used: • • • • •

future AND “higher education” future AND university future AND “tertiary education” “trends in higher education” “trends in tertiary education”

Emphasis was placed on terms, such as future, trends, and higher education, and associated synonyms. The analysis included research published since 2015 up to and including 2021. It should be emphasized that the analysis took place in February 2021 and only a limited number of publications from 2021 were included.

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The main results of this brief bibliometric analysis were network illustrations of various keywords. These research maps were illustrated via VosViewer.5 6 The bibliometric analysis did not result in any additions to the existing list of keywords concerning trends in higher education. After completing the bibliometric analysis, the list of keywords was connected to the theoretical and conceptual foundations of this thesis (Part II). This step focused on viewing trends in higher education in relation to the three central tasks of higher education institutions and complementary institutional characteristics of EHEIs. A first set of projections was developed based on various combinations of the described topics. This long list included 34 projections. However, in the process of developing projections, a gradual consolidation was necessary. This was because Delphi surveys that include a comparatively large number of elements usually have lower response rates (Gargon et al., 2019). The basis for defining a target size concerning the set of projections was contemporary prospective studies that use Delphi. These studies integrate 12 to 15 projections in their surveys (Beiderbeck et al., 2021a; T. Meyer et al., 2021; von der Gracht et al., 2021). Against this background, the target size for the set of projections in this thesis was set at 10 to 12 projections. Consolidation was conducted via expert assessments. The long list was presented to four experts. They were asked to assess the 34 projections in terms of their relevance from the perspective of higher education and futures research. To take different perspectives into account, two experts each had a background in educational research and practice and two experts had a background in futures research. The result of these expert assessments was a consolidated set of projections. This list included 21 projections. To reach the target size, a workshop was held with two experts from educational research and educational management. The workshop particularly focused on the projections’ relevance from the point of view of educational research. This workshop resulted in the consolidation of the list into a short list, which comprised 10 projections. These 10 projections were refined and finalized in an exchange with the experts from the expert assessments and the workshop. Both groups of experts were selected in line with the presented expert selection process of the Delphi survey and its inclusion criteria. 5

In the VosViewer tool, the data extracted from the databases were illustrated. Here, the option of illustrating keywords with their frequency in the titles and abstracts of the available publications was chosen. A similar approach can be found in the detailed bibliometric analyses by Fergnani (2019). 6 For a detailed description of how the tool works, see van Eck (2011).

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One challenge in the context of defining projections was to prevent interpretation discrepancies among experts (Salancik et al., 1971, p. 65). The background of this challenge is that people process information differently, especially information that is temporally, spatially, or emotionally distant from them (Markmann et al., 2020, pp. 3–4). Therefore, the specific formulation of projections is a special challenge in Delphi surveys. When formulating projections, different quality criteria should be considered and the content of projections should not be too specific. The optimal length of projections is 20 to 25 words (Salancik et al., 1971, p. 73). Individual projections should not contain too much information. Finally, the projections should possess a low level of abstraction (Markmann et al., 2020). Based on the described process and against the background of the quality criteria outlined, the following 10 projections were included in the Delphi survey in this thesis (see Table 6.4): Table 6.4 Overview set of projections No.

Projection7

Short Title

1

Real output* has established itself as a central element in the evaluation of higher education institutions, such as rankings.

Real output evaluation

2

Higher education institutions have shifted Focus on personality development their content focus unrestrictedly to the development of their students’ personalities.

3

Individual institution-independent researchers have established themselves as the fundamental instance of knowledge production in higher education.

4

Virtual education platforms* have pushed Virtual educational platforms higher education institutions into the role of suppliers of educational content.

5

Higher education institutions have evolved from life stage-related institutions to lifelong and lifewide learning companions.

Institution-independent researchers

Lifelong and lifewide education

(continued)

7

Projections 1, 4, 6, and 9 included a note explaining terms, potentially unknown to the experts. These are indicated in this table via asterisks.

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Table 6.4 (continued) No.

Projection

Short Title

6

Education of excellent academics and practitioners has shifted to centralized, physical environments, called education cities*, while virtual mass learning opportunities have expanded.

Education cities

7

Intelligent digital systems have established themselves as an effective alternative to traditional lecturers in the teaching of technical and methodological knowledge.

Intelligent digital systems

8

With increasing competition throughout the New funding models higher education sector, institutions have started utilizing alternative financing models (e.g., venture capital, fundraising, technology licensing).

9

Performance certificates* have become the central admission and examination criteria in higher education institutions.

Performance certificates

10

Formal educational degrees are awarded by default by a consortium of several institutions.

Consortial degrees

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Results of the Real-time Delphi Survey

The standardized form of Delphi in combination with its qualitative nature result in a special feature, which explicitly affects data analyses. On the one hand, Delphi surveys are evaluated qualitatively, following the nature of the method. On the other hand, quantitative evaluation procedures are also used (Häder & Häder, 2019, p. 705). Day and Bobeva (2005, p. 112) have pointed out the merging of positivistic and quantitative ideals with interpretative and qualitative ideals in the Delphi method. Against the background of quantitative evaluation procedures and their claims, and the associated statistical challenges of Delphi (see Section 6.1), it should be emphasized that quantitative data and associated analyses in this real-time Delphi survey served the purpose of classifying qualitative data. The following chapter provides an overview of the results of this Delphi survey. It starts with an explanation of how the study was conducted. The final expert panel is described before the structure of the survey is specified. The qualitative and quantitative results of the study are then examined in detail. Evaluation methods applied in this study are presented, and their selection is justified.

7.1

Results for Each Delphi Projection

In this subchapter, the procedure of the real-time Delphi survey in this thesis, methods for data analyses, and detailed results of the projections within the realtime Delphi survey are presented. In a first step, an outline of the real-time Delphi survey is presented. Subsequently, an overview of methods used for quantitative Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/978-3-658-40712-4_7. © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_7

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and qualitative data analyses are illustrated. The results of these analyses for each Delphi projection are presented. This presentation of results is structured in the same way for each projection. At the beginning, the projection and its underlying topics are described. Descriptive statistical metrics are then illustrated with the four central dimensions of each projection (EP, I, D, and C). An overview of the quantity and structure of qualitative contributions is followed by a presentation of central lines of argumentation that experts provided for EP and I. Finally, a summary is provided for the results of each projection. At the end of this subchapter, concluding, overarching remarks concerning the described results are presented. In advance of the illustration of results for each projection, it must be noted that the frequency of responses to different projections varied. This fact was dealt with by listing the respective number of assessing experts per projection. The number of experts assessing a projection was displayed as n.

7.1.1

The Real-time Delphi Survey in Detail

The real-time Delphi survey was conducted over a period of 11 weeks between May 2021 and August 2021. The original planning allowed for a survey period of eight weeks. Due to the response situation within the survey, it was decided to extend the study twice after it began. At the conclusion of the survey on August 10, 2021, 184 individuals had entered the online tool. In some cases, after viewing the study, a notice was sent to the author that participation was declined due to lack of expertise. Other experts did not participate in the study without explanation. This real-time Delphi survey conclusively included 114 expert participants. Participation was, thus, at 9.53% (114 of 1,196 invited experts). Full responses to the questionnaire1 , including personal information, were received from 90 experts. After adjusting for misunderstandings during the qualitative data analysis (see Subchapter 7.1.1), 81 full participations with demographic data were recorded. Therefore, between 81 and 114 people assessed the entire questionnaire. The ratio of invitations to full participation was 6.77% (81 of 1,196). Nevertheless, the targeted size of the expert panel was achieved. In the online tool, the experts encountered a welcome screen. On this screen, various aspects of the study were explained, such as the topic, objective, background, and contact persons. On the second page, an overview of the content and 1

An overview of the survey questionnaire can be found in the electronic supplementary material.

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timeline of the survey was illustrated. In addition, a tutorial on the method of the Delphi survey was presented to enable methodological transparency. Following these two organizational pages, each of the 10 projections could be assessed on separate pages. In this context, the three dimensions of expected probability of the development (EP), impact on higher education institutions (I), and desirability of the development (D), which are common for prospective Delphi studies, were assessed by each expert. With reference to the assessment of the EP, two scales are currently in use, which are the fixed and flexible formats. The former refers to scale-based assessment of EP at one or more fixed points in time. The latter focuses on the time of occurrence. Here, an EP of 100% is assumed and experts assess when the development is likely to occur (Beiderbeck et al., 2021b, p. 8). In the context of this thesis, EP was assessed on a fixed scale. Four time periods were presented and assessed according to their EP on a scale of 0% to 100%: in 5 years (2026) (EP5), in 10 years (2031) (EP10), in 15 years (2036) (EP15), and in 20 years (2041) (EP20). This format was selected to complement the fixed scale using elements of the flexible scale. Complementary scales have the advantage of including shortand medium- and long-term perspectives and elements (Beiderbeck et al., 2021b, p. 8). A similar structure in the context of assessing EP was used by T. Meyer et al. (2021). Assessments of I and D were made with 5-point Likert scales.2 The experts were asked to provide qualitative assessments in the form of text contributions on EP and I. Beyond these three central dimensions, experts were asked to rate how confident they were in their assessment. This self-assessed confidence (C) represents an indicator for classifying the level of expertise of the expert panel. This is used to retrospectively examine the extent to which unsuitable experts were selected. It enables the categorizing of experts. This dimension can be considered useful, since significant differences in the assessments of experts with high and low self-perceived expertise could be observed in further studies (Best, 1974, p. 450). Asking experts for their self-perceived confidence can thus contribute to the identification of cognitive biases.3 Subsequently, the experts were asked for their general opinion on the relevance of EHEIs in the future. Therefore, experts’ personal predispositions were incorporated in the survey, since these can have an unconscious influence on expert 2

For the application of Likert scales in relation to dimensions I and D, see, among others Ecken et al. (2011); T. Meyer et al. (2021); Roßmann et al. (2018, p. 138). 3 For the use of self-perceived confidence in Delphi studies, see, among others, Beiderbeck et al. (2021b); T. Meyer et al. (2021).

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assessments (Loye, 1980). Following Beiderbeck et al. (2021b), the locus of control (LoC) framework was used. The framework was first described by Rotter (1966). Locus of control is also referred to as internal versus external control of reinforcement (Rotter, 1990, p. 489). The key point of the concept is examining control beliefs of individuals. It captures the extent to which individuals believe that events are influenced by their own actions or unfold independently of themselves. The former is understood as internal control of reinforcement (IC). The latter is described by the concept of external control of reinforcement (EC) (Rotter, 1966, p. 1, 1990, p. 489). At the end of the survey, the experts were asked to provide information on various personal dimensions (e.g., age and country of residence) before gratitude was expressed and an overview of all assessed projections was provided.

7.1.2

Data Analyses

The starting point for data analyses in this Delphi survey was descriptive statistical measures regarding the projections. First, for each dimension of a projection the mean of quantitative expert assessments was presented. In addition, the consensus of the expert panel was highlighted to classify the results. A wide variety of indicators are suitable for representing consensus in Delphi surveys.4 A combination of mean and standard deviation (SD) is often used. Consensus is achieved within the frame of these indicators if SD is less than 41% of the underlying scale (Gracht, 2012, pp. 1530–1531).5 In other publications, consensus is measured via the majority of opinions. Here, consensus was reached when the majority of opinions were located on (less than) one-third of the underlying scale (Hsu & Sandford, 2010, p. 344).6 Beyond mean and SD, and majority opinion, a combination of mean and IQR is regularly used to determine consensus in Delphi surveys (Birko et al., 2015, 4; von der Gracht, 2012, p. 1531). The IQR shows in which range 50% of the experts opinions are located. von der Gracht (2012, p. 1529) has described the threshold concerning consensus measurement via IQR as depending on the study.

4

For a comprehensive account of, and discussion of, the various metrics used to represent consensus in Delphi surveys, see Birko et al. (2015); von der Gracht (2012). 5 The original presents an SD of + /− 1.64 which accounts for 41% of the underlying 4-point scale. 6 The original presents 80% of opinions within a 2-point range on a 7-point scale.

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Usually, consensus is achieved when IQR is less than 14% to 33% of the underlying scale.7 Meaning, 50% of opinions can be found within 14% to 33% of the scale. Concerning this real-time Delphi study, a combination of mean and IQR was used as a measure to adequately show dimensions that reached consensus. This is because IQR “is generally accepted as an objective and rigorous way of determining consensus” (von der Gracht, 2012, p. 1531). The threshold was defined as an IQR less than, or equal to, 25% of the respective scale, which is in line with Kisgen (2017, p. 222) and within the range presented above. Consequently, consensus is reached for EP if IQR is ≤ 25 (100-point scale). For I and D, consensus is reached when IQR is ≤ 1.25 (5-point scale). Dimensions for which the experts reached consensus are highlighted in the following subchapters. Qualitative text contributions were evaluated with the help of a brief syntax analysis and qualitative content analysis (QCA). In syntax analysis, the experts’ text contributions are categorized. This aims at obtaining an overview of the nature and structure of qualitative contributions. This represents an indicator of the experts’ level of engagement. Within the framework of the syntax analysis, the concept proposed by Förster and von der Gracht (2014) was used. This focuses on classifying text contributions into three categories of whole sentences, phrases, and catchwords (Förster & von der Gracht, 2014, p. 225). A high number of whole sentences indicates a higher engagement of the experts, while a high number of catchwords indicates a lower engagement. QCA focuses on the specific content of text contributions. It is a method that processes texts that arise during collecting data, for example, transcripts of open-ended interviews or responses to open-ended questionnaires (Mayring, 2014, p. 43; Mayring & Fenzl, 2019, p. 633). In the context of today’s QCA, the originally quantitatively oriented content analysis8 is extended by basic ideas of hermeneutics. The focus is not only on the manifested text content, but also on its meaning (Kuckartz, 2018, p. 21; Kuckartz & Rädiker, 2022, p. 35). Different forms of QCA exist. In this work, the content structuring QCA (German original: inhaltlich-strukturierende qualitative Inhaltsanalyse) was used (Kuckartz, 2018, pp. 97–123; Kuckartz & Rädiker, 2022, p. 104 f.). This form of QCA focuses on identifying topics and subtopics, structuring them and analyzing their relations (Kuckartz, 2018, p. 123; Kuckartz & Rädiker, 2022, p. 157). In 7

In the original, different values are shown for different scales, such as, 1 point on a 7-point scale, 2 points on a 10-point scale, 1 point on a 4- or 5-point scale, and 1 point on a 3-point scale. 8 For an overview of the limitations of quantitative-oriented content analysis and the necessity of integrating hermeneutic elements, see Kracauer (1952).

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the process, qualitative data is analyzed iteratively. According to Mayring (2014, 2015, 2020), the material is coded and recoded several times. Content-structuring QCA deals more intensively with individual cases and, thus, with a smaller amount of data material than other forms of QCA, such as evaluative qualitative content analysis (German original: evaluative qualitative Inhaltsanalyse).9 In the context of QCA, coding is the first step, after transcribing collected data. This step can be deductive or inductive (Mayring, 2002, p. 142).10 In the inductive approach, data are gradually reduced by summarizing the text into paraphrases. Subsequently, the text is systematically analyzed by means of a selection criterion, which is the code definition (Mayring & Fenzl, 2019, p. 637). The deductive approach is characterized by the fact that codes are deduced from a theoretical background before the data are collected. These codes can be revised repeatedly during the QCA (Mayring & Fenzl, 2019, p. 638). Coding within this thesis was conducted in a mixed form of inductive and deductive approaches. Main codes were defined deductively based on the structure of the Delphi survey. Subcodes were also developed deductively alongside the Delphi design. During the data analysis, further subcodes were defined inductively based on the data. Resulting from coding, the original 939 text contributions were itemized as 1,099 text passages. The second step of QCA after coding is data reduction. Data reduction consists of three basic steps of paraphrasing, generalizing, and reducing (Kuckartz, 2018, pp. 73–76; Kuckartz & Rädiker, 2022, p. 83 f.; Mayring, 2015, p. 72). Each categorized text passage is first paraphrased. Then, the paraphrase is generalized before being reduced and summarized in the last step. Reducing and summarizing the data is viewed by Mayring (2015, p. 72) as a first and a second reduction. The former refers to the deletion of redundant text passages. The second step involves the subsequent summary of text passages that possess the same meaning (Mayring, 2015, p. 72). This procedure was applied in the context of the data analysis within this thesis. A central aspect of QCA is interrater reliability, also called intercoder reliability or intersubjective agreement. This aims at ensuring the reliability of results (Mayring, 2014, p. 42). Krippendorff (2018, p. 215) has described the process of examining interrater reliability as two or more individuals independently analyzing the same items with the same guidelines. The focus is on comparing the 9

For evaluative qualitative content analysis, see Kuckartz (2018, pp. 123–142). Later works add Mayring and Fenzl’s (2019, p. 637) explication, as a third variant of category formation and application. Explication is also called context analysis. Here, “individual unclear text passages are made the object” (Mayring & Fenzl, 2019, p. 637).

10

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143

coding of two independent coders. “In research projects working with relatively small bodies of data, the second coder usually codes a subset of the data coded by the primary coder, checking for reliability” (Cornish et al., 2014, p. 82). For investigating interrater reliability, various methods and associated metrics have been developed over the past decades. Holsti’s, respectively Osgood’s method, Scott’s Pi (π), or Krippendorff’s Alpha (α) can be mentioned here (Holsti, 1969; Krippendorff, 2018; Osgood, 1959; W. A. Scott, 1955).11 These methods and their metrics differ concerning their scale level. Only some of them consider coincidental matches of distinct coding results. The latter factor determines whether a metric is perceived as liberal or conservative (Lombard et al., 2002, p. 594). Regardless of method and metric, there is no consensus concerning the level of interrater reliability to interpret results as reliable. Krippendorff (2018, p. 241) has suggested setting the threshold at 80% accordance of interrater codings. An interrater analysis result of 66.7% to 80% can serve as a basis for cautious interpretation (Krippendorff, 2018, p. 241). In further studies, a threshold value of 90% is considered generally accepted. Moreover, there is widespread consensus even at a value of 80%. Strong dissent exists at lower values (Neuendorf, 2002, p. 143). Lombard et al. (2002, p. 594) have emphasized that in exploratory studies the use of a threshold of 70% is sufficient. In the context of this research, Holsti’s (1969) method was applied to test for interrater reliability. The method compares ratings of various coders by viewing the number of coders in relation to the number of consistent codings and all codings integrated in the analysis. Within this frame a metric is calculated via the following equation: 2 * number of consistent codings (number of codings for coder 1 + number of codings for coder 2) The threshold for interrater reliability was set at 70% since this research is exploratory. Lombard et al.’s (2002, pp. 600–602) recommendations were considered and their suggested procedure was applied. A second coder was familiarized with the coding system. Subsequently, a pretest was carried out. Ten text passages, one text passage per projection, were coded by both coders. Afterwards the results and coding of the data were discussed. Coding of the data was performed by the author. For the second coder, a representative sample of randomly selected data for each projection was extracted. 11

For a comparative overview of procedures and metrics with respect to interrater reliability, see Lombard et al. (2002); Krippendorff (2018).

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The second coder coded 102 text passages, which account for approximately 10% of the data. Finally, agreement of the coders, according to Holsti (1969), was calculated. This calculation focused on the sample of 102 text passages. Analysis resulted in a value of 74.51% for the calculated metric. Considering the threshold value of 70%, interrater reliability in coding the data was achieved. It should be noted, however, that this is partly due to the comparatively low setting of the threshold.12

7.1.3

Projection 1—Real Output Evaluation

Various tools are used to determine the quality of higher education institutions. One of these is institutional rankings. In academic literature, these are often criticized for lack of validity and reliability.13 Aspects in the foreground concern research and teaching (Kroth & Daniel, 2008, p. 554). Especially third mission activities14 of institutions are not taken into account in current rankings (Urdari et al., 2017). However, those activities, also referred to as innovation in this thesis, are highly relevant for a holistic view of higher education institutions. Therefore, modifying evaluations of higher education institutions, such as rankings, may become relevant in the future. For this reason, a central topic in the context of projection development was incorporating real output, in the form of societal innovations (see Part II, Subchapter 4.4), into the evaluation of higher education institutions. The first projection focuses on this very topic. It aims at identifying to what extent experts think that integrating real output into evaluation of higher education institutions will happen and become relevant in the future. Table 7.1 provides an overview of the projection and associated quantitative assessments of the experts. 114 experts assessed this projection. They estimated that EP in the next five years is likely to be at 27.03%. The development was thus not viewed as probable in the short term. However, during the subsequent five to 15 years, EP increases. In 20 years, the experts saw EP at more than 55%, which can be regarded as a high probability. The impact of development on higher education institutions

12

A detailed overview of interrater reliability analysis in this thesis is provided in the electronic supplementary material. 13 For a detailed description of the criticism of rankings, see Part II, Subchapter 4.5.1. 14 For a detailed description of the third mission of higher education institutions, see Part II, Subchapter 4.4.

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Table 7.1 Descriptive statistics regarding projection 1 No.

Projection for the future

1

Real output has established itself as a central element in the evaluation of higher education institutions, such as rankings.

n = 114 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

27.03

45

3.75

1

3.59

2

3.39

1

2031

40.38

40

2036

48.37

55

2041

55.58

60

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact on higher education institutions if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved. IQR ≤ 25% of the respective scale equals consensus, and for EP ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

was also rated as high, and the same applied for the perceived desirability of the development (see Table 7.1). As a measure of consensus, IQR showed that the expert panel was characterized by dissent and strong dissent concerning EP at each of the specified points in time. Therefore, experts offered diverse opinions on the probability of occurrence of the described development. The experts also disagreed on the desirability of the event. Regarding impact, consensus was observed. Hence, the panel agreed that the development had a high impact on higher education institutions (see Table 7.1). Experts justified their assessments by contributing arguments via text. A total of 115 text contributions were made in the context of this projection. Syntax analysis showed that more than 80% of the contributions (94 out of 115) were complete sentences. All other contributions were structured in the form of phrases. No contribution consisted of catchwords (see Table 7.2). This result implies a high rate of engagement by the experts regarding Projection 1. In view of the diversity of experts’ opinions, it was reasonable to investigate the contentual structure of qualitative text contributions in addition to their syntax (see Table 7.3).

146 Table 7.2 Details of qualitative arguments for Projection 1

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Results of the Real-time Delphi Survey

Projection for the future

1 Real output evaluation

Quantity of arguments

115

Syntax Whole sentences Phrases Catchwords

94 21 0

Table 7.3 Contentual structure of qualitative arguments for Projection 1 Quantity of paragraphs

176 Overall

EP

I

Supportive arguments (PRO)

58

35

23

Nonsupportive arguments (CONTRA)

66

64

2

Indifferent arguments

20

General remarks

29

Misunderstandings

1

Coding resulted in 176 text passages that were derived from originally 115 text contributions. Of the text passages, 58 were identified that supported the projection and 67 that did not support the projection.15 In the supportive text passages (PRO), 35 were concerned with EP and 23 focused on I. The experts provided 64 nonsupportive arguments (CONTRA) for EP and two for I. The contentual structure, thus, reflected the strong dissent regarding EP. A majority of PRO arguments was visible for I. Only one misunderstanding of the projection was identified. Arguments that were PRO concerning EP could be described with two lines of argumentation. First, the experts pointed to the increasing relevance of real output delivered by higher education institutions. This line of argumentation focused on external pressures on institutions. The experts argued that disruptive societal challenges significantly increase the relevance of applying knowledge and consequently creating real output. Additionally, real output from institutions was increasingly demanded by politics, business and society in general. For example, 15

Supporting the projection meant, in the context of EP, justifying a high probability of occurrence. Regarding I, this meant arguing for a high impact of the development on higher education institutions. The nonsupportive text passages should be understood as diametrically opposed to this.

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one expert said: “I am fully convinced that the pressure (socially and ecologically) that companies are already experiencing (source = consumers) will do nothing but increase and also hit higher education institutions”. Second, the experts described reasons for the relevance of real output in the evaluation of higher education institutions. It was emphasized that a focus of evaluation on real output becomes necessary. One argument here highlighted the weaknesses of current evaluation systems in higher education that necessitate a changed focus on real output, and continued, “rankings and evaluations will include more qualitative aspects in the future, as only they allow a full picture”. The experts thought that qualitative aspects in evaluation and the underlying need for a holistic evaluation of higher education institutions will increase. Central CONTRA-arguments for EP can be described alongside three lines of argumentation. The most frequently cited reason against a high probability of occurrence of Projection 1 referred to methodological problems that the concept of real output entails. The essence of the argument was reflected by a rhetorical question one expert posed: “How would you measure innovations or impact on society?”. From some experts’ point of view, real output was thus not quantifiable, which obstructs the entry of the projection. A further line of argument for low EP of the projection related to a lack of support by relevant stakeholders. The experts argued that existing structures would be actively sustained due to a lack of support from politics and the labor market. This argument focused on the idea that politics and the economy do not want to see real output integrated in the evaluation of higher education institutions. This was because both stakeholder groups emphasized training capable employees over creating real output in higher education. This could also be seen as an argument for sustaining existing evaluation structures. In this context, one critical expert addressed the aspect of power that various groups of people have gained through the commercialization of higher education and its implication for changes: “Higher education … has become primarily a commercial activity that the administrators and tenured ‘experts’ will fight to defend against change”. Concerning I the experts saw various areas of impact in higher education institutions. The curriculum needs to adapt to real output. This referred to programs, teaching, educational content, and the evaluation of students. Additionally, the experts said that selection processes of institutions would change. The experts predicted a change in the focus of higher education institutions. Prioritizing research and teaching was supplemented by the element of innovation. In this context, one expert pointed out the danger that a change in focus on real output

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could bring: “This will most likely result in strong managerial control over all academic activities with the aim to ensure maximal impact”. In summary, diverse assessments were made by the experts. The occurrence of the projection could be probable because external pressure made it necessary. On the other hand, the projection could not occur because relevant stakeholders did not support it and the concept of real output was not quantifiable. The impact of development on higher education institutions was perceived by the experts to be multifaceted, from curriculum, to selection, to institutional focus. Likewise, the dangers of such a development were addressed. These served as indicators of dissent regarding the desirability of the projection.

7.1.4

Projection 2—Focus on Personality Development

The fundamental task of leadership is to create an innovative and creative future for human communities.16 Achieving this task requires a creative personality (W. G. Faix & Mergenthaler, 2015). Personality development can only be achieved by individuals themselves. It emerges from an education that is oriented toward certain standards (Tippelt, 2013, p. 249). Education in the sense of enabling personality development is a central aspect within the concept of contemporary EHEIs in this thesis.17 In the context of projection development, the focus was on the relevance of personality development for shaping society’s future. It was of interest what implications a widespread integration of personality development into higher education in the future would have on EHEIs. Against this background, Projection 2 focused on the question of whether higher education institutions would shift their focus to the personality development of their students in the future (see Table 7.4). EP for Projection 2 was seen at less than 15% in five years. The short-term entry of this development into higher education was, therefore, perceived as unlikely. Overall, the experts did not see a strong increase in the EP of the projection until 2041. The final assessment was at 31% in 20 years. The impact of a changed focus of higher education institutions on personality development was rated high, with desirability being medium. An IQR of 22.5 indicates that the 103 experts agreed on the assessment of short-term EP. Likewise, there was consensus on the strong impact the projection would have on institutions if it were to occur. Concerning 16 17

For details, see Part II, Subchapter 3.2. For details, see Part II, Subchapter 4.2.

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Table 7.4 Descriptive statistics regarding Projection 2 No.

Projection for the future

2

Higher education institutions have shifted their content focus unrestrictedly to the development of their students’ personalities.

n = 103 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

14.92

22.5

3.59

1

2.97

2

3.52

1

2031

20.36

30

2036

26.31

42.5

2041

31.05

50

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus: for EP ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

medium- and long-term EP, the panel was characterized by dissent. The experts also disagreed regarding the desirability of the projection (see Table 7.4). Qualitative justification of assessments comprised a total of 77 text contributions. Syntax analysis illustrated that 69% of contributions (53 of 77) were formulated in complete sentences. In addition, 23% of contributions consisted of phrases and 8% of catchwords (see Table 7.5). These results display a lower level of engagement of experts than in Projection 1. Nevertheless, it indicates an overall high level of engagement, since more than two-thirds of contributions were formulated as whole sentences. Table 7.5 Details of qualitative arguments for Projection 2

Projection for the future

2 Focus on personality development

Quantity of arguments

77

Syntax Whole sentences Phrases Catchwords

53 18 6

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Coding of the 77 text contributions resulted in 84 text passages. The structure of content was characterized by CONTRA arguments concerning EP and PRO arguments concerning I. A ratio of six PRO and 24 CONTRA arguments for EP underlined the low values regarding quantitative expert assessments. I comprised 24 PRO arguments and one CONTRA argument. This result also reflects the prevailing quantitative opinion of the panel. For Projection 2, two misunderstandings were identified within the qualitative data (see Table 7.6). Table 7.6 Contentual structure of qualitative arguments for Projection 2 Quantity of paragraphs

84 Overall

EP

I

Supportive arguments (PRO)

30

6

24

Nonsupportive arguments (CONTRA)

25

24

1

Indifferent arguments

7

General remarks

20

Misunderstandings

2

Despite the many CONTRA arguments on EP of Projection 2, some experts believed that the development was probable. The reasons for this were structured alongside two lines of argumentation. The first was student demand and an increased focus on student needs. For example, one expert said that “if students are the driver of content demand, supply side must change rapidly”. On the other hand, various societal developments that exert pressure on higher education institutions were raised as an argument for high EP. For example, digital technologies that enable free access to knowledge reduce the relevance of knowledge. Consequently, a reorientation of higher education institutions to a more holistic education is necessary. One expert stated: Knowledge of facts becomes less and less important as you can Google everything or ask your robot assistant to do so in the future. Hence, the evaluation, integration, valuation, discussion, and assessment, and combination of the facts and strands of knowledge is what people are going to be educated for. And for this, values, emotions, and world views, and the ability for critical thinking and reflection is central.

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Another expert added that society’s development toward a knowledge society creates the need for reorientation of higher education institutions. This reorientation should be directed toward humanistic education18 . According to the experts on the other side of the argument, higher education institutions would continue to focus on imparting knowledge and skills in the future. On the one hand, this is because institutions tend to be conservative and would therefore retain their traditional areas of expertise, as some experts stated. On the other hand, experts said that science and higher education institutions should not focus on personality development, but solely on scientific progress. Individual experts argued that the development of curricula was not oriented to student demands but to topics. Another CONTRA argument related to the late stage in students’ lives at which higher education institutions intervene. At this stage, students’ personalities are already extensively developed. Consequently, institutions would not need to focus on personality development of their students in the future. Some experts took a critical view on formalizing personality development because defining formal educational goals with a focus on personality development entails an essential problem. One expert pointed out that Projection 2 would trigger a profound discourse about what the target personality of higher education should look like. Defining this was perceived by some experts as undemocratic: it is simply difficult, even very problematic from a democratic point of view, to link personal development with formal education. In democracies, individuals are absolutely free to develop in ways and toward goals of their own choosing. A specification of educational goals, however, contradicts this free development.

Mass education, the diversity of the student body and the individualization of education lead to high resource requirements in higher education. Other experts pointed out that these needs could not be met by institutions regarding the personality development of their students. Therefore, a lack of resources in higher education institutions opposed the occurrence of Projection 2. Finally, experts argued for a low EP of Projection 2 because it lacked support from relevant stakeholder groups. The development would have to be supported by politicians and by lecturers for it to be successful. The experts did not see this as probable. They elaborated that institutions would only initiate a change of their focus if the labor market demanded it. One expert also pointed out that students are not interested in such a development: “Unfortunately today’s students want to get their education quickly and 18

For details on humanistic education, see Part II, Subchapter 4.2.

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there is limited time to introduce them to all of the areas we would like to introduce them to”. The experts particularly saw changes in curricula as an impact of the development on higher education institutions. Curricula focus more on individual students, for example, in teaching. They become more open and flexible to cater to the different learning types within the student body. Overall, experts said that the focus of institutions would shift because of the development. Instead of knowledge, personality, and thus individualized education were coming to the fore. In their assessment, some experts distinguished between the positive and negative impact of the development. It results in a positive impact on the students: “Encouraging personal development would have a strong impact on society and would likely create a different perspective. In principle, all trainees would have the chance to develop to their full potential”. A negative impact was particularly evident regarding resources of institutions, as the development necessitates high financial investments in infrastructure. The development leads to increased private competition compared to public institutions. Overall, the role of institutions would also change: “As in the meantime more than 50% of a particular age group starts to study, universities take on the role of the skilled trade and will deliver the modern version of vocational training”. In summary, the occurrence of the projection was probable because such a development is demanded by students and pressure is created by the decreasing relevance of knowledge. Nonoccurrence of the projection was justified by the lack of resources and support. It was argued that higher education institutions would not recede from their traditional and existing expertise. The integration of personality development into formal education was criticized. Arguments on the development’s impact focused on changes in the curriculum. Institutional focus, institutional role, and influence on institutional resources were addressed as impacts.

7.1.5

Projection 3—Institution-Independent Researchers

For several decades a societal trend toward individualization has been evident (e.g., Frank & McEneaney, 1999). “... Individualization refers not to individuation, but to a structural change of the relation between individual and society resulting in the individual taking precedence over society or social communities” (Rasborg, 2017, p. 230). Individuals thus increasingly align their actions to their

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own interests, goals, and situations. They aim at avoiding external determination of their lives driven by society. This focus on individuals, their interests, goals and situations is also evident in higher education. As early as the last century, scientists wrote about the individualization of teaching (Goldschmid & Goldschmid, 1974). A focus of the scientific literature is on the individualization of learning. In this context, a key term is “adaptive learning” (Saba & Shearer, 2018). Against the background of individualized teaching and learning, approaches to individualizing entire curricula are already being designed (Moye, 2019). The preceding remarks, and underlying literature, show that individualization of teaching and learning in higher education is already deeply investigated. The first central task of higher education institutions is thus already considered in connection with the social trend of individualization. For this reason, the projection development focused on the second central task of institutions in connection with individualization: research. The final projection design focuses on a structural change in research in the future, that is induced by individualization. It describes individual researchers, independent of higher education institutions, as the new central instance of knowledge production (see Table 7.7). Beyond individualization and research the projection implicitly integrated the question of whether and to what extent people can work together without being bound to institutions (e.g., Bondar, 2013). The 91 experts estimated the EP of this projection to be low. This was particularly true for the short and medium term. In the long term (up to 2041), the average estimate for EP increased to approximately 29%. Within this frame, I was perceived as medium. The experts viewed D as low. Consensus in the expert panel could be identified concerning EP in 2026 (IQR = 25). Further temporal dimensions of EP were characterized by dissent. The experts also disagreed on medium I and low D of the development. This is indicated by an IQR of 2 for both dimensions (see Table 7.7). Syntax analysis for Projection 3 showed that the experts submitted 85 text arguments and 69.4% (59 of 85) were formulated as complete sentences, and 20% (17 of 85) took the form of a phrase. Consequently, just over 10% (9 of 85) of the arguments were formulated as catchwords. Therefore, analogous to the previous projections, the expert panel displayed a high engagement for this projection (see Table 7.8). During coding, the 85 arguments were categorized in the form of 97 text passages. The content structure of these text passages reflected the results of the quantitative assessments. Only four text passages were identified in the dimension EP that argued for the occurrence of the development. In contrast, 16 passages

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Table 7.7 Descriptive statistics for Projection 3 No.

Projection for the future

3

Individual institution-independent researchers have established themselves as the fundamental instance of knowledge production in higher education.

n = 91 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

17.48

25

3.27

2

2.24

2

3.35

1

2031

22.11

35

2036

25.74

45

2041

28.64

50

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus: for EP ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

Table 7.8 Details of qualitative arguments for Projection 3

Projection for the future

3 Institution-independent researchers

Quantity of arguments

85

Syntax Whole sentences Phrases Catchwords

59 17 9

were provided that argued for why the development would not occur. Regarding I, 39 of 41 text passages were identified that argued for a high impact of development on higher education institutions. A total of 28 text passages were general comments on the development described in the projection. In addition, three text passages could be identified, which indicated a misunderstanding concerning Projection 3 (see Table 7.9). Experts who argued for high EP of the development listed developments in society that promoted it. One such development was increased freelancing as an employment model in the economy. One expert stated that “there is a trend in the direction of more freelance work, especially in the US. Maybe such a

7.1 Results for Each Delphi Projection

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Table 7.9 Contentual structure of qualitative arguments for Projection 3 Quantity of paragraphs

97 Overall

EP

I

Supportive arguments (PRO)

43

4

39

Nonsupportive arguments (CONTRA)

18

16

2

Indifferent arguments

5

General remarks

28

Misunderstandings

3

development is also possible in science”. This is accompanied by demand for time and location flexibility in the workplace. According to the experts this demand is expressed by employees and therefore also researchers at higher education institutions. The Open Science movement was seen as a factor that promoted a high EP for this projection. Another expert provided a different argument. He wrote that digital platforms offer the opportunity for individual researchers to position themselves and build a strong personal brand that goes beyond the visibility of an institution. The arguments of experts who viewed the projection as improbable can be considered along two lines of argumentation. First, they pointed out that the current research system is robust. Hence, it functions and has established structures that are difficult to change. According to some experts, the conditions for raising financial resources make the existence of institutions and their structures indispensable. Institutional resources are the second argument used against the occurrence of Projection 3. Another argument for low EP was that individual researchers lack both financial and time resources for institution-independent research. In addition, one expert said, “I think it’s a given situation that research needs a strong network of science and money only institutions can provide”. Consequently, the experts did not see the will of researchers to promote the projection since institutions offer an established infrastructure and social capital in the form of extensive networks (see also, Part II, Subchapter 5.5). The impact of the projection on higher education institutions was viewed in a variety of ways. Experts saw a shift in higher education institutions’ focus. Institutions turn away from research and increasingly move toward education, that is, teaching and learning. Ways of integrating new knowledge into curricula are improved. One expert described the impact as, “less long-term knowledge

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production. At the same time: more chances to get really good and interesting new knowledge into your own institution”. According to the expert panel, with this change in focus, the institutions lose their leading position in the field of research and thus part of their reputation. Consequently, financial support for the institutions would diminish, which would reduce their capacity for large infrastructure. Further negative impact results from changes in the research process. Individualized, institution-independent research results in less collaboration. Consequently, interdisciplinarity suffers. In addition, basic research decreases and the research process becomes more complicated. Beyond institutions, some experts described an impact on general aspects of higher education. Decreasing independence of research was described as a risk. The development results in increased commercialization of research and knowledge production. Therefore, ties to third party interests concerning research and science increase. The work situation in higher education was discussed. According to the experts, new employment models, flexible working and goal-oriented zero-hour contracts find their way into higher education. Finally, a change in the entire higher education system was highlighted. One expert pointed out that “educational institutions are still very hierarchical and conservative. Such developments could help to soften this and create freer structures and opportunities for science and those concerned”. The stratification of higher education through research would thus decline and allow heterarchy to replace hierarchy in the future. In summary, the experts saw the occurrence of the projection as probable due to various current developments in society, such as increased freelancing. On the other hand, arguments regarding currently established structures and the problem of too few resources for institution-independent research were provided concerning low EP. The I of the development was expressed through various changes within higher education institutions, in their environment, and in the higher education system. For example, the focus and role of institutions could shift from research to education. The structure of the higher education system could evolve toward less hierarchy.

7.1.6

Projection 4—Virtual Educational Platforms

The previous projection focused on individualization. Simultaneously the trend of digital transformation proceeds. This involves establishing digital processes and systems in organizations of all kinds. Digital transformation supports individualization as it enables decentralization. This can also be observed in (higher)

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education. Castañeda and Selwyn (2018) have offered an example since they write about the (hyper-) individualization of education through digital technologies. In light of the two aforementioned trends, “universities face the question whether their ‘old business model’ of residential learning is still valid or whether online learning might substitute for it” (Huber, 2016, p. 95). As a results, higher education institutions have been adapting to digital developments for several years by cooperating with providers of digital learning platforms or establishing such platforms themselves. These virtual learning platforms are also being studied in detail in research. The focus here is, for example, on integrating platforms into traditional study formats and on improving the quality of learning (Valencia et al., 2017) or the effect of platforms on the improvement of students’ skills (Ahmed & Hasegawa, 2019). Projection development centered on connecting aspects of the first central task of higher education institutions and digital transformation, in the form of virtual education platforms. The final projection focused on the increased entry of digital platforms into the field of higher education and its implications for the role and function of higher education institutions (see Table 7.10). Table 7.10 Descriptive statistics for Projection 4 No.

Projection for the future

4

Virtual education platforms* have pushed higher education institutions into the role of suppliers of educational content

n = 93 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

34.22

40

3.61

1

2.65

2

3.7

1

2031

44.73

50

2036

51.52

50

2041

55.91

55

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus: for EP ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

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This projection was assessed by 93 experts. According to the expert panel the occurrence of the development in the next five years is moderately probable (34.22%). By the year 2041, the estimated probability of occurrence increased by 21% to approximately 56%. Overall, the experts regarded the projection as probable in the long term. However, there was no consensus on this within the expert panel. Interquartile range for each EP dimension was at least 40, indicating strong dissent. The panel also disagreed on the medium desirability of the development (see Table 7.10). Beyond this dissent, the experts agreed on the assessment of I with a value of 3.61 and an IQR of 1. The experts agreed that the projection would have a strong impact on higher education institutions if it was to occur. An IQR of 1 concerning C indicates that the level of dispersion was also low. Therefore, the experts were confident in their respective assessments (C = 3.7). The 93 experts provided a total of 84 text contributions. Not every expert provided a qualitative assessment for the projection. However, the syntax of contributions reflected a high level of engagement on the part of the experts. Of the arguments, 82% (69 of 84) were formulated as complete sentences. In addition, 12 contributions had the structure of a phrase. Thus, only three contributions were formulated as catchwords (see Table 7.11). Table 7.11 Details of qualitative arguments for Projection 4

Projection for the future

4 Virtual educational platforms

Quantity of arguments

84

Syntax Whole sentences Phrases Catchwords

69 12 3

The 84 text contributions were itemized into 94 text passages as part of the QCA. The content structure of the text passages underscored the quantitative assessments of the experts. In the EP dimension, nine PRO and 10 CONTRA arguments were submitted. This indicates disagreement among experts for this dimension. Concerning I, 44 out of a total of 49 passages were submitted regarding high impact of the projection on higher education institution. This also reflected the assessment and associated consensus depicted in the descriptive metrics. Nine further text passages suggest an indifferent assessment by some experts (see Table 7.12).

7.1 Results for Each Delphi Projection

159

Table 7.12 Contentual structure of qualitative arguments for Projection 4 Quantity of paragraphs

94 Overall

EP

I

Supportive arguments (PRO)

53

9

44

Nonsupportive arguments (CONTRA)

15

10

5

Indifferent arguments

9

General remarks

17

Misunderstandings

0

Arguments that were PRO for the occurrence of Projection 4 focused on the COVID-19 pandemic and the accompanying increased digital transformation (of higher education). One expert focused his argument on the need for higher education institutions to deliver content virtually, which results from the pandemic. Another expert further reinforces the relevance of online education, even after the pandemic. “Covid has demonstrated the feasibility of large-scale online education. It’s now a fixture, whatever happens with the pandemic”. Here, the focus was particularly on the financial potential of virtual education for higher education institutions. Opposing experts argued that virtual education was gaining relevance, but that institutions were not being displaced from their role by platform providers. There were various reasons for this. One expert thought that higher education institutions would establish their own platforms. Another expert did not see that the established structure of today’s higher education system would change as much as described in the projection. This was justified by the statement that higher education institutions “are strong institutions with a big lobby, they cannot be pushed aside so easily”. Therefore, good networks of higher education institutions contradict a radical development described in the projection. Experts saw a wide variety of impacts of the projection for higher education institutions. Some experts spoke of increasing competition in the higher education system, both between institutions and with new competitors from the technology sector. Another expert saw the possibility for institutions to enter collaborations with platform. According to the experts, one negative impact of the projection is increased resource consumption for higher education institutions. Establishing a digital infrastructure, including their own platforms, requires high financial investments on the part of the institutions, both in hardware and software and in trained personnel.

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Other experts argued for a negative impact on the role of higher education institutions resulting from the projection. Consequently, the role changes and institutions become suppliers of educational content. Concerning this argument, one expert said that “It would be bad—it would deprive the university from a broader social role of socializing, building citizens, engagement, etc.”. In this new role, institutions would lose their role as social agents. The experts saw another impact of the projection regarding higher education institutions’ structures and, in particular, curriculum changes. This applied for educational content, curricular structure, and the role of teachers. According to the experts, a positive aspect of this would be the possibility of offering flexible courses of study. Considering the two described aspects of the new role and the changed curriculum in an integrated way, the experts saw a loss of quality in higher education. One expert warned, “This could be a significant problem—outsourcing education to businesses is usually a mistake—they lack a purpose that aligns with the purposes of higher education—and would limit the value and quality of the type of education provided”. Viewed in summary, the experts considered the occurrence of the projection to be probable, especially due to current digital developments. The opposing side did not deny these digital developments, but did not attribute the radical power of change to them implied by the projection. One reason for this was the strength of existing structures of higher education. Both the adaptive potential of institutions (establishing their own platforms) and the established network (lobby) were addressed. When the projection occurs, the experts saw profound changes for higher education institutions. The fundamental role of institutions would change. This would be accompanied by changes in structures, such as curricula and digital infrastructure.

7.1.7

Projection 5—Lifelong and Lifewide Education

Beyond individualization and digital transformation the demographic change is a further relevant development impacting society. This hypernym includes various developments in the social structures, particularly of developed countries. According to the European Commission (2020), the term includes, for example, longer life expectancies and falling birth rates, which in combination result in an aging of society as a whole. The three trends of digital transformation, individualization, and demographic change, both individually and collectively, have profound implications for higher

7.1 Results for Each Delphi Projection

161

education. Society’s demands on institutions are changing. For example, digital technologies are enabling broader access to higher education, and potential students want and need to continue their education regardless of location and age. From the perspective of educational research, the starting point for overcoming these changed requirements could be the concept of lifelong learning.19 This conceives of education as a task that spans all phases of life, from childhood to adolescence and young adulthood, to older adulthood and the elderly (Tippelt, 2018, p. 106). Understanding education and learning as a lifelong project underscores the relevance of second and third educational pathways (Schmidt-Hertha, 2014, p. 29). Various superordinate forms of learning are integrated in lifelong learning. These are formal, nonformal, and informal learning (Eckert & Tippelt, 2017; Schmidt-Hertha, 2014, p. 20 f.; Tippelt, 2018, p. 106). The prior remarks illustrate the relevance of observing demographic change, and of observing it in combination with digitalization and individualization, and their impacts on the higher education sector in the future. Projection development focused on changing requirements for (higher) education resulting from those trends in the form of the political and theoretical idea of lifelong learning.20 The final projection focuses on the role of higher education institutions in a world that can be characterized by the changes and requirements described above. Particularly, the changing role of higher education institutions from a lifestagerelated institution to a lifelong (time) and lifewide (location) learning companion was the focal point (see Table 7.13). The EP of this projection was assessed by the 94 experts similarly to Projection 4. While EP was viewed as low in five years, it increased to 63.17% by 2041. In the long term, the experts, therefore, considered the projection to be probable. The impact of the projection on higher education institutions was rated as high with a value of 4.04. The experts also rated the desirability of the projection as high (see Table 7.13). Concerning Projection 5, dissent was again identified for EP in general. An IQR of 27.5 for EP in five years was slightly above the threshold for consensus (IQR ≤ 25). Therefore, dissent for this dimension of the projection was low. In contrast, an IQR of 57.5 for EP in 20 years indicated strong dissent with respect to the long-term perspective. The expert panel also disagreed on the desirability 19

For an overview of societal changes and the associated need for lifelong learning, see Schmidt-Hertha et al. (2020). 20 According to Schmidt-Hertha (2014, p. 29), lifelong learning can be interpreted as an aim of educational policy and a paradigm of educational theory. Both comprehensions were included in the projection development.

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Table 7.13 Descriptive statistics for Projection 5 No.

Projection for the future

5

Higher education institutions have evolved from lifestage-related institutions to lifelong and lifewide learning companions.

n = 94 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

26.76

27.5

4.04

1

4

2

3.73

1

2031

42.13

40

2036

54.97

50

2041

63.17

57.5

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus: for EP ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

of the development. However, consensus among the experts was achieved concerning the strong impact that the development would have on higher education institutions. In addition, the experts’ self-assessments again showed low dispersion (IQR = 1). This indicates that the individual experts were confident in their assessments. Syntax analysis showed that 73.6% of contributions were formulated as complete sentences and paragraphs (53 of 72). Overall, the results again indicated a high level of engagement by the experts (see Table 7.14). Nevertheless, this holds only true for experts who submitted qualitative arguments. Only 77% of experts who assessed Projection 5 also commented qualitatively on their quantitative assessments (72 text arguments by 94 experts; see Table 7.14). Table 7.14 Details of qualitative arguments for Projection 5

Projection for the future

5 Lifelong and lifewide education

Quantity of arguments

72

Syntax Whole sentences Phrases Catchwords

53 16 3

7.1 Results for Each Delphi Projection

163

The 72 text contributions could be categorized in the form of 90 text passages. The content structure of the text passages showed that a large proportion of qualitative data concern a high EP (27 of 36) and a high I (24 of 25) of the projection. In total, 20 text passages could be identified as general thoughts on the projection. Additionally, one text passage indicated a misunderstanding (see Table 7.15). Table 7.15 Contentual structure of qualitative arguments for Projection 5 Quantity of paragraphs

90 Overall

EP

I

Supportive arguments (PRO)

51

27

24

Nonsupportive arguments (CONTRA)

10

9

1

Indifferent arguments

8

General remarks

20

Misunderstandings

1

The experts’ central argument for high EP for the projection focused on several developments that unfold within society. One expert wrote: With advances in health care, medicine, and improvements in general living conditions and due to automation and technology, society will have many workers who will require new ways of working and there will be a high demand for upskilling, reskilling and retraining.

Moreover, the experts argued that higher education institutions must cope with the increasingly diversified life courses of individuals. In addition to new needs for reskilling and upskilling that emerge, one expert viewed increased competition in the field of higher education as a reason for the high EP of the development. Against the background of competition, one expert pointed to a decreasing level of state funding for higher education institutions. This results in increased funding from private sources, which in turn pushes institutions to reach out to additional target groups. Experts on the opposite side saw low EP of the projection since prevailing structures of higher education would be difficult to change. One expert said that “the life stage provision is so embedded in compulsory education policy that the major postsecondary role will be difficult to shift”. This argument, concerning strong linkage of higher education institutions with secondary education that prevents the occurrence of the projection, was supported by further experts. Another

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expert saw structural barriers in the context of lifelong learning as a reason for a low probability of occurrence of the projection. A different kind of argument related to students’ perceptions of higher education institutions. One expert wrote: “I just think that … people seeking higher education do it first and foremost to get a degree. Therefore, they want to leave again asap [as soon as possible]”. Therefore, needs for lifelong learning are not met by higher education institutions. Rather the institutions are perceived by students as fulfilling the purpose of awarding titles. The experts saw a high impact of the projection regarding structures of higher education institutions. On the one hand, it affects the business model. One expert wrote: “Higher education institutions may need to start moving toward a subscription model, where people will have access to programs by a payment … thus potentially retaining the loyalty of their students across their lifetime”. On the other hand, curricula are strongly influenced. According to the experts teaching and educational settings change. The former is set up more flexibly and enriched with the help of new methods, while the latter is increasingly shifted to an online environment. The focus of higher education institutions is shifting. They serve new target groups with new needs. For this reason, adult learning theories are being integrated into curricula. In addition, one expert saw the projection as a way for higher education institutions to align themselves with the demands of modern society. In summary, the experts provided a wide range of arguments for and against the occurrence of the development and its impact on higher education institutions. Experts in favor of a high probability of occurrence emphasized the need for higher education institutions to develop into lifelong and lifewide learning companions. Various societal developments, such as demographic change or digital transformation, and the resulting relevance of constantly changing demands on individuals, give rise to such a necessity. While experts who saw the probability of occurrence as low did not dispute that such a development may be necessary, they argued that the established structures in higher education make the change improbable. From the expert panel’s perspective, the development would have a strong impact on the structures of higher education institutions, from the overarching organizational structure to the specific structure of curricula.

7.1 Results for Each Delphi Projection

7.1.8

165

Projection 6—Education Cities

In contemporary societies people increasingly migrate to cities. This societal trend is called urbanization. It results in steady and rapid growth of cities into supercities and megacities, which promote a centralization of society (van der Zwaan, 2017, pp. 109–111). Another politically driven trend that drives such centralization is deglobalization. This refers to the increased focus of societies on national and regional issues. It leads to the end of the era of globalization (van der Zwaan, 2017, p. 111; Witt, 2019). Urbanization and deglobalization also promote centralization in the field of higher education since society increasingly demands higher education institutions to form (future) knowledge centers (van der Zwaan, 2017, p. 110). Consequently, a connection of both trends with the educational setting as an element of curricula at higher education institutions (see Part II, Subchapter 4.2) can be established. These thoughts constitute the foundation for projection development. The main question is to what extent centralization of higher education occurs in the form of education cities.21 Education cities are an idea based on the aforementioned concept of knowledge centers. To do justice to the opposing trends of globalization and deglobalization, the projection was formulated in a differentiated manner. The focus of centrally organized higher education is on excellence. Against the background of expanding education, however, digital developments with a focus on mass education are also taken into account in the final projection (see Table 7.16). Projection 6 was evaluated by 89 experts. They perceived the occurrence of the development as improbable. The EP was below 15% in five years and rose to a maximum of 30.82% in 2041. I on average was perceived as medium to high (see Table 7.16). D was rated as low by the expert panel, while the level of confidence was assessed as medium. Consensus can be observed regarding the first two EP dimensions. An IQR of 15 and 25, respectively, indicate that the experts agreed on low EP in five and 10 years. It would increase to a maximum of 20.13% in 2031. The expert panel agreed on the medium-to-high impact of the projection on higher education institutions. There was slight dissent (IQR = 33) on the 21

Education cities must be distinguished from the concept of learning cities. According to Eckert and Tippelt (2017) and Tippelt et al. (2009), the latter concept includes (political) ambitions to foster lifelong learning in a decentralized manner, thus, strengthening regions, for example, by developing learning regions. The former, on the other hand, is to be understood as a concept of a physical, centralized place in the future that is established by institutions to separate themselves from virtual competition, as von der Gracht and Becker (2014) describe.

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Table 7.16 Descriptive statistics for Projection 6 No.

Projection for the future

6

Education of excellent academics and practitioners has shifted to centralized, physical environments, called education cities,* while virtual mass learning opportunities have expanded.

n = 89 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

14.75

15

3.51

1

2.35

2

3.18

1

2031

20.13

25

2036

25.66

33

2041

30.82

40

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus, and for EP, ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

probability of occurrence in 15 years and the low desirability of the projection (IQR = 2). Strong dissent was evident for long-term EP (see Table 7.16). The expert panel substantiated its quantitative assessments in the form of 99 text contributions. Syntax analysis here indicated a high level of engagement by the experts. A total of 75 contributions were formulated in the form of complete sentences and paragraphs. Contributions in the form of phrases totaled 18. Only six text contributions were formulated as catchwords. This corresponded to just under 6% of all contributions submitted for Projection 6 (see Table 7.17). Table 7.17 Details of qualitative arguments for Projection 6

Projection for the future

6 Education cities

Quantity of arguments

99

Syntax Whole sentences Phrases Catchwords

75 18 6

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167

The 99 text contributions could be categorized into 104 text passages. The content structure of the text passages illustrated a surplus of arguments for low EP of the projection. Of the text passages 20 of 27 for EP focused on low EP. Regarding I, about 94% of text passages (34 out of 36) argued in favor of a high impact of the projection. This showed that the experts submitted more arguments for I than for EP. Ten text passages indicated an indifferent opinion of experts for both dimensions. In addition, 30 text passages reflected a general opinion on the projection (see Table 7.18). Table 7.18 Contentual structure of qualitative arguments for Projection 6 Quantity of paragraphs

104 Overall

EP

I

Supportive arguments (PRO)

41

7

34

Nonsupportive arguments (CONTRA)

22

20

2

Indifferent arguments

10

General remarks

30

Misunderstandings

1

The experts provided reasons for high EP along two lines of argumentation. First, they addressed the desires and demands of students. The experts argued that students would continue to have a strong desire for higher education in attendance. Furthermore, students aim at becoming part of exclusive circles. This would proceed in the future. Second, the described development was perceived as a result of the current pandemic situation. One expert said, “We might see indeed that the pandemic and other factors will develop stronger city campuses for life action of faculty, practitioners, and students with mass higher education added as virtual”. This expert perceived the concept of education city as an evolution of the current campus model. Other experts saw a low EP of the projection for three central reasons. They argued that achieving such a development would require the support of a wide range of stakeholders. According to the experts, this support would be denied. A lack of support by politics was highlighted by one expert as an example. Beyond stakeholder support, the experts argued that the projection would be opposed by higher education institutions themselves. This was because the projection opposed higher education institutions’ self-interest since they aim at producing productive and satisfied learners.

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Further argumentation for low EP focused on the projection undermining the ideal of social equality. According to the experts, the development leads to increased injustice in the system since only groups of people that are on top of the social hierarchy would be able to afford, for example, housing in education cities. Consequently, fundamental democratic values of modern societies would be suppressed. The expert panel proceeded to name various societal developments that directly oppose the projection. One of these developments was the expansion of higher education. According to the experts, mass education and the associated masses of institutions cannot be centralized. For this reason, virtual education in combination with presence elements would prevail. Another development opposing the projection was decentralization. Following this argument, currently decentralization rather than centralization is taking place. Education cities are thus emerging, if at all, in the form of virtual communities. The experts saw the impact of the projection in two areas, which were structures of higher education institutions and social implications. One expert wrote that higher education institutions would increasingly consolidate. Their focus is shifting to building communities and relevant networks. One expert stated, Science, research, and education would not be restricted any more to one college or university at a place but the hi. ed. institutions would have to organize and arrange most of their activities by direct and close coordination with several other partners or they might merge into a new organization or institution of a new type.

As a result of centralization, one expert also pointed out that institutions would increasingly evolve from being places of learning to being places of lifestyle. Many experts focused on negative social implications of the projection. It reinforces existing power structures. It contributes to the devaluation of mass education, which, as one expert put it, would be “a step backwards”. Consequently, the experts feared that access to high-quality higher education would decrease. In summary, the experts’ arguments for high EP focused on students’ demands and the projection being a result of the current pandemic. The experts justified low EP by reciting societal developments that oppose the projection and a lack of support from higher education institutions’ stakeholders. The impact of the development particularly includes the structures of higher education institutions and the expansion of social inequality and injustice.

7.1 Results for Each Delphi Projection

7.1.9

169

Projection 7—Intelligent Digital Systems

Corresponding to Projection 4, this projection addresses the trend of digital transformation. However, the focus here is not on higher education institutions’ business models. Rather, it is on educational methodology and educational setting, as explained in Part II, Subchapter 4.2. The projection includes methods and settings that higher education institutions use to conduct their first central task of education. Within this frame, massive open online courses (MOOCs) offer a virtual opportunity for higher education institutions. The first MOOCs were used in higher education as early as 2008 (van der Zwaan, 2017, p. 141). Providing a digital platform, MOOCs allow human instructors to teach to a mass of students. Projection development goes one step further than MOOCs. It focuses on the rapid development of new technologies and the (partial) absence of human lecturers. The projection addressed the following question: “To what extent can AI facilitate or even manage the process of teaching and learning itself?” (Bates et al., 2020, p. 4). Consequently, the final projection focused on the possibility of technologies and technological applications (partially) assuming responsibility for the teaching process in higher education institutions (see Table 7.19).22 90 experts assessed the projection. They perceived EP in the short term as low. The mean value of EP for 2026 was 19.28%. Up to the year 2036, the value for EP doubled. However, it conclusively reached a medium level of EP since the value for EP in 2041 was assessed at 46.47%. According to the expert panel, the impact of the projection on higher education institutions was at a high level, while desirability reached a medium level. A value of 3.58 regarding C indicated that the experts were confident in their assessment (see Table 7.19). Consensus in the panel was reached for one dimension. The experts agreed that the probability of occurrence in five years was low. Slight dissent can be seen concerning EP in ten years, while the other two EP dimensions are characterized by medium (in 15 years) to high dissent (in 20 years). Slight dissent is also evident for I and D. The experts submitted 118 text contributions. Therefore, Projection 7 was the most discussed projection in this thesis concerning the absolute number of text contributions. Syntax analysis emphasized the discussion concerning Projection 7. Approximately 87% of contributions were shaped as complete sentences and 11%

22

The technological applications are referred to as intelligent digital systems. This formulation was intended to stimulate the creativity of the experts and not to limit the assessments to individual technologies, such as artificial intelligence.

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Table 7.19 Descriptive statistics for Projection 7 No.

Projection for the future

7

Intelligent digital systems have established themselves as an effective alternative to traditional lecturers in the teaching of technical and methodological knowledge.

n = 90 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

19.28

18.75

3.83

2

2.83

2

3.58

1

2031

28.99

30

2036

38.06

38.75

2041

46.57

55

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus, and for EP ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

were formulated in the form of phrases. Only two of the 118 contributions were catchwords. This corresponds to only 1.69% of the contributions (see Table 7.20). Table 7.20 Details of qualitative arguments for Projection 7

Projection for the future

7 Intelligent digital systems

Quantity of arguments

118

Syntax Whole sentences Phrases Catchwords

103 13 2

Coding of the text contributions resulted in 149 text passages. Of these, 31 could be assigned to EP and 49 to I. The former shows a slight surplus of CONTRA arguments. This result supports the experts’ quantitative assessments of EP. The same applies to I. With a ratio of 48 to 1, a majority of text passages argued in favor of a strong impact of the projection. Beyond that, 57 general comments were made in the context of the projection and its subject. Finally, it should be

7.1 Results for Each Delphi Projection

171

mentioned that two text passages could be identified as misunderstandings (see Table 7.21). Table 7.21 Contentual structure of qualitative arguments for Projection 7 Quantity of paragraphs

149 Overall

EP

I

Supportive arguments (PRO)

62

14

48

Nonsupportive arguments (CONTRA)

18

17

1

Indifferent arguments

10

General remarks

57

Misunderstandings

2

Qualitative contributions of the experts illustrated various reasons for the occurrence of the projection. The experts did not view the development as probable but rather as necessary. The central reason for this is that higher education would otherwise not be able to cope with the rapidly increasing student numbers in the future. One expert says: “This development is very unfortunate but is likely to happen—if the system of higher education is to be a universal system, there will be a need to teach a variety of students”.23 Another key argument for the high EP of the projection was the high speed of current digital developments. Intelligent digital systems are rapidly maturing and ready for use. One expert predicted that artificial intelligence would be standard in higher education in the future. However, another expert pointed out that the comprehensive adaptation of technologies in higher education takes time. Therefore, it is a long-term concern. The central argument for the low EP of the projection was the absence of social elements of education, which cannot be substituted by technologies. Within the scope of such social elements are, for example, dialogue, mentoring, networking, discussions, relationships, and personal experiences. In the context of this argument, one expert summarized by saying, “learning is and will be a social experience and one success factor of learning is and will be that there is a social context”. Critical experts have seen the strength of intelligent systems in the transmission of knowledge. However, these systems contribute little to competence development. 23

The quoted expert mentioned the concept of the universal system of higher education. For further details, see Part II, Subchapter 3.1.

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Another argument put forward by the experts was conservatism in higher education. Resulting from a conservative mindset in higher education institutions, technologies would not become established. The experts doubted whether students would support a shift toward digital teaching. Finally, one expert expressed ethical concerns related to intelligent digital systems. These must be resolved before the development described in the projection can occur. Impacts of the development were viewed in manifold ways. The experts perceived an increased access to higher education and increased resource efficiency as positive impacts. This manifests itself, for example, in lower personnel costs for institutions or in more available time resources for lecturers, who are enabled to concentrate on the social aspects of education. However, the experts confined this argument by pointing out that initially high investments in technological infrastructure are necessary if the projection was to occur. The expert panel saw further impact regarding the curricula of higher education institutions. In particular, the role of teachers was changing. They would be able to focus exclusively on social aspects, such as mentoring students. They would be responsible for maintenance and testing of intelligent digital systems. Beyond the role and tasks of instructors, the projection changes how educational content is selected, how educational settings are designed, to what degree programs are flexible and how interaction is conducted. With reference to the curriculum, the experts also saw negative effects. Due to the absence of human instructors, the quality of teaching was reduced. Furthermore, the role of institutions changes. They lose their status as a social place. Beyond the aforementioned impacts, one expert saw in the development a fundamental restructuring of the system of higher education worldwide: “Our societies would need only a few hi. ed. institutions which host and apply those intelligent digital systems (AI). These systems could offer their teaching to many clients worldwide at the same time”. Following this expert’s argumentation, the projection can reduce the number of higher education institutions. Few institutions would be necessary to serve students and their needs. In summary, the experts see the occurrence of this projection as probable because the speed of digital developments enables it. Moreover, the projection is seen as a necessity because no alternative to cover the increased volume of students exists. The opposing side argued that the social aspect of education would be neglected. In addition, ethical concerns, and the lack of student support for the development were at the forefront of the argument for the low EP of the projection. The impacts of the projection were perceived as manifold. Positively, the expert stated that access to higher education was increased. Negatively, the neglect of social aspects of education was pointed out. Overall, the scope of the

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173

development’s impact may extend from changes in the curriculum to fundamental structural changes in the system of higher education.

7.1.10 Projection 8—New Funding Models The trends already described have a wide variety of implications for higher education institutions. Digital formats in higher education, such as MOOCs (van der Zwaan, 2017) or virtual learning platforms (Valencia et al., 2017) enable increased access to higher education. As a result of these and other developments, for example, from the social or political sphere, higher education systems are increasingly characterized by diversified student bodies and types of institutions (Teichler, 2004). The latter can be separated simplified into public and private higher education institutions. Private institutions differ from public institutions regarding their source(s) of funding.24 “However, even this distinction is becoming increasingly blurred because universities are steadily diversifying their funding sources” (Golowko, 2021, p. 20). Following these remarks, a central aspect of current changes in higher education institutions is the diversification of funding sources. By its nature, a direct link can be described to the central task of education in higher education institutions, as funding is a core element of curricula (see Part II, Subchapter 4.2). The diversification of funding sources in higher education was the focal point of this projection development (see Table 7.22). 90 experts assessed Projection 8. Concerning EP in general, the panel assigned a medium to high probability of occurrence to the projection. Probability of occurrence in 2026 was rated at 40.5%. Five years later, the experts were already seeing an average probability of occurrence of 50.11%. This value increased up to 64.71% by 2041. Aside from that the experts saw a strong impact of the development on higher education institutions. Desirability, however, was rated medium, even showing a tendency toward low desirability (see Table 7.22). IQR for all EP dimensions was valued at 50 to 60. Therefore, the experts’ assessments of a medium to high EP for the projection were characterized by strong dissent. There was slight dissent with regard to the high impact of the projection. Finally, consensus was reached concerning D of the development. 24

The simplistic distinction between private and public institutions regarding funding sources is not uncontroversial. A competing concept of distinction for higher education institutions presents four new institutional types based on the aforementioned. See, for this, Marginson (2018, p. 331). However, in the context of projection development in this research, a simple distinction alongside institutions’ funding types is sufficient.

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Table 7.22 Descriptive statistics for Projection 8 No.

Projection for the future

8

With increasing competition throughout the higher education sector, institutions have started utilizing alternative financing models (e.g., venture capital, fundraising, technology licensing).

n = 89 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

40.57

50

3.83

2

2.63

1

3.57

1

2031

50.11

55

2036

57.47

55

2041

64.71

60

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = Impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus and for EP ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

The experts submitted 64 text contributions for Projection 8. Therefore, this projection features the lowest absolute number of qualitative arguments of any projection. However, the structure of contributions indicates a high level of engagement on the part of contributing experts. Among the contributions, 47 of 64 were drafted as whole sentences. Phrases accounted for 17 contributions. No contributions were phrased as catchwords (see Table 7.23). Table 7.23 Details of qualitative arguments for Projection 8

Projection for the future

8 New funding models

Quantity of arguments

64

Syntax Whole sentences Phrases Catchwords

47 17 0

Coding translated the 64 text contributions into 86 text passages. The content structure of the text passages showed that qualitative discussions of the projection primarily focus on impact. A total of 54 text passages could be assigned to I and,

7.1 Results for Each Delphi Projection

175

in particular, to associated PRO arguments. Only two arguments were submitted for EP, both also as PRO arguments. Eight text passages indicate an unclear opinion of the experts. In addition, 22 text passages could be identified as general remarks on the projection (see Table 7.24). Table 7.24 Contentual structure of qualitative arguments for Projection 8 Quantity of paragraphs

86 Overall

EP

I

Supportive arguments (PRO)

56

2

54

Nonsupportive arguments (CONTRA)

0

0

0

Indifferent arguments

8

General remarks

22

Misunderstandings

0

Concerning a high probability of occurrence, the experts argued that a trend toward diversification of funding sources in higher education can already be seen today. They stressed that this trend would continue in the future. The panel proceeded to point out that vast amounts of financial resources are available in the private sector. This motivates public institutions to diversify their sources of funding. According to one expert, “looking for money was always important and will be important. As in the moment in the private sector money is accumulated, it is obvious that the importance of this sector for research will increase”. A special feature of this projection compared to the others was that no argument spoke against its occurrence. However, some experts offered a differentiated opinion. One expert described how the probability of occurrence of this projection varied depending on region. According to the expert, such a development would unfold more quickly in higher education systems that include a large proportion of private institutions. The experts saw a positive impact of the projection in improved access to higher education. Institutions would be able to offer their services to a greater number of people as a result of better funding. In addition, the panel saw an improvement in the quality of curricula since new monetary resources allow for expanding and optimizing learning environments. Experts saw a positive impact originating from close collaborations between educational institutions and industry. Correspondingly, collaborations result in improved student employability. The availability of more monetary resources for research was also perceived as a positive impact of the projection.

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Further impacts were perceived by the panel as negative. In particular, the experts criticized an increased commercialization of higher education institutions. According to this argument, higher education institutions are increasingly using economic indicators and targets instead of the previous education-related indicators as a result of new funding. Overall, some experts perceived the projection as a threat as it could push higher education to develop into a profit-oriented system, rather than a learning system. In this context, experts emphasized that new funding sources are creating new dependencies of higher education institutions on private funders whose interests they must consider. Following this logic, other experts argued that new inequalities were emerging from this projection. This referred both to inequalities between institutions and to inequalities between disciplines. Some experts issued a warning that such a development could suppress the disciplines of the liberal arts. This is because the disciplines have little relevance to the economic interests of funders. In summary, the experts’ argument for the occurrence of the projection was based on the observation that diversification of funding sources is already visible in higher education. Due to the monetary resources in the private sector, this would also extend to public institutions. No expert argued for a low probability of occurrence of the projection. However, it was emphasized that regional differences were perceived regarding the probability of the occurrence. The expert panel observed positive impacts of the projection in the increased availability of financial resources for higher education institutions. The projection leads to diversified higher education systems. The experts highlighted the commercialization of higher education and an associated increase of inequalities. Both aspects were described as negative.

7.1.11 Projection 9—Performance Certificates W. G. Faix et al. (2021) have described in the preface of their book that humanity can only overcome its great challenges if a certain kind of people emerges from its educational systems—people with creative personalities. A central aspect of such personalities is that they make use of their knowledge and skills. This manifests itself in the form of performance. Performance is not understood here as student performance in the sense of measured performance in examinations (Prifti, 2019, p. 161). Rather, performance is to be seen as the expression of a certain competence (Gruschka, 2007, p. 26).

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In this context the definition of performance, according to Mergenthaler and Faix (2022), can be used. In this definition, performance is reflected in a contribution that benefits a community. It contributes to the preservation and the positive development of the community (Mergenthaler & Faix, 2022, p. 47). Performance and higher education thus are two concepts that should be considered together, especially from a future-oriented perspective. For this reason, such a consideration is the focus of projection development. Performance is linked to various elements of the conceptual background of this thesis. On the one hand, it is connected to the central task of education, particularly the educational objective (see Part II, Subchapter 4.2) in higher education institutions. On the other hand, it is linked to the complementary institutional characteristic of selection (see Part II, Subchapter 4.5). The final projection focuses on performance especially performance certificates, in the context of admission and examination procedures in higher education institutions (see Table 7.25). Table 7.25 Descriptive statistics for Projection 9 No.

Projection for the future

9

Performance certificates* have become the central admission and examination criteria in higher education institutions.

n = 86 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

24.94

33.25

3.41

1

3.26

1

3.37

1

2031

50.11

35

2036

57.47

40

2041

64.71

60

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus, and for EP, ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

This projection was assessed by 86 experts. The EP for the year 2026 was estimated at 24.94%. Therefore, the experts saw low probability of the projection occurring in the short term. Viewing a time horizon of 10 years, this value

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almost doubles to 50.11%. According to the experts, this increase in probability of occurrence continues in the following years. However, this happens at a reduced rate. In conclusion, the experts saw a probability of occurrence for the projection of 64.71% in 2041. The impact of the projection on higher education institutions was perceived to be medium. This also applied to desirability (see Table 7.25). Concerning EP, slight dissent was evident for the short- to medium-term future. This was highlighted by IQR values of 33.25 in 2026 and 35 in 2031. For the long-term periods, there was strong to very strong dissent in the expert panel. Consensus was identified for both I and D. Therefore, the experts agreed on the medium impact and medium desirability of the projection (see Table 7.25). The 86 experts submitted a total of 69 qualitative text contributions to Projection 9. Syntax analysis showed that 78% of all contributions were drafted in the form of complete sentences. In addition, 12 contributions were formulated as phrases. Three further contributions were phrased as catchwords (see Table 7.26). Table 7.26 Details of qualitative arguments for Projection 9

Projection for the future

9 Performance certificates

Quantity of arguments

69

Syntax Whole sentences Phrases Catchwords

54 12 3

Coding of the text contributions resulted in 79 text passages for Projection 9. Of these, 40 could be assigned to the PRO-category for EP and I. Seven text passages indicated an opinion that did not support the projection. A total of 16 text passages indicated an indifferent opinion from some experts. With regard to EP, more CONTRA-arguments than PRO-arguments (5 out of 9) were submitted. Therefore, despite quantitative assessments reflecting a high probability of occurrence, more (qualitative) text passages focused on low probability. The underlying equilibrium of arguments concerning EP reflected the dissent in the expert panel. Concerning I, pro-arguments were predominant. In the text passages in the dimension, 36 of 38 described high impacts of the projection (see Table 7.27). Arguments for high EP can be illustrated alongside various lines of argumentation. One focal point of argumentation was an increased demand for performance and performance certificates by various stakeholder groups of higher education institutions. Hence, the labor market increasingly demands proof of applying

7.1 Results for Each Delphi Projection

179

Table 7.27 Contentual structure of qualitative arguments for Projection 9 Quantity of paragraphs

79 Overall

EP

I

Supportive arguments (PRO)

40

4

36

Nonsupportive arguments (CONTRA)

7

5

2

Indifferent arguments

16

General remarks

13

Misunderstandings

3

competencies in the form of performance. According to the experts, this development would extend to higher education. Furthermore, performance is becoming increasingly relevant in the context of lifelong learning. The second line of argument focused on the inadequacies of current forms of measuring learning and learning outcomes. These were perceived as inadequate because they focus on the retrieval of knowledge. According to the experts, however, it is more important for people to be able to show that learned knowledge has been understood and that learned skills can be used in practice. Performance certificates act as an improved instrument for this purpose. On the opposing side of the argument, experts argued that higher education is too conservative for such an integral change to the existing system to be implemented. Additionally, the experts said that performance certificates would need to be established extensively within this frame. This includes secondary education. However, existing structures prevent this extensive implementation. Other experts argued from a different direction. Here, the focus was not on the system. Rather, they emphasized that performance certificates as an admission and examination criterion could exceed various resource limits. Accordingly, a comprehensive integration of such certificates was not feasible from the perspective of higher education institutions. As an example, one expert said, “establishing performance as a metric will be a transition for many universities. Because during a study program, opportunities must be created to show performance”. To achieve this in a system of universal higher education did not seem possible to the experts. Beyond that, the development was unlikely to occur because there are few opportunities for potential students to show performance before they graduate. Impacts of the projection were seen, especially regarding curricula. The experts wrote that problem- and project-based learning would come to the fore.

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Within this frame, real-world experiences are integrated into curricula as opportunities to demonstrate performance. As a result of this new curricular focus, the use of theory-based learning vanishes. The projection also impacts how examinations are conducted in higher education institutions. Overall, the experts foresaw an increased integration of innovative pedagogical approaches and curricular elements. Furthermore, the projection results in new evaluation models both in and for higher education institutions. The experts saw impacts beyond curricula concerning selection of students. In this context, changing profiles of potential students result in a diversification of the student body. As an overarching effect, the experts argued that institutions are increasingly focusing on social impacts. In summary, the experts believed that the projection has a high probability of occurrence because the labor market and society demand the development. Moreover, performance certificates could replace current, inadequate alternatives for measuring learning and learning outcomes. Opposing arguments focused on the conservatism of higher education and the lack of resources for implementation on the part of students. The expert panel saw impacts of the development especially for the curricula of higher education institutions. However, the experts also pointed to the changing evaluation systems both for evaluating institutions and for evaluation within institutions.

7.1.12 Projection 10—Consortial Degrees Digital transformation is changing the structure of economic, political, and cultural systems worldwide (Schuler & Day, 2004, p. 1). Technologies support people in reaching unprecedented levels of interconnectedness. Castells (2010) has coined the term network society to describe a new form of society, which is characterized by networks. In industry, the result of this new form of society is a shift toward more cooperation. Value creation is increasingly performed in networks (Castells, 2010, p. 172 f.). Network society and the described form of value creation in networks are the focus of projection development. Within this frame the latter is transferred to the context of higher education. Castells (2010, p. 428) has already conducted a similar transfer in his work, focusing on educational setting and educational methodology. This projection development focuses on the cooperation of higher education institutions in modular curricula. Institutions cooperate in consortia by default, and formal educational degrees are awarded only by such consortia. Consequently, a connection between conceptual frame and trends was

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established concerning network society and the central task of education in the form of educational setting and methodology, and the complementary institutional characteristic of networks (see Table 7.28). Table 7.28 Descriptive statistics for Projection 10 No.

Projection for the future

10

Formal educational degrees are awarded by default by a consortium of several institutions.

n = 81 Year

EP (mean, in %)

IQR

I (mean)

IQR

D (mean)

IQR

C (mean)

IQR

2026

15.44

20

3.33

1

2.88

2

3.31

1

2031

21.53

30

2036

27.83

45

2041

33.31

45

Legend: EP = estimated probability of occurrence (0–100%); IQR = interquartile range; D = desirability of occurrence (1 = very low, 5 = very high); I = impact if the development occurs (1 = very low, 5 = very high); C = self-assessed confidence in own assessments (1 = very low, 5 = very high). Note: Italic numbers indicate that consensus among experts was achieved; IQR ≤ 25% of the respective scale equals consensus, and for EP, ≤ 25, D ≤ 1.25, and I ≤ 1.25, respectively.

This last projection was assessed by 81 experts. The panel estimated a low EP for the described development. This applied to both the short-term perspective and the long-term perspective. Thus, the mean value of EP increased from an initial 15.44% in 2026 to 33.31% in 2041. The impact of development was perceived as medium. Likewise, the experts viewed the desirability of the projection as medium (see Table 7.28). Consensus in the expert panel was reached regarding the low probability of occurrence in 2026 (see Table 7.28, IQR = 20). The experts agreed on the medium impact of the projection on higher education institutions. Slight dissent can be seen for EP in 2031 and D. An IQR value of 45 indicated strong dissent for EP in 2036 and 2041 (see Table 7.28). The 81 experts provided a total of 113 qualitative text contributions for Projection 10. Of the text contributions, 75% were drafted as whole sentences, 16% were formulated as phrases, and only 9% could be identified for the category of catchwords (see Table 7.29).

182 Table 7.29 Details of qualitative arguments for Projection 10

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Results of the Real-time Delphi Survey

Projection for the future

10 Consortial degrees

Quantity of arguments

113

Syntax Whole sentences Phrases Catchwords

85 18 10

The result of coding these 113 text contributions were 137 text passages. In this context, 43 text passages were assigned to the PRO-category and 27 to the CONTRA-category. Regarding EP, a tendency toward CONTRA-arguments was visible. Of the passages for EP, 25 out of 35 could be assigned to this category. This outcome supported the quantitative assessments of the experts for a low probability of occurrence of the projection. Within the frame of I, 33 of 35 passages indicated a PRO-opinion. Finally, 46 text passages were general expressions of opinion on the projection (see Table 7.30). In Projection 10, approximately 10% of text passages (14 of 137) indicated a misunderstanding. Hence, phrasing of the projection was less successful compared to the nine other projections. However, 90% of text passages indicated that a large proportion of experts understood the meaning of the projection correctly. Consequently, the misunderstandings were not of great significance. They were considered in quantitative and qualitative analyses by revising, for example, descriptive statistics and n for each projection retrospectively. Table 7.30 Contentual structure of qualitative arguments for Projection 10 Quantity of paragraphs

137 Overall

EP

I

Supportive arguments (PRO)

43

10

33

Nonsupportive arguments (CONTRA)

27

25

2

Indifferent arguments

7

General remarks

46

Misunderstandings

14

Concerning high EP, some experts said the occurrence of this projection was only probable if political initiatives promoted it. The experts emphasized that networking is and would continue to be highly relevant, particularly in the field

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of education. A further argument for high EP was the current development toward individualized, distributive, and international education. Experts in favor of a low probability of occurrence argued in particular with the lack of support for the development by important stakeholder groups. On the one hand, policymakers are pursuing national goals, which makes the development unattractive to them. Therefore, the development could be prevented by regulations. On the other hand, the development was not compatible with the goals of students. Educational institutions themselves would not support the development since their financial self-interests interfere at this point. In addition, some institutions might fear losing their reputation and institutional identity. A central impact of the projection would be a change in the structure of higher education institutions. According to the experts, in a system of higher education designed for networking, it would be necessary for institutions to reorganize themselves. Current models of cooperation become obsolete and new models come to the fore. One reason for this is the new interdependence of interorganizational processes. Beyond fundamental structural elements, the structure of education in higher education institutions would change. One expert said, “this will have greatest impacts because this is a shift from monolithic to hybrid, open, and multicomplex organizations”. The quality of education increases. The experts justified this increase in quality with the key reasons of cooperation and networking through which institutions expand their range of expertise and combine resources. The experts saw higher education institutions evolving into highly specialized institutions as a result of this projection. Resulting from increased specialization, mass consolidations are initiated, changing the structure of the higher education. New regulations are established in institutions by politicians and accreditation institutions. The experts saw a negative impact of the development in the fact that institutions lose their independence. Autonomy and individuality disappear in this changed system of higher education. In summary, the expert panel provided two reasons for a high probability of occurrence of the projection, which were the increasing relevance of networking and the individualization of education. However, the experts saw the development as probable only if it is supported by political initiatives. Reasons against the occurrence of the projection related in particular to the lack of support, both from politics and the student body, and from the institutions themselves. Perceived impacts of the development were manifold. The system of higher education would be restructured as the basic structure of institutions changes. Institutions would work together as highly specialized organizations. As a result, new regulations would have to be established for this new system.

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Overall, the experts thought that resulting from the projection the quality of higher education would increase at various levels. However, the loss of autonomy and individuality was perceived as an additional negative impact.

7.1.13 Concluding Remarks on the Results Against the background of the results described for the respective projections, the following subchapter discusses overarching results and insights gained. In a first step, the quantitative assessments for EP, I, and D are examined. This is followed by a detailed discussion of qualitative aspects. Table 7.31 gives an overview on descriptive statistics for all projections: On average, Projection 8 has the highest values across all four EP dimensions. The experts perceived the development that higher education institutions would diversify their funding sources in the future to be the most probable. Focusing on the occurrence of projections in the short-term, Projection 4 ranked second. Over the next five to 10 years, the experts saw the entry of virtual education platform providers into the higher education sector as probable. In the long term, however, Projection 4 is replaced in second place by Projection 5. According to the experts, in 15 to 20 years, it is probable that higher education institutions develop toward being lifelong and lifewide learning companions (see Table 7.31). Projection 5 offered the highest estimates for I. The experts saw the greatest impact on higher education institutions in their development toward lifelong and lifewide learning companions. Projection 7 ranked second in this dimension. Consequently, the expert panel viewed the impact of intelligent digital systems in teaching methodological and technical elements as high. In addition, the experts saw a strong impact regarding the diversification of funding sources for higher education institutions described in Projection 8 (see Table 7.31). Similar to its high impact, the experts assessed Projection 5 to be the most desirable. This was followed by Projection 1. The panel thus wished to see more evaluation of higher education with indicators that measure real output. The same applied to evaluations within higher education institutions. In third place among the projections with the highest average desirability was Projection 9. The experts perceived as desirable that examinations and admission procedures in higher education institutions should be increasingly shifted toward performance certificates (see Table 7.31).

27.03

14.92

17.48

34.22

26.76

14.75

19.28

40.57

24.94

15.44

2

3

4

5

6

7

8

9

10

20

33.25

50

18.75

15

27.5

40

25

22.5

45

21.53

31.14

50.11

28.99

20.13

42.13

44.73

22.11

20.36

40.38

EP10

Mean

Mean

IQR

EP5

1

No.

30

35

55

30

25

40

50

35

30

40

IQR

27.83

36.94

57.47

38.06

25.66

54.97

51.52

25.74

26.31

48.37

Mean

EP15

45

40

55

38.75

33

50

50

45

42.5

55

IQR

33.31

42.76

64.71

46.57

30.82

63.17

55.91

28.64

31.05

55.58

Mean

EP20

Table 7.31 Overview of descriptive statistics for all projections

45

60

60

55

40

57.5

55

50

50

60

IQR

I

3.33

3.41

3.83

3.83

3.51

4.04

3.61

3.27

3.59

3.752

Mean

1

1

2

2

1

1

1

2

1

1

IQR

D

2.88

3.26

2.63

2.83

2.35

4

2.65

2.24

2.97

3.558

Mean

2

1

1

2

2

2

2

2

2

2

IQR

C

3.31

3.37

3.57

3.58

3.18

3.73

3.7

3.35

3.52

3.389

Mean

1

1

1

1

1

1

1

1

1

1

IQR

81

86

89

90

89

94

93

91

103

114

n

7.1 Results for Each Delphi Projection 185

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Overall, 60 projection-related dimensions25 were assessed by the experts. Consensus was identified for 15 of these 60 dimensions. Concerning EP, consensus was achieved for six of the 40 dimensions. Five of these six measurements related to EP5. The experts thus agreed on half of their assessments with regard to the probability of occurrence in five years. The sixth value was measured for EP10. For the long-term perspective (EP15 and EP20), there was dissent or strong dissent across the expert panel. Regarding I, Consensus was achieved for seven of the ten projections. With regard to D, consensus was found for two projections. The 10 confidence-dimensions were considered separately since they performed a different function than EP, I, and D. Across all projections, the average level of confidence of the experts was in a range of 3.18 to 3.73. An IQR of 1 was calculated for each projection. There was consistently low dispersion in the C dimension. Therefore, the experts’ average confidence in their own assessments was at a medium or high level across all projections. The experts provided a total of 896 text contributions on the 10 projections. In addition, 43 arguments were submitted on the question of the relevance of EHEIs in the future. On average, 93 experts provided 90 text contributions for each projection. Consequently, not every expert commented on their own quantitative assessments. Nevertheless, a high level of commitment and discussion potential could be identified, considering the experts who participated in qualitative discussions. This is indicated, among other things, by the results of an overarching syntax analysis. Of the 896 arguments on all projections, 76.57% were phrased as complete sentences. Considering arguments regarding the relevance of EHEIs in the future, 77.74% of the contributions were formulated as complete sentences (730 of 939 contributions). The absolute number of qualitative contributions highlighted that Projection 1 (115 contributions), Projection 7 (118 contributions) and Projection 10 (113 contributions) were strongly discussed. However, an isolated consideration of this absolute number is not sufficient. This is because each projection possesses a different n. A metric must be calculated that considers the number of contributions and the number of assessing experts in relation. For this purpose, the number of contributions per expert was calculated for each projection. The result of this calculation illustrates that the experts strongly engaged in discussing Projection 6 (1.112 contributions per expert), Projection 7 (1.311 contributions per expert), and Projection 10 (1.395 contributions per expert). 25

For the 10 projections, each expert assessed four EP-dimensions, one I-, and one Ddimension.

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187

Beyond the absolute and relative number of contributions per projection, an overarching syntax analysis provided support for identifying the most discussed projections. The analysis showed that Projection 1 (81.74%), Projection 4 (82.14%), and Projection 7 (87.29%) possessed the highest rates of text contributions, which were phrased as complete sentences. It can be assumed that the experts considered the five mentioned projections (Projections 1, 4, 6, 7, and 10) as the most interesting and relevant. Projection 7 stands out in this analysis since it ranks first in absolute text contributions and relative number of text contributions in the form of whole sentences. In addition, the projection ranks second in terms of the number of contributions per expert. The overarching results of qualitative analyses indicated that Projection 7 and thus intelligent digital systems in teaching of methodological and technical knowledge in higher education institutions were perceived as most relevant by the experts. In Delphi studies, it is regularly observed that discussions decrease the more extensive the study regarding the number of rounds or the length of the questionnaire (e.g., Gargon et al., 2019, p. 111; Gary, 2012, p. 394; Gary & von der Gracht, 2015, p. 137; Holey et al., 2007, p. 6). In the context of the present Delphi study, such a phenomenon was not observed. Considering absolute values, it is visible that for the first five projections an average of 87 contributions were submitted. For the further five projections, this value was 93 text contributions. The contributions per expert reflected a similar picture. For the first five projections, an average of 99 experts submitted 0.87 contributions per person. For Projections 6 to 10, this value increased to 1.07 contributions per expert, with an average of 87 experts discussing it. Hence, on average, more experts assessed Projections 1 to 5 than 6 to 10. However, these experts submitted less text contributions per person. On the one hand, this analysis indicated that there was a fundamental interest among the experts in the subject area of the study, as the discussions did not decrease. On the other hand, the results of this analysis showed that the projections designed in advance represented relevant topics for the field of higher education. These results also retrospectively confirmed the decision to use a compact design for the Delphi questionnaire. In addition, the key figures described gave an indication of the topics that experts perceived to be most interesting or relevant. The focus of the most discussed projections was on the evaluation of higher education institutions with the help of the element real output (Projection 1). Furthermore, virtual education platforms entering the higher education sector were focal point (Projection 4). Likewise, the concept of education cities (Projection 6)

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was strongly discussed. The key figures attributed particular relevance to developments surrounding the use of intelligent digital systems in teaching (Projection 7). Finally, the approach of establishing consortial degrees as a new standard in higher education (Projection 10) was also intensively discussed. Identifying and analyzing the most discussed projections and topics in this thesis must be limited. This is because Projection 10 offered the highest number and rate of misunderstandings in the context of the text passages in comparison to all other projections. How the content of the projection was to be understood was thus part of projection-related discussions in the form of text contributions. However, the discussion was not distorted by this fact since misunderstandings were identified and highlighted as such in the QCA.

7.2

Dissent Analysis

Regardless of the main objective of a Delphi study, the method also aims at structuring a group communication process (see Subchapter 6.2). In the previous subchapter, descriptive key figures for the individual projections were explained and described. The consensus of the expert panel was considered regarding various dimensions and projections. Equally relevant is dissent within an expert panel. Analyzing dissent provides important impulses for classifying and interpreting data (Beiderbeck et al., 2021b, p. 14). This creates transparency concerning study results and ensures the quality of qualitative research. A variety of analyses may be integrated into dissent analysis. Warth et al. (2013) have suggested a five-step approach. This consists of a stakeholder group analysis, a desirability bias analysis, an outlier analysis, a bipolarity analysis, and a latent class analysis (Warth et al., 2013, pp. 573–575). Based on this, Beiderbeck et al. (2021b, pp. 14–15) have proposed a four-step procedure that includes the aforementioned analyses, excluding the latent class analysis. In the context of this thesis, a group analysis in combination with a desirability bias analysis was performed. This approach can be considered adequate since it provides sufficient insight into the dissent regarding the experts’ opinions as the following subchapters illustrate. Against the backdrop of complex and multifaceted issues discussed in Delphi surveys, systematic disagreements may arise between different social groups (Warth et al., 2013, p. 573). For this reason, group analysis is a common method of examining dissent in Delphi studies (Beiderbeck et al., 2021b). It assumes that different experts in the panel have different motivations and, therefore, different opinions. As part of group analysis, the expert panel is divided into groups of

7.2 Dissent Analysis

189

people regarding different dimensions. It aims at enabling group comparisons of assessments concerning the projections (Warth et al., 2013). A common clustering of the expert panel in prospective Delphi studies is the division into three stakeholder groups of academia, industry, and politics, or politics and associations (T. Meyer et al., 2021, p. 6; von der Gracht et al., 2021, p. 20; Warth et al., 2013, p. 572). Group analysis in this thesis did not focus solely on stakeholder groups according to the traditional definition given above. Instead, different personal dimensions were brought to the fore to expand the scope of analysis. The personal dimensions could be specified by the experts in the Delphi survey in the form of questions that followed the assessment of the 10 projections. Within dissent analysis, only the experts who provided answers to the personal questions were included. As a result, the number of experts concerning different forms of dissent analysis varied. This is considered by illustrating n for each form of dissent analysis in the following subchapters.

7.2.1

Personal Dimensions for Group Analysis

The first personal dimension was the typical division of experts into the sectors of academia, industry, and politics or associations. This division was based on personal information the experts provided on their professions. Available options for selection in the questionnaire were designed on basis of expert groups that were defined within the frame of the preceding expert identification process. Figure 7.1 provides an overview of the distribution of experts by sector. Within this Delphi survey, 74% of the experts had a background from academia, 20% were located in industry, and 6% in politics and associations. It should be noted that n = 90 experts submitted details concerning their profession. Considering the size of the academia sector, an additional group comparison was conducted within subgroups of this sector. A dedicated query of experts’ professions enabled identification of different subgroups in this context. This approach of subgroup analysis was adapted from Warth et al. (2013, p. 573) who performed a similar analysis when they identified significant differences among stakeholder groups in their Delphi survey. In addition to sectoral groups, region was retrieved as a personal dimension. This dimension aimed at examining to what extent differences between the assessments of experts from different regions prevail. As part of the survey, experts were enabled to submit the countries in which they were based. Regional groups were defined with the seven continents. A peculiarity lay in the fact that one

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5

18

67

Academia

Industry

Politics and Associations

Figure 7.1 Professions within the expert panel. (Own illustration)

expert submitted the country Russia. Due to its location across two continents, the expert’s answers were assigned to the group Europe for the sake of simplicity. Table 7.32 provides an overview of the assignment of the 90 experts to their respective continents: The third group within this analysis was the experts’ age. The main question here was whether assessments in respect to age groups of the experts differed. As part of the questionnaire, experts could submit their year of birth. A total of 83 experts offered data regarding this question. This was arranged in three clusters: A, B, and C. Cluster A integrated the answers of experts who were born between the years (before) 1936 and 1956. This cluster included 15 experts (18% of respondents). Cluster B contained the experts who submitted a year of birth between 1957 and 1976. With 36 experts (43%), this cluster was the largest of

7.2 Dissent Analysis

191

Table 7.32 Overview of group analysis by region North America

South America

Europe

Asia

Australia

Africa

Antarctica

Number of 16 experts

2

63

3

3

3

0

Relative number of experts

2.2%

70.0%

3.3%

3.3%

3.3%

0.0%

17.8%

the three established. Finally, Cluster C included experts who were born between 1977 and 1996. The size of this cluster was 32 experts (39%). The fourth personal dimension was gender. In the survey, experts had four options to choose from: male, female, diverse, prefer not to say. Of the 90 experts who responded, 38 said they were female (42%). In addition, 50 experts were male (56%). Two of the experts indicated that they preferred not to reveal their gender (2%). These remarks indicate that a balanced degree of gender diversity was achieved in this real-time Delphi survey. Given that “detailed information about the personality of participants can shed a different light on results and should therefore be considered in all Delphi studies” (Beiderbeck et al., 2021b, p. 15), the aspect of personality was also integrated into the design and evaluation of the Delphi survey in this thesis. Beiderbeck et al. (2021b) have suggested conducting a sentiment analysis of different factors. This sentiment analysis was integrated into the group analysis. Two indicators for sentiment were retrieved as a personal dimension for the group analysis. First, the experts’ general opinion on the future relevance of EHEIs was retrieved. Here, analogous to assessing projection dimensions I, D, and C, the experts could rate the personal dimension on a 5-point Likert scale. In this context, one implies a low relevance and five implies a high relevance. This question was integrated into the analyses as the dimension of general opinion. The experts were able to justify their assessment with the help of qualitative arguments. Second, IC and EC of the experts were retrieved. For this purpose, the shortened version of the LoC questionnaire presented by Kovaleva (2012) was utilized. This comprised two factors for both respectively, IC and EC (Kovaleva, 2012, p. 81). The factors were designed as statements which, in the context of this research, could be rated by the experts on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Subsequently, a control indication (CI) could be

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calculated for each expert and their respective control of reinforcement to enable group comparisons.26 With reference to the general opinion a total of 88 experts submitted assessments. Within this framework, descriptive statistics can be described. The mean value of assessments was 3.8. Therefore, the experts perceived EHEIs as relevant in the future. An IQR of 1 indicated that the expert panel agreed on this. For group analysis three groups of general opinion were formed. The first group included experts who saw a low relevance for EHEIs in the future (NEG = assessment of 1 or 2), the neutral group (NEU = assessment of 3) and the group that saw a high relevance of the institutions in the future (POS = assessment of 4 or 5). The NEG group included 11 experts. Fourteen experts were assigned to the NEU group, and, with 63 experts, the POS group was identified as the largest. Descriptive statistics can also be presented for LoC of the expert panel. Regarding the first factor for IC a mean value of 3.74 was calculated. An IQR of 1 indicates consensus in the expert panel. With regard to the second factor of IC the mean value was at 3.81. Considering the IQR of 1 the experts agreed on this statement. These statistics showed that the individual experts on average agreed with both statements concerning IC. The presented values for IQR highlighted that the expert panel was characterized by consensus. Considering factors for EC, a mean value of 1.96 was calculated for the first factor. Here, an IQR of 0 indicated consensus in the expert panel. The mean value for the second factor for EC is 2.25. In addition, an IQR value of 2 was calculated. There was thus no consensus on the statement. These descriptive statistics illustrate that on average the individual experts did not agree on statements for EC. The expert panel as a whole agreed on this assessment. However, this applies only for the first factor for EC. The second factor for EC was characterized by dissent in the expert panel. In a last step, CI for the whole expert panel was calculated. The results of the LoC framework indicated an internal control of reinforcement regarding the expert panel. This implied that the experts thought that future developments could be and would be influenced by their own actions rather than happening independent of them (Rotter, 1966). Beyond descriptive LoC results, experts’ assessments were divided into three groups alongside CI. The first group included all experts who showed a negative difference in the LoC factors considered and thus a CI of external control beliefs, 26

CI for experts’ control of reinforcement was the difference between the sum of values submitted for IC factors and the sum of values submitted for EC factors. It indicated the extent to which an expert possessed an internal or external control of reinforcement.

7.2 Dissent Analysis

193

or external control indication (ECI). They assumed that developments unfolded independently of their behavior. Five experts belonged to this group. The second group was the neutral group of experts. These offered a CI of 0, or a neutral control indication (NCI). This group included six individuals. The third group was composed of those experts whose assessments indicated internal control beliefs, or internal control indication (ICI). This group consisted of 77 experts. Therefore, it represented the largest group in the personal dimension LoC.

7.2.2

Procedure for Group Comparisons

For the purpose of group comparisons, a Kruskal Wallis test was performed concerning each personal dimension and their defined groups (Kruskal & Wallis, 1952).27 This test is used for comparative analysis that includes more than two groups. Its results indicate if significant differences between groups persist. Consequently, it does not illustrate which groups’ assessments differ significantly. In some research, hypotheses are defined concerning which groups differ significantly. However, this did not apply to this thesis. Therefore, conducting a subsequent analysis of individual, pair-wise group comparisons was useful. For this purpose, the nonparametric Wilcoxon-Mann-Whitney test was applied (Mann & Whitney, 1947; Wilcoxon, 1945). This test was utilized because two nonpaired groups, whose data was not normally distributed, were the focus of comparison. Within the frame of both, the Kruskal-Wallis test and the Wilcoxon-MannWhitney test a p-value is calculated. A level of significance (α) is defined. In this thesis α was set at less than or equal to 0.05. A p-value below this threshold indicates a significant difference between groups that were considered in the comparison. This procedure, and the threshold for p were in line with Warth et al. (2013). The group comparisons and tests were performed using the programming language R (Ihaka & Gentleman, 1996).28 In the following, the results of group analysis are presented. Within this frame the personal dimensions and respective results of individual groups are presented. Differences are highlighted. This presentation of results focuses solely

27

In the Kruskal-Wallis test, the Bonferroni correction was applied to ensure valid results. For the Bonferroni correction, see Benjamini and Hochberg (1995); Hochberg (1988). 28 For an overview of the use of R in science, see Tippmann (2015).

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on projections and personal dimensions in which significant differences were identified. At this point, it should be noted that again different values for n can be found in the following presentation. This concerns projections, personal dimensions, and projection-related dimensions (EP, I, D, C).29 The focus of group analysis is on personal dimensions. Therefore, different values for n regarding projection-related dimensions do not distort the results of comparisons for personal dimensions. For this reason, with full transparency of the procedure, this issue is negligible. It should be noted, however, that in some projections the projection-related dimensions I, D, and C possessed an n + 1 compared to EP. This is because one expert who answered a question on the personal dimension did not provide all the information for projection-related dimensions.

7.2.3

Group Analysis for Projection 1

Group comparisons for Projection 1 showed that significant differences existed in the three personal dimensions of profession, region, and gender. Regarding profession, p-values of the projection-related dimensions EP5 to EP20 were within a range of 0.02 to 0.035. Therefore, significant differences existed in these four projection-related dimensions. As an example, the mean values of assessments of various groups can be considered. These illustrate that experts from academia saw EP20 of the projection at 51.55%. Representatives of politics and associations estimated this dimension at 85% and industry assessed the value at 67.5% (see Table 7.33). These comparisons indicate that experts from politics and associations viewed a change in the evaluation of higher education institutions in the form of indicators concerning real output more probable than experts from industry and academia. The former experts offered the highest average rating for desirability of the development. Academia can be viewed as the most conservative in regard to EP of Projection 1. Concerning the personal dimension, “region”, significant differences can be identified for EP20 (p = 0.042). Considering average values, it is obvious that differences in the assessments prevailed. This particularly applied to the two largest groups. The 16 experts from North America estimated EP20 at 39.38%, while 29

In the context of group analysis, the terms personal dimension and projection-related dimension can be replaced by the statistical terms independent variable (for the personal dimension) and dependent variable (for the projection-related dimension).

2

N/A

3

Africa

36

3

Australia

50

3

Asia

Female

61

Europe

Male

2

South America

18

Industry

16

5

Politics

North America

65

Academia

n

0.9601

25

27.22

25.38

0.6319

23.33

6.67

20

27.7

15

26.81

0.02035

17.78

58

25.98

EP5

Significant group differences are indicated with italics.

p-value

Gender

n = 88

p-value

Region

n = 88

p-value

Profession

Group

Table 7.33 Results of group analysis for Projection 1

n = 88

0.531

45

44.44

37.26

0.2411

25

25

28.33

44.75

50

30.5

0.03233

41.56

75

37.38

EP10

0.4596

62.5

53.08

44.9

0.08888

21.67

33.33

40

54.2

75

33.75

0.02776

57.22

79

43.94

EP15

0.342

80

61.56

52.3

0.04298

28.33

46.67

51.67

61.98

100

39.38

0.03544

67.5

85

51.55

EP20

0.6489

4

3.97

3.8

0.05748

3.33

4

3.33

4.05

4.5

3.31

0.4802

4.11

3.80

3.82

I

0.1542

5

3.72

3.52

0.6393

3.67

3.67

2.67

3.7

4.5

3.44

0.5589

3.78

4.20

3.55

D

0.007704

4

3.72

3.2

0.2365

3.33

2.67

2.67

3.52

3.5

3.38

0.1336

3.22

4.00

3.45

C

7.2 Dissent Analysis 195

196

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Results of the Real-time Delphi Survey

the 61 experts from Europe viewed it at 61.98% (see Table 7.33). Therefore, European experts in the expert panel see real output in the evaluation of higher education institutions as more probable, than experts from North America. In the third personal dimension, “gender”, significant differences could also be identified for one projection-related dimension. This dimension was C. The results of analysis indicate that female experts are more confident in their assessment for Projection 1 than male experts. On average, they submitted a high rating for level of confidence (3.72), while male experts displayed a medium rating (3.2) (see Table 7.33).

7.2.4

Group Analysis for Projection 2

In the context of Projection 2, group comparisons again illustrated significant differences in three personal dimensions. As in the context of Projection 1, significant differences were identified in the personal dimensions of profession and region. In addition, significant differences were also identified in the dimension of age. Significant differences in the dimension of profession concerned the project-related dimensions EP10, EP15, and EP20. Overall, experts from academia rated EP10, EP15, and EP20 lower than experts from politics or industry (see Table 7.34). Results of group comparisons indicate that experts from industry viewed a new focus of higher education institutions on personality development more probable than experts from academia or politics. Academia submitted the most conservative assessments. This was manifested, for example, in assessments for EP20 by experts from industry, which were nearly twice as high as assessments by experts from academia (see Table 7.34). In the personal dimension, region, significant differences were identified for the projection-related dimensions EP10, EP15, EP20, I, and D. South American experts offered the highest assessments for these five dimensions. However, this was not convincing since the group consisted of only two experts. Large differences could be seen concerning the two groups including most experts. European experts perceived the projection as more probable compared to North American experts. In addition, they assumed a greater impact of the projection. European experts perceived the development as medium desirable, while North American experts rated D as low (see Table 7.34). In the third personal dimension, age, significant group differences could be identified for I. Experts from Clusters C (25 to 44 years) and A (65 to 85 years) rated the impact of the projection as high. Experts from Cluster B (45 to 64

64 5 18

Politics

Industry

31

3

Africa

C = 25 to 44 years

3

Australia

35

2

Asia

14

61

Europe

B = 45 to 64 years

2

South America

A = 65 to 85 years

16

North America

0.8268

15.32

15.2

16.07

0.24

10

0

10

17.46

15

8.88

0.1423

23.06

20

12.06

EP5

Significant group differences are indicated with italics.

p-value

Age

n = 87

p-value

Region

n

Academia

nEP = 87 nI, nD & nC = 88

p-value

Profession

Group

Table 7.34 Results of group analysis for Projection 2

n = 80

0.7928

21.84

19.37

22.14

0.01142

10

0

0

23.97

40

10.94

0.01293

34

31

15.31

EP10

0.463

29.35

22.57

31.07

0.002599

10

0

0

31.8

55

12.19

0.003586

46.11

38

19.61

EP15

0.1557

33.55

24.86

43.93

0.002393

10

0

0

37.67

65

14.38

0.0041

51.67

45

23.95

EP20

0.0001957

4.03

3.03

4.21

0.0419

3.33

2.67

3

3.84

4.5

2.94

0.0976

4.11

3.40

3.48

I

0.1128

2.97

2.71

3.64

0.01779

2.33

1.33

1.67

3.3

4

2.31

0.1628

3.50

3.40

2.80

D

0.4438

3.59

3.54

3.21

0.1395

3.33

4

2

3.54

2.5

3.81

0.4701

3.28

3.60

3.58

C

7.2 Dissent Analysis 197

198

7

Results of the Real-time Delphi Survey

years) rated it in the medium range (see Table 7.34). This result indicates that experts of the age of 45 years to 64 years perceived the impact of a new focus of higher education institutions on personality development as significantly lower than respectively younger and older experts.

7.2.5

Group Analysis for Projection 3

Group comparisons for Projection 3 include significant differences in two personal dimensions. First, the dimension of age. Second, the dimension of LoC. Within the personal dimension, age, significant differences were identified for C. This was indicated by a p-value of 0.0169. The mean values illustrate that both Clusters A and B judged the confidence in their own assessment higher than Cluster C. For Projection 3, experts between the age of 45 and 85 thus were more confident in their assessments than younger experts. Cluster A assessed confidence on average as high as Cluster B while Cluster C was moderately confident in their own assessments (see Table 7.35). Significant differences for LoC were displayed for one projection-related dimension. This was illustrated by a p-value of 0.0486 in the context of EP5. Here, experts showing an ECI or an ICI estimated the short-term probability of the projection occurring to be 17% to 18% higher than people who indicated an NCI (see Table 7.35). Experts that were located on one of the ends of the spectrum, thus, perceived EP5 significantly higher than experts who displayed a neutral opinion on their control of reinforcement.

5 6 71

Internal control indication (ICI)

30

C = 25 to 44 years

Neutral control indication (NCI)

33

B = 45 to 64 years

External control indication (ECI)

14

A = 65 to 85 years

0.04862

18.18

0.83

19

0.6085

14.17

17.76

21.79

EP5

Significant group differences are indicated with italics.

p-value

LoC

n = 77

p-value

Age

n

Group

Table 7.35 Results of group analysis for Projection 3

n = 82

0.06047

22.42

2.5

26

0.8988

20.5

21.03

25

EP10

0.1115

25.87

5

33

0.9476

27.33

22.94

24.64

EP15

0.2276

28.89

8.33

37

0.9307

29.77

24.67

29.57

EP20

0.3743

3.19

3.33

4

0.2046

3.48

3

3.43

I

0.09009

2.35

1.33

2.2

0.4449

2.48

2.15

2.29

D

0.3362

3.36

3.83

2.8

0.01689

2.97

3.58

3.43

C

7.2 Dissent Analysis 199

200

7.2.6

7

Results of the Real-time Delphi Survey

Group Analysis for Projection 4

Projection 4 displays significant differences in two personal dimensions. The first dimension is age and the second dimension is gender. Comparison of different groups regarding age highlighted significant differences for all four EP dimensions. The p-values here ranged from 0.0049 to 0.0328. Lower values for EP 10 and EP15 illustrated that the probability of significant differences was higher for these two dimensions than for EP5 and EP20. These values highlighted that Cluster C perceived the projection’s occurrence as more probable than Clusters A and B. Overall, these results indicated that the older the experts, the less probable they perceive a future predominance of virtual educational platforms in higher education. An exception to this is provided concerning EP20. Here, the assessment of Cluster A was higher than that of Cluster B (see Table 7.36). Table 7.36 Results of group analysis for Projection 4

Age

Group n

EP5

EP10

EP15

EP20

I

D

C

A = 65 14 to 85 years

22.86

30

39.64

51.07

3.93

2.57

3.93

B = 45 34 to 64 years

31.21

38.68

44.29

47.76

3.41

2.62

3.71

C = 25 31 to 44 years

44.03

58.71

65.32

67.94

3.69

2.69

3.59

p-value

0.01964 0.004881 0.007999 0.03282 0.2968

0.8881 0.3118

n = 79 Gender Male

p-value

48

31.83

38.54

44.4

49.25

3.41

2.43

3.61

Female 36

39.56

53.33

63.33

68.22

3.81

2.92

3.86

N/A

7.5

40

32.5

32.5

5

3

4

0.1058

0.09773

0.01801

0.01603 0.03388 0.1963 0.3646

2

Significant group differences are n = 86 indicated with italics.

The personal dimension, gender, offers significant differences for three projection-related dimensions. The p-value for EP15, EP20, and I were between 0.016 and 0.0338. Mean values of these three dimensions indicated that female experts estimated the occurrence of the projection in 15 and 20 years, and the

7.2 Dissent Analysis

201

impact on higher education institutions, higher than male experts. Their estimates for the two EP dimensions were between 60% and 70%, while the male experts’ estimates were between 40% and 50%. The male experts saw a medium impact for the projection, while the female experts estimated it as high (see Table 7.36).

7.2.7

Group Analysis for Projection 6

The group comparisons for Projection 6 reveal significant differences in two personal dimensions, profession and LoC. Significant differences of experts from different professional background can be illustrated for I. This was indicated by a p-value of 0.0487. The average values of the three groups considered showed that experts from politics and associations estimated the impact of the projection as very high. Experts from academia and industry saw only a medium impact on higher education institutions for Projection 6. While there are no significant differences regarding EP, the average scores of experts from politics and associations in this dimension were higher than those of the other two groups. Overall, these results indicate that politics and associations perceived the establishment of education cities for excellent education and research more probable than industry and academia. However, these differences were not significant. Nevertheless, politics and associations viewed the impact of the projection significantly higher than the other groups (see Table 7.37). Locus of control featured significant differences for EP15. Within this frame p was valued at 0.047. The absolute values highlighted a declining trend across the three groups concerning all EP dimensions. Here, experts who displayed neutral control beliefs provided the lowest estimates, experts who displayed internal control beliefs provided the highest values, and experts who displayed external control beliefs provided values in-between (see Table 7.37).

5 18

Politics

Industry

72

Internal control indication (ICI)

Significant group differences are indicated with italics.

p-value

6

External control indication (ECI)

Neutral control indication (NCI)

n = 83

5

62

Academia

n

0.05329

16.57

1.67

13

0.398

14.44

35

13.68

EP5

n = 83

0.0764

22.25

4.17

16

0.6677

22.06

38

18.79

EP10

0.04725

28.18

6.67

18

0.4805

29.17

43.6

23.56

EP15

0.06127

33.58

9.17

22

0.3579

34.83

49.8

28.34

EP20

0.8806

3.51

3.17

3.4

0.04879

3.17

4.6

3.48

I

0.2002

2.44

1.67

2

0.6461

2.17

2.6

2.38

D

0.4599

3.1

3.67

3.2

0.2626

3.39

3.6

3.06

C

7

LoC

nEP = 85 nI-C = 86

p-value

Profession

Group

Table 7.37 Results of group analysis for Projection 6

202 Results of the Real-time Delphi Survey

7.2 Dissent Analysis

7.2.8

203

Group Analysis for Projection 7

Projection 7 showed significant differences for age. These differences related to D and C, as p-values of 0.0377 (D) and 0.030 (C) illustrate. Regarding the former, experts from Clusters A and C perceived a moderate desirability. Experts from Cluster B estimated desirability of the projection as low. Values for dimension C highlighted that Cluster B was less confident of their assessments (medium) than Clusters A and B. Although not significant, the assessments of the four EP dimensions also differed. Here, too, the middle class submitted the lowest assessments for all four points in time. These results indicate that younger and older experts in the panel perceived intelligent digital systems concerning teaching in higher education institutions as moderately desirable. Experts of intermediary age on the other hand perceived this digital development as not desirable. Furthermore, younger and older experts indicated that they were more confident in their assessments than experts of intermediary age (see Table 7.38). Table 7.38 Results of group analysis for Projection 7

Age

Group

n

EP5

EP10

EP15

EP20

I

D

C

A = 65 to 85 years

15

20.67

30

38

45.33

4.13

3

4.07

B = 45 to 64 years

34

13.82

23.06

31.91

41.65

3.74

2.44

3.32

C = 25 to 44 years

31

24.19

33.87

43.71

52.48

3.84

3.31

3.66

0.6513

0.7198

0.4835

0.4323

0.2998

0.03771

0.03015

p-value

Significant group differences are indicated with italics.

7.2.9

n = 80

Group Analysis for Projection 8

Group comparisons for Projection 8 showed significant differences for two personal dimensions, which are region and general opinion. With respect to region, p-values for EP5 (p = 0.0234), EP10 (p = 0.0231), EP15 (p = 0.0337), and EP20 (p = 0.0432) indicated significant differences. In all four EP dimensions, the

204

7

Results of the Real-time Delphi Survey

mean scores showed different assessments from different regions. An examination of the two largest groups highlighted that North American experts estimated the occurrence of the projection significantly higher than European experts. This applied to all four points in time. This difference was set at a value of more than 33% for EP5, 31% for EP10, 29% for EP15, and 24% for EP20 (see Table 7.39). Overall, North American experts estimated the long-term probability of occurrence of the projection (EP20) at 86.25%. European experts saw a probability of 61.07% for the occurrence of Projection 8 (see Table 7.39). Experts from North America were nearly certain that higher education institutions would establish alternative sources of funding in the long-term future. European experts on the other hand perceived this as significantly less probable. Regarding the experts’ general opinion on the future relevance of EHEIs, EP5 displays significant differences. Overall, experts with neutral or positive attitudes toward the relevance of EHEIs perceived EP as higher than those with negative attitudes. This difference in opinion was significant in the short-term future, as indicated by the p-value of 0.0462. While the CONTRA group assigned an EP5 of 22% to the projection, the PRO group estimated it at 42.07% and the neutral group at 49.62% (see Table 7.39).

7.2.10 Overarching Remarks on Dissent Analysis Within the frame of this dissent analysis, overarching observations can be described. No significant differences were found within the defined personal dimensions and associated groups for Projections 5, 9, and 10. Further projections provided significant differences for each personal dimension in at least one case. Table 7.40 provides an overview of this. This overview (see Table 7.40) provides insights into the origin of dissent within the expert panel of this Delphi survey. In the various personal dimensions, it was generally possible to observe divergent opinions by group. However, these differences were significant in only 7.6% of investigated cases (32 of 420 individual comparisons).30 Consequently, this analysis only partially explains dissent in this study. This implies the usefulness of conducting further dissent analyses. In this Delphi survey, the largest proportion of experts could be assigned to the profession group of academia. Due to the high level of dissent in the expert panel, 30

In total 420 individual comparisons were carried out. This total arises out of the six personal dimensions which were compared regarding seven projection-related dimensions for 10 projections.

10 13 61

Neutral general opinion (NEU)

Positive general opinion (POS)

3

Africa

Negative general opinion (NEG)

2 3

60

Europe

Australia

2

South America

Asia

16

North America

n

Significant group differences are indicated with italics.

p-value

Opinion

n = 86

p-value

Region

Group

Table 7.39 Results of group analysis for Projection 8

0.04621

42.07

49.62

22

0.02336

27

36.67

25

35.08

17.5

68.75

EP5

n = 84

0.06375

51.15

59.23

29

0.02309

35

43.33

35

45.25

25

76.88

EP10

0.09438

59.02

65

37

0.03369

43.33

50

50

52.83

35

81.56

EP15

0.09653

66.89

69.92

44

0.04317

48.33

56.67

50

61.07

40

86.25

EP20

0.1118

3.95

3.5

3.4

0.1116

4.67

3.33

3

3.98

3.5

3.31

I

0.7353

2.69

2.5

2.5

0.3886

1.33

2.67

3

2.7

2.5

2.69

D

0.1245

3.52

4

3.2

0.3376

3.67

3.33

3

3.5

3

4.06

C

7.2 Dissent Analysis 205

206 Table 7.40 Overview of quantity of significant differences

7

Results of the Real-time Delphi Survey

Personal dimension

Quantity of significant differences (projection-related dimensions)

Profession

7

Region

10

Age

8

Gender

4

Locus of control (LoC) 2 General opinion

1

Overall

32

investigating potential significant differences within this largest group of experts was found useful. For this purpose, a comparison of subgroup assessments within academia was conducted. However, no significant differences were identified in the context of this intra-group comparison.

7.2.11 Supplementary Dissent Analysis: Desirability Bias In the context of dissent analysis, the projection-related dimension ‘desirability’ offers a supplementary possibility for analysis. The focus here is on identifying a potential desirability bias in the expert panel. Desirability bias assumes a polarization of opinions. Assessments for EP alongside its perceived desirability can lead to dissent since people who perceive the desirability of an event to be high (low) also estimate its occurrence to be high (low) (Ecken et al., 2011). The test for desirability bias was performed with the described procedure of group analysis, already conducted for each projection. Within this frame, D was treated as a personal dimension rather than a projection-related dimension. It was thus treated as an independent variable. Consequently, five intra-dimensional groups (1 = very low D; 5 = very high D) were formed and their assessments for EP were compared. In the following, the results of this desirability bias analysis are presented in an aggregated form, since a detailed consideration exceeds the scope of this thesis.31

31

A detailed overview of the results for the desirability bias analysis is provided in the electronic supplementary material.

7.2 Dissent Analysis

207

Desirability bias analysis identified significant differences between the groups for 37 of the 40 considered EP dimensions (four EP dimensions per projection). Exceptions to this were EP5 and EP10 of Projection 5 and EP5 of Projection 8. Regarding 26 of the 40 EP dimensions, the calculated p-value was below 0.001. The existence of significant differences here is substantially more likely in these dimensions than regarding the group comparisons of personal dimensions described in the previous subchapter. For these, the p-value of only one comparison was below 0.001.32 The significant differences concerning desirability do not indicate an overarching desirability bias. This would prevail if significant differences existed only between the pairs of groups “very low / low” and “very high / high”. However, significant differences were also observed for EP assessments of the pairs of groups medium and high, and medium and low (e.g., Projection 6, EP15 and EP20 or Projection 3, EP5). Nevertheless, the results indicate a widespread desirability bias within this real-time Delphi survey. Overall, the number of significant differences observed provides an indication of the origin of the dissent in the expert panel. Significant differences in 37 of the 40 cases examined indicate that the desirability of projections determine EP assessments and thus dissent for these dimensions. In summary, it can be said that the perceived desirability of a projection influences the assessed probability of occurrence.

32

See Subchapter 7.2.4, personal dimension age and projection-related dimension I.

8

Scenario Technique

Chapter 7 provided insights into experts’ perception of the future of various aspects of higher education. It looked at diverse developments in higher education whose occurrence is possible in an isolated way. In the context of the foresight process, this isolated view does not yet provide a basis for transfer to practice. Therefore, an additional step must be integrated into the research process. This consists of interpreting the real-time Delphi survey’s results in an interconnected way. This chapter describes the step of interpreting the results in the form of scenarios. It includes a focus on higher education institutions to enable the transfer of research results.

8.1

Scenario Development

The starting point of scenario development is the definition of the term scenario. In principle, scenarios are to be understood as alternative perspectives on the future (e.g., Gausemeier et al., 1998; Wack, 1985). For the term scenario, a wide variety of definitions can be found in the research literature. However, they all possess common characteristics (Spaniol & Rowland, 2019, p. 11). Scenarios for planning must always be plausible and internally consistent. They include the description of possible futures under inclusion of most diverse external factors. Scenarios are traditionally written in a narrative and retrospective style (Markmann et al., 2013, p. 1822; Spaniol & Rowland, 2019, p. 11). Furthermore, scenarios describe a multitude of futures. Therefore, scenarios are developed in sets. The different scenarios in a set are to be regarded as equivalent alternatives to each other (Spaniol & Rowland, 2019, p. 11). Scenarios are particularly used in a business context, and specifically in strategic management (e.g., van der Heijden, 1996; Wack, 1985). Considering © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_8

209

210

8

Scenario Technique

economic organizations, scenarios aim at creating a basis for identifying and focusing on future potentials. They promote an active approach to the future in organizations and serve to cope with growing uncertainties (Gausemeier et al., 1998, p. 113). Generally, scenarios are a wide-ranging tool for the integrated consideration of various future developments. For example, they are used to describe probable, possible, (un)desirable futures, wild cards, and the paths leading to these futures (Bañuls & Turoff, 2011). From the perspective of the social sciences, scenarios aim “to broaden mental frontiers and to develop a greater open-mindedness toward new knowledge …. They [scenarios] seek to stimulate debate on the future and facilitate conversation on what is happening and may happen in the world around us” (Masini & Vasquez, 2000, pp. 51–52). In the scientific literature, scenarios are classified by their objectives. Vergragt and Quist (2011) have described two types of scenario, which are the explorative and the normative scenario. Other authors propose a third type, the predictive scenario (Börjeson et al., 2006, p. 725). Predictive scenarios try to predict what will happen in the future (Börjeson et al., 2006, p. 726). Explorative scenarios, on the other hand, focus on the question of what might happen in the future. The focus is therefore on describing possible developments and situations in the future. This should be carried out from different perspectives (Börjeson et al., 2006, p. 727). Normative scenarios are oriented toward specific goals of persons or institutions. They address the question of how certain goals can be achieved and which developments influence achieving these goals (Börjeson et al., 2006, p. 728). As part of a new approach, a fourth scenario type was developed by Erdmann and Schirrmeister (2016), which is the transformative scenario. Transformative scenarios differ from the other typologies because of their focus, not their goal. They emphasize transformations of systems and their implications for various actors (Erdmann & Schirrmeister, 2016, p. 239). Börjeson et al. (2006, p. 728) have similarly described the transforming scenario as a manifestation of the normative scenario type. Nowack et al. (2011) have criticized the normative scenario type. These authors have argued that both exploratory and predictive scenarios can contain normative elements (Nowack et al., 2011, p. 1605). Overall, the development of scenarios is useful with regard to the research questions of this thesis. They offer the possibility to integrate different perspectives in combination, to exploit the space of possibilities for the future of an issue, and to illustrate it in a comprehensible way (e.g., Gausemeier et al., 1998). This research focuses on developing explorative scenarios that include normative elements. The focus is on identifying and describing developments that influence

8.1 Scenario Development

211

higher education institutions, and deriving implications and initial options for action for the institutions. The reason for choosing explorative scenarios is the overarching explorative nature of this research. The underlying Delphi survey already pursues explorative goals in its qualitative design. This thesis does not aim to use scenarios normatively or to predict a “correct” future (see Chapter 7). The focus is on developing scenarios that illustrate possible futures in whose interconnections the actual expected future lies. The use of Delphi surveys as a basis for scenario development is widespread. Various authors recommend this procedure. The reasons for this are the opportunity of easily integrating Delphi results into the scenario development process and that Delphi surveys provide high-quality data, generated from multiple perspectives, as a basis for scenarios (von der Gracht & Darkow, 2010, p. 49). Various approaches have been established for the development of scenarios as Nowack et al. (2011) have illustrated in their overview. Following one of these approaches, a Delphi survey was used in this research work to provide a foundation for scenario development. Especially for the development of explorative scenarios, the Delphi survey can be used to generate new ideas (Nowack et al., 2011, p. 1611). In foresight studies, scenarios consider the possible, probable, and desirable futures, regardless of the scenario type (Mietzner, 2009, pp. 26–27). Recent foresight studies also consider futures that go beyond, for example, weak signals and wild cards. These futures include developments whose probability of occurrence is low, but whose associated impacts are enormous if they occur (Kisgen, 2017, p. 331). One goal of futures research and thus also of scenarios is the development of knowledge for action and orientation (Kreibich, 2008, p. 10). This knowledge is created across multiple perspectives. According to Mietzner (2009, p. 35), four perspectives can be included, which are society (sociocultural developments), economy (economic developments), politics, and technology. These four perspectives are reflected in the STEEP model (see Part II, Subchapter 4.4), which was used in this thesis. From this concept the ecological perspective can be added. Within the diversity of scenario development methods, there is no standardized procedure. Often, authors use a scenario axis matrix (e.g., van der Heijden, 1996, p. 215; Wack, 1985). Others see the process of scenario development as a procedure “that draws on the knowledge and creativity of the participants to constructively work out alternatives, expressing and analyzing ideas in a free and creative environment” (Masini & Vasquez, 2000, p. 52). Consequently, structured and creative approaches to scenario development exist.

212

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Scenario Technique

This distinction is also made in the scientific literature. Here, model-based and intuitive scenario development are two concepts (e.g., Mietzner, 2009). Other authors write of a formal as opposed to intuitive process design for scenario development (van Notten et al., 2003, p. 426). The former is considered to be quantitatively oriented. Scenarios are based on a rational-analytical approach to development. The latter focuses on the creativity of individuals. Qualitative data and creative techniques are in the foreground (Mietzner, 2009, p. 140 f.; van Notten et al., 2003, p. 427). Considering these two approaches, Bishop et al. (2007, pp. 18–19) have given an overview of specific techniques for developing scenarios. These include, for example, model-based cross-impact analysis (CIA) and intuitive judgment (Bishop et al., 2007, p. 18) In the context of this research, the intuitive development of scenarios is focused on. This qualitative approach was chosen because it can be directly integrated into the overarching research design. It is useful in relation to the exploratory goals of this thesis. For this purpose, a scenario team was included analogous to van der Heijden (1996) to enable a multiperspective approach. Model-based and intuitive scenario development behave analogous to qualitative and quantitative research methods. As with the latter, a mixed-methods approach can also be used in scenario development. This mix of methods is reasonable to prevent the weaknesses of the respective approaches. For this reason, the qualitative-intuitive approach of scenario development was supplemented by elements of the quantitative, model-based approach. In this thesis, the Delphi survey represented the starting point for scenario development. To consolidate the results, the first two scenario development steps were conducted using models. In this context, portfolio analysis and CIA were applied. These analyses provided a first insight into the interdependencies of the individual developments described in the 10 Delphi projections. Results of these analyses were the first outlines of guiding ideas for the explorative scenarios. This quantitative approach was the foundation for further work using intuitive methods. The scenario outlines were thus discussed and structured in a scenario workshop by the scenario team in December 2021. Subsequently, the scenarios were further developed alongside the qualitative Delphi results. Portfolio analysis is a way to use quantitative data from Delphi studies to develop scenarios. In this framework, the experts’ mean assessments of project-related dimensions are illustrated and considered in the context of scatter plots. This form of presentation enables identifying thematic clusters within the data (Kisgen, 2017; Markmann et al., 2013). Based on these clusters, initial relationships can be established between developments described in the projections.

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Given the complexity of the subject matter, it can be assumed that developments described in the projections are interrelated, interconnected, and interdependent. Such interconnections between projections are not considered in portfolio analysis. For this reason, a CIA was performed following the portfolio analysis. Cross-impact analysis is considered to be an analysis of influence (Mietzner, 2009). It considers how different developments influence each other. The analysis focuses on the cross-impact of events and developments (T. J. Gordon & Hayward, 1968, p. 101). Cross-impact analysis was developed in response to the weaknesses of the Delphi survey, according to which Delphi considers complex phenomena and underlying interdependent developments in isolation (Bañuls & Turoff, 2011, p. 1580). Consequently, the combination of CIA with the Delphi method should be considered a reasonable approach in the context of scenario development. Similarly, the combination of Delphi, portfolio analysis, and CIA is common (e.g., Kisgen, 2017; Markmann et al., 2013). The aim of both analyses was to provide impetus for further qualitative development of scenarios. The focus here is on the qualitative data of the Delphi survey. However, the clusters formed in portfolio analysis, and interdependencies identified in CIA are relevant since they indicate which qualitative data should be considered in conjunction. Against this background, the qualitative data on the projection clusters were used and discussed in the scenario team. The aim here was to develop lines of argumentation for the interconnectedness of the projections and to check their plausibility. Scenarios do not illustrate mere trend exploration. This is because the latter is thought to be too linear (Helmrich & Zika, 2019, p. 237). Consequently, scenarios are more complex than trend exploration. For this reason, it is necessary to maintain a high quality in the scenario development process. This is achieved by applying quality criteria for the scenario development process. In this thesis, the quality criteria for foresight defined by Helmrich and Zika (2019) were applied. It is essential to define a specific time horizon for scenarios. References to theory must be drawn and the process of scenario development must be presented transparently. In addition, unexpected events should be included. Finally, the consistency of the subject area should be maintained (Helmrich & Zika, 2019, p. 234). The latter refers to the already mentioned internal consistency of scenarios. Consequently, individual scenarios within a scenario set should be based on the same structure. The following subchapters provide an overview of the design of portfolio analysis and CIA. The subsequent process of intuitive scenario development is

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described. Finally, the developed scenarios are illustrated and their implications for higher education institutions are presented.

8.2

Prescenario Analyses

As part of the scenario development in this research work, two prescenario analyses were conducted in advance of the creative work in the scenario team, which were portfolio analysis and CIA. The use of both methods, and the results derived from them, are described, and classified in detail in the following subchapter.

8.2.1

Portfolio Analysis

Portfolio analysis is a business management tool. It is based on the premise that companies are composed of several activities. Therefore, a company consists of a portfolio of activities (Pidun, 2019, p. 55). In the context of portfolio analysis, a company itself is considered as all activities or parts of the company specifically. This approach is often applied to products, services, or strategies of an organization. Objective of portfolio analysis is illustrating activities along different axes. This enables clustering and subsequent evaluation of said activities against the background of the respective objective of the analysis (Pidun, 2019, pp. 55–94). In the context of this thesis, portfolio analysis was conducted in line with Kisgen (2017). The focus was on clustering the 10 Delphi projections. Foundation for this is provided by the quantitative assessments of the expert panel. In the first step, a portfolio of the projections was set up along the dimensions EP and I. In the second step, this was carried out for the dimensions EP and D. Finally, clusters were formed based on the proximity of projections regarding EP and I, and EP and D. Usually only one EP dimension is queried in prospective Delphi surveys (e.g., Engelke et al., 2016; Gary & von der Gracht, 2015). In comparison, four EP dimensions were assessed by the experts in this thesis. For this reason, the procedure according to Kisgen (2017) could not be applied unchanged. Consequently, the mean was calculated from the assessments at all four points in time for each projection. The objective was enabling a neat presentation in the form of a meanportfolio. A portfolio for each of the four respective EP dimensions was created for the 10 projections. The latter was used to examine to what extent the same clusters as in the mean-portfolio can be identified for the individual points in time.

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Figure 8.1 provides an overview of the mean-portfolio and the identified clusters when considering EP and I. 5

P5

Impact of development

4

P7

P2 P6 P10 P3

P1

P8 P4

P9

3

2

1

0

0

10

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30

40

50

60

70

80

90

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Expected probability (in %)

Figure 8.1 Portfolio analysis for expected probability and impact of the development. (own illustration)

Overall, three clusters can be identified for the 10 projections. The first cluster is characterized by low EP and moderate I. It consists of four projections. The two projections in the second cluster on average possess a moderate EP and I is also rated medium to high by the experts. The third cluster is composed of four projections. These are characterized by medium to high EP and high I. Cluster verification in the form of individual portfolios for the four points in time (EP5-EP20) also reveals three clusters in each case. For EP5, the clusters are slightly different from the mean-portfolio since Projections 1, 5, and 7 were assessed as having a lower EP. From EP10 on, the same clusters as for the meanportfolio are visible. These do not change in the following EP dimensions. The average view reflects robust clusters comparing it to the individual points in time.

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Figure 8.2 provides an overview of the mean-portfolio for EP and D. Three clusters could also be identified within this frame. The first cluster includes four projections with low to medium D and low EP. The second cluster includes two projections also characterized by low and medium D but medium EP. The third cluster focuses on projections that possess medium to high D and high EP.

Desirability of development

5

P5

4 P1 P9 P2 P10

3

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P6 P3

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1

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Expected Probability (in %)

Figure 8.2 Portfolio analysis for expected probability and desirability of the development. (own illustration)

These clusters consist of the same projections as the cluster of the meanportfolio for EP and I. Overall, however, the points within the clusters are further apart than in the first portfolio. Based on the detailed examination of the four EP dimensions, the clusters become clearly identifiable from EP15 onwards. For EP5, Projections 1, 5, and 7 show a lower EP. For EP10, this outcome applies to Projections 1 and 5.

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Overall, the mean-portfolios reveal nearly identical clusters. In the detailed examination of the four EP dimensions regarding I and D, the three proposed clusters can be identified in six of eight individual portfolios. For this reason, these three clusters are the focus of further scenario development. Once the clusters have been identified, their associated qualitative arguments of the experts are the focal point. The three identified clusters are composed of different sets of topics. The first cluster comprises Projections 2, 3, 6 and 10. The scenario development focuses on a new focus of higher education institutions on personality development, individual researchers who are independent of institutions, centralized education cities for excellent education and the awarding of degrees in consortia as standard. Cluster 2 contains Projections 7 and 9. The focus of a scenario developed from this cluster should be the establishment of intelligent digital systems in the teaching of technical and methodological knowledge. The new focus on and integration of performance certificates in the selection and examination procedures of higher education institutions is focused on. Cluster 3 includes Projections 1, 4, 5, and 8. Central developments in a scenario derived from this are, therefore, the evaluation of higher education institutions on the basis of factors relating to real output and the entry of virtual education platform providers into the field of higher education. The changing role of higher education institutions toward lifelong and lifewide learning companions is a focal point. Finally, such a scenario includes the aspect of alternative financing models for higher education institutions.

8.2.2

Cross-impact Analysis

The described clusters give an impression of the thematic basis of three scenarios, which can be developed on the foundation of the Delphi results. However, the Delphi results and clusters continue to consider the projections in isolation from each other. Against this background, further scenario work examines how the projections are interconnected within and across the clusters (interconnectedness). The focus is on how they influence each other (interdependence). This process highlights how CIA addresses the weakness of the Delphi survey (Bañuls & Turoff, 2011, p. 1580).

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Cross-impact analysis is one of the most common tools used for developing scenarios (Bañuls & Turoff, 2011, p. 1580). As part of the method, different developments are compared in a cross tabulation. Originally, this illustrated how one development affects the probability of occurrence of another development (T. J. Gordon & Hayward, 1968). The goal was to adjust the probability of occurrence of the developments with the results of the analysis (T. J. Gordon, 1994a, p. 4). This decided calculation of the mutual influence of the probability of occurrence of the projections is not very useful in this research work. The reason for this is that the analysis results are used in the context of further intuitive scenario development. They are not understood as accurate forecasts, but indicators. Consequently, a simplified variant of CIA, as Bañuls and Turoff (2011) describe, was applied. Within this frame, the focus is not on the probability of occurrence of the projections on an interval scale, but on the general mutual impacts of the projections using an ordinal scale (Bañuls & Turoff, 2011, p. 1580). In this thesis, CIA followed Kisgen’s (2017) procedure by using a scale from 0.0 to 1.0.1 Assessing mutual impacts of the projections can be performed by the research team (e.g., Lechler et al., 2019; Spickermann, Grienitz, & von der Gracht, 2014). Likewise, it is possible to involve experts for the estimation (Kisgen, 2017). In the context of this research, the latter option was chosen. Four experts were recruited from the Delphi survey. Experts from different groups of profession were focal point. These experts were asked to bilaterally assess the mutual influence of the projections using a matrix. Subsequently, mean values were calculated for each field of the matrix and reflected back to the experts for validation (Kisgen, 2017, p. 309 f.). Using the discussed mean values of expert assessments, various key ratios were calculated to interpret the relationships between projections. Table 8.1 provides an overview of the key ratios and their calculation.

1

This scale was converted from text form to numerical form. The value should be interpreted as follows: 0.0 = no impact; 0.3 = weak impact; 0.5 = medium impact; 0.7 = high impact; 1.0 = very high impact.

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Table 8.1 Indicators within cross-impact analysis, according to Kisgen (2017)2 Ratio

Definition

TA = Total active

Overall impact of one projection on other projections

Calculation  TA = of all values in the line of a projection

TP = Total passive

Overall influence of all projections on one projection

A = Intensity of activity

Ratio of total active to total passive

A = TA / TP

A&I = Intensity of activity and interconnectedness

Interrelation of a projection with other projections

A&I = TA * TP

 TP = of all values in the column of a projection

Results of CIA are usually illustrated in a network-matrix. This provides an overview of interdependencies between considered projections and provides insight into systemic relationships (Mietzner, 2009, p. 122). Table 8.2 illustrates this network-matrix for the 10 projections in this thesis. Lines indicate the impact of one projection on the other projections. Columns show how much each projection is influenced by others. The calculated key ratios are presented. Free fields refer to the logical impossibility of a projection to impact itself. From the four experts’ point of view, various projections influence each other strongly to very strongly. The highest mean values were rated at 0.9. According to this analysis, Projection 2 (focus on personality development) has a very strong impact on Projection 5 (lifelong and lifewide learning). Likewise, Projection 5 exerts a very strong influence on Projection 2. Beyond this, a strong influence of Projection 9 (performance certificates) on Projection 1 (real output evaluation) is visible (see Table 8.2). Projection 1 has a strong influence on Projections 2 and 9, as does Projection 2 on the first projection. Projection 3 (institution-independent researchers) strongly influences Projection 8 (new funding models). Projection 4 (virtual educational platforms) is estimated to have a strong influence on Projections 5 and 7 (intelligent digital systems). Projection 5 shows the most frequent number of high or very high impacts. In addition to the very strong impact on Projection 2, this 2 Kisgen (2017) has calculated the index of leverage for all projections in addition to the mentioned key ratios. This indicator was not integrated in the context of this thesis since the information to be gained by this index can already be identified with the help of the aforementioned key ratios.

Real output evaluation

Focus on personality development

Institution-independent researchers

Virtual educational platforms

Lifelong and lifewide education

Education cities

Intelligent digital systems

New funding models

Performance certificates

Consortial degrees

1

2

3

4

5

6

7

8

9

10

TP

Short Title

No.

Table 8.2 Results of cross-impact analysis

5.4

0.4

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0.6

0.8

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0.7

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2

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9

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10

TA

4.4

4.2

4.8

4.5

4.6

5.9

4.9

3.9

3.7

4.5

A

0.9

1.0

1.0

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1.2

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0.8

21.4

17.6

22.8

17.9

18.2

29.0

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14.9

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24.0

I

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projection has a strong impact on Projections 1, 4, 9 (performance certificates), and 10 (consortial degrees). Projection 6 (education cities) strongly influences Projection 10. For the seventh projection, a strong influence on Projection 4 is visible (see Table 8.2). Projection 8 is in the second place in terms of frequency of strong impact on other projections. It strongly influences both Projections 1 and 3 and Projection 10. Projection 9 is estimated to have a strong impact on Projection 2. Finally, the experts saw a strong impact of Projection 10 on Projection 5 (see Table 8.2). Beyond mean values for each projection and its connections, the previously described key ratios are viewed in more detail. Figure 8.3 gives an overview of total active (TA) and total passive (TP) values for the individual projections.

Projection 1

-4,9

Projection 2

4,4 -4,2

4,2

-4,8

Projection 3

4,8

Projection 4

-4

Projection 5

-4

Projection 6

4,6

-5

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Projection 7

-4,1

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-3,9

4,9 3,9

-5,1

Projection 9 Projection 10

4,5

3,7

-5,4 -6

4,5 -4

-2

0

2 TP

4

6

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TA

Figure 8.3 Total active and total passive for each projection. (own illustration)

This overview indicates which projections are influencing others rather than being influenced by others. Influencing projections include those that possess a higher TA than TP value. This applies to Projections 4, 5, 6, and 7. The influenced projections, on the contrary, possess a higher TP than TA value. This applies to Projections 1, 9, and 10. Further projections can be viewed as neutral projections. They are as much influenced as they influence. This applies to Projections 2, 3, and 8.

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2

Intensity of activity

1,5

P6

P4

P5

P7 1

P3

P9

P8 P10 P1 P2

0,5

0 0

5

10

15

20

25

30

35

Interconnectedness

Figure 8.4 Intensity of activity and level of interconnectedness for each projection. (Own illustration)

Figure 8.4 illustrates the projections along their intensity of activity (A) and their intensity of activity and interconnectedness (A&I). Projection 5 stands out. Although it possesses a similar A as Projections 4 and 6, it protrudes as having the highest interconnectedness of all projections. Lifelong and lifewide learning as the new standard of higher education can thus be considered the most active and interconnected development within this real-time Delphi survey. Projection 3 possesses the lowest level of interconnectedness. Hence, the establishment of institution-independent researchers is comparatively disjointed from the other projections. The most inactive projection according to its A is Projection 2. From the experts’ point of view, a new focus of higher education institutions on personality development is thus the most passive development within this Delphi survey.

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8.2.3

223

Concluding Remarks on Portfolio Analysis and Cross-impact Analysis

Combining CIA and portfolio analysis, a differentiating illustration of this network of influences can be presented. Within this frame intra- and inter-cluster influences are distinguished. Five out of the overall 19 connections that possess a strong to very strong impact can be located within the three established clusters. These influences can be assigned to the following projection pairs: 4 on 5, 5 on 4, 5 on 1, 6 on 10, 8 on 1. Further strong to very strong impacts relate to inter-cluster connections. Cross-impact analysis also illustrates the extent to which the clusters of portfolio analysis contain influencing, influenced, or neutral developments (TA and TP). Clusters can be examined regarding the extent to which they include active and interconnected developments (A and A&I). Cluster 1 consists of two neutral (Projections 2 & 3), one influenced (Projection 6), and one influencing projection (Projection 10). This cluster includes projections that offer all four possible characteristics concerning A and A&I. They are inactive (Projection 2), active (Projection 6), interconnected (Projection 10), and disjointed (Projection 3). Cluster 2 consists of one influencing (Projection 7) and one influenced projection (Projection 9). One of the projections is active (Projection 7) and the second projection is moderately active (Projection 9). Both projections show a moderate AI compared to other projections. Cluster 3 consists of one influenced (Projection 1), one neutral (Projection 8) and two influencing (Projections 4 & 5) projections. Analogous to Cluster 1 this cluster also includes the various expressions of A and A&I. The previous subchapters and remarks illustrate that the three clusters derived from portfolio analysis offer various combinations of developments that influence or are influenced. The distribution of active and inactive and interconnected projections is balanced within the clusters. Overall, the results highlight that Projections 4, 5, 6, and 7 can be considered drivers. Further projections are rather influenced or neutral. These findings from CIA provide new insights for further qualitative scenario development, which extends the foundation already derived from portfolio analysis.

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Three Scenarios for Higher Education in 2040

Based on the results presented in the previous subchapters, scenarios were developed in a creative and intuitive process. For this purpose, a scenario team, as suggested by van der Heijden (1996) was formed. This scenario team consisted of the author and four experts who had already participated in the Delphi survey. These four experts were selected by means of their availability and willingness to participate in the scenario development process. Their expertise was assumed since they were selected within the expert selection process with the inclusion criteria described in Subchapter 6.2.2.1. As a basis for intuitive scenario development in the scenario team, the results of portfolio analysis and CIA were used to structure the qualitative Delphi results. Focus was on assigning qualitative data to the three identified clusters, and considering interdependencies of the projections. Resulting from this step were initial outlines of the content of three scenarios for the future of higher education and higher education institutions. These outlines were discussed at a workshop of the scenario team in December 2021. Discussions focused on scenario content. Subsequently, the scenario outlines were further developed and the scenarios were written. In the last step, the scenarios were completed with the help of a literature review. Regardless of the objective and process design in the context of scenarios and their development, scenario content possesses different characteristics. Overarchingly, scenarios can be designed to be complex or simple. Both attributes refer to the five characteristics of temporal nature, nature of variables, nature of dynamics, level of deviation, and level of integration (van Notten et al., 2003, p. 426). Temporal nature refers to the narrative of the scenario description. Two types of temporal nature can be distinguished. On the one hand the developmental and on the other hand the end-state nature. Developmental scenarios include describing the path of developments, from the present to a targeted point in time. This point in time, the end-state, is illustrated. End-state scenarios are only illustrating the end-state. However, developments toward it are implicitly considered (van Notten et al., 2003, p. 433). Nature of variables includes the types and quantity of variables used to describe a scenario. A distinction is made between heterogeneous and homogeneous sets of variables in scenarios. Heterogeneous scenarios consist of a large number of variables of different types. Homogeneous scenarios, on the other hand, contain only a small number

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of variables. Additionally, these variables can be assigned to a few different types (van Notten et al., 2003, p. 433). Nature of the dynamics of a scenario focuses on the degree of extrapolation within a scenario description. This centers on the extent to which scenarios linearly extrapolate current developments. Within this frame, two types of dynamics and scenarios can be distinguished. A scenario based on the extrapolation of trends and developments is called a trend scenario. Extrapolation in this case is perceived as not dynamic. In contrast, there are scenarios that include improbable and extreme developments. These scenarios are called contrast scenarios. They exhibit a low degree of extrapolation and thus a high degree of dynamics (van Notten et al., 2003, p. 433). Level of deviation refers to the diversity of the set of scenarios. This can be designed as alternative or conventional. Alternative scenarios are characterized by the fact that they differ greatly in content. Each scenario represents a partly diametrical alternative to the other scenarios.3 Conventional scenarios can be regarded as sets of trend scenarios. They focus on the extrapolation of trends and thus often overlap regarding their content (van Notten et al., 2003, pp. 433–434). Finally, level of integration describes the degree of interconnectedness considering various aspects of the object of investigation within a scenario. Considering these aspects in an interconnected and holistic manner indicates a high level of integration. In contrast, scenarios that focus on considering aspects individually display a low level of integration (van Notten et al., 2003, p. 434). These five characteristics each include two opposing types. It must be critically noted at this point that scenarios can rarely be assigned distinctly to one of the two types. Therefore, the individual characteristics are to be classified on a continuum. Scenarios tend to be oriented in one direction or the other and cannot always be distinctly assigned to one type. End-state scenarios can also explicitly describe developments toward the end-state. Depending on the scope of these descriptions, such scenarios would not fulfill the requirements for a developmental scenario. However, it would not be possible to distinctly assign it to the type of end-state scenario. The scenario content in this thesis were derived from available data from the Delphi survey. Therefore, characteristics of scenario content were predefined to a certain degree. Considering the expressed criticism of the typology, the characteristics were not distinctly assigned to one type. Rather, focus was on considering 3

One technique for developing scenarios that emphasizes alternative scenarios is the scenario axis technique. Here, four scenarios are formed along two axes and the combination of the opposite poles. See Kosow and Gaßner (2008); Wack (1985).

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tendencies toward the characteristics’ types. Figure 8.5 provides an overview on the characteristics of scenario content in this thesis. Grey bars illustrate individual characteristics of scenario content and their respective types. In addition, a white bar indicates where the characteristic is located on a continuum between the two types. Temporal nature Developmental

End-state

Nature of variables Heterogenous

Homogenous

Nature of dynamics Contrast scenarios

Trend scenarios

Level of deviation Alternative scenarios

Conventional scenarios

Level of integration High

Low

Figure 8.5 Overview of scenario content with the five characteristics. (own illustration)

The temporal nature of scenario content in this thesis is end-state oriented. However, the mass of qualitative contributions enabled the outlining of various developments that lead to the end-state. The nature of variables is homogeneous. Hence, few variables were considered and variables can be assigned to few types. Considering the nature of dynamics, the scenarios were characterized as contrasting. The focus was not simply on extrapolating trends. Nevertheless, the scenarios differ in terms of their nature of dynamics. The scenarios approximate to the form of trend scenarios and contrast scenarios. The level of deviation in the scenario set indicates predominance of alternative scenarios. Even though overlapping points of the scenarios exist, the scenarios can be considered as sufficient alternatives to each other. The level of integration within the scenarios is high. Developments and aspects are not considered in

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isolation, but interconnectedly. This is already indicated by the CIA conducted in the previous subchapter (see Subchapter 8.2). The following subchapters provide an overview of the three scenarios developed. Within this frame, the individual scenarios are described. The scenarios are classified and viewed in connection with the conceptual part of this thesis. For this purpose, changes regarding EHEIs are highlighted. Additionally, initial implications for higher education institutions are derived for each scenario. To ensure internal consistency of the scenarios, scenario dimensions were defined inductively, alongside the Delphi results. These dimensions were derived from aspects of the Delphi results that could be identified for each cluster of portfolio analysis and thus each scenario. Consequently, the dimensions represent the underlying structure of the scenarios. Table 8.3 provides an overview derived scenario dimensions and their definitions. Table 8.3 Overview of scenario dimensions Scenario dimension

Definition

The world …

Includes a description of global developments and aspects that influence society and the way in which they might influence it.

The education system …

Includes a description of developments and aspects that influence educational systems in general and the way in which they might influence them.

The central purpose(s) of higher education institutions …

Includes a description of changes within the main tasks of higher education institutions.

Studies at higher education institutions …

Includes a description of changes in the studies at higher education institutions.

Research in higher education institutions …

Includes a description of changes in research at higher education institutions.

Cooperation of higher education institutions …

Includes a description of changes in cooperation of higher education institutions.

In line with the methodology described, each scenario was given a bold title. Descriptions of the scenarios were prepared in narrative form. In addition, an explicit time horizon for the scenarios was defined. 2040 was chosen as a target year since it was included within the long-term time horizon assessed in the Delphi survey. Preceding the description of scenarios alongside the scenario dimensions in the following subchapters, is a short introduction on the foundation

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of each scenario. This includes referring to the cluster of portfolio analysis the respective scenario is based on.

8.3.1

Scenario I—Renaissance of the University

The foundation for this scenario is Cluster 1 of portfolio analysis. This cluster is composed of Projections 2, 3, 6, and 10. Integrated developments are a focus of higher education institutions on the personality development of students, the establishment of institution-independent researchers, the establishment of education cities, and the awarding of degrees in consortia as the new standard. The scenario description is derived from the qualitative results of the Delphi survey. The results of the CIA were also considered. The systematically developed scenario reads as follows: The world is highly individualized in the year 2040. The needs, interests, and knowledge of individuals are at the center of economic, political, and social actions. New technologies enable people’s individual life situations to be considered in all areas of life. The education system focuses on individual requirements of learners. The expansion of education has continued over the past 20 years. The number of potential participants in higher education has increased rapidly. Today, in the year 2040, the education system is faced with the task of meeting the needs of the many individuals. Focus here is in particular on enabling personality development. This also applies to the area of higher education. Due to the late entry of individuals, considered in relation to the developmental stages, focus is on targeted further development of competencies of individuals and their performance, as part of their personality and thus preparing students for a highly individualized and digital world. In 2040, higher education institutions have returned to their core mission. Their central purpose is focusing on the education (Bildung) of their students. Establishing a safe environment for students and their personality development is of utmost importance for the institutions. Consequently, they have moved away from their increasing focus on the labor market, as visible in the 2020s, and adopted a student-centered perspective. Student-centeredness manifests itself in particular in the fact that institutions have to meet the individual requirements of students. To cope with this, they are highly specialized. Overall, the field of institutions has diversified further; they can be found in the most diverse forms, as highly specialized institutions. These

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highly specialized institutions work together in consortia to provide students with opportunities for holistic development. Expansion of education has led to an increased demand from society for greater cohesion and integration of higher education institutions. In response to this development, institutions have decided to establish regional hubs in different regions. These centralized locations of consortia of highly specialized higher education institutions are called education cities, or by some, pejoratively, “production sites”. They function as spaces of life and learning for students. The initial situation has changed in that institutions have specialized to meet student needs. Consequently, they now work together in consortia to pool their expertise. Due to this new situation, some institutions changed their role in general and are now focusing on modern vocational training. Studies at higher education institutions are conducted in the format of classroom teaching. This aims at providing students with the social aspects of learning and it enables a university experience that is based on human interaction and contributes to students’ personality development. Digital formats serve as a supplement to meet the individual life situations of students. Teaching is conducted in tandems of academics and practitioners. The goal here is to address student needs from different perspectives. Teaching and learning focus on opportunities for students to develop their personality. Real-world experiences are the key point of study programs. These are conducted collaboratively, in networked project teams. Resulting from the individualization of society, diversified forms of employment were introduced in the labor market. Consequently, freelancing has established itself as a central form of work in almost all areas of the working world. This also applies to research. Research in higher education institutions has relocated. Researchers are no longer employed by higher education institutions and research funds are acquired for them. Instead, they work as freelancers and gig workers in projects for various institutions at the same time or independently of these institutions in research groups. Cooperation with higher education institutions is established to draw back on infrastructure and to bring current research results into the institutions. Digital platforms are used worldwide to raise funds and promote research results. This development has not only led to the expansion of open science, but also to individual researchers intensively taking advantage of opportunities for personal branding on these platforms. Due to decentralization, trust in research has decreased in parts of society. This is because they suspect that research is increasingly influenced by third party interests. Others, however, see decentralization as an advantage and a measure for quality assurance in research.

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Cooperation of higher education institutions are pursued in particular with other higher education institutions. This aims at forming highly connected consortia, to account for the hyper specialized nature of the institutions. In addition, targeted collaborations are established with individual researchers or research groups to improve institutional reputation and ensure access to new knowledge. Classification of Scenario I considering Part II of this thesis In the main tasks of higher education institutions (see Part II, Chapter 4), it is assumed that institutions focus on all three tasks in a balanced way. Within the frame of Scenario I, this focus shifts significantly. The core of what higher education institutions do does not change, rather it shifts. Education becomes the central factor considered in higher education institutions. The institutions return to their original task. Innovation arises as a byproduct of the students’ personality development. It is promoted in particular on the foundation of educational possibilities provided within studies. Radical changes can be seen in the area of research. Research is losing much of its relevance in institutions since individual researchers are building their own infrastructures to conduct research. The elite status of higher education institutions is composed in particular of aspects of education. The focus is on excellent execution of teaching and creation of excellent environments for students’ personality development in education cities formed by consortia. Elite higher education institutions are highly individualized and focus on the needs of their students to enable leadership education. They collaborate in a centralized (globalized and clustered) manner in their consortia and introduce new certification mechanisms. In the year 2040, degrees are awarded jointly by consortia partners. This includes alliances between higher education institutions and alliances of higher education institutions and businesses. Considering research, EHEIs cooperate with the most prestigious researchers to integrate the “best” research results directly into their curricula and to strengthen their reputation. Research funds are acquired by the institutions in cooperation with their partner researchers. Some consortia have evolved into large, overarching, but loosely-coupled institutions. The elite institution per se is a thing of the past. Instead, the focus is on elite consortia. These are characterized by the fact that they work together with the best researchers. They offer an excellent framework for their students to develop their personalities. They distinguish themselves through the quality of teaching and learning on-site.

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Implications for higher education institutions For higher education institutions today, this scenario implicates several options for actions. A research system operated independently of institutions would result in the elimination of a large component of institutions’ funding. Consequently, cooperation with research institutes or individual researchers should thus be sought. In particular, institutions that aspire to achieve an elite status should enter into collaborations with high-potential or already established top researchers. The study infrastructure of the institutions is changing in such a way that mere knowledge queries, such as tests, are becoming obsolete. Focus on personality development leads to real-world experience gaining relevance. In such a scenario, institutions should therefore start today to align their study programs to practical experience and opportunities for education (Bildung) instead of knowledge. Institutions today should note that it must be explicated which personality traits should be the objective of higher education. In this scenario, higher education institutions are strongly specialized. Consequently, it seems reasonable to define a specific focus today and to identify which institutions offer complementary opportunities for cooperation. The innovation aspect of higher education institutions is represented in this scenario by educated people who drive innovation. The institutions should therefore focus more on the innovation aspect of their studies. Networks become highly relevant in this scenario. This is particularly true for elite institutions. Higher education institutions should expand their networks and strategically choose which partners to include in their consortium to achieve development toward an elite consortium, if desired. Some institutions may change their focus altogether. They can decide not to focus on the personality development of students, but rather on modern vocational training. An early decision here offers the possibility of achieving “market” leadership in this area in the future.

8.3.2

Scenario II—The Commercial Higher Education Institution

The foundation of this scenario is Cluster 2 of the portfolio analysis. This cluster is made up of Projections 7 and 9. On the one hand, the focus was on the establishment of intelligent digital systems in teaching at higher education institutions. On the other hand, a focus of the institutions on performance certificates was integrated.

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In 2040, the ongoing digital transformation of all areas of life is extensively advanced. The world is characterized by short innovation cycles and rapid digital developments. The relevance of technologies is higher than ever before. Access to knowledge is also at an all-time high since knowledge can be accessed in seconds. As a result, differentiating people by knowledge and qualifications is becoming more difficult. The education system is forced to focus on new indicators. Formal and informal education is becoming more purpose-driven and benefit creation becomes more relevant. The performance of individuals becomes a central factor in education. Against the background of the economic character of the concept of performance, the link between economic practice and scientific theory has increased. Consequently, power of businesses in the education system has increased. Access to education, including higher education, expands worldwide at unprecedented levels. Universal access becomes possible and this new accessibility leads to diversification and destratification of the higher education landscape. The central purpose of higher education institutions has changed. Against the background of increased cooperation between higher education institutions and economic organizations, higher education has gained further importance as an economic asset. Consequently, performance in the economic sphere is the target of the main tasks of higher education. Higher education institutions are, in particular, a place for preparation for working life. They impart appropriate technical and work-specific knowledge and skills. They are less a central instance of education than a supplement to digitally accessible knowledge. Thus, the institutions supplement digital self-learning as certifying institutions. They now act as service providers for businesses. Consequently, the aspects of research and knowledge are losing relevance in the institutions. Innovation comes to the fore as a task. Especially economic innovation, as it supports cooperation partners from the economic sphere. Further areas of society lose relevance in this context, and social sciences also suffer, for which the institutions are strongly criticized. Institutions are measured by new evaluation systems. These focus on how they as an overall institution perform. Performance of academic staff in research, of graduates after graduation, and of students in their studies are relevant factors in such evaluation systems. Studies in higher education institutions are designed in a hybrid format. Technical and methodological knowledge is taught via intelligent digital systems. The artificial intelligence integrated in these systems is so advanced that it can cover these aspects better than human lecturers. It extracts the latest knowledge from the global pool of scientific publications and prepares it for students in a way that

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is appropriate for the target group. Higher education institutions integrate entire departments to maintain and optimize these systems. Human lecturers focus on the social aspects of learning in the form of problem- and project-based teaching and learning. Digital enhancements and continuous curricular adaptations to the requirements of the working world are key points. Digital infrastructure is highly relevant and expanded in resource efficiently. Different types of performance certificates are becoming the focus of selection and examinations for both students and employees at the institutions. Business projects are integrated into the curriculum to adequately reflect performance. The performance of students is analyzed with the help of comprehensive data analyses in the sense of learning analytics. Due to the resulting transparency of performance, the term “glass students” is established by critics. Research in higher education institutions is conducted in the model of the 2020 s. Financial resources are provided in particular from the private sector since institutions fostered decentralization from state- to self-regulation. Public funds are allocated to research institutes. In terms of content, research focuses in particular on topics relevant to the labor market and economic disciplines. The focus is particularly on current problems of economic cooperation partners. Research on liberal arts disciplines, and basic research, has decreased significantly, which is a development that is strongly criticized. Cooperation of higher education institutions has developed in a linear fashion since the 2020 s. Digital technologies are shaping it. Commercialization of higher education is being driven forward. One reason for this is the increased cooperation with commercial enterprises. This, in turn, is due to the abundance of financial resources in the private sector. Degrees and study programs offered in cooperation between higher education institutions and companies are the new normal. New models of cooperation between higher education institutions and companies are also being established to promote economic innovation. Classification of Scenario II considering Part II of this thesis In the main tasks of higher education institutions (see Part II, Chapter 4), it is assumed that they focus on all three tasks in a more or less balanced way. In this scenario such a focus shifts significantly. Research, especially basic research, loses relevance in higher education institutions. Education for the labor market and economic innovations come to the fore. Against the background of purpose orientation with a focus on economic benefit, focus is on the performance of students, employees, and educational institutions. Against this background higher education institutions align their processes to promote performance through project- and problem-based teaching and learning.

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Elite institutions are characterized within this frame by their performance. Staff and graduates and students display the best results within the frame of new evaluation systems. These concentrate on performance, especially in the economic field. In addition, elite institutions best serve the needs of the labor market. They maintain the closest ties and establish the strongest collaborations in and with the business community. They are constructed similarly to today’s elite institutions but are significantly more digital. They illustrate a highly commercialized type of the contemporary entrepreneurial university model since they serve a less holistic spectrum of societal sectors and business is the primary focus. The elite status of an institution thus is not based on responsibility. Rather, the focus is on (economic) performance. Therefore, the view of society as a whole is abandoned in favor of a focus on the economic sphere. Implications for higher education institutions For contemporary higher education institutions, this scenario offers various implications. In particular, the new focus of institutions on the aspect of performance stands out. If an institution pursues the goal of achieving an elite status, it is necessary in the context of this scenario to demonstrate the greatest performance in comparison to other institutions. This refers to applicants of the institutions and to students and staff. Regarding applicants, it may be useful for the institutions to adjust their selection procedures to the factor “performance” already today. Institutions should recruit applicants who have demonstrated the strongest performance prior to their studies to achieve elite status. Concerning students, institutions can change their study programs to focus on performance. Consequently, projects in (business) practice can be integrated into curricula. A logical consequence of this is a change of examination systems. These would have to be radically changed and converted to performance certificates. For holistically displaying performance, it seems reasonable for the institutions to establish performance as a central indicator regarding the hiring of new employees. In addition, target and bonus systems for established employees can be adjusted toward performance. Focus here is on demonstrating elements of performance that are relevant for the respective position. Beyond performance, collaborations are becoming more relevant in this scenario. These should be increasingly sought, especially with businesses. It is also reasonable to initiate collaborations with developers of intelligent digital systems to align curricula in a digital and future-oriented manner. Consequently, it can be

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relevant for higher education institutions today to invest in digital infrastructure and to plan for such investments in the future. The expansion of information technology competence within higher education institutions should also be considered in this context. Finally, it seems reasonable for the institutions to establish mechanisms today that lead to promoting research in liberal arts disciplines as part of economically oriented research and associated funds. The result of this would be preventing effects that harm liberal arts disciplines and thus anticipating associated criticism.

8.3.3

Scenario III—Lifelong and Lifewide Learning Companions

The foundation for this scenario is Cluster 3 of the portfolio analysis. This cluster is composed of Projections 1, 4, 5, and 8. This scenario focuses on the evaluation of higher education institutions alongside indicators of real output. In addition, virtual education platforms have established themselves in higher education. Lifelong and lifewide learning and the new role of higher education institutions as learning companions are key points. The establishment of alternative sources of funding for the institutions is integrated. The world is characterized by constant uncertainty in the year 2040—and can be described as volatile, uncertain, complex, and ambiguous (VUCA). The term “VUCA world”4 reflects the status quo more than ever. Radical and disruptive developments, such as pandemics, regularly challenge society. In all areas of society, demands on individuals are changing almost continuously. Likewise, changes are expressed through incremental developments, such as the demographic change, whose effects are now increasingly being felt. People are living longer and are healthier for longer. Digital developments have progressed linearly without achieving the predicted visionary breakthrough. Nevertheless, digital platforms have established themselves as the means of choice in a wide variety of areas. As a result of constant incremental and radical changes, individuals increasingly need new knowledge and new skills. This places new demands on the education system. Educational institutions must adapt to the greatly shortened half-life of knowledge. New knowledge must be produced more quickly and distributed to individuals across their lifespan, regardless of location. Digital tools are therefore being used 4

For the term VUCA world, see N. Bennett and Lemoine (2014); Millar et al. (2018).

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at every level of the education system. The challenges of the VUCA world are changing the needs of learners. Disciplinary learning and the isolated consideration of subjects are no longer useful. Interdisciplinarity and transdisciplinarity, and the interconnected consideration of a wide variety of topics, have become more relevant. Within this frame, a focus on creating continuous innovations was established. These serve as an instrument for dealing with the developments of the VUCA world. The central purpose of higher education institutions has changed. They no longer represent lifespan-related instances of education. Rather, they have evolved from a transitional place to lifelong and lifewide institutions that continuously accompany learners of different ages. The institutions’ objective is preparing individuals to successfully address VUCA challenges and to enable them to initiate and implement innovations. As a result of this change in the role of higher education institutions, the perception of the higher education system in society has also changed. Institutions are no longer perceived as situational in a person’s life. While in the past they were particularly associated with individuals in (young) adulthood, they are now perceived as lifelong learning companions. Virtual education platforms have aggressively entered the system of higher education. Some social media, in their role as exchange platforms, now serve as future hubs for developing radical ideas in terms of positive societal change. In terms of their new role as learning companions, some higher education institutions have entered into competition with digital platform providers. They adopt a hybrid approach and operate their own platforms. As a result of competition, higher education institutions have diversified their funding sources. Beyond public funding, various other types of domestic and international financing, such as venture funding, have established themselves as alternatives. Economic constraints on institutions have receded into the background due to the resulting secure financial situation. This allows institutions to act more freely. However, parts of society view this development critically. Fears have risen that the independence of higher education institutions, especially with regard to research, suffer from third party interests. Studies in higher education institutions have undergone massive changes as a result of the institutions’ changed role. Traditional study programs are a thing of the past. Higher education institutions offer flexible learning opportunities to enable skilling, upskilling and reskilling for all age groups. Learning opportunities are designed in the form of modules that cover a wide range of disciplines.

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The traditional enrollment model has become obsolete. Students enroll in higher education institutions using a subscription model similar to streaming services from the 2020 s. In everyday language the terms “a la carte university” and “invisible university” have established themselves for this new model. This is because students pay monthly fees and can attend modules of their choice at a fixed price, depending on their needs and situation. These modules can be attended both in digital and on-site form. They conclude with the award of a certificate. It is also possible to attend modules at various institutions at the same time, which results in blurred institutional borders, making the traditional standalone institution invisible. The subscription model is particularly popular in institutions with private sponsorship. Research in higher education institutions has developed linearly compared to studies. It is conducted in the traditional model already known from the 2020 s. Digital technologies enable optimized execution of research projects and efficient distribution of research results. As a result, new knowledge is generated, integrated into curricula, and disseminated to society more quickly. In terms of content, research focuses in particular on topics that contribute to overcoming the greatest epoch-related challenges. Focus is on cross-disciplinary knowledge creation. Trans- and interdisciplinary research is consequently particularly encouraged. Cooperation of higher education institutions has diversified. The institutions are in constant competition with virtual education platforms. These platforms take on a role similar to that of the institutions, but do not offer on-site education. Some institutions, especially smaller ones, are cooperating with platforms in the sense of coopetition5 to accommodate their new purpose. Other institutions cooperate with each other to pool financial resources to operate their own platform. Some institutions of higher education have escaped competition and cooperation with digital platform providers by focusing exclusively on research. They have now taken the form of traditional research institutes. Classification of Scenario III considering Part II of this thesis In the main tasks of higher education institutions (see Part II, Chapter 4), it is assumed that they focus on all three tasks in a more or less balanced way. In this scenario, this focus shifts depending on the institution. Some institutions focus on education, promoting societal innovation, and thus creating a positive 5

The term is a hybrid originating from cooperation and competition. It refers to a gametheoretical model of cooperation that focuses on collaborations with competitors. See Brandenburger and Harborne Jr. (1996); Brandenburger and Nalebuff (1996).

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impact in society. Others are withdrawing from these fields due to strong competition. These institutions focus entirely on research. They cooperate with virtual education platforms or institutions of higher education by acting as suppliers of educational content. In this scenario, a changed constitution of EHEIs is identifiable. The institutions represent excellent learning companions. They flexibly meet the needs of the most able students, anytime, anywhere. To this end, they have established excellent frameworks in the three areas of skilling, upskilling, and reskilling. Elite institutions cover the needs of learners digitally with the help of their own educational platforms. They also offer face-to-face modules at various locations around the world. The aim here is in particular to promote innovation among students and former students. Research at elite institutions is characterized by its high quality. It is strongly inter- and transdisciplinary. In addition, it is conducted in cooperation with a wide variety of institutions in society. Further research cooperation is established with the most prestigious research institutes. This also includes former higher education institutions, which have emerged as the best new research institutes. Implications for higher education institutions Various implications for current higher education institutions arise from the scenario described. On the one hand, these serve to prepare institutions for such a future. On the other hand, they serve as an impetus for institutions striving to achieve an elite status in view of such a future. Higher education institutions can already adjust themselves to their new role as lifelong and lifewide learning companions. Here, the focus is particularly on designing their own processes in a more flexible manner. With the help of investments in digital infrastructure and the establishment of new international locations, increased location independence can be created for learners. Depending on the financial possibilities of an institution, initial collaborations with providers of digital platforms can be created. New stakeholder groups can be focused on. Consequently, it is reasonable to extend communication and recruitment of students to further age groups. In the context of these new target groups, changed learning theories, such as adult learning theories must be integrated into curricula to address the respective needs. Regarding their own staff, especially in teaching, institutions should start investing in continuing education today. In addition, inter- and transdisciplinary teaching should be promoted. This also applies to research. With a view to increasing institutions’ commitment to addressing societal challenges, research should also be conducted cross-institutional.

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Finally, it can be highlighted that it is useful for higher education institutions to identify and establish alternative sources of funding today. These should go beyond classic public funding. Within this frame, it could be advantageous for institutions to define mechanisms that preserve academic independence to prevent negative developments in this respect.

9

Conclusions from Part III

Part III constitutes the empirical part of this thesis. It illustrated the overarching empirical research process, underlying methodology, and results of this research. It aimed at answering Research Questions 3 and 4: “How do higher education institutions change in the future and which factors determine these changes?” and “What do possible future images of higher education and especially EHEIs up to the year 2040 look like?”. Chapter 6 focused on the empirical research process within this thesis. In general, the research process of this thesis is exploratory and deductive as described in Part I. Within this frame empirical work was designed qualitatively. The main reason for qualitative design was that Research Questions 3 and 4 focus on subjective perceptions of individuals (Wichmann, 2019). In the context of this qualitative approach methods of the metadiscipline futures research (R. A. Slaughter, 2002) were applied since this thesis adopts a future-oriented perspective and focuses on processes (Gray & Hovav, 2011). The overarching empirical research process was designed in an integrated manner. This is because the phases of the traditional research process of social sciences (Hussy et al., 2013) were integrated into the phases of the generic foresight process (futures research) (Voros, 2003). The empirical research design consisted of three phases: (1) input and data collection, (2) foresight and data analyses, and (3) outputs and documentation. On this foundation the specific method for data collection, the Delphi survey, was explicated. In a first step, the methodological foundations were explained. Within this frame the types and variants of the Delphi survey were described (Häder, 2014; Niederberger & Renn, 2019). Weaknesses of the method were presented and the type and variant applied in this thesis were justified. The focus was on a real-time Delphi survey (Gnatzy et al., 2011; T. Gordon & Pease, 2006). © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_9

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In a next step, the specific execution of Delphi in this thesis was the central point. The real-time Delphi survey was designed in line with Gnatzy et al. (2011). Additionally, the expert selection process and expert inclusion criteria for the survey were described. Here, a focus was on selection by external cues and personal involvement (Mauksch et al., 2020) and inclusion criteria included surface and deep-level aspects (Spickermann, Zimmermann, & von der Gracht, 2014). Projections for this real-time Delphi survey were formulated based on Part II of this thesis. Supplementing the heuristic theoretical concept of the constitution of EHEIs as the central point, a literature review on the future of higher education and a brief bibliometric analysis on the topic were conducted. In addition, four experts offered their opinions on the various defined projections for the Delphi survey. These three steps were conducted to form a solid foundation for the survey. The final 10 Delphi projections were formulated considering various quality criteria proposed by Salancik et al. (1971) and Markmann et al. (2020). In Chapter 7, the results of the real-time Delphi survey were presented. In this context the specific design of the survey and the timeline were presented. Focal points of the survey were four dimensions that experts had to assess: expected probability (EP) for five, 10, 15 and 20 years (von der Gracht et al., 2021); impact on higher education institutions (I); and desirability of the development (D). Personal aspects, such as demographic data, self-assessed confidence in own assessments (C) and locus of control (LoC), were integrated into the survey (Beiderbeck et al., 2021b). Depending on the projection, the number of experts discussing the projections ranged from 81 to 114. These experts came from a wide variety of fields and countries. A majority of the experts could be assigned to the professional group, academia. Concerning the region in which experts were based, Europe and North America represent most of the expert panel. Greater diversity in the panel is displayed by age and gender of the experts since these are rather equally distributed. In a first step of data analysis, the data on the individual projections of the Delphi survey were evaluated quantitatively and qualitatively. The quantitative evaluation included the presentation of descriptive statistics regarding the dimensions EP (EP5, EP10, EP15, EP20), I, D, and C. Within the scope of EP, the experts attributed the highest values to the projection focusing on the establishment of alternative sources of funding by higher education institutions. The experts also saw the development of institutions toward lifelong and lifewide learning companions and the entry of virtual educational platforms into the field of higher education as probable. The experts assigned the lowest values for probability to the projection that described a

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research system based on individual researchers independent of institutions. Equally improbable in the experts’ view is the establishment of centralized education cities. The experts attributed the highest I to the projection describing the development toward lifelong and lifewide learning companions. Intelligent digital systems and alternative sources of funding were also perceived to have a high impact. The lowest values for the impact of developments were in the medium range. Institution-independent researchers and awarding of degrees in consortia as the default were attributed such a medium impact. Considering D, the experts on average rated lifelong and lifewide learning the highest. They perceived the establishment of real output in the evaluation of higher education institutions as desirable. Institution-independent researchers and education cities were perceived as less desirable. The experts rated the establishment of alternative sources of funding for higher education institutions as moderately desirable. The interquartile range was used in quantitative analysis to classify the extent to which consensus or dissent prevails for each dimension and respective projection (von der Gracht, 2012). Overall, it was determined that consensus in the expert panel was low. A broad consensus could be observed for I. Further dimensions were characterized by dissent. The qualitative analysis of the Delphi survey was conducted using qualitative content analysis (QCA) (e.g., Mayring, 2002, 2015), and specifically the variant of content structuring qualitative content analysis (Kuckartz, 2018; Kuckartz & Rädiker, 2022). A syntax analysis of the qualitative data (Förster & von der Gracht, 2014) was integrated into the qualitative analysis of the Delphi results. The experts provided 939 qualitative text contributions in the Delphi survey. Of these 77.74% were formulated in the form of complete sentences. Considering absolute and relative key figures within the frame of the syntax analysis, projections were identified that can be described as strongly discussed. The overarching themes of these projections were digital transformation, urbanization, and the third mission of higher education institutions. Within the frame of QCA overarching thematic aspects could be identified. Societal trends and developments were frequently offered as an argument for the high EP of projections. Opposed to this the experts argued for many projections that they have a low EP or would never occur, because higher education is conservative, and traditions will not be overcome. A further argument for low EP was the lack of resources of institutions and students. Furthermore, the lack of support by relevant stakeholder groups for some developments described in the projections was addressed.

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The impact of the projections on higher education institutions was perceived as multifaceted. Mostly, changes regarding the focus of higher education institutions and their structures were addressed. The impact on curricula was stressed by the experts. In addition, many projections were considered to have an impact on the financial or human resources of higher education institutions. Finally, the experts described negative impacts on higher education institutions, such as decreasing independence and increasing inequalities. Beyond the projections, dissent in the expert panel, which could be identified by the descriptive statistics, was analyzed. Group comparisons were performed. Independent variables were groups formed via personal dimensions (LoC, age etc.). Dependent variables were projection-related dimensions (EP, I, D, C). These initial group comparisons offered little insight into the origin of dissent in the expert panel. Consequently, a supplementary desirabiliy bias analysis (Ecken et al., 2011) was performed. Its results highlighted that the perceived desirability of a development influenced the experts’ assessment of the EP of a projection. Therefore, it was the central aspect of explaining dissent in the expert panel of this survey. In Chapter 8 the described results of the Delphi survey were used as a foundation for further conducting the foresight phase and initiating the output phase of the empirical research process. This chapter focused on images of the future of higher education institutions in scenarios (Gausemeier et al., 1998). In this context the various types of scenarios were described. The process of scenario development in this thesis was explicated. The main point was the development of explorative scenarios, which included normative aspects (Nowack et al., 2011). For developing the scenarios a mixed design, combining model-based and intuitive methods was used (Mietzner, 2009). This process consisted of a portfolio analysis and a CIA and creative work in a scenario team (Kisgen, 2017). The latter was made up of four experts from the Delphi survey. Subsequent to describing the process of scenario development the prescenario analyses (portfolio analysis and CIA) and their results were presented. Portfolio analysis was conducted for two combinations of dimensions “EP and I” and “EP and D”. Both combinations resulted in the formation of the same three clusters. Cross-impact analysis included an assessment by four experts who previously participated in the Delphi survey. They assessed the interconnectedness of projections. Results were overviews of projections that actively influenced other projections, projections that were influenced by others and interconnectedness of projections. This offered a basis for transforming the isolated view of the three clusters from portfolio analysis into an interconnected view. Therefore, it was a central step in creating plausibility for the final scenarios.

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Three scenario outlines were developed in the scenario team from the three clusters. Results from the creative work in the scenario team were supplemented by results from the literature review on the future of higher education. Table 9.1 gives an overview on the three developed scenarios and their core characteristics. Scenario I focused on a highly individualized future. Higher education institutions are particularly committed to the personality development of their students. Research is carried out independently of the institution by researchers in networks. Scenario II centers on a highly digitized future. As a result, knowledge is easily accessible. Higher education institutions consequently focus on the demonstrated performance of their students. During this, business collaborations are strengthened, making the field of higher education highly commercialized. Scenario III focuses on a deeply volatile future. This future is characterized by the constantly changing requirements and needs of society. Higher education institutions support society in meeting these requirements. Within this frame institutions are lifelong and lifewide learning companions of learners. These three distinct scenarios combine Part II and Part III of this thesis since they respectively implicate changes in the tasks and thus the constitution of EHEIs in the future. In Scenario I, EHEIs are mainly defined by their responsibility in the form of their educational task. Rather than traditional institutions they operate in elite consortia offering an excellent framework for personality development. In Scenario II, EHEIs are defined by their institutional performance specifically concerning economic performance. This performance is the central aspect of the institutions’ responsibility. In Scenario III, EHEIs are characterized by excellently operating as a lifelong and lifewide learning companion. They mainly focus on their educational task and its time and location flexibility. These scenarios represent the foundation for the transfer phase of the foresight process, which was explicitly not integrated in the research process of this thesis since it is usually conducted by leaders in practice or politics. Therefore, it follows the conclusion of this thesis.

• focus on individual learner requirements • rapid increase of participants • task: meeting individual needs • enable personality development • preparation for a highly digitalized and individualized world

The world …

The educational system …

The central purpose(s) of • return to their core mission higher education institutions … • focus on education (Bildung) • safe environment for personality development of students • student-centered perspective • highly specialized • organized in education cities • some institutions focus on modern vocational training

Scenario I • highly individualized • needs, interests and knowledge of individuals are focal point • new technologies enable considering individual situations

Dimension

Table 9.1 Comparative overview of scenarios

• constant uncertainty • volatile, uncertain, complex and ambiguous world • radical and disruptive developments • linear digital developments

Scenario III

• economic needs are focal point • higher education as an economic asset • institutional performance • preparation for the labor market • research and knowledge lose relevance

• companion for lifelong and lifewide learning • preparing institutions for VUCA challenges • virtual education platforms are strong competition • diversified funding sources • striving for creating innovations • some operate as research institutes (continued)

• universal access to knowledge • new demands and education • relevance of continuously new knowledge and new • purpose-driven skills • focus on creating benefits • shortened half life of • performance as the central knowledge factor • lifelong and lifewide learning • inter- and transdisciplinarity

• digital transformed • short innovation cycles and rapid digital developments • high relevance of technology

Scenario II

246 9 Conclusions from Part III

Scenario I • classroom teaching • university experience • digital formats as supplements • teaching by practitioners and academics • real-world experience in curricula • networked education • institution independent research • freelancing researchers • open science • digital platforms

• working in consortia • targeted collaborations with individual researchers / research groups

Dimension

Studies at higher education institutions …

Research in higher education institutions …

Cooperation of higher education institutions …

Table 9.1 (continued)

• traditional research model • application of digital technologies • more efficient research projects • focus on trans- and interdisciplinary research

• flexible learning opportunities • skilling, reskilling and upskilling • focus: all age groups • modules instead of study programs • subscription model

Scenario III

• digital cooperation • cooperation with virtual • increased cooperation with education platforms businesses • coopetition • study programs in • cooperation with further cooperation between higher education institutions businesses and institutions are the new normal • new models of cooperation

• traditional research model • funding especially from private sources • content of research: current problems from the economy

• hybrid format in curricula • intelligent digital systems in teaching continuous curricular adaptions • performance certificates • real-world (business) projects • learning analytics

Scenario II

9 Conclusions from Part III 247

Part IV Conclusions

Findings and Implications

10

The preceding Parts II and III constitute the main part of this thesis. On this foundation, the following subchapters provide an overview of the findings and implications of this thesis. Within this frame, the main results are summarized for Parts II and III. Based on this, implications from this research for higher education practice and policy are described in aggregated form.

10.1

Main Findings of Research

The central aim of this thesis was to investigate the phenomenon of EHEIs from a pedagogical, institution- and future-oriented perspective. To this end, an overarching research question was defined. This was then broken down into four underlying research questions. The following subchapter provides an overview of the main findings of this thesis. It provides insight into answering the research questions and thus an approximation in achieving the central objectives of this thesis. Research Question 1 focused on the constitution of EHEIs. Its answer was sought through a traditional literature review. From the literature, various definitions for the terms elite and elite education were identified. The particular definition of the terms depends on the underlying perspective of an investigation. The central perspectives on the concept of elite education are the sociological, the pedagogical their interdisciplinary, system-oriented perspective (see Part II, Subchapter 3.1.2). This work focused on the pedagogical and interdisciplinary, system-oriented perspective with a particular focus on the field of higher education. Elite education in higher education can be divided into three distinct objects, which are the system, the institution, and the individual (e.g., Brezis & Hellier, 2018; Kwiek, © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_10

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2016; Trow, 1973). This thesis focused on elite higher education as an institution. An elite higher education institution is defined in this thesis as an institution of higher education that is at the top of a hierarchy and holds an elite status. The attribution elite thus results from the position in the field of other higher education institutions. An elite status results from a position on top of a reputation hierarchy (see Part II, Subchapter 3.1.2). The foundation for attaining an elite status and a position at the top of the institutional hierarchy in this thesis is the notion of the responsibility elite (Bohlken, 2011). Elites obtain their status by assuming a certain responsibility toward the society in which they are located (see Part II, Chapter 4). The responsibility of higher education institutions in society can be outlined along their central institutional tasks. These were also derived from the literature and form the basis for describing the constitution of contemporary EHEIs. The main tasks of contemporary higher education institutions can be summarized in the three concepts of education, research, and innovation (e.g., Cloete et al., 2018; Sam & van der Sijde, 2014; Spiel et al., 2018). These tasks can be broken down into an impact and the process leading to it (Pinheiro & Benneworth, 2018). Consequently, the central tasks are to be considered from a process and impact perspective. Education at higher education institutions includes the process of personality development. Focus is not on accumulating knowledge, but on people who work on holistically developing themselves in an interactional process with their environment (e.g., W. G. Faix & Mergenthaler, 2015; Klafki, 2007b). Higher education institutions have the task of enabling this process. The resulting impact in the task of education are educated persons. From the perspective of the institutions, research involves two parts of a process. One is the research activities, and the other is the creation of an adequate environment for research. The former refers to activities of an individual, and activities of an institution or several institutions in different types of research. The types of research are basic research, applied research, and experimental development. In this framework, research activities are execution of research, supervision of research and continuing education for research (OECD, 2015) The research environment should provide an excellent framework for research and scientific excellence, especially with regard to elite institutions. This is achieved through large institutional research capacity, multidisciplinary research clusters, support for researchers in terms of resources and an enabling and supportive research culture (Falola et al., 2020; Huenneke et al., 2017; Torres Zapata, 2019). Impact of research at higher education institutions is newly generated, relevant knowledge. However, this knowledge is not only produced. It is also aggregated, processed, distributed, and promoted by institutions. Produced knowledge can be

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classified into two categories: homogeneous and disciplinary produced Mode 1 knowledge and heterogeneous and interdisciplinary produced Mode 2 knowledge (Gibbons et al., 2010; Wolhuter, 2018). Both types of knowledge are produced in EHEIs. Here the institutions are leading in terms of quality and quantity. The final main task of innovation involves the process of creating societal innovations (Rammert, 2010). Institutions focus on innovations that cover all areas of the STEEP field (Kotler, 2002). The process perspective includes promoting innovation and creating institutional innovativeness. Both aspects are achieved through innovative research, innovative pedagogical interventions, and innovative organizational structures (Etzkowitz, 2013; Tierney & Lanford, 2016). Elite institutions perform these two parts of the process excellently. A current model of a higher education institution that promotes innovation is the entrepreneurial university (Etzkowitz, 2013). However, it concentrates especially on economic and technological innovation. The impact of innovation is progress in different spheres of society. Beyond the central tasks of higher education institutions and their excellent execution, further characteristics of EHEIs were identified in the literature on elite education institutions. These possess both overlaps and complementary aspects to the three central tasks. The four central further characteristics are reputation, academic excellence, selection, and network. Research Question 2 focused on embedding leadership education in the context of EHEIs. Answering this research question was integrated into the previously mentioned literature review and thus the heuristic theoretical concept of the constitution of contemporary EHEIs. Elite higher education institutions stand out from others regarding their educational task because they execute it excellently. Excellent execution is synonymous with providing an excellent framework for personality development. In such a framework, special persons, for example, leaders, develop. Leadership education can thus be seen as part of the educational task in EHEIs. This comprises seven curricular elements that pursue the goal of fostering the (further) development of creative personalities (Kisgen, 2017). These personalities in turn are capable of leading (A.-V. Faix, 2020; W. G. Faix & Mergenthaler, 2015). Research Question 3 focused on the future of higher education institutions. It aimed at investigating what this future might look like and what changes lead to this future. Empirical work applied methods of futures research. The future was treated as “futures” which are alternative constructions of present perceptions on the future. Consequently, Research Question 3 investigated current perceptions of the future of higher education institutions. The focus of empirical work was on a real-time Delphi survey (Gnatzy et al., 2011; T. Gordon & Pease, 2006). The

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real-time Delphi survey was designed based on Part II of this thesis. In addition, a literature review on the future of higher education, a brief bibliometric analysis in the topic area, and collaboration with experts were conducted to form a solid foundation for the survey (see Part III, Subchapter 6.2). Finally, 10 projections were defined. These were to be evaluated quantitatively and qualitatively by experts from various stakeholder groups of higher education institutions. Depending on the projection, the number of experts discussing the projections ranged from 81 to 114. These experts came from a wide variety of fields and countries. A majority of the experts could be assigned to the professional group of academia. Concerning the region in which experts were based, Europe and North America represented the majority of the expert panel. Greater diversity in the panel was displayed in the ages and genders of the experts (see Part III, Subchapter 7.2). In a first step, the data on the individual projections of the Delphi survey were evaluated quantitatively and qualitatively. The quantitative evaluation included the presentation of descriptive statistics regarding the dimensions expected probability of the development in 5, 10, 15, and 20 years (EP5, EP10, EP15, EP20), impact of the development (I), and desirability of the development (D). Within the scope of EP, the experts envisaged the establishment of alternative sources of funding by higher education institutions as probable. This applied to both the short-term and long-term perspectives. In the long term, the experts also foresaw the development of institutions toward lifelong and lifewide learning companions and the entry of virtual education platforms into the field of higher education as probable. The experts assigned the lowest long-term probability to a research system based on individual researchers independent of institutions. Equally improbable in the experts’ view was the establishment of centralized education cities (see Part III, Subchapter 7.1). The experts attributed the strongest impact on higher education institutions to the development toward lifelong and lifewide learning companions. Intelligent digital systems and alternative sources of funding were also perceived to have a strong impact. The lowest values for the impact of developments were in the medium range. In particular, institution-independent researchers and awarding of degrees in consortia as the default were attributed such a medium impact (see Part III, Subchapter 7.1). Regarding D of the projections, lifelong and lifewide learning was again rated highest on average. The experts also saw the establishment of real output in the evaluation of higher education institutions as desirable. Institution-independent researchers and education cities were perceived as less desirable. The experts

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rated the establishment of alternative sources of funding for higher education institutions as moderately desirable (see Part III, Subchapter 7.1). The interquartile range was calculated for each of these dimensions as part of the quantitative analysis. This is used to classify the extent to which consensus or dissent prevails for each dimension and respective projection. Overall, it was determined that consensus in the expert panel was achieved for 15 of the 60 points considered. Distributed among the project-related dimensions, consensus was found five times with regard to EP (5 out of 40 points), seven times for I (7 out of 10) and three times for D (3 out of 10). Thus, there was a broad consensus with regard to I. Further dimensions were characterized by dissent (see Part III, Subchapter 7.1). Qualitative data analysis was conducted using qualitative content analysis (QCA) (e.g., Mayring, 2002, 2015), and specifically the variant of content structuring qualitative content analysis (Kuckartz, 2018; Kuckartz & Rädiker, 2022). A syntax analysis of the qualitative data (Förster & von der Gracht, 2014) was integrated into the qualitative analysis of the Delphi results. The experts provided 939 qualitative text contributions on projections and additional questions in the Delphi survey. Of these contributions, 77.74% were formulated in the form of complete sentences. Considering absolute and relative key figures within the frame of the syntax analysis, projections were identified that can be described as strongly discussed. The overarching thematic focus of these projections was digital transformation, urbanization and centralization, and the third mission of higher education institutions. The syntax analysis illustrated that the length of the Delphi questionnaire was adequate. This can be concluded from the fact that discussions of the projections did not decrease over the course of the questionnaire (see Part III, Subchapter 7.1). Results of QCA were strongly dependent on projections. Nevertheless, some overarching aspects can be addressed. Regarding high EP of the projections, societal trends and developments were frequently offered as an argument. Following this argument, individualization, digitalization and demographic change lead to changes in society, which cause people to place changed demands on higher education institutions. These requirements are addressed by developments described in the respective projections.On the opposite side, the experts argue for low EP or nonoccurrence of the projection using conservatism and tradition. The argument is that the field of higher education is conservative and the traditionally established structures are rigid and strong. Consequently, radical changes, which are described in the projections, would not become established in the sector. Another argument for low EP, which has been applied in the context of various projections, is the lack of resources on the part of institutions or students and the lack of

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support for the development described in the projection, by relevant stakeholder groups. An overarching aspect of the impact of the projections on higher education institutions was changes regarding the focus of higher education institutions and their structures. In particular, the impact on curricula with regard to educational content, educational methodologies, and the educational setting, and learning and teaching, was addressed by the experts. In addition, many projections were considered to have an impact on the financial or human resources of higher education institutions. Finally, the experts provided qualitative input on some projections that explicitly described negative impacts on higher education institutions. The focus here was on a decreasing independence of the institutions on the one hand and the strengthening of inequalities on the other (see Part III, Subchapter 7.1). Beyond the projections, dissent in the expert panel, which could be identified by the descriptive statistics, was analyzed. For this purpose, group comparisons were performed. Groups were compared for 420 data pairs (e.g., EP5 assessed by age of the experts). Within this frame, 32 differences that can be considered significant were identified. These differences spanned all defined personal dimensions in which groups were established (see Part III, Subchapter 7.2). This result provided little insight into the reasons for dissent in the expert panel. Therefore, a supplementary test for desirability bias was performed. Within 40 individual data pair comparisons, 37 significant differences were identified here. Consequently, the perceived desirability of a development in particular influenced the experts’ assessment of the probability of occurrence of a projection. Desirability was therefore a relevant reason for the dissent in the expert panel of this Delphi survey (see Part III, Subchapter 7.2). Research Question 4 explicitly focused on images of the future of higher education institutions. Against this background, scenarios for higher education institutions were derived from the results of the real-time Delphi survey. A mixed design was used for this purpose. The first two steps were portfolio analysis and CIA. These two tools are model-based. Based on this, the process of scenario development proceeded intuitively and creatively in the scenario team. This scenario team consisted of four experts who participated in the Delphi survey. The result of the analyses and creative work were three scenarios, which represented alternative present constructions of the future of higher education institutions. Scenario I focused on a highly individualized future. Higher education institutions are particularly committed to the personality development of their students. Research is carried out independently of the institution by researchers in networks. Elite higher education institutions are defined especially by their educational task. Their innovation task has evolved linearly. The research task on

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the other hand has been outsourced. The focus here is not on elite institutions in the classic sense, but on elite consortia that carry out their educational and innovation task, especially in-person, worldwide. Scenario II focused on a highly digitized future. As a result, knowledge is easily accessible. Higher education institutions consequently focus on the demonstrated performance of their students. In the course of this, business collaborations are strengthened, making the field of higher education highly commercialized. Consequently, institutions focus on economic interests and issues. They are dedicated to creating high-performing individuals for the workplace and to innovation in the field of business. The institutions position themselves as entrepreneurial universities. In this framework, elite institutions are defined by their institutional performance. They produce the best performers for the workplace and provide an excellent framework for economic innovation. They also feature the strongest partners from the business sphere. Scenario III focused on a deeply volatile future. This is characterized by constantly changing requirements and needs of society. Higher education institutions support society in meeting these requirements. They focus not only on higher education but also on continuing education of individuals, that is, skilling, reskilling and upskilling. In addition, they open their view and focus on further age groups. Independence of time and place is highly relevant. Within this frame institutions are lifelong and lifewide learning companions of learners. They are in strong competition with virtual education platforms that assume the same role. Elite higher education institutions are characterized by their flexibility regarding location and time of their educational task. They operate their own virtual platforms and thus operate outside the new competition. This brief summary of the three scenarios illustrates that higher education institutions may develop in distinct directions in the future from the perspective of their stakeholders. At least three directions could be derived from this research. The directions are the higher education institution as an institution of education and innovation as a concomitant of that education, the higher education institution as a supplier of competent individuals to business and the workplace, and the higher education institution as a facilitator of learning across life stages and locations. With these three directions in view, achieving an elite status would not change in its nature for higher education institutions. Rather, it would change in its design, depending on the scenario one observes. In one scenario, elite higher education might be designed in consortia rather than individual institutions. Focus here is on education and cooperation. In another scenario, an elite status is

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achieved by the high performance of an institution especially regarding resulting economic benefits. In a third scenario, EHEIs are flexible organizations that function as excellent learning companions over the course of an individual’s life. Leadership education, resulting from the concept presented in Part II has not been a specifically discussed aspect of the Delphi survey. Rather, it is an integral part of the educational task of EHEIs. In the context of the scenarios developed, this task would continue to exist in the future, especially in view of the societal developments described and the resulting need for competent leaders in society. The foregoing remarks, considered in combination, provide an answer to the overarching research question of this thesis: “In what respect will higher education change, how are elite higher education institutions constituted in this context, and where is leadership education located within this nexus?”.

10.2

Implications for Higher Education

The results presented in the previous subchapter have various implications for educational policy and practice. In a first step, the constitution of contemporary EHEIs can be viewed to derive these implications. The introduction to this thesis illustrated that a focus on research in political excellence initiatives in higher education is insufficient. This was emphasized by the constitution of contemporary EHEIs, derived from the literature. A focus on research covers only one of at least three central tasks of higher education institutions. This provides an opportunity for education policy to broaden the foundation of excellence initiatives, from research to the aspects of innovation and education. In this context, it is recommended that innovations initiated and produced by higher education institutions or factors that constitute an innovative environment be integrated in evaluations. The curriculum should also be considered. Evaluations in the context of excellence initiatives could evaluate curricula of higher education institutions with the elements and requirements of leadership education. For higher education institutions, the presented heuristic theoretical concept offers a holistic view of their own tasks. Institutions that aspire to achieve an elite status have the opportunity to position themselves concerning the three central tasks. It seems reasonable for the institutions to proactively work toward integration of a holistic view of higher education institutions in evaluation systems, such as accreditations or funding schemes. The three scenarios that have been developed offer further initial implications for educational practice. In an individualized future, as Scenario I presents it, it

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seems reasonable for higher education institutions to focus on the original task of the university: education. In this sense, they should gear their curricula toward personality development of students in order to enable education. The focus is on providing opportunities for education, for example, in the form of real-world projects. These should be integrated into the study programs. It seems reasonable against the background of Scenario I to strengthen cooperation in the field of research, especially with individual researchers. The future depicted in Scenario II is characterized by rapid digital developments. It therefore seems obvious that higher education institutions should already begin investing heavily in their own digital infrastructure. This is not limited to the distribution of learning content. The focus should be on implementing intelligent digital systems that support both teachers and researchers. Institutions can also invest in the digital training of their staff. In this scenario, the commercialization of higher education was strongly promoted. To prepare for such a future, it makes sense for institutions to push for collaborations with businesses. In this context, performance becomes more relevant. Consequently, higher education institutions should integrate opportunities in curricula in which students can show (economic) performance. They should align their internal evaluation systems for various stakeholders, such as selection procedures or examinations, with the aspect of performance. The future in Scenario III is shaped by the VUCA world. Higher education institutions focus on skilling, upskilling, and reskilling. The focus is on providing lifelong and lifewide learning opportunities. To achieve this, higher education institutions should already be driving forward the flexibilization of their own processes and structures. Investments in digital infrastructure enable the flexibilization of the educational setting. Expanding the target groups to include additional age groups diversifies them. Consequently, it is necessary to integrate changed learning theories (e.g., adult learning theories) into the curricula. Due to the decreasing half-life of knowledge, institutions should invest in the continuing education of their staff. It seems reasonable to promote inter- and transdisciplinary collaborations in a wide variety of fields. Finally, the institutions should begin establishing alternative or complementary sources of funding.

Contributions of Research

11

The aim of any scientific work is to create valuable contributions for the research field in which it is conducted. Especially in the field of educational research, a contribution of scientific work to practice is relevant. The following chapter provides an overview of the scientific and practical contribution of this thesis. Naturally, the contributions of the two parts of this thesis are distinctly designed. Part II as the theoretical-conceptual part provides more contributions on a scientific level, while Part III as the empirical part focuses on practical contributions. Both types of contributions are illustrated below for the two parts of this thesis.

11.1

Scientific Contributions

The literature review on which Part II is based has shown that elite education is investigated mainly from a sociological perspective. This thesis has identified and illustrated perspectives beyond this to broaden the strong focus on social implications of the phenomenon. This is firstly in terms of a pedagogical perspective and secondly in terms of an interdisciplinary, systems-oriented perspective. The focus is on the consideration of institutions and their characteristics. Such a consideration has received little attention in the context of the concept of elite. Focusing on institutions and excellence, the concept of the world-class university is brought to the fore in the literature. Literature that focuses on elite education from a pedagogical perspective and from an interdisciplinary, system-oriented perspective is thus supplemented by this thesis. In previous research on elite education, various elite theories have been used to explain the phenomenon of elite education institutions, such as the functional or the performance elites. This work adds at this point, a linkage of the phenomenon of EHEIs with the concept of responsibility elite. This link connects © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_11

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the institution-oriented view of elite (higher) education with a philosophicalnormative concept of elite. It is the first work to apply the notion of responsibility elite to institutions rather than individuals. Consequently, this thesis contributes to the literature on elite higher education and elite institutions in general. Based on the understanding of leadership as a social process (1) and elements of certain people within this process (2), the term leadership education was defined. In this context, this thesis provides a scientific contribution to the delimitation of the terms leadership education and leadership development. Leadership development is to be understood as an element of leadership education, which focuses on the organizational context and formal educational programs. This work establishes a concept of EHEIs. This concept can be considered original. In itself, it makes a contribution to research since it provides an alternative to existing views on EHEIs. Already the basis of the concept of responsibility elite stands out. The further linking of responsibility with tasks and elite status of higher education institutions adds to the literature on elite education that focuses on institutions. Considering the constitution of EHEIs from a pedagogical perspective is also to be perceived as a contribution of this thesis. The heuristic theoretical concept of the constitution of an EHEI complements the existing literature and provides a basis for elaborating a new model of higher education institutions, the “elite institution” and for conducting further research. Two further contributions can be pointed out. First, the innovation task of higher education institutions was explicated. This emphasizes moving away from the current focus of institutions on economic innovations to a holistic view of the innovation task. Second, the integration of the phenomenon of leadership education in the context of EHEIs can be seen as a contribution to science. Concrete approaches to linking both phenomena are scarce in the literature so far. Scientific contributions can also be derived from Part III of this thesis. Methodologically, this thesis creates value in that it adds to the literature that applies methods of futures research in the context of educational research. The integrated view of the foresight process and the research process of the social sciences in the underlying research design is a contribution of this thesis. This underlines the multidisciplinary character of the thesis and illustrates the meaningfulness of cross-disciplinary research, also on a methodological level. The results of the empirical work of this thesis contribute to the literature regarding the future of (higher) education. Topics relevant to higher education from experts’ perspectives were illustrated and investigated. A contribution to the research of today’s perception of future issues in the field of higher education was provided.

11.2 Practical Contributions

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Practical Contributions

Concerning practice, this thesis provides a contribution for two stakeholder groups. On the one hand for educational policy and on the other hand for educational practice. Parts II and III of this thesis together provide a central contribution, which is related to one objective of this thesis. They raise awareness for various issues that could change higher education in the future. This sensitization shall support institutions and politics to act in a holistic and future-oriented way. In this context, the contribution of this thesis increases with the number of institutions and organizations that are sensitized for future-oriented thinking and acting. Part II results in concrete contributions to provide impulses for action in educational policy. The focus here is on supplementing the basis for excellence initiatives with regard to the holistic view of higher education institutions, and the inclusion of the responsibility of the institutions in such initiatives. The empirical work in Part III also provides contributions to education policy. The results of the Delphi survey and the resulting scenarios enable the future-oriented design of further (excellence) initiatives, for example, by focusing on promoting lifelong learning or leadership education. For educational institutions, one contribution of this work is the explication of the space of opportunity for the next two decades. This provides a broader basis for goal and strategy development in the institutions since key opportunities can be identified. Risks perceived by experts resulting from various developments were presented. This enables institutions to preemptively deal with risks and opportunities. Another practical contribution of this thesis is the generated expert knowledge on the future of higher education. This can be seen as knowledge for action and orientation. On this basis, a concrete scenario transfer can be conducted in higher education institutions. The expert knowledge can be used to encourage the leadership of higher education institutions to think extensively about the future of their sector. For institutions that have already established foresight, the factors identified and described can be used to validate or supplement their own foresight activities. In view of institutions that have not established a foresight process, this thesis provides a contribution to the establishment of such processes. It exemplifies a mixed (structured and intuitive) approach to the process of scenario development for higher education. It thus proposes an exemplary approach to investigating the future of the sector systematically. Beyond the applied design

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for scenario development and Delphi, alternative designs were presented. Connected with the main tasks of higher education institutions this could serve as a methodological guideline for practitioners. Finally, this thesis also provides a practical contribution beyond the field of higher education. The developments underlying the Delphi projections affect a large number of sectors. At a minimum, impacts can be identified for sectors adjacent to higher education, such as secondary or continuing education. Virtual education platforms or the development toward lifelong and lifewide learning companions can be highlighted as two examples. Both contain the potential to change the mentioned adjacent areas integrally. Consequently, a contribution of this work is sensitizing further areas and highlighting possibilities for the establishment of foresight in these areas.

Limitations and Outlook

12

This thesis, as the previous chapter shows, makes diverse contributions to educational research, practice, and policy. Despite these contributions, this scholarly work must also be critically reflected upon since every scientific work offers limitations. For this reason, the following chapter elaborates on the limitations of this thesis regarding its two main parts (II and III). On this foundation, it provides a concluding outlook on future research derived from the various steps of the research process in this thesis.

12.1

Limitations of this Thesis

In Part II, limitations can be highlighted regarding the concept of the constitution of EHEIs. Due to its underlying level of consideration, this can only be regarded as heuristic. Aspects of the central tasks at a detailed level are not integrated. It does not explicitly consider how education, research, or innovation are implemented in practice at elite institutions. The focus is on the theoretical understanding of these tasks, derived from the literature. The underlying understanding of education in this thesis also presents a limitation. In the context of the sociocultural background of this work, the focus was especially on an understanding of education in the German tradition of Bildung. This is based on humanistic assumptions. It understands education as deeply human. Aspects going beyond humanistic educational approaches were not integrated. Another limitation of Part II of this thesis is the foundation of the developed concept. It was developed exclusively with the help of a literature review. Empirical work was not conducted at this point. This can be seen as a limitation since empirical work in advance of the conceptualization could have expanded the basis and level of consideration of the concept. © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2023 N. Lange, Future Perspectives for Higher Education, https://doi.org/10.1007/978-3-658-40712-4_12

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Empirical work was not integrated until Part III of this thesis. This also possesses its own limitations. As a starting point, a typical limitation of qualitative research can be mentioned. The results of this qualitative-explorative empirical work are not representative. However, this is also not intended with regard to the method of the Delphi survey. Considering the Delphi survey, one limitation is the small number of projections discussed. This was chosen because of the better manageability by participants to consequently minimize dropouts. Nevertheless, a larger number of projections would have had the potential to generate a larger amount of expert knowledge, as further developments can be integrated. This would have enriched the foundation for scenario development. Beyond the projections, limitations must also be highlighted concerning the expert panel. This panel was characterized in particular by experts from academia. The qualitative content analysis showed that the experts attributed a high degree of conservatism to this area. The impression arises that a more diversified panel could have provided further interesting and relevant insights. Beyond the profession of experts, it was also observed that the majority of experts were based in Europe. It is thus possible that the results of the Delphi survey and scenarios derived from them are strongly influenced by European culture. Regarding the scenarios, both the process of scenario development and the scenario content offer limitations. In scenario development, attention can be drawn to the small number of experts who participated in the CIA. A larger group of experts in this step had the potential of altered assessments. Since the CIA was only used as an indication, not as a quantitative instrument, this limitation is less critical. In the context of the scenario content, it should be noted that the three scenarios developed do not explicitly integrate wild cards or weak signals, that is, surprising and improbable developments. One reason for this is that the scenario development did not focus particularly on the probability of occurrence of developments. Another reason is that the expert panel provided little input with regard to surprising developments. Overall, it must be mentioned that none of the scenarios claim to illustrate the one and real future. They can never grasp every element of a potential future. They mirror what experts from higher education today perceive as potential future developments for higher education. For this reason, the present scenarios are based exclusively on the results of the Delphi survey. Limitations regarding the implications of this thesis can be highlighted. The implications are to be understood as initial impulses. Concrete recommendations or normative results can be achieved with the help of further research on the underlying detailed aspects of the developed concept and scenarios.

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Finally, a general limitation, concerning all parts of this thesis is addressed. The foundation for the conceptualization and the empirical work was a definition of the term “elite education” derived from the literature. This resulted in the implicit application of a consistent definition of the term over the course of this thesis. Considering that the concepts of elite and elite education are controversially discussed in the literature and understandings of the term vary greatly, for example, by discipline, institutions, or even within institutions (faculties, institutes etc.), this implicit application can be viewed as a limitation of this work. This holds especially true concerning the real-time Delphi survey since it did not consider differences in experts’ perceptions of the term elite education.

12.2

Outlook on Future Research

Building on the limitations of this thesis presented in the previous subchapter, an outlook on directions for future research derived from this thesis is given below. In total, 10 lines for future research were identified within this frame. 1. A first line of future research emerges from the research interest and theoretical basis of this thesis. In Part I, it was presented that excellence initiatives of nations aim at establishing new excellent institutions on the one hand and improving established institutions on the other hand. In Part II, it was presented that higher education systems are stratified. These systems are currently structured into elite and mass institutions. Combining these remarks results in a further interesting topic of research, which is investigating the subsystem of the origin of EHEIs. The focal point here would be on the respective subsystem of where EHEIs should originate from—should they be developed within the prevailing elite subsystem of higher education or should mass institutions be fostered to develop into elite institutions. The perception of various stakeholder groups of higher education could be integrated. Such research would enable deriving normative directions for educational policy and practice in an environment characterized by competition (between institutions, nations etc.). 2. The linkage of the pedagogical-institutional perspective on elite education with the philosophical-normative notion of responsibility elite established in this thesis should be further investigated. In particular, a detailed examination of EHEIs against the background of their responsibilities to the common good could provide interesting further insights into the phenomenon. With the help of such investigations, another facet could be added to the theoretical

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Limitations and Outlook

framework outlined in this thesis, which mainly focuses on traditional elite theories. Based on the developed heuristic theoretical concept of the constitution of contemporary EHEIs, concrete types of the model of an elite institution in higher education can be elaborated. Here, it would be interesting to focus on different manifestations of the three central tasks and examine them with different variables. Exemplary variables are the regional context, the sponsorship of the institutions, also against the background of the underlying forms of government in the regions, or the size of the institutions. Against the background of the theoretical basis for the developed concept a validation and supplementation with the help of empirical work is reasonable. In the tradition of this thesis, another Delphi survey could be conducted. In comparison to the Delphi survey presented here, such a survey would pursue the goal of achieving consensus regarding the concept of contemporary EHEIs. In the traditional sense, a validation with the help of quantitative methods would also be useful and interesting. Based on the heuristic theoretical concept in this thesis, the status quo in the practice of EHEIs could be investigated as a further line of research. The focus here could be on aspects of the concept that go beyond the research task and thus existing literature on rankings and world-class universities. Examples are the investigation of the integration of leadership education or the promotion of innovation in the institutions, which could provide interesting insights for educational practice and policy. The previous line of future research focuses on investigating the current situation in educational practice. This kind of research could also be adapted to educational policy. On the foundation of the heuristic theoretical concept in this thesis, it can be investigated which characteristics of EHEIs are already integrated in educational policy in different nations. This could offer valuable impetus for policy makers since it would explicate potential gaps. Qualitative contributions in the Delphi survey provide insights into further areas of research. First, it would be of interest to investigate how to quantify the factor of real output in higher education institutions. This facet was pointed out by some experts as a central problem of Projection 1. In the context of real output, current research on the entrepreneurial university and the third mission of higher education institutions focuses on the creation of economic innovations. Therefore, as a second research aspect, the question of how higher education institutions can stimulate innovation beyond the economic sphere can be posed. Finally, a further research aspect emerges from Projections 4 and 7. Many experts provided the argument that digital systems

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cannot integrate the social aspects of education. Consequently, an investigation of precisely this integration in the context of educational processes in institutions would be reasonable. 8. The dissent analysis in the context of the present Delphi survey provides approaches for further research. It would be interesting to conduct a Delphi survey focusing on the structuring of the group communication process. Here, different hypotheses can be tested, which are indicated in this thesis. Within the frame of results of the dissent analysis in this thesis, three initial outlines of hypotheses could be derived. Experts from industry have a more liberal view of future developments than experts from academia (1). Female experts are more open to (radical) changes than male experts (2). European experts see universities as having greater social responsibility than North American experts (3). 9. Three future lines of research emerge from aspects described in the developed scenarios. First, it may be useful to elaborate and analyze key scenario aspects in detail in the future. For example, it could be evaluated to what extent higher education institutions integrate the aspects of lifelong and lifewide learning into their current processes and structures. Second, it would be interesting to investigate the degree of commercialization of contemporary higher education institutions in terms of activities that possess an economic focus. Finally, analysis of the current use of alternative sources of funding for higher education institutions, for example in the form of an international comparative analysis, could provide interesting and relevant insights. 10. A further line of future research addresses the general limitation presented at the end of the previous subchapter. Since diverse understandings of the term elite education were not included in this thesis, a succeeding study with a differentiated focus on the concept would be interesting and enriching. Within this frame, qualitative and quantitative research on the perception of the concept by various stakeholder groups of higher education on different levels (e.g., types of institutions, disciplines, ownership etc.) could be conducted to add to the existing body of literature.

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