Gamification of Electronic Negotiation Training (Gabler Theses) 3658382600, 9783658382605

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Gabler Theses

Andreas Schmid

Gamification of Electronic Negotiation Training

Gabler Theses

In der Schriftenreihe „Gabler Theses“ erscheinen ausgewählte, englischsprachige Doktorarbeiten, die an renommierten Hochschulen in Deutschland, Österreich und der Schweiz entstanden sind. Die Arbeiten behandeln aktuelle Themen der Wirtschaftswissenschaften und vermitteln innovative Beiträge für Wissenschaft und Praxis. Informationen zum Einreichungsvorgang und eine Übersicht unserer Publikationsangebote finden Sie hier.

Andreas Schmid

Gamification of Electronic Negotiation Training

Andreas Schmid Stuttgart, Germany Dissertation University of Hohenheim, Germany, 2021 D100

ISSN 2731-3220 ISSN 2731-3239 (electronic) Gabler Theses ISBN 978-3-658-38260-5 ISBN 978-3-658-38261-2 (eBook) https://doi.org/10.1007/978-3-658-38261-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 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. Responsible Editor: Marija Kojic This Springer Gabler 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

Digital business negotiations are an important instrument of inter-organisational coordination and essential for company success. The digitalisation requires digital skills as well as more traditional negotiation skills. To teach such skills, negotiation training is employed. Existing approaches to negotiation training are often ineffective as trained negotiators still choose mediocre agreements or do not exploit the full potential of electronic negotiation support systems. Consequently, the goal of the present research is to design, implement and evaluate a novel negotiation training for electronic negotiations with gamified elements to enhance the negotiators’ motivation. In times of the global pandemic, digitalisation of business negotiations became ever more important. Negotiators were unable to meet in person and realised the advantages of electronic negotiations. Nevertheless, electronic negotiations are challenging as they not only require negotiation skills but also digital system skills. Therefore, a dedicated electronic negotiation training is required that motivates future negotiation experts and prepares them in the best possible way. Gamification is a promising approach to create a lasting training success. The present research is highly relevant for negotiation researchers who work on the digitalisation of negotiations. Likewise, negotiation practice benefits from the novel results as companies will now know how best to prepare their negotiators. In addition, the research shows the successful design, implementation, evaluation and optimisation of a gamified information system which can serve as a blueprint for system designers.

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I hope that this excellent work finds its way into new research approaches and novel business practices. Hohenheim May 2022

Professor Mareike Schoop, PhD

Preface

The information systems discipline is a relatively new but established research area with a long history focussing on the productive and efficient use of technology to process information for organisational needs. Consequently, information systems research deals with socio-technological systems. Having an information systems degree and having worked as a software developer during my studies, I have always been interested in how technological innovations shape and are shaped by users and organisations. Technological innovations affect our everyday lives and have especially changed the way we teach and learn in the last decades. New research streams started to investigate how learning can be facilitated by the use of digital processes, applications, and tools. Gamification—using game elements in learning contexts without creating a fully-fledged game—is a novel approach from the information systems discipline to motivate learners and improve their learning. The game elements, which create powerful emotions, attract, and sustain our attention in games, are expected to evoke the same powerful effects in non-game contexts such as learning. Although the term gamification has recently gained attention, the idea behind game-based or gamified learning is not new: In 1981 Malone already described how game elements can create an intrinsically motivating learning experience. Nowadays, with the widespread adoption of digital tools, apps, or learning management systems, it is quite convenient to include game elements in a digital learning environment. I had the privilege to investigate the potentials of gamification in the very interesting domain of digital negotiation training. Negotiations are an important soft skill topic and require manifold communication and decision-making skills. Striving to improve the training participants’ motivation and digital negotiation skills with a gamified digital negotiation training to be designed, I started my PhD project at the end of 2017. This thesis ultimately presents my PhD journey

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beginning with understanding what constitutes human motivation, how learners’ motivation can be improved, how games and game elements motivate users, and analysing negotiation training and negotiation processes. Subsequently, a gamified digital negotiation training was iteratively designed, implemented, and evaluated, demonstrating the effectiveness of the developed gamified training. Both gamification designers and gamification researchers may gather interesting insights in how to design gamified systems for a particular application context and which effects for the game elements are observed. Completing a PhD thesis is always a challenging task, and it has become an even more challenging task to complete the thesis in the middle of the COVID19 pandemic. Thus, I would like to take this opportunity to thank the numerous people who supported me during the PhD project in many ways. First of all, I want to thank my supervisor Prof. Mareike Schoop, PhD for supporting my interest in research very early and giving me the opportunity to pursue this new and interesting topic. I am thankful for all the conversations and discussions with her that helped me to expand my knowledge about research and for her enormous support and encouragement during the entire time. Furthermore, I would like to thank Prof. Dr. Markus Voeth for co-supervising my thesis and Prof. Dr. Henner Gimpel for chairing the board of examiners. I would also like to thank my fellow colleagues at the Information Systems Department I at the University of Hohenheim Muhammed Kaya, Dr. Michael Körner, Dr. Annika Lenz, Dr. Philipp Melzer, Marlene Meyer, Azuka Mordi, Dr. Bernd Schneider, Stefan Ullmann, and Josepha Witt for the pleasant working atmosphere, the regular discussions and talks, and their support in all these years. I thank the Negoisst development team, which I had the honour to manage and lead for a long time, for implementing the game elements together with me in the Negoisst system. I further want to express my deep gratitude to my parents Gabi and Gerhard, my brother Matthias, and my girlfriend Benita for their constant encouragement and their support during this PhD project. Additionally, a special thank goes to Matthias, who proofread several articles and chapters of this PhD thesis and provided valuable feedback. I am deeply grateful to have you all as my family. Herrenberg May 2022

Andreas Schmid

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Research Goal and Research Questions . . . . . . . . . . . . . . . . . . . . . . 1.2 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 4 6

2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Motivation & Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Self-Determination Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Flow Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Goal Setting and Achievement Goal Theory . . . . . . . . . . . 2.2 Gamification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Definition, Conceptualisation & Related Concepts . . . . . . 2.2.2 Game Design Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Designing Gamified Systems . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Gamification in Education & Training . . . . . . . . . . . . . . . . 2.3 E-Negotiation Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 E-Negotiation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Negotiation Support Systems . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Negotiation Skills & Training . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Inherent Game Characteristics of E-Negotiations . . . . . . .

9 9 10 14 15 19 19 22 27 29 31 32 34 39 45

3 Design Science Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Design Science Research in Information Systems . . . . . . . . . . . . . 3.2 Applied Approach and Contribution . . . . . . . . . . . . . . . . . . . . . . . . .

51 51 54

4 A Framework for Gamified Electronic Negotiation Training . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 State-of-the-Art in Electronic Negotiation Training . . . . . . . . . . . .

59 60 62

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4.3 Motivation Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Self-Determination Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Flow Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Achievement Goal Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Gamified Electronic Negotiation Training . . . . . . . . . . . . . . . . . . . . 4.4.1 General Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 A Framework for Gamified Electronic Negotiation Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Discussion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Gamifying Electronic Negotiation Training—A Mixed Method Study of Students’ Motivation, Engagement and Learning . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Gamification & Education . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Negotiations & Electronic Negotiation Training . . . . . . . . 5.3 System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Participants & Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Data Collection & Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Quantitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Qualitative Survey Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Qualitative Interview Results . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Discussion & Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Rankings or Absolute Feedback? Investigating Two Feedback Alternatives for Negotiation Agreements in a Gamified Electronic Negotiation Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Decision Support in E-Negotiations . . . . . . . . . . . . . . . . . . . 6.2.2 Gamified E-Negotiation Training . . . . . . . . . . . . . . . . . . . . . 6.2.3 The Relation between Goals, Feedback and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Feedback for Negotiation Agreements . . . . . . . . . . . . . . . . . 6.3 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Data Collection & Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .

64 64 65 66 67 67 71 73 75 76 78 78 79 81 84 84 86 87 87 90 91 93

97 98 99 99 100 101 103 104 107 107 108

Contents

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6.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Conclusion & Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Gamification of Electronic Negotiation Training: Effects on Motivation, Behaviour and Learning . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Motivation & Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Gamification in Education & Training . . . . . . . . . . . . . . . . 7.2.3 Electronic Negotiation Training . . . . . . . . . . . . . . . . . . . . . . 7.3 System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Participants & Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Experiment Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 Data Collection & Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 Impact on Participants’ Motivation . . . . . . . . . . . . . . . . . . . 7.5.2 Impact on Engagement and Learning . . . . . . . . . . . . . . . . . 7.5.3 Evaluation of Integrated Components . . . . . . . . . . . . . . . . . 7.5.4 Impact on Subsequent System Use . . . . . . . . . . . . . . . . . . . 7.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Conclusion and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

117 118 120 120 121 122 125 131 131 132 133 135 136 137 140 141 142 148 149

8 Discussion & Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Integrative Discussion of Contributions . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Gamified E-Negotiation Training as an Innovative Artefact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Design Principles for Gamified Interventions . . . . . . . . . . . 8.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Towards a Holistic E-Negotiation Training . . . . . . . . . . . . . 8.4.2 Future Research Avenues for Gamification Research . . . .

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

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Abbreviations

ANOVA AVE BATNA CET CI CR DP DSR DSS ICT IS IT IU JU M Mdn MSV NSA NSS OIT SD SDT TAM TNT

Analysis of Variance Average Variance Extracted Best Alternative to a Negotiated Agreement Cognitive Evaluation Theory Contract Imbalance Composite Reliability Design Principle Design Science Research Decision Support System Information and Communication Technology Information Systems Information Technology Individual Utility Joint Utility Mean Median Maximum Shared Variance Negotiation Software Agent Negotiation Support System Organismic Integration Theory Standard Deviation Self-Determination Theory Technology Acceptance Model Tactical Negotiation Trainer

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List of Figures

Figure 1.1 Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 2.6 Figure Figure Figure Figure

2.7 2.8 2.9 2.10

Figure 3.1 Figure 3.2 Figure 3.3 Figure 4.1 Figure 5.1 Figure 5.2 Figure 6.1

Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different Types of Motivation Posited by OIT (Ryan and Deci 2000b, p. 72) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Flow Channel (Csikszentmihalyi 1990, p. 74) . . . . . . . The 2 × 2 Achievement Goal Model (Elliot and McGregor 2001, p. 502) . . . . . . . . . . . . . . . . . . . . . . . . . Gamification and Related Concepts (Deterding et al. 2011, p. 13) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptualisation of Gamification (Koivisto and Hamari 2019, p. 193) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Game Element Hierarchy (Werbach and Hunter 2012, p. 82) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preference Elicitation in Negoisst . . . . . . . . . . . . . . . . . . . . . Message Composition in Negoisst . . . . . . . . . . . . . . . . . . . . . The History Graph in Negoisst . . . . . . . . . . . . . . . . . . . . . . . The Dual Concern Model for Negotiation Strategies (Lewicki et al. 2010, p. 112) . . . . . . . . . . . . . . . . . . . . . . . . . Information Systems Research Framework (Hevner et al. 2004, p. 80) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Theory Conceptualisations . . . . . . . . . . . . . . . . . . . . . Design Science Research Knowledge Contribution . . . . . . . Framework for Gamified Electronic Negotiation Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual and Joint Utility Rankings . . . . . . . . . . . . . . . . . . Home Screen of the Gamified NSS . . . . . . . . . . . . . . . . . . . . Screenshot of an Anonymised Joint Utility Ranking . . . . . .

8 13 16 19 20 22 24 37 38 40 49 52 54 57 72 83 84 103

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List of Figures

Figure 6.2 Figure Figure Figure Figure Figure Figure Figure

7.1 7.2 7.3 7.4 7.5 7.6 8.1

Screenshot of the Pareto graph with Individual Utilities (0 to 100) as Axes . . . . . . . . . . . . . . . . . . . . . . . . . . Writing a Message in Negoisst . . . . . . . . . . . . . . . . . . . . . . . Level Overview with Levels 1 to 3 . . . . . . . . . . . . . . . . . . . . Anonymised Joint Utility Ranking . . . . . . . . . . . . . . . . . . . . Pareto Graph Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Badge Page Showing all Unlocked Badges . . . . . . . . . . . . . Home Screen of the Gamified Negoisst System . . . . . . . . . GitLab Contribution Chart . . . . . . . . . . . . . . . . . . . . . . . . . . .

105 124 126 128 129 130 131 165

List of Tables

Table 5.1 Table 5.2

Table 6.1 Table 6.2 Table 6.3

Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 7.7 Table 8.1

Convergent and Discriminant Validity and Reliability Analysis for the IMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive Statistics. Learning Outcomes for E-Negotiation Skills Could Range between −6 and + 6 Points; Learning Outcomes for System Skills between −13 and + 13 Points . . . . . . . . . . . . . . . . . . . . . . . . . . Variables and Cronbach’s Alpha . . . . . . . . . . . . . . . . . . . . . . . . Descriptive and Test Statistics for Survey and Engagement Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Negotiation Outcomes for the Levels Showing Agreement Rates, Achieved Individual Utilities (IU), Joint Utilities (JU) and Contract Imbalance (CI) . . . . . . . . . . . Descriptive and Test Statistics for Participants’ Use of Electronic Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive Statistics and Correlations for Motivation Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive Statistics for Engagement and Learning Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mean Values per Gender for Engagement and Learning Outcome Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Participants’ Evaluation of the Training using Scores . . . . . . . Mean Values for the Evaluation of the Integrated Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System Feature Use after the Training . . . . . . . . . . . . . . . . . . . Design Principles for the Design of Gamified Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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89 109 111

112 136 137 138 139 139 141 142 172

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1

Introduction

The ability to negotiate effectively has become a major issue for individuals as they are confronted with negotiation situations almost everywhere in their life— whether these occur in their private life, within or between political organisations, or in their business life. Negotiations are conducted by at least two parties who cannot achieve their goals through unilateral actions, and, therefore, are required to find a compromise solution by engaging in interdependent communication and decision-making tasks until they settle on an agreement (Bichler et al. 2003). Negotiating effectively is not a trivial, but a cognitively challenging and complex task, and requires manifold skills for the decision-making and communication tasks that arise during the negotiation process. Skilled negotiators are valuable for business companies, since they are able to settle on agreements that increase the companies’ financial performance and improve the companies’ relationships with their business partners and customers (ElShenawy 2010). The negotiations that occur in one’s business life can range from simple salary and budget negotiations to more complex negotiations concerning non-standardised products or services and collaborations between companies or organisations. Obtaining the skills to conduct such negotiations is therefore both in the interest of the individuals as well as for business organisations. The development of these skills is facilitated through dedicated negotiation training offered both in university courses for management and related study programmes as well as in corporate training. Most training courses teach negotiation theory and additionally engage the participants in practical experiences, such as role plays and negotiation simulations, in which the participants can apply the learned theory and, consequently, the development of their skills is facilitated (Lewicki 1997). Negotiations are either conducted face-to-face, requiring the parties to be physically present together at one location, or via electronic media. Nowadays, negotiations are to a large extent electronic negotiations (e-negotiations) and are © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_1

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Introduction

most of the time conducted via asynchronous media such as email (Schoop et al. 2008). In a recent study among German managers, almost half of the participants expected an increased importance of e-negotiations (Voeth et al. 2019). With the advent of the COVID-19 pandemic in 2020 and the arising difficulties to meet and negotiate face-to-face, it can be expected that the importance of enegotiations continues to increase. E-negotiations impose additional burdens on the negotiation process through missing social cues (Köszegi and Kersten 2003), but also provide new opportunities to settle on better agreements through use of support tools (Bichler et al. 2003). Consequently, additional skills are required to conduct e-negotiations and dedicated e-negotiation training is offered in addition to traditional face-to-face negotiation training (Köszegi and Kersten 2003; Melzer and Schoop 2017). Such e-negotiation training engages participants in negotiation simulations conducted via negotiation support systems (NSSs) to facilitate the development of e-negotiation skills (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2016; Schoop 2020; Vetschera et al. 2006). The effectiveness of a training and whether its participants successfully develop the targeted skills depend, in general, on the quality of training methods, the characteristics of the organisational environment, the participants’ cognitive abilities, and the participants’ motivation (Salas and Cannon-Bowers 2001). Training methods and the organisational environment in which an e-negotiation training is embedded have been sufficiently elaborated in the past and are established (Köszegi and Kersten 2003; Melzer and Schoop 2016). Less emphasis has been put on the motivation of the participants, which is, as a prerequisite, assumed to be high (Melzer and Schoop 2015). However, there are problems that question such an assumption: First, although participants in an e-negotiation training receive both a training for the system as well as for conducting enegotiations (Melzer and Schoop 2015), many support features that NSSs offer are not used despite their positive impact on the negotiation process and outcomes (Druckman et al. 2012). Second, many negotiators still settle on inefficient agreements (Gettinger et al. 2016). Third, we observe that the NSSs do not offer sufficient feedback for training participants (Schmid and Schoop 2019). Given that spending more time in a negotiation training positively impacts an individual’s skill acquisition (ElShenawy 2010) and negotiation experience manifests in better negotiation behaviour and outcomes (e.g. Thompson 1990b, 1990c), motivating the participants, increasing their engagement in the e-negotiation training simulations, providing feedback to them within the NSS, and, in consequence, improving their learning outcomes and skill acquisition appears to be a promising solution to solve the above mentioned issues.

1

Introduction

3

Motivating training participants, especially students, is a major challenge for education and training (Dichev and Dicheva 2017). Some scholars consider the millennials—often called as the generation Y—as particularly hard to motivate through traditional teaching methods (Lee and Hammer 2011; Putz et al. 2020). Others have adopted the term attention economy—describing attention as an individuals’ rarest resource (Davenport and Beck 2001)—in the education domain, claiming that learning tasks are competing against distracting and more interesting activities (Buckley and Doyle 2017). However, individuals are required to engage in their learning tasks to obtain new knowledge or skills. Engagement is a multifaceted construct and involves behavioural engagement (e.g. time spent on a task, participation), emotional engagement including positive and negative reactions, and cognitive engagement (Fredricks et al. 2004). The qualities of an individual’s engagement depend on their motivation (Ryan and Deci 2017). Education and training seek to facilitate an individual’s intrinsic motivation— i.e. the motivation to perform a task for its inherent satisfaction—since intrinsic motivation of an individual is associated with high engagement, high persistence when facing difficult tasks, higher quality learning, and other favourable outcomes (Malone 1981; Ryan and Deci 2000a). There is an extensive body of research confirming that both an individual’s motivation and, in consequence, their engagement predict learning outcomes and academic achievement (Kahu 2013; Reschly and Christenson 2012; Ryan and Deci 2017). The information systems (IS) discipline develops and offers various solutions that create motivating and engaging learning environments. Many solutions have been introduced into negotiation training: For example, by transforming a negotiation training course to a flipped classroom (Melzer 2018), using virtual reality technology to offer an enjoyable face-to-face negotiation training (Ding et al. 2020), and by developing game-based approaches for negotiation training (Dzeng et al. 2014; Gratch et al. 2016; Kim et al. 2009). The latter approach has gained significant attention in education, since playing computer games is very popular among children and younger adults (Bitkom 2020). Games are intrinsically motivating, i.e. individuals play for dozens of hours for the enjoyment that these games provide and individuals are often completely immersed (Chen 2007; Przybylski et al. 2010; Ryan et al. 2006). As one of the first researchers, Malone (1981) characterised the game elements that create such an intrinsically motivating experience and argued for their transfer to learning environments. First approaches lead to development of educational and serious games, where learning content and instructions are embedded within the gameplay (Charsky 2010; Oblinger 2004).

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Introduction

Another solution to motivate individuals that has recently gained attention in IS and education is the use of game design elements in non-game contexts, also referred to as gamification (Deterding et al. 2011). Using only particular game design elements such as badges, points, or leaderboards instead of developing a fully-fledged game, gamification is expected to evoke similar positive experiences and motivational processes that games traditionally do (Huotari and Hamari 2012). Designing educational or serious games requires more development effort than merely incorporating game design elements in a targeted information system. Furthermore, a game often requires sacrificing some of the real-worlds system’s functionality to maintain the game’s entertainment value, whereas gamification retains the target system’s functionality and additionally incorporates game design elements (Liu et al. 2017). Game-based negotiation training approaches either support purely synchronous communication with the negotiation partner that simulates face-to-face negotiation (Gratch et al. 2016; Kim et al. 2009) or the communication aspect is completely neglected (Dzeng et al. 2014), which makes them unsuitable for the training of e-negotiations that are mostly performed via asynchronous media. To motivate participants in an e-negotiation training, increase their engagement, and improve their learning outcomes and skill acquisition, it would instead be reasonable to retain the currently used systems in such training—namely negotiation support systems—and enhance these systems with game design elements. Reviews examining the effects of gamification in education and training are predominantly positive (Dichev and Dicheva 2017; Dicheva et al. 2015; Majuri et al. 2018; Sailer and Homner 2020), therefore, the present thesis investigates a new approach for an improved e-negotiation training by gamifying an NSS.

1.1

Research Goal and Research Questions

The overall research goal of this thesis is to improve the motivation, engagement, and learning outcomes of participants in an e-negotiation training through gamification. More precisely, an established and sophisticated NSS that is used within such training will be enhanced with game design elements that are deemed to be suitable to reach these objectives. Designing a gamified system is, however, not a trivial task and more than just adding the most frequently used game design elements (Liu et al. 2017). Instead it involves a careful analysis of the application context, its users, and profound knowledge in game design and human motivation (Morschheuser et al. 2018). Since little empirically validated guidance is available for selecting the right game design elements for a given context and, therefore, to

1.1 Research Goal and Research Questions

5

achieve the desired goals (Liu et al. 2017), this work follows the design science research methodology (Hevner et al. 2004) and reports on the design and evaluation of different versions of the gamified NSS. In order to reach the ultimate research goal, the following research questions will be answered in the thesis: Research Question 1: How can gamification be applied to the context of e-negotiation training? As a first step of the design process, the present work analyses motivation theories, gamification, and e-negotiation training as the application context to derive the requirements for the design. This step grounds the design on a solid theoretical basis. After the requirements have been defined, the game design elements that are expected to fulfil these requirements are chosen and implemented in the NSS Negoisst (Schoop et al. 2003; Schoop 2010, 2020). Following an iterative design process, which is suggested both for design science research as well as for gamification design (Hevner et al. 2004; Morschheuser et al. 2018), the effects of the chosen game design elements will need to be evaluated in order to continually improve the design. Research Question 2: Which effect does a gamified e-negotiation training have on participants’ motivation, engagement, and learning outcomes? The second research question evaluates to which extent the research goal has been fulfilled by the iteratively developed system versions. Using both qualitative and quantitative evaluation methods, the holistic system design is evaluated and compared with an established e-negotiation training to outline whether the design can be further improved. Successful gamified learning or training interventions require a holistic thinking about the learning experience (Dicheva et al. 2019). Such a learning experience, and the achievement of the defined goals for a gamified system, emerge from the chosen game design elements and the interaction among these game design elements (Liu et al. 2017), whose combined effect can be greater than the sum of their individual effects (Dicheva et al. 2019). Research Question 3: How do the incorporated elements contribute to the learning experience? To guide other researchers and practitioners in designing gamified systems, research question 3 addresses how the game design elements and the combination of game design elements are perceived by the participants in terms of their impact on motivation, engagement, and learning. This is achieved by comparing two alternative gamified designs (cf. chapter 6), evaluating different versions of the gamified design that were continually refined (cf. chapters 5 to 7), and

6

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Introduction

assessing participants’ perceptions of the individual game design elements (cf. chapters 5 and 7).

1.2

Structure of the Thesis

The structure of this cumulative doctoral thesis is outlined in Figure 1.1 and includes four studies that have been conducted in order to achieve the research goal and answer the research questions. First, chapter two gives an overarching theoretical introduction, including explanations and established theories for what motivates individuals and determines their behaviour, an introduction to gamification and gamification design, and a description of negotiations and in particular e-negotiation training representing the application context for this thesis. The third chapter presents the used design science research methodology, both from a theoretical perspective as well as the applied approach and the contributions of the thesis. The first study by Schmid & Schoop (2019; cf. chapter 4) integrates the theoretical background described in chapter 2, including motivation theories, gamification, and e-negotiation training to derive requirements for the design. Addressing research question 1, a framework for gamified e-negotiation training is presented. Chapter 5 (Schmid et al. 2020) describes a first design of a gamified enegotiation training realising the previously derived requirements and, thus, answers research question 1 with a focus on the actual artefact implementation. The study further includes an evaluation of the artefact and compares it with an established e-negotiation training using students as subjects. Employing a mixed-methods evaluation design allowing participants to choose among the gamified, the conventional, or both types of training, the study provides first insights with regard to the research questions 2 and 3 and outlines areas for further improvements. Since NSSs already include several feedback elements to evaluate the settled agreement, chapter 6 (Schmid 2021) investigates two different types of gamified training. The study examines whether the absolute feedback, provided by a negotiation feedback element, or whether the relative feedback provided by the often-used rankings in gamified applications exerts better psychological and behavioural outcomes. Therefore, this study again addresses research questions 2 & 3—however, with a different focus—and provides a holistic overview on various psychological outcomes within a gamified training.

1.2 Structure of the Thesis

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The last study by Schmid & Schoop (2022; chapter 7) reports on a large quantitative evaluation of the gamified training, comparing it with a conventional training. Specifically, the effects on motivation, engagement, learning outcomes, and subsequent behaviour (RQ 2) are investigated, and an evaluation of all the integrated game design and feedback elements is presented (RQ 3). Chapter eight concludes the thesis by presenting an overarching discussion of all included studies, summarises and generalises its contributions, critically describes its limitations, and presents an outlook on future research directions.

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Figure 1.1 Structure of the Thesis

1

Introduction

2

Theoretical Background

Gamification is expected to evoke motivational processes similar to conventional games (Huotari and Hamari 2012). Motivated individuals reveal higher engagement, better learning outcomes, and other favourable outcomes. It is therefore a necessary prerequisite for gamification designers to gain a profound understanding of human motivation (Morschheuser et al. 2018). Consequently, the theoretical background chapter is structured as follows: First, four theories from the area of psychology are described, providing an explanation for what motivates individuals or learners to engage in certain tasks in a particular way. Section two defines and conceptualises gamification as a new means to motivate individuals and examines how the employed game elements can affect motivation. Finally, in the third section the application context for gamification in this thesis is presented, i.e. e-negotiations and their training.

2.1

Motivation & Behaviour

Motivation is at the heart of everything we do—affecting our behaviour. When we are motivated, we are moved to do something. To explain behaviour, we could simply ask ourselves the question, to which extent we feel motivated to do something. However, research has acknowledged that there are different types of motivation that impact our engagement and quality of actions (Ryan and Deci 2000a). The most basic distinction is made between intrinsic and extrinsic motivation. When we are intrinsically motivated, an activity is performed because of its inherent satisfaction, i.e. we experience this activity as inherently interesting and enjoyable. When we are extrinsically motivated, we aim to achieve a separable outcome, e.g. to achieve a reward, or to avoid punishments or other negative consequences. In general, being intrinsically motivated leads to better © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_2

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activity outcomes such as enhanced performance or better learning than when one is extrinsically motivated (Ryan and Deci 2000a, 2000b). In the following, four theories for individuals’ motivation are presented, all of which have been proven to be valuable theoretical frameworks for gamification research. Among the frequently adapted theories in gamification are flow theory (e.g. Kapp 2012; Liu et al. 2017; Treiblmaier et al. 2018) and goal-setting and achievement goal theory (e.g. Hamari 2017; Landers et al. 2015; Tang et al. 2020). Besides these three theories, especially self-determination theory is the most adopted theory in gamification research (Liu et al. 2017; Seaborn and Fels 2015; Treiblmaier et al. 2018; Tyack and Mekler 2020; Xi and Hamari 2019) and will be described first, followed by flow theory and goal-setting and achievement goal theory.

2.1.1

Self-Determination Theory

Self-Determination theory (SDT) is a macro-theory of human motivation, growth, and well-being and investigates the individual and social-contextual factors that facilitate or hinder human motivation and well-being (Ryan and Deci 2000b, 2017). It is rooted in the domain of positive psychology and claims that every human individual is inherently curious, physically active, and social (Ryan and Deci 2017). SDT includes six mini-theories that deal with different phenomena and reasons around individuals’ motivation, growth, and well-being (Ryan and Deci 2017), from which the most central ones are elaborated in the following. First of all, SDT proposes that every individual has three innate basic psychological needs and the degree to which these needs are fulfilled while pursuing an activity influences motivation and well-being (Deci and Ryan 2000). Specifically, these three needs are the need for autonomy, competence, and relatedness. Autonomy refers to volition and not to independence during an activity (Ryan and Deci 2017). When an activity is self-endorsed or congruent with one’s personal interests and values, the need for autonomy is likely to be satisfied. Competence describes an individual’s need to feel effective in an activity and to experience mastery. Last, relatedness refers to the need of feeling socially connected with others. For the need of relatedness to be satisfied, at a minimum a secure social base is necessary in which individuals feel cared for by others. Additionally, it may include feelings of belonging to a group or feeling significant to others (Ryan and Deci 2017).

2.1 Motivation & Behaviour

11

The satisfaction of the previously presented basic psychological needs is important for intrinsic motivation to flourish, as explained by SDT’s Cognitive Evaluation Theory (CET). CET focusses on the social-contextual factors that facilitate or diminish intrinsic motivation (Ryan and Deci 2000b). Intrinsic motivation will flourish if factors that positively impact an individual’s perceived autonomy and competence are present and, at a minimum, a secure social base is given. Notably, both autonomy and competence need satisfaction is necessary for intrinsic motivation to occur. Among the factors or events that facilitate feelings of competence and, therefore, satisfy the psychological need for competence, are rewards, positive feedback, and optimal challenges. Likewise, negative feedback diminishes feelings of competence and, in consequence, intrinsic motivation (Ryan and Deci 2000b). To facilitate feelings of autonomy, environments in which an individual experiences an activity as volitional are necessary. Feeling controlled by someone or something diminishes perceived autonomy, as the activity is less perceived to be volitional or in other terms, the activity’s perceived locus of causality becomes external. Therefore, threats, deadlines, directives, imposed goals, and rewards diminish perceived autonomy and intrinsic motivation. On the contrary, choice, opportunities for self-direction, or teachers behaving autonomysupportive instead of controlling facilitate intrinsic motivation (Ryan and Deci 2000b). Especially rewarding an activity and its effects on intrinsic motivation have been extensively investigated in the past. Deci et al. (1999) conclude that the individual’s perception of the reward is crucial. If a reward is considered as informational feedback, it can enhance intrinsic motivation, whereas a reward perceived as controlling undermines intrinsic motivation through diminished feelings of autonomy. Another SDT mini-theory, the organismic integration theory (OIT), is concerned with the various forms of extrinsic motivation (Ryan and Deci 2000b). In fact, many activities at work, in education, or in private life are not performed intrinsically for their inherent satisfaction, but to yield an outcome separable from the activity itself. This is what is typically labelled as extrinsic motivation. However, extrinsically performed activities can greatly vary, depending on the degree to which the extrinsic regulations and values enforcing the behaviour corresponds to one’s own values and beliefs. As an example, a student may learn vocabulary because they fear the negative consequences from their parents if they were to receive a bad mark in their next exam. A second student may learn vocabulary because they think that speaking other languages will be beneficial for their career. Both students do not study because of the inherent satisfaction that learning vocabulary provides, but to attain a separable outcome. Apparently,

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their type of extrinsic motivation differs and, therefore, the extent to which they autonomously engage in the activity. At the core of OIT is the distinction between four different types of extrinsic motivation, which form a continuum between the qualities of amotivation and intrinsic motivation and vary in their relative autonomy (Ryan and Deci 2000b). It further postulates an internalisation process, i.e. the process of taking in external values, beliefs, or behavioural regulations and transforming them into one’s own. This internalisation process to assimilate and integrate external regulations is an inherent tendency in every individual. Therefore, OIT also deals with the factors that hinder or facilitate the internalisation and integration of external regulations (Ryan and Deci 2000b). The four different types of extrinsic motivation between the amotivation and intrinsic motivation differ to the extent that the regulation for a behaviour corresponds with one’s own beliefs and values and the perceived locus of causality (see Figure 2.1). The four types of extrinsic motivation are external regulation, introjected regulation, identified regulation, and integrated regulation (Ryan and Deci 2000b). At the far left of the motivation continuum is amotivation, a state of lacking any intention to engage in an activity. To its right is external regulation, a form of motivation where the perceived locus of causality is completely external and the individual acts only to obtain rewards or avoid punishments. The first student in the example above fearing negative consequences corresponds to this type of extrinsic motivation. For introjected regulation, the regulation is partly integrated into one’s own, but not fully accepted and is still in some conflict with one’s values and beliefs. This kind of motivation is characterised by self-control and ego-involvement, e.g. when a student learns vocabulary to avoid experiencing feelings of guilt or to maintain feelings of worth. It can be described best by a sense of one “should” or “must”, while the perceived locus of control is still somewhat external. Identified regulation means that an activity is considered as personally important. The activity is positively valued and is conducted in a more self-determined manner than for introjected regulation. The previously mentioned type of student, studying for their career, belongs to this type of extrinsic motivation. Last, integrated regulation represents the most autonomous form of extrinsic motivation, where the activity is congruent with one’s values and the perceived locus of causality is internal. As an example, a student learning vocabulary because it is completely consistent with their goal to learn new languages falls into this category. The activity is completely congruent with the student’s values, goals, and beliefs. Still, integrated regulation differs from intrinsic motivation, as the activity is not performed for its inherent satisfaction (Ryan and Deci 2017).

2.1 Motivation & Behaviour

13

Figure 2.1 Different Types of Motivation Posited by OIT (Ryan and Deci 2000b, p. 72)

Depending on the type of motivation, the qualities of an individual’s behaviour greatly differ. Intrinsic motivation is the desirable type of motivation for learning, as it leads to creativity, enhanced performance, persistence, and high-quality learning (Malone 1981; Ryan and Deci 2000b). For extrinsic motivation, the qualities of one’s behaviour depend on the perceived locus of control and how well regulations are internalised. When individuals experience a conflict with their values and beliefs, and experience the perceived locus of control as external, their behaviour will be more half-hearted or dutiful. However, more autonomous forms of extrinsic motivation are associated with higher engagement, better performance, and higher quality learning (Ryan and Deci 2000b). SDT and its related mini-theories have been studied in various areas, including the areas of education and video games. The intrinsic motivation and immersive experiences sparked by video games are evoked by the satisfaction of the three basic psychological needs for competence, relatedness, and autonomy (Ryan et al. 2006). Inherently, games are played voluntarily, i.e. playing a game is already an autonomous behaviour. Within the game itself, autonomy can be afforded by opportunities of choice, allowing players for example to choose their own goals and strategies, giving them the freedom to decide about their actions, or choosing an avatar as self-representation. The individual should not feel controlled by game instructions nor by the feedback that is provided by the game (Ryan et al. 2006). Most game designers focus on the satisfaction of the player’s need for competence, by which players experience mastery and success. The challenges provided by the game should be suitable for the player’s current skills and enable them to improve these skills. Clear goals provided by optimal challenges and combined with positive feedback enhance feelings of competence. Nowadays, several games

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include a level structure, by which a player completes a level through attaining clear proximate goals and, subsequently, moves up to a higher level including more difficult goals. Often, the progression while levelling up is accompanied by the availability of more tools or capacities for the player (Ryan and Deci 2017). Finally, video games can provide social interaction through means of competition or cooperation. Being recognised by others, helping others, or feeling connected with others facilitates feelings of relatedness (Ryan and Deci 2017). Since SDT is valuable in explaining the motivational power of games, it has been adopted to study and explain the effects when using selected game design elements within a different context than games, which has been termed as gamification. Game design elements and their effects in relation to SDT will be explained in detail in section 2.2.2.

2.1.2

Flow Theory

Flow is a subjective experience occurring when an individual is performing an intrinsically motivated activity (Csikszentmihalyi 1990). It describes a state of optimal experience that is characterised by full immersion of the individual with the current activity. Essentially, flow experiences can occur for any kind of activity, e.g. during sports, while drawing a picture, programming, or playing a video game. For the latter activity, together with research on video game play, flow theory provides valuable explanations for the immersive qualities of games and why individuals invest dozens of hours playing games for their own enjoyment (Chen 2007; Sweetser and Wyeth 2005) and has also been adopted in gamification research (Kapp 2012; Liu et al. 2017; Treiblmaier et al. 2018). According to flow theory, a flow experience is characterised by the following eight elements (Csikszentmihalyi 1990): 1. A challenging activity that requires skills and is perceived as attainable 2. An activity that is perceived as intrinsically rewarding 3. The merging of action and awareness, i.e. that the person’s attention is completely absorbed by the activity 4. Clear goals and immediate feedback about the progress towards these goals 5. Intense concentration on the activity at hand 6. A sense of control about one’s actions 7. The loss of self-consciousness 8. An altered sense of time

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There is one important condition for the experience of flow. The individual must perceive the activity as challenging but attainable, requiring the use and stretch of existing skills (Csikszentmihalyi 1990). In other words: A balance between the current skills of an individual and the skills required to perform the activity is required. Importantly, the current skills of the individual must be adequately stretched while performing the activity to facilitate personal skill development. Figure 2.2 illustrates this so-called flow channel as a result of the relationship between skills and challenges. For example, an individual with low skills may start with a rather easy but, nevertheless, appropriate and challenging activity in state S1. When the individual engages in the activity, the individual’s skills will improve and, after some time, the activity in S1 is no longer perceived as challenging. Thus, in order to stay within the flow channel, the individual requires to engage in a more challenging activity, for example in the activity in state S2. If the activities challenge level is not adapted to the current skills of the individual, they will drift into a state of boredom. However, when the activity is too challenging and exceeds the current skills of the individual by far, the individual will instead experience anxiety (Csikszentmihalyi 1990). Importantly, the individual’s experience depends on its subjective challenges and subjective perception of skills and cannot be assessed objectively (Nakamura and Csikszentmihalyi 2002). Therefore, the described relationship between challenges and skills holds important implications e.g. for sport coaches, teachers, or game designers on how to maintain their target audience in the flow channel (Chen 2007).

2.1.3

Goal Setting and Achievement Goal Theory

Most individuals—if not all—define and strive towards goals. Besides factors such as one’s ability and knowledge, conscious goals affect an individual’s behaviour and performance. The idea behind goal setting theory (Locke and Latham 1990) originated in the late 1960 s and explains how goals and several moderators affect one’s performance, e.g. when an individual sets an easy or a difficult goal to reach. The term goal is a generic one and encompasses other related terms such as intention, task, deadline, and objective (Locke and Latham 1990). The presence of goals impacts one’s performance through self-regulatory processes towards achieving these goals. Essentially, Locke and Latham (2002) define four mechanisms by which goals affect performance: First, goals direct an individual’s attention and effort towards goal-relevant behaviours. Individuals give less attention to information and less effort to behaviours that they consider as irrelevant to reach their goal. Second, goals energise behaviour and effort, with

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2 Theoretical Background

Figure 2.2 The Flow Channel (Csikszentmihalyi 1990, p. 74)

more difficult goals producing greater effort than lower goals. Third, they increase an individual’s persistence. Last, goals indirectly affect behaviour, because individuals seek to discover or reuse task-relevant knowledge and strategies in order to reach the goal. For example, individuals may use skills or knowledge from related situations to attain a goal or may deliberately plan a strategy to attain their goal in completely new situations. The theory further defines four moderators for the goal-performance relationship. First, for goals to be effective, individuals must be committed to reach them. Goal commitment is most important for difficult goals, as they are less likely to be reached and require more effort and persistence (Klein et al. 1999). Goal commitment can be induced by a number of factors, but the two key constructs are the personal importance of the goal for the individual and self-efficacy, i.e. the confidence to possess the relevant abilities to attain the goal (Locke and Latham 2002). Feedback is another moderator, implying that goals and feedback positively impact performance, since individuals need to track their progress towards these goals and feedback supports them to adapt their strategies should they turn out to be unsuccessful (Locke and Latham 2002). The third moderator is task complexity. When tasks become more difficult, the effect of goals also depends on the abilities and task strategies of the individual. Therefore, the effect of goal setting is stronger for easy tasks than for difficult tasks (Locke and Latham 2002).

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The last moderator is situational constraints, such as time constraints and support that an individual receives (Locke and Latham 2019). Given the respective abilities to attain a goal, goal setting theory advances a linear relationship between goal difficulty and performance (Locke and Latham 1990). Indeed, quite early it was demonstrated that hard goals produce higher performance than easy goals (Locke 1968). Furthermore, specific goals are more effective than “do-your-best” goals, since the latter are interpreted more subjectively. Therefore, the most effective type of goals are specific and difficult (Locke and Latham 2019). In addition, when a complex and distal goal is defined, performance and self-efficacy can be improved by additional proximal goals (Latham and Seijts 1999). The innate nature of individuals to set and pursue goals has led to the development of achievement goal theory within educational settings. The theory originated when researchers independently began to recognise students’ different reactions towards challenging tasks and failure. Dweck (1986) for example noted, that students that are confronted with difficult challenges either seek to master these challenges and invest a lot of effort, or they reveal a behavioural pattern including challenge avoidance, low persistence, and helplessness. Importantly, these observations are independent of the students’ cognitive abilities. Her conclusion was that students either pursue learning goals, i.e. they seek to increase their competence and improve their abilities, or they pursue performance goals where they seek to demonstrate their competences and abilities relative to others. Therefore, students with performance goals tend to avoid difficult challenges that could question their competences. When these students experience failure, they attribute failure as a lack of their own competences, reduce their efforts, and show helpless responses (Elliott and Dweck 1988). Students with learning goals, however, view failure as an important feedback for their progress and increase their effort to succeed and improve their competences (Elliott and Dweck 1988). While similar conceptualisations were proposed in parallel (see e.g. Murayama et al. (2012) for an overview), finally, the distinction between mastery goals (i.e. learning goals in terms of Dweck’s notion) and performance goals emerged. They differ in the way an individual defines competence by using a standard or referent to evaluate competence: There are absolute standards (the requirements of the task itself), intrapersonal standards (an individual’s past attainment or maximum potential attainment), and normative standards referring to the performances of others (Elliot and McGregor 2001). To evaluate their performance, individuals pursuing mastery goals use absolute and/or intrapersonal standards, which share many conceptual and empirical similarities. Individuals with normative standards pursue performance goals (Elliot and McGregor 2001).

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In general, mastery goals are considered to be more effective than performance goals, but only distinguishing between these two goals could not explain all variances in individuals’ behaviour. Therefore, researchers began to investigate valence, i.e. whether individuals strive for positive outcomes (success) or try to avoid negative outcomes (failure). Valence was first included for performance goals only (Elliot and Harackiewicz 1996) and subsequently to mastery goals (Elliot and McGregor 2001), leading to the 2 × 2 achievement goal model shown in Figure 2.3. Individuals with performance-avoidance goals strive to not perform poorly compared to others. Performance-avoidance goals are for example associated with lower intrinsic motivation, surface processing, disorganisation, and negative exam performance (Elliot et al. 1999; Elliot and Harackiewicz 1996). Individuals with a performance-approach goal focus on demonstrating their ability and outperforming others. This goal is associated with surface processing, but also with persistence, effort, and—at least in the short term—positive exam performance (Elliot et al. 1999). The most beneficial behaviour is observed for individuals with mastery-approach goals, who seek to develop new competences and abilities. These individuals reveal high persistence and effort when facing difficult tasks, use deep learning strategies, and show positive exam performance (Anderman and Patrick 2012; Elliot et al. 1999). Finally, Elliot and McGregor (2001) introduced mastery-avoidance goals, where individuals focus on not performing worse than before or not failing to master a task. Examples include individuals in their late stage of their career or lives, who start to focus on retaining important skills or abilities, or perfectionists focussing on not making any mistakes (Elliot and McGregor 2001). The effects of mastery-avoidance goals are worse than for mastery-approach goals, but they are more positive than for performance-avoidance goals (Elliot and McGregor 2001; Murayama et al. 2012). Harackiewicz et al. (2002) note that students pursue multiple goals and, therefore, may pursue both mastery-approach and performance-approach goals. As an example, a student may aim to master a difficult task and subsequently strive to outperform others in an exam. Nonetheless, an environment that influences individuals to set and pursue mastery goals and approach these goals in a positive manner is beneficial for education and training. Goals can be affected by a purposeful designed environment and an appropriate gamification design can support individuals’ goal adoption, as has been recently described by Tang et al. (2020).

2.2 Gamification

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Figure 2.3 The 2 × 2 Achievement Goal Model (Elliot and McGregor 2001, p. 502)

2.2

Gamification

Having reviewed the theories in the previous section that explain individuals’ motivation and how they consequently engage in activities, this section explains the concept of gamification that is expected to yield motivational power for the individuals. First, gamification will be defined, conceptualised, and distinguished from related concepts such as serious games. Then the game design elements and their psychological outcomes will be described, before the last two parts focus on the design of gamified systems and gamification in education and training.

2.2.1

Definition, Conceptualisation & Related Concepts

The term gamification emerged in 2008 (Deterding et al. 2011) and has gained significant academic attention since around 2010 (Hamari et al. 2014). The majority of researchers adopt the definition by Deterding et al. (2011), defining gamification as the use of game design elements in non-game contexts. Game design elements include all elements characterising a game’s design, e.g. its interface patterns, mechanics, and game models (Deterding et al. 2011). Therefore, game design elements can be described at various abstraction levels, e.g. from concrete interface patterns such as badges and points to more abstract ones such as challenges and competition. Hence, input devices for games such as controllers

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are excluded by this definition. Since only elements of game design are adopted in a non-game context, e.g. in an IT-system or a lecture, no full-fledged game is created. Therefore, this definition allows for broad usage scenarios, restricting gamification neither to certain purposes nor to specific usage contexts (Deterding et al. 2011). Using the definition above, gamification can be distinguished from several other related concepts such as playful design or serious games. Research on games uses the concept by Caillois (2001), who distinguishes between paidia (i.e. “playing”) and ludus (i.e. “gaming”). The term paidia refers to something that children often do and describes a free-form, exploratory, and improvisational activity. Ludus refers to activities governed by rules where individuals strive towards the achievement of goals (Deterding et al. 2011). Gamification primarily refers to the latter category but may—to some extent—also afford playful experiences (Högberg et al. 2019). Per definition, gamification only uses game elements without creating a full-fledged game and, therefore, can be further distinguished from games on a parts/whole dimension (Deterding et al. 2011). Figure 2.4 depicts both dimensions and contrasts gamification with games, playful design, and toys. Playful design affords the free-form and exploratory qualities of playing resulting in enjoyable experiences (Arrashvuori et al. 2011), but without being a toy itself. Using the two dimensions, serious games—defined as games with educational or nonentertainment purposes instead of pure entertainment (Charsky 2010; Michael and Chen 2006)—fall into the same category as traditional games designed for pure entertainment.

Figure 2.4 Gamification and Related Concepts (Deterding et al. 2011, p. 13)

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21

While the definition by Deterding et al. (2011) focusses on the design perspective and is applicable in many contexts, other definitions explicitly highlight the goals of gamification and/or contextualise its application. Liu et al. (2017) provide an IS perspective and define gamification as “[…] the incorporation of game design elements into a target system while retaining the target system’s instrumental functions” (p. 1013). The IS research discipline has a long history in seeking to develop knowledge around the productive and efficient use of systems that serve organisational needs (Hirschheim and Klein 2012). Productivity-oriented systems are also referred to as utilitarian systems, which provide instrumental functions that support users’ task performance (van der Heijden 2004). In the definition of Liu et al. (2017) it is this type of IS that is enhanced with game design elements without scarifying its instrumental functions. NSSs, for example, can be described as utilitarian systems providing negotiators with communication and decision support, document management, and conflict management (Schoop 2010, 2020) in order to save transaction costs, find agreements in less time, and reach agreements of higher quality (Bichler et al. 2003). When an NSS is gamified, all of its instrumental functions are retained, and game elements are additionally incorporated. For gamified systems, Liu et al. (2017) introduce the term meaningful engagement to emphasize the dual outcomes of a gamified system: on the one hand, the incorporation of game design elements should result in enjoyable experiences and increased engagement, and on the other hand, it should additionally enhance instrumental outcomes of the system’s use. This term is (partially) reflected in several other definitions of gamification, highlighting that gamification provides motivational affordances (Hamari et al. 2014), intrinsically or extrinsically motivates users (Kapp 2012; Treiblmaier et al. 2018), results in gameful experiences (Hamari et al. 2014), promotes behavioural outcomes or changes (Hamari et al. 2014; Treiblmaier et al. 2018), and facilitates learning when adapted in an educational context (Kapp 2012; Landers 2014). The game elements implemented in a gamified system are expected to invoke the same gameful experiences (i.e. psychological outcomes) as games generally do (Huotari and Hamari 2012). When individuals play games, they typically experience mastery, competence, autonomy, enjoyment, immersion, and flow (Chen 2007; Csikszentmihalyi 1990; Przybylski et al. 2010; Ryan et al. 2006). These experiences are characteristic for intrinsically motivated behaviour and high levels of voluntary engagement. Gameful experiences arise from the presence of affordances—sometimes referred to as gameful or motivational affordances—provided by games or game elements (Deterding et al. 2011; Deterding 2015; Huotari and Hamari 2012). For understanding how these experiences emerge, SDT and the

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satisfaction of basic psychological needs have proven to be a valuable theory for the effects of games (Przybylski et al. 2010; Ryan et al. 2006) and gamification (Sailer et al. 2017; Xi and Hamari 2019). If, therefore, game elements satisfy an individual’s psychological needs for autonomy, competence, and relatedness, intrinsic motivated behaviour and high levels of engagement are likely to occur (Ryan and Deci 2000b), and, as a result, improved behavioural outcomes such as learning outcomes are facilitated (Koivisto and Hamari 2019). On an abstract level as shown in Figure 2.5, gamification is situated in a given context and includes the affordances provided by the game elements, the psychological outcomes resulting from the interaction with these game elements, and the related behavioural outcomes arising from the psychological outcomes (Hamari et al. 2014). However, choosing the right game elements to promote the desired behavioural outcomes in a given context remains a challenging task for gamification designers.

Figure 2.5 Conceptualisation of Gamification (Koivisto and Hamari 2019, p. 193)

2.2.2

Game Design Elements

Game design elements (or game elements used synonymously for sake of brevity in the following) are one of the core components in the gamification definition by Deterding et al. (2011). Depending on the selected game elements, their concrete implementation, and their visualisation, different motivational affordances are present in a gamified system, leading to different psychological outcomes. Thus, a first and important question for gamification designers after defining objectives and requirements is, what are the potential game elements that can be incorporated in a system? While some researchers provide a list of elements (e.g. Kapp 2012), such lists are criticised for producing a constrained set of alternatives lacking room for creativity (Deterding et al. 2011). Instead, game elements are described as elements that are characteristic for games, play a significant

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role in gameplay and can, therefore, be found in most, but not necessarily all, games (Deterding et al. 2011). However, the decision on whether an element is characteristic for a game and should be regarded as a game element remains a subjective one. This subjective definition of game elements that constitute to gameplay is reflected in the different views of game designers and players. While game designers would define the basic building blocks and algorithms (e.g. points and levels) that form a game as the game elements that constitute towards a game experience, players rather perceive the interactions with the game (e.g. in the form of challenges and progression) as the observable game elements (Hunicke et al. 2004). Therefore, when discussing about game elements it is useful to view them at different abstraction levels. In this vein, Werbach and Hunter (2012) present a specific game element hierarchy for the context of gamification and distinguish between components, mechanics, and dynamics (see Figure 2.6). Dynamics are the big-picture aspects of a gamified system that need to be considered but cannot be manipulated directly. Instead, dynamics such as curiosity, positive emotions, feelings of progression, or feelings of status within a social group, emerge from the underlying mechanics and are perceptions of the user, i.e. they are user dependent. Mechanics are the basic processes that drive the user engagement in a gamified system and lead to the arousal of the dynamics, e.g. through means of competitions, cooperation, and rewards. Finally, the most concrete level are the components contributing to the emergence of mechanics and dynamics. Components are the basic building blocks of a gamified system and include elements such as badges, leaderboards (synonym: rankings), levels, or points. Importantly, a component can contribute to one or more mechanics, and a mechanic can induce one or more dynamics (Werbach and Hunter 2012). For example, a ranking provides feedback and can induce competition among the users, which in turn can lead to positive or negative emotions depending on the position in the ranking and feelings of progression. Clearly classifying an element as a component or mechanic is sometimes difficult as, to date, clear definitions of the elements are missing. As an example, Werbach and Hunter (2012) define achievements as reaching defined objectives and categorise them as a component, whereas Xi and Hamari (2019) use the term achievement-related features (including e.g. badges, points, status bars, levels, and leaderboards) more in the sense of a mechanic. Nonetheless, from a designer perspective it is helpful to consider game elements at different abstraction levels, since the overall objectives with regard to desirable psychological outcomes can be achieved by multiple mechanics, which in turn are implemented through a set

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of concrete components. Therefore, different combinations of components can yield identical or similar psychological outcomes.

Figure 2.6 Game Element Hierarchy (Werbach and Hunter 2012, p. 82)

To date, no taxonomy of game elements exists and the terms for game elements are sometimes used differently (Liu et al. 2017). New taxonomies have been proposed recently (e.g. Schöbel et al. 2020b; Toda et al. 2019), but have not yet received wide-spread adoption. Therefore, and to avoid confusion due to ambiguous and inconsistent use of the game element terms, the relevant game elements that are used in the present thesis are defined in the following. Furthermore, their relation to the mechanics and dynamics, and their expected psychological outcomes, are described. Points are the most simple game element and frequently used in gamified systems (Koivisto and Hamari 2019) and can provide a foundation for other elements such as rankings or levels. Points are received when specified activities within the gamified system are accomplished and represent the individual’s progress (Werbach and Hunter 2012). Depending on their purpose, points can take various forms, including experience points, reputation points, scores, or currencies (Schöbel et al. 2020b). Points function as a reward and provide immediate feedback for an individual’s behaviour (Sailer et al. 2013). By awarding defined activities, they function as a positive reinforcement for these activities (Sailer et al. 2013). However, when only points are implemented to track an individual’s progression, there is no specific goal for the individual to pursue, other than collecting these points (Landers et al. 2017b).

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Rankings (synonymously used for leaderboards) can be used to provide transparency among individuals regarding their achieved points or other success metrics (Werbach and Hunter 2012). They help individuals to evaluate their performance relative to others. However, they are one of the most debated game elements, since individuals may finish at the bottom of the ranking, probably leading to decreased feelings of competence and motivation. For individuals at the top of a ranking feelings of competence can arise (Sailer et al. 2013). Besides providing informational feedback, rankings induce social comparison and competition among the individuals. Social comparison behaviour enhances individuals’ perceived pressure—even for well-performing individuals (Huschens et al. 2019)—which in turn has been shown to diminish intrinsic motivation (Reeve and Deci 1996). Rankings can help users to set a goal to strive for, e.g. to be among the top 5 users or to achieve a midfield position, but the choice which goals users pursue is left to the individual (Landers et al. 2017b). Therefore, task performance between individuals can greatly differ depending on their goal setting. Badges are visual representations for achievements in a gamified system (Werbach and Hunter 2012). If the requirements to earn the different badges are known, they provide clear goals for the individual to work for and thus have a goal setting function (Hamari 2017). Such badges also provide an overview for the individual on what the system is capable to do and what possible actions the individual can perform (Werbach and Hunter 2012). Badges are rewarding and provide feedback. When individuals receive a badge, they experience feelings of mastery and competence (Sailer et al. 2013). Transparency among the individuals regarding their achieved badges can also lead to a sense of group identification for individuals with the same badge and allows individuals to share their capabilities (Werbach and Hunter 2012). Levels, in their simplest form, are a progress indicator providing feedback, e.g. when achieving the next higher level requires collecting a certain number of points (Mekler et al. 2017). Consequently, achieving the next level is associated with feelings of competence. Moving to the next level or achieving the highest level further provides clear goals for the individual (Glover 2013). However, levels do not necessarily rely on points and moving to the next higher level can also be achieved by completing a specific task (Kim et al. 2018). Individuals start with rather easy tasks to complete a level. While they are progressing through the different levels, the tasks within higher levels become typically more difficult and require more effort and skills (Kim et al. 2018). Completing such challenging but attainable tasks tailored to the current skills of the individual leads to feelings of mastery and competence (Przybylski et al. 2010). When the individual can define

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and implement their own strategies to solve the task, feelings of autonomy can arise (Ryan et al. 2006). Meaningful stories (often also referred to as storytelling) provide a narrative context for the tasks in a gamified system (Kapp 2012). Such stories add meaning, provide context, and guide action for the tasks (Kapp 2012). The story may be completely fictional (e.g. fighting against dragons), or it is connected to the real world or even analogous to the real world (Nicholson 2015). If such a story is in line with the personal interests of the individual, it can spark interest for the situational context, enhance experiences of meaningfulness and, consequently, enhance feelings of voluntary participation and autonomy (Sailer et al 2017, Xi & Hamari 2019). The expected effects have been investigated in various studies, which are based on SDT and the satisfaction of the three basic psychological needs in their study design. A study by Sailer et al. (2017) showed that the combined presence of badges, rankings, and performance graphs positively influence competence need satisfaction and task meaningfulness, which is considered as an experience of autonomy because the task at hand conforms with individuals’ goals and values. Avatars, meaningful stories, and teammates positively affect experiences of relatedness (Sailer et al. 2017). Similarly, Xi and Hamari (2019) also conducted a holistic research on achievement-related elements (e.g. badges, points, status bars, and leaderboards), immersion-related elements (e.g. avatars, personalisation features, and stories), and social-related elements (e.g. cooperation, competition, and networking features). While immersion-related features were only positively associated with autonomy need satisfaction, achievement-related elements and social-related elements positively affected all three basic psychological needs. Achievement-related features were the strongest predictor for autonomy and competence. Mekler et al. (2017) tested the individual effects of points, rankings, and levels against a control group condition for an image annotation task. They did not find any effects on participants’ perceived competence nor their intrinsic motivation, possibly due to the rather trivial image annotation task. These results suggest that the effects of the game elements vary depending on the task complexity and the context of their application. Consequently, the application context and its users must be considered when adding game elements to design gamified systems (Hamari et al. 2014; Nicholson 2012).

2.2 Gamification

2.2.3

27

Designing Gamified Systems

Designing a gamified system is more than a process of adding the most used game design elements (i.e. points, badges, or rankings) to a system (Liu et al. 2017). Instead, it includes a careful analysis of the context and its users, a definition of the desirable psychological and behavioural objectives, and an understanding of the potential game design elements that can be incorporated and their motivational affordances. Especially, profound knowledge in game design and human motivation is necessary for a creative but nonetheless theoretically-guided search process to identify suitable game elements (Morschheuser et al. 2018). In an iterative process, the most suitable game design elements are chosen and incorporated, their effects are evaluated and monitored regarding the desirable objectives, and the gamification design might, therefore, be further optimised (Morschheuser et al. 2018). A starting point for the design is to understand the users, their motivation and needs, and the characteristics of the context in which gamification is employed (Morschheuser et al. 2018). The task or system to be gamified may have deficiencies that can be resolved using gamification, e.g. if a task lacks feedback game elements providing feedback can be incorporated (Liu et al. 2017). However, one shortcoming of many gamified systems is the focus on reward-based gamification as one form of feedback, potentially resulting from a lack of understanding of human motivation (Nicholson 2012, 2015). Rewards are usually considered as extrinsic motivators for behaviour. Therefore, reward-based gamification is considered as suitable and effective for short-term system use, but should be used with care for long-term system use (Liu et al. 2017; Nicholson 2015). When an individual receives a reward for an activity, the reward is likely to be perceived as controlling the activity, which undermines autonomy and intrinsic motivation (Deci et al. 1999). However, a reward can also be perceived to provide informational instead of controlling feedback for an activity, which then enhances perceived competence and facilitates intrinsic motivation (Deci et al. 1999). The satisfaction of the three basic psychological needs is, therefore, an important starting point for the design of gamified systems in order to facilitate individuals’ motivation (Tyack and Mekler 2020). Nonetheless, the reward example also highlights that the arising psychological experiences when interacting with a gamified system can largely differ between individuals. The subjective experiences may further depend on individual’s goals and their goal setting characteristics (Hamari et al. 2018; Landers et al. 2017b) or certain psychological needs (e.g. the need for competence) being more cherished than others (van Roy and Zaman 2019).

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In summary, both the context’s characteristics as well as the individual’s characteristics and its subjective experiences determine the success of a gamification design. Therefore, Morschheuser et al. (2018) highlight the necessity to follow an iterative design process and to test gamification ideas as early as possible. This enables the continuous improvement of the gamification design and individuals’ resulting psychological experiences. A systematic approach to guide gamification designers regarding the choice of game elements for an activity or system is proposed by Deterding (2015). In line with game design literature, he considers challenges to be at the heart of gameplay, giving rise to motivating and enjoyable experiences through different mechanisms. First, overcoming non-trivial challenges creates feelings of competence and, second, choosing which challenge to overcome using self-selected strategies facilitates feelings of autonomy (Przybylski et al. 2010; Ryan et al. 2006). Third, relatedness can be facilitated when the challenge is collaboratively solved with others (Ryan and Deci 2017). Last, the uncertain outcome of a non-trivial challenge arouses curiosity and interest (Malone 1981), leading to increased attention and can finally result in flow experiences (Csikszentmihalyi 1990). Therefore, in his method for gameful design Deterding proposes to identify the challenges that are inherent in individual’s activities and goal pursuit (Deterding 2015). Instead of adding new challenges to an activity, the inherent challenges of an activity are restructured into nested and interlinked feedback loops of goals, actions, objects to act upon, rules, and feedback affording motivating experiences. Thus, game elements are chosen to support individuals to overcome these challenges by providing immediate and overall progress feedback, which facilitate feelings of competence and support the individuals’ existing goals and needs. Consequently, this method combines and emphasises both user and context analysis and focuses on the identification of inherent skill-based challenges and user motivation within a given context. Using Deterding’s method, gamification designers can avoid what has been labelled “pointsification” (Robertson 2010), i.e. adding the simplest game elements without delivering value. Instead, it enables designers to create gamified systems that are actually meaningful to the users, i.e. systems that align with the users’ goals and their motivation as well as the application context without relying on rewards only that undermine intrinsic motivation (Nicholson 2012).

2.2 Gamification

2.2.4

29

Gamification in Education & Training

The concept of applying game elements in non-game contexts has especially drawn attention in the education domain, which was right from the beginning—when the term gamification was established—among the most frequently investigated research areas and nowadays is the most frequently studied application context (Hamari et al. 2014; Koivisto and Hamari 2019). Playing games is an activity performed for the inherent satisfaction of the games itself and playing such games is very popular among children and younger adults (Bitkom 2020). This might explain why so many educators and researchers attempt to evoke similar positive experiences by applying game elements in their educational settings. Although the potential application contexts of gamification in education can include traditional classroom settings too, gamified education mostly uses web-based, scalable, and asynchronous systems (Buckley and Doyle 2016). Especially Learning Management Systems provide an ideal basis for the application of gamification, since they include all the necessary functionalities to provide learning content in various ways, support collaborative work, and allow tracking of learners’ progress and activities (Glover 2013). In an educational context, intrinsic motivation and autotelic learning are considered as one of the highest pinnacles of successful education (Malone 1981; Ryan and Deci 2000a). Intrinsically motivated learners show high engagement, high persistence when facing difficult tasks, deep learning processing, and, in consequence, better learning outcomes (Ryan and Deci 2000a). This is well reflected in educational gamification studies: The most frequently studied outcomes in gamified education are motivational learning outcomes, behavioural learning outcomes, and cognitive learning outcomes (Sailer and Homner 2020). Reviews in the education domain predominantly report positive outcomes, but there are also several studies reporting null effects, mixed effects, or even negative effects (Dichev and Dicheva 2017; Dicheva et al. 2015; Majuri et al. 2018; Sailer and Homner 2020). Gamified education should attempt to satisfy the three basic psychological needs (namely autonomy, competence, and relatedness) in order to evoke intrinsically motivated learning behaviour (van Roy and Zaman 2017). At the core of many gamified learning interventions are game elements providing feedback (Sailer et al. 2019). Feedback is a crucial element for learning, as it helps learners to pursue their learning goals, informs them about how well they are currently working towards achieving these goals, and what steps they should or could perform next (Hattie and Timperley 2007). Feedback can be presented immediately or with delay. When learners are working on a task, immediate feedback is most

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effective (Sailer et al. 2019). It is especially the need for competence that is addressed by feedback elements, and a study by van Roy and Zaman (2019) has shown that learners cherish the need for competence more than the other two needs. Although SDT provides several implications on how to provide feedback to facilitate learners’ intrinsic motivation, several gamified learning interventions are rather simplistic by providing reward-based feedback only, implementing extrinsic motivators at the expense of facilitating autonomous and intrinsic motivation (van Roy and Zaman 2017). Therefore, such gamified interventions can have detrimental outcomes for already motivated learners, whose intrinsic motivation is undermined by external rewards (Deci et al. 1999; van Roy and Zaman 2017). A study by Hanus and Fox (2015) demonstrated that the use of badges and rankings within a course led to less motivation and, subsequently, lower exam scores. Although badges are competence-confirming and rankings can provide informational feedback supporting individual’s competence, they may also be perceived as controlling their learning behaviour diminishing the individual’s intrinsic motivation. Furthermore, leaderboards could have shifted participants’ attention away from the mastery of new learning content to outperforming others. As a general recommendation, Glover (2013) suggests to strictly separate the game elements from grading and not create a parallel formal assessment route via gamification (Glover 2013). In summary, the design of a gamified learning intervention including reward-based game elements can be effective if they provide competence-confirming feedback. However, the designer should not neglect the other two psychological needs, must consider the interplay between the game elements and how they affect all three basic psychological needs and intrinsic motivation (van Roy and Zaman 2017). More matured gamified learning interventions that go beyond reward-based feedback require a holistic thinking about the learning experience facilitated by the game elements (Dicheva et al. 2019) and an alignment between gamification design and learning design (Rabah et al. 2018). The learning experiences provided through serious games support active learning, problem-based learning, and experiential learning (Oblinger 2004). Similarly, researchers have argued that gamification suits the same learning paradigms, i.e. active learning approaches (Glover 2013) and experiential learning (Grund 2015; Nicholson 2015). Furthermore, a recent study confirmed that active learners have a positive perception of gamified learning interventions (Buckley and Doyle 2017). To support these learning paradigms, gamified learning interventions should include goal-focused and challenging activities in which the learners engage along with elements providing feedback about their progress. Challenging but attainable activities can

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spark the learners’ interest for the activity (Malone 1981). Clear goals that set their focus on the mastery of a learning activity are particularly effective (Landers et al. 2017a). When the activity allows more than one strategy to be pursued, feelings of autonomy can arise in addition to feelings of competence by the provided constructive feedback (van Roy and Zaman 2017). In addition to goals themselves, it has been argued to divide the overall learning goals into smaller sub-goals that inform learners about their overall progress and give them a sense of accomplishment (Glover 2013; Urh et al. 2015). Last, when goals are challenging and their achievement is uncertain, individuals may also experience failure. Therefore, gamified learning interventions need to be designed as safe learning places, where learners do not fear failure but acknowledge failure as an important feedback for their activities, facilitating their reflections and learning (Dichev and Dicheva 2017; Lee and Hammer 2011). In summary, the theoretical background concerning gamification has demonstrated that gamification—especially in learning contexts—is more than a process of adding the typical game elements such as points and badges to an activity or system. Given the stressed importance to understand the context in which gamification is applied (Hamari et al. 2014; Morschheuser et al. 2018) and the necessity to understand the activities within this context as well as the motivational and de-motivational aspects characterising these activities (Deterding 2015; Dicheva et al. 2019), the following section will elaborate on the context to be gamified in this thesis.

2.3

E-Negotiation Training

Negotiations are defined as “[…] an iterative communication and decision making process between two or more agents (parties or their representatives) who 1) cannot achieve their objectives through uniliteral actions; 2) exchange information comprising offers, counter-offers and arguments; 3) deal with interdependent tasks; and 4) search for a consensus which is a compromise decision.” (Bichler et al. 2003, p. 316). Importantly, the negotiation parties are interdependent as they need each other to achieve their desired objectives and cannot reach their objectives alone. Today, many individuals at the workplace are expected to conduct business negotiations, which are intraorganizational or interorganisational negotiations conducted at various levels of an organisation. The complexity of business negotiations differs and can range from simple salary or budget negotiations to complex negotiations about non-standardised products or services and collaborations between organisations. While auctions are a suitable, partly automated

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mechanism for the allocation of standardised goods or services, the parties in complex negotiations often deal with incomplete information about the product or service in question. Therefore, such complex negotiations cannot be automated and require the active involvement of the negotiation parties in the communication and decision-making tasks to exchange information, resolve conflicts, and find potential solutions (Schoop 2020). Nowadays, business negotiations are to a large extent conducted electronically (Schoop et al. 2008) and with the continual digitalisation and the COVID-19 pandemic starting in 2020 the importance of e-negotiations can be expected to continue to grow even further. However, negotiations are a non-routine and challenging activity for individuals, since they require complex skills for their communication and decision-making tasks throughout the negotiation process. Negotiations conducted via electronic media impose additional burdens and require additional skills (Köszegi and Kersten 2003). To facilitate the development of negotiation and e-negotiation skills, university courses offer dedicated training. The following subsections elaborate on the context for which game design elements are going to be applied. First, characteristics of e-negotiations and an e-negotiation process model are described. Second, negotiation support systems are presented, which provide support for individuals conducting e-negotiations and are used in e-negotiation training. The third subsection elaborates on the negotiation skills required to conduct (e-)negotiations and how to facilitate their training. Last, the inherent game characteristics of e-negotiations are described, serving as a starting point for the analysis of e-negotiations and the application of game design elements.

2.3.1

E-Negotiation Process

The term e-negotiation and its synonym digital negotiation are often differently defined. Some use the term e-negotiation to describe a negotiation conducted via electronic media. The present thesis uses the definition of an e-negotiation in its narrow sense as defined in the Montreal Taxonomy, defining an e-negotiation as a negotiation “[…] restricted by at least one rule that affects the decision-making or communication process, if this rule is enforced by the electronic medium supporting the negotiation, and if this support covers the execution of at least one decision-making or communication task” (Ströbel and Weinhardt 2003, p. 147). Therefore, an e-negotiation is not only conducted via ICT, but the ICT used to

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conduct the negotiation provides additional value to the negotiators by providing at least decision-making or communication support. Schoop (2010) adds to the definition above that ICT may also support document management, since the documents exchanged during the negotiation (e.g. different contract versions and formal offers) are a central component in business negotiations. An e-negotiation must be structured by a process model and protocol that all negotiation parties and their representatives adhere to. Different negotiation process models have been proposed for face-to-face negotiations (e.g. Adair and Brett 2005; Gulliver 1979). The original eight-phase model by Gulliver (1979) has been adapted by Kersten (1997) for the context of e-negotiations, and was subsequently refined by Braun et al. (2006). In the following, the process model by Braun et al. (2006) will be described to understand the different activities conducted by the negotiation parties throughout an electronic negotiation process. The phases of their process model are: 1) Planning. In this phase the negotiators select the negotiation arena where the negotiation takes place and the communication mode. They define the negotiation problem, i.e. the negotiation issues and the possible options for these issues to be negotiated about. The negotiators individually determine their objectives. For each issue, the aspiration level (i.e. the value among the options the negotiator wants to achieve) and the reservation level (i.e. a value which the negotiator cannot violate) is defined. Furthermore, the negotiators need to consider the best alternative to a negotiated agreement (BATNA) that comes into play when the negotiation parties do not settle on an agreement (Fisher et al. 1991). The better the BATNA of a negotiation party, the higher is the pressure that the negotiation party can exert on the other parties to enforce its position (Lewicki et al. 2010). After collecting information about their negotiation partners, the negotiators decide about their overall strategy and initial tactics to be used. 2) Agenda setting and exploring the field. The negotiators discuss about the issues and their meanings, resulting in issues and options that may be deleted or added to the negotiation agenda. Afterwards, they might revise their preferences determined in the planning phase and may adapt their strategy and initial tactics. Additionally, they may decide about the negotiation protocol to be followed and other constraints enforcing the timing of exchanges and deadlines. 3) Exchanging offers and arguments. In this phase the negotiators learn about the partner’s preferences and priorities and identify critical areas of disagreement as well as areas to develop mutually beneficial solutions. The negotiators will

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realise whether there is a zone of possible agreements or whether this zone is empty. This phase is characterised through intensive exchange of information. As a consequence, negotiators may further adapt their strategies and revise their aspiration levels. 4) Reaching an agreement. The negotiators enter this phase when they have elaborated on the areas for an agreement and realise that the negotiation has been successful. Joint proposals are developed to settle on a final agreement. 5) Concluding the negotiation. In the final phase, the negotiators reach an agreement and evaluate the compromise for possible improvements. The process described above can be performed entirely by a human negotiator or—in parts—by negotiation software agents (NSAs). NSAs are actively involved in a significant part of the negotiation and make decisions on behalf of a human or artificial principal (Jennings et al. 2001; Kersten and Lai 2007a). For example, the human principle may define the issues and objectives in the first two phases, but the actual exchange of offers, the search for an agreement and the conclusion of the negotiation is performed by the NSA. Thus, the process is partly automated, which can be suitable for standardised business negotiations (Vahidov et al. 2012). For complex business negotiations with incomplete information, however, it is the human negotiator that should engage in the continual decision-making and communication tasks. To support human negotiators with these tasks in e-negotiations, negotiation support systems have been developed where the negotiators are still in control of the negotiation process (Schoop et al. 2003).

2.3.2

Negotiation Support Systems

Early negotiation support systems (NSSs) emerged as a further development of decision support systems (DSSs) applied to e-negotiation processes (Kersten & Lai 2007). DSSs help users to understand and formalise their preferences and objectives and, therefore, help them to understand the problem and to search for solutions. Besides providing decision support, NSSs facilitate the communication process between the negotiation partners in e-negotiations and may also include document management (Schoop 2010, 2020). NSSs enable to find agreements of higher quality, settle on agreement within a quicker time period, and to save transaction costs in business negotiations (Bichler et al 2003). During the last two decades NSSs are also used to train future negotiators and facilitate their

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development of the required skills for e-negotiations (Köszegi and Kersten 2003; Melzer and Schoop 2016; Schoop 2020; Vetschera et al. 2006). All NSSs follow a negotiation protocol that guides them through the negotiation process, defines a set of possible actions for the negotiation partners and the permittable sequence of these actions (Bichler et al. 2003). It includes clear rules defining when the negotiation is concluded with an agreement or when the negotiation is concluded unsuccessfully without an agreement. Since many NSSs were developed based upon a decision support perspective, the communication support provided by these NSSs and implemented in their negotiation protocol is often rather simplistic. Oftentimes, only offers including a selection of the preferred values for each issue and an unstructured text message to underpin one’s offer can be sent until an agreement or disagreement is reached (e.g. Kersten and Noronha 1999). However, achieving good communication in e-negotiations involves more than an exchange of offers and counteroffers. Instead, it requires that the negotiators avoid ambiguous messages, ensure transparency, make coherent utterances, engage in mutual clarification efforts, develop a shared understanding, and finally build trust and a relationship among the negotiators (Schoop et al. 2010). Therefore, communication theories can form a valuable basis for the design of NSSs to support the communication process. As an example, the NSS Negoisst is based on speech act theory (Searle 1969) and the theory of communicative action (Habermas 1981). Negoisst requires to explicitly choose a message type defining the intention of the message to be sent, supports informal communication to clarify questions, and enables the negotiator to link the selected values with their written text message, which, therefore, prevents semantic and pragmatic misunderstanding and facilitates the negotiators’ mutual understanding (Schoop et al. 2003; Schoop 2010, 2020). NSSs further provide decision support since the negotiators possess limited cognitive abilities to evaluate complex offers and are prone to bounded rationality in business negotiations. During the planning phase, each negotiator determines their preferred values for all negotiation issues and ranks the issues according to their importance (Schoop 2010, 2020). Based on these preferences, a utility function is computed by the NSS that is used to evaluate each offer. Usually, the utility function computes a utility value for each offer that ranges between 0 and 100%, where 0% equates that the offer does not correspond to one’s defined preferences at all whereas 100% equates that the offer perfectly corresponds to those preferences. The utility value does not only help negotiators to determine whether the received offer is acceptable or not, but also to track the concessions made by the negotiation partner over time. Furthermore, the utility value helps

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in assessing the appropriate concessions when generating the next offer (Schoop 2010). In addition to a utility value computed based on one’s defined preferences, NSSs may also offer additional visualisations to support the negotiator’s decisionmaking. For example, a history graph helps the negotiator to track the negotiation process regarding own concessions made and the ones made by the negotiation partner (Kersten and Noronha 1999; Schoop 2010, 2020). Another alternative is the dance graph (Gettinger et al. 2012b). Finally, NSSs may offer means to evaluate the settled agreement, e.g. whether they have exploited the negotiation potential or whether further improvements are possible. NSSs can propose alternatives to the settled agreement in order to improve the negotiation outcome (Gettinger et al. 2016). The NSSs that were developed in the past have different foci: Systems such as Inspire (Kersten and Noronha 1999) or SmartSettle (Thiessen and Soberg 2003) merely provide decision support, whereas for example WebNS (Yuan et al. 1998) provides communication support only. The web based Negoisst system, however, is the only NSS that provides extensive communication and decision support as well as document management (Schoop et al. 2003; Schoop 2010, 2020). Since it offers the highest level of support among all current NSSs and has been used in enegotiation training for almost two decades (Schoop 2020), it is the most suitable choice for the present thesis to apply game design elements for an improved enegotiation training. Its core features with regard to the provided communication and decision support are summarised in the following. Once the negotiators have discussed and agreed on the issues to be negotiated in the planning phase, Negoisst supports each negotiator with their preference elicitation (Schoop 2010, 2020). Using sliders, the negotiators first determine the relative importance of the issues so that the sum of the ratings for the issues equates to 100%. Afterwards, the negotiators rate the alternatives for each issue. Negotiation issues are either categorial or numeric. The issue price in Figure 2.7 is a numeric issue. Any value between the allowed range can be chosen and the negotiator determines a worst and best case. Environment-friendliness in Figure 2.7 is a categorial issue, which requires to explicitly specify all possible values and to rate them. When the preferences are elicited, a utility function is calculated which is the basis for the evaluation of offers in the following phases, computing a utility value for each offer. The elicited preferences can be viewed or edited at any time during the following negotiation phases. In addition to the utility value, viewing these preferences while constructing an offer can be helpful to make rational and appropriate concessions to the negotiation partner. An empirical study has shown that initial offers and the concessions made during the

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negotiation closely correspond to the defined individual preferences (Vetschera 2007).

Figure 2.7 Preference Elicitation in Negoisst

The negotiators then enter the phase of information and argument exchange. Similar to email communication, the negotiator chooses a title and writes a text message in natural language (see Figure 2.8). In addition, the negotiator must choose a message type which corresponds to the intention behind the message (Schoop 2010, 2020). Negoisst provides formal message types (i.e. offer, request, counteroffer, accept, and reject) and informal message types (i.e. question and clarification). The negotiation is over once a negotiator chooses to accept a counteroffer agreeing to the deal or when one party chooses to reject the whole negotiation, leaving the negotiation without an agreement. On the right-hand side in Figure 2.8, the agenda including all negotiation issues is shown. When constructing a new formal message, the negotiator selects their preferred values in the agenda. In the current example, the negotiator has chosen a price of 150 and

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a pigmented colour type. Since Negoisst can deal with partial offers—leaving some issues open for later discussion—no choice was made for environmentfriendliness yet. Therefore, the utility value of the current counteroffer ranges between 54% in the worst case and 86% in the best case. In theory, the negotiator can choose certain values in the negotiation agenda but write about totally different ones in the natural language message. To avoid inconsistencies and to ensure mutual understanding, Negoisst offers a feature called semantic enrichment (Schoop 2010, 2020). The feature links the issues and selected values from the agenda with the natural language message, retaining the flow of the natural message. In the Figure 2.8, the issue “colour type” and its value “pigmented” were included in the natural language message. While typing, the user can include other issues or their values in the message.

Figure 2.8 Message Composition in Negoisst

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Every message exchanged between the negotiators is stored persistently in the database and can be accessed at any time. This enables the negotiators to track the negotiation process and to assess their prior decisions. Furthermore, the numeric utility values of the formal message exchange are displayed in the history graph as shown in Figure 2.9. The black line shows the utilities for the offers and counteroffers made by the negotiator, whereas the orange line shows the utilities for the offers and counteroffers made by the negotiation partner. All utility values in the graph are based on the negotiator’s utility function and do not reveal anything about the partner’s utility values (Schoop 2010, 2020). The graph visualises one’s own concessions made over time—if any—and the concessions made by the negotiation partner as evaluated by one’s own utility function.

2.3.3

Negotiation Skills & Training

Early on Lewicki (1986, p. 16) noted that “negotiators are made, not born” and that the skills to conduct negotiations effectively can be developed and improved throughout an individual’s life. A negotiation conducted face-to-face or via electronic media requires manifold and diverse communication and decision-making skills that are needed from the planning phase until an agreement is finally settled. Developing these skills requires extensive training, which is either offered as a corporate training course for employees involved in negotiation processes or as part of a university course for management and similar study programmes. Lewicki was one of the first scholars to teach negotiations during the early 1970s and noticed that negotiations are not a topic that can only be learned theoretically and developing negotiation skills requires practice (Lewicki 1997). Therefore, at a relatively early stage he applied one of the basic concepts for almost every negotiation training: Combining teaching theory and research findings together with their practical application in case methods, role playing, and other forms of practical experiences. He argued that either implicitly or explicitly, most negotiation training courses follow the experiential learning methodology of Kolb (1984), whereby knowledge is created through the transformation of experiences. The experiential learning process includes four phases: 1) 2) 3) 4)

having a new experience; reflecting on that experience; drawing conclusions from the experience; and active experimentation in new situations and planning the next steps.

Figure 2.9 The History Graph in Negoisst

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This process highlights that having an experience alone does not lead to the creation of new knowledge. Instead, the reflection phase is crucial and must be facilitated by the training for example through structured debriefings or journal entries (Köszegi and Kersten 2003; Lewicki 1997). A plethora of different negotiation training methods emerged that follow this experiential learning process: Training methods include principle learning, trial-and-error learning, observational learning, learning via feedback, and analogical learning (Loewenstein and Thompson 2006; Nadler et al. 2003). They differ in the way that the experience is created or presented, e.g. by observing someone else’s negotiations or one’s own active involvement in a role play or negotiation simulation. Learning through experiences such as role plays and simulations is considered as an effective way to teach negotiations, because it actively engages the participants, fosters participants’ connection between the theory and its practical application, and these experiences remain long after the theory is lost (Lewicki 1986, 1997). Negotiating effectively is not a single skill but a complex collection of skills, from which the negotiator might apply a subset depending on the current negotiation situation (Lewicki 1997). The development of negotiation skills is facilitated by learning through experiences and active participation in role plays and negotiation simulations. Negotiation training is considered as effective if the participants attain negotiation skills that they can transfer to different situations and new problems (Movius 2008; Roloff et al. 2003). The literature presents numerous lists of negotiation skills (e.g. Lewicki 1997; Lewicki et al. 2010; Roloff et al. 2003; Susskind and Corburn 2000) and, due to their complexity, elaborating on all these negotiation skills is beyond the scope of this thesis. A short summary of the most important skills is given below and the interested reader is referred to the given literature for further information. First, preparation and planning skills are rated as very important among professional negotiators (Roloff et al. 2003). Negotiators must determine their goals and preferences and select a negotiation strategy. Determining one’s goals that should be achieved through a negotiated agreement, the BATNA, and the reservation and aspiration levels for each negotiation issue, helps negotiators to behave more rationally when evaluating and making offers or concluding the negotiation. The planning steps also include an analysis of the negotiation partner, i.e. their most important negotiation issues, their preferences, and their available alternatives to settling on an agreement (Lewicki et al. 2010). Analysing on whether relational aspects between the negotiators come into play, e.g. when that the negotiation deals with more than a simple procurement negotiation and establishing a longlasting relationship is of importance, also affect the self-defined goals and the choice of the negotiation strategy.

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There are two fundamentally different approaches to resolve a negotiation conflict: Either, the negotiator follows a distributive approach and attempts to win the negotiation, or the negotiator follows an integrative approach and attempts to find solutions that are beneficial for all negotiators (Lewicki et al. 2010). In distributive negotiations the negotiators compete with each other over a fixed resource that is split among the negotiators and, therefore, claim value. On the contrary, in integrative negotiations the interaction between the negotiators is predominantly characterised by cooperation and trustful information exchange, i.e. the negotiators try to find mutually beneficial solutions that create value. Choosing one of these approaches depends on the negotiation context, the partner, and requires analysing whether there actually is integrative negotiation potential. However, most negotiation situations are neither purely distributive nor integrative. Instead, they are often mixed-motive, including elements of distributive bargaining and integrative negotiation, where negotiators try to create value and also claim value in order to maximise their own and mutual benefits (Lewicki et al. 2010). When a negotiator aims to claim value and improve their own position, the message exchange is often characterised by extreme opening offers, single issue offers, providing few and small concessions, substantiation, or presenting “last chance offers” (Roloff et al. 2003; Vetschera 2013). Distributive negotiators do whatever is necessary to improve their position, which can also result in unethical behaviour such as the use of deceptive tactics (Lewicki et al. 2010; Roloff et al. 2003). It is crucial for distributive negotiators to be well prepared, i.e. to understand or even improve their BATNA and to present strong and convincing arguments. Furthermore, during the exchange of offers and information the negotiator must assess and additionally try to modify the partner’s target and resistance point (Lewicki et al. 2010). A good preparation is also key for a successful integrative negotiation in order to create value and find an agreement that is beneficial for all negotiation partners. However, negotiators often fail to realise the integrative potential of a negotiation and have a fixed pie assumption (Thompson 1990a). They often assume that the negotiation partner’s interests are completely opposite to their own interests and, consequently, engage in distributive bargaining behaviour. Therefore, one crucial element of negotiation training is to overcome the fixed pie assumption and to train participants in identifying solutions that create value. This starts with the notion that winning a negotiation through distributive bargaining is not necessarily beneficial when long-term relational aspects with the negotiation partner are important, that the preferred values for the issues are not always completely opposite, and that not every issue is equally important for the negotiation partners. Creating value includes creative tasks such as expanding the set

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of feasible agreements by inventing new options that create mutual gain, e.g. by adding new negotiation issues to the agenda (Lewicki et al. 2010). Most commonly, integrative negotiators create a logrolling strategy (Pruitt 1981; Roloff et al. 2003). Negotiators trade concessions on issues that are of lower importance to them in exchange for receiving concessions on issues that are of higher importance. Rather than using trial and error concessions, logrolling behaviour is more efficient to move the negotiators towards an integrative agreement (Pruitt 1981). An integrative negotiation process has four important steps: 1) identification and definition of the problem; 2) understanding the problem and focussing on the interests and needs; 3) generating potential solutions for the problem; and 4) evaluating these potential solutions, before selecting the optimal solution (Lewicki et al. 2010). Unlike distributive bargaining behaviour, integrative negotiations require an open and trusting information exchange. Information exchange about one’s interests leads to more beneficial agreements (Thompson 1991). Indeed, a key role is to focus less on the positions of the negotiators and instead try to figure out the actual interests behind them (Fisher et al. 1991). Obtaining information about the other negotiator’s priorities is required for effective logrolling and demands active listening in order to understand the negotiation partner’s interests (Lewicki et al. 2010). The way that concessions are made by the negotiation partner enables the negotiator to reason about the preferences of the partner and helps to elaborate on potential solutions (Adair and Brett 2005). Furthermore, integrative negotiators are seen as problem solvers (Roloff et al. 2003). Skilled integrative negotiators, however, set reasonably high goals for themselves, otherwise they could apply cognitive heuristics such as finding compromise solutions for each issue. Setting high goals requires looking beyond such easy strategies and investing effort to seek a creative and integrative solution (Roloff et al. 2003). The last step to evaluate the integrative solutions involves claiming value to maximise one’s own outcome (Lewicki et al. 2010). Finding integrative solutions is a difficult task and requires experience. Negotiators logrolling behaviour improves when they gain experience (Thompson 1990b). Negotiators with logrolling experience are less subject to the fixed pie assumption and achieve better negotiation outcomes (Thompson 1990c). In general, negotiators that spend more time in a negotiation training gain more skills and perform better in negotiations (ElShenawy 2010). E-negotiations are often conducted using asynchronous media (Schoop et al. 2008) and impose additional burdens to the negotiation process. However, they also provide new opportunities to settle on better agreements. Therefore, in addition to the aforementioned negotiation skills, specific e-negotiation skills are required. First, in contrast to face-to-face negotiations the asynchronous mode

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does not require an immediate reaction or reply. Instead, it enables the negotiator to spend more time for their preparation, to assess offers and counteroffers in detail, and to make use of time pressure tactics (Köszegi and Kersten 2003). The digital media filters out social cues such as mimics, gestures, or tone of voice (Sproull and Kiesler 1986), which can be a source of ambiguous utterances and misunderstandings in e-negotiations (Schoop 2020). Therefore, negotiators must learn to read between the lines and express their intention clearly to avoid such misunderstandings (Köszegi and Kersten 2003). They also need to learn how to establish trust and build a relationship using digital media. Last, negotiators can use analytical support tools. These tools require gaining experience in order to use them to their advantage (Köszegi and Kersten 2003). E-negotiation training frequently uses NSSs to facilitate the development of these skills (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2016; Schoop 2020; Vetschera et al. 2006). These systems inherently provide the negotiators with analytical support and use an asynchronous digital media imposing the burdens described before. Therefore, an e-negotiation training using an NSS includes two elements: First, a system training for the NSS to be used is required, where the negotiators gain experience with the system and understand the support features. Second, the e-negotiation training must facilitate the development of the additional e-negotiation skills (Melzer and Schoop 2015). The development of these skills is achieved by engaging the participants in negotiation simulations conducted via the NSS following the experiential learning methodology (Köszegi and Kersten 2003; Melzer and Schoop 2016). E-negotiation training is heavily intertwined with traditional face-to-face negotiation training. The face-to-face negotiation skills used for the planning phase, for performing distributive bargaining, and for conducting an integrative negotiation, are also needed for successful e-negotiations. According to Köszegi and Kersten (2003), the e-negotiation training incentivised their participants to experiment with different negotiation strategies, tactics, approaches and negotiation styles, which would be more difficult in face-to-face negotiations. Since e-negotiations heavily draw upon knowledge and skills already gained for face-to-face negotiations, an e-negotiation training is usually embedded in a traditional negotiation course or training (Köszegi and Kersten 2003; Melzer and Schoop 2016). First, participants receive a standard training including negotiation theory and its practical application for the development of face-to-face negotiation skills, before they move towards developing the additional e-negotiation skills (Köszegi and Kersten 2003; Melzer and Schoop 2016). The present thesis, developing a gamified e-negotiation training, is embedded in such a negotiation course (Melzer 2018; Melzer and Schoop 2017) and it is therefore assumed that the participants have

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already gathered knowledge and developed skills for face-to-face negotiations, before they enter the subsequent e-negotiation training.

2.3.4

Inherent Game Characteristics of E-Negotiations

The game element hierarchy in Section 2.2.2 already included well-known elements and phenomena from the negotiation domain such as competition and cooperation, which are inherent in the negotiation processes that the negotiators face. Negotiations are indeed sometimes referred to as a game, where negotiators for example dance around each other, play with different strategies, follow rules and protocols, and aim to achieve the ultimate goal of an agreement. In a prior study, Schmid and Schoop (2018) elaborated in detail on these inherent game characteristics of e-negotiations conducted in NSSs. Since these characteristics further help to understand the context and might serve as an important starting point for the incorporation of game elements in an NSS, the core findings of this study are shortly summarised in this section. Gaming activities are referred to as goal-focussed activities structured by rules (Deterding et al. 2011). Therefore, a necessary prerequisite for e-negotiations to be perceived as game-like is that they have at least one goal and impose rules on the process. The ultimate goal of a negotiation is the achievement of an agreement (Bichler et al. 2003). The negotiation process to settle on such an agreement is structured by a negotiation protocol defining the possible actions each negotiator can take in a given situation (Kersten and Lai 2007b). Consequently, it can be concluded that the prerequisites to perceive e-negotiations as game-like are fulfilled (Schmid and Schoop 2018). The negotiation process itself can be further characterised as an interactive process between at least two negotiators (Bichler et al. 2003). Many games offer social interaction opportunities for the players, e.g. through competition, cooperation, or communication via chat between them (Ryan and Deci 2017; Sweetser and Wyeth 2005). Similarly, the negotiators are engaged in negotiation-specific communication by exchanging offers, counteroffers, or informal messages, striving towards the joint goal of mutual understanding and an agreement as well as their individual goals (Schoop et al. 2010; Schoop 2010). Although the negotiators define their individual goals, their strategies, and their tactics in the planning phase, they constantly adapt their actual behaviour according to the behaviour of the negotiation partner. Since the negotiators aim to settle on a compromise solution that cannot be achieved through unilateral actions (Bichler et al. 2003), negotiations require social interaction including communication elements such

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as arguing, convincing, accommodating, threatening, enquiring, or clarifying to settle on a compromise solution (Schoop et al. 2010). Furthermore, the negotiators’ chosen strategies affect the complete negotiation process and shape the social interaction process. The overall approach of a negotiator is either distributive or integrative and can be implemented using several tactics (Lewicki et al. 2010). A negotiation includes a strong competition element if the negotiators follow a distributive approach and attempt to win the negotiation. If they decide to follow an integrative approach, the social interaction process is characterised by cooperation and the desire to create a winwin situation for each negotiator. Furthermore, in negotiations with more than two parties coalitions that aim to achieve mutually desirable goals and that cooperate with each other can be built up (Lewicki et al. 2010). Hence, it can be concluded that negotiations include a strong social interaction element characterised by competition and/or cooperation (Schmid and Schoop 2018). In addition to the game definition given above, other researchers argue that players must experience a sense of control over their actions, which lead to observable changes in the game world (Sweetser and Wyeth 2005). Negotiators conducting an e-negotiation via an NSS only receive support and advice for their communication and decision-making tasks, but, in contrast to NSAs, are still in control of their own actions and the negotiation process (Schoop et al. 2003). Furthermore, the actions performed in the e-negotiation lead to observable changes. First, at the level of the negotiation protocol a completed action leads to the selection of new tasks that are to be completed by the negotiator or the negotiation partner, i.e. the protocol proceeds to a new protocol state (Kersten and Lai 2007b). Second, the actions might lead to changes at the content-related level of the negotiation. For example, the negotiator might have successfully convinced the negotiation partner to make some concessions or recognises other reactions of the negotiation partner towards the negotiator’s own previous action. Thus, it can be concluded that the negotiators experience a sense of control over their actions and observe or recognise different reactions towards their completed actions (Schmid and Schoop 2018). Characteristic to many games is that they offer various choices during the game to the players, allowing them to select among strategic, tactical, and expressive options, or to choose the goals they wish to pursue (Charsky 2010; Ryan et al. 2006). In the planning phase, the negotiators individually define the goals they aim to achieve and develop a strategy including several tactics to implement the chosen strategy (Lewicki et al. 2010). Similar to games, different strategies and tactics can be used to achieve a defined goal in a negotiation and there is no standard procedure to follow. However, the initial strategy that was defined in

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the planning phase might turn out to be unsuccessful during the following negotiation process, since the process and the outcome of a negotiation are almost always uncertain (Schmid and Schoop 2018). Similar to players, skilled negotiators can adapt their strategy to a given situation if their initial strategy and tactics no longer seem suitable to pursue their goals (ElShenawy 2010). The development of one’s own strategy and tactics also refers to another element that is fostered by several games, namely creativity (Bowman et al. 2015). Creativity is a required negotiation skill to successfully conduct integrative negotiations (Lewicki 1997), where the negotiators search for a mutually beneficial compromise and, thus, need to invent options that create mutual gain. Combining both choices and creativity, setting one’s own goals and choosing a strategy and tactics in order to reach them is a frequently discussed topic in game and gamification literature because it can enhance intrinsic motivation (Charsky 2010; Nicholson 2012). Negotiators always define their own goals, but negotiators in an e-negotiation training are less bound to a serious business situation and may, therefore, try new approaches that they would not test in a serious situation. In particular, Köszegi and Kersten (2003) report that training participants experiment with different negotiation styles, strategies, and tactics. Therefore, setting own goals in a negotiation training does not necessarily only refer to the typical goal-setting tasks in the planning phase, but may also include the goal to experiment with new negotiation styles and strategies and evaluate their impact on the process and outcome (Schmid and Schoop 2018). Feedback is an inherent and important element in every game and is often provided in various forms (Ryan et al. 2006; Sweetser and Wyeth 2005). Usually, players have at least a status, a score, or a level indicating their overall progress. Feedback also refers to immediate feedback that is provided for one’s actions (Sweetser and Wyeth 2005). In NSSs, the negotiators receive various forms of feedback that help them to track their progress towards reaching their ultimate goal, namely settling on an agreement (Schmid and Schoop 2018). First, to settle on an agreement the negotiators have to proceed through each of the negotiation phases, which were previously described using the phase model of Braun et al. (2006) in Section 2.3.1. Each of these phases must be concluded and the completion of such a phase represents an intermediate goal (Schmid and Schoop 2018). The negotiation in an NSS is structured by a negotiation protocol which enables the negotiators to comprehend their current status within the different phases (Kersten and Lai 2007b). The protocol further guides the users through these phases and they can observe their progress towards reaching an agreement (Schmid and Schoop 2018). Second, the negotiators receive immediate feedback when they construct an offer or counteroffer, displaying a score that helps them to

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avoid non-desirable outcomes and to achieve desirable ones. This score is usually known as the utility value, supporting the negotiators in evaluating their offer or counteroffer (Schoop 2010, 2020). Third, these utility values can be visualised in a graph such as the history graph (see Figure 2.9, Section 2.3.2) tracking the negotiation process in terms of the concessions made over time. A converging line, such as given in the figure, denotes a higher probability for reaching an agreement soon, whereas diverging lines indicate that more time might be required until an agreement is found (Schmid and Schoop 2018). Last, the received offer or counteroffer from the negotiation partner contains important feedback, which helps to evaluate whether the negotiator could achieve some concessions or whether the current tactics are deemed as unfruitful to reach progression. Depending on the negotiation partner’s reaction it may be necessary to adapt or change one’s own behaviour (Schoop et al. 2010). Finally, challenges are one of the most frequently used game elements and are provided at various levels of difficulty to match with the current skills and the progress of the player (Charsky 2010; Chen 2007; Przybylski et al. 2010; Sweetser and Wyeth 2005). Providing optimal challenges tailored to the current skills of the player enables and maintains the player’s flow experience (Chen 2007; Sweetser and Wyeth 2005). Similarly, negotiations require skills to overcome a challenge, namely, to achieve an agreement with the negotiation partner (Schmid and Schoop 2018, 2019). Business negotiations are usually quite complex, but they may differ in their level of difficulty in many aspects. First, the number of issues that are to be negotiated determine the level of difficulty. Single-issue negotiations (e.g. about a price or a salary) are quite easy to handle and the negotiator might not even need decision support through means of a utility value to evaluate the offers. However, effectively negotiating multiple issues is much more difficult and requires support to evaluate offers and counteroffers. In an integrative negotiation, it involves the careful consideration of various solutions that create a mutual beneficial solution (Lewicki et al. 2010; Schmid and Schoop 2018). Second, a negotiation can be further characterised by its type and the number of parties involved, e.g. as a bilateral or a multilateral negotiation (Bichler et al. 2003). In a bilateral setting only two parties negotiate with each other to settle on an agreement, whereas multilateral negotiations involve more than two parties and demand that all involved parties agree on a compromise. Considering and satisfying the interests of several negotiation parties in a multilateral setting is more challenging than meeting the interests of only one party (Lewicki et al. 2010). Third, the level of difficulty depends on the negotiation partner and their chosen strategy. Lewicki et al. (2010) describes four basic strategies, namely avoidance, accommodation, competition,

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and collaboration, that differ regarding the negotiator’s concern about their own interests and regarding their concern of the relationship with the negotiation partner (see Figure 2.10). Negotiating with a partner that is less concerned about their own outcome is obviously easier than negotiating with someone following a collaborative strategy. The hardest challenge is to negotiate with a partner following a competitive strategy, because their only interest is to pursue their own goals with little concern for establishing or maintaining a good relationship. In summary, the complexity and the difficulty of negotiations can differ in many aspects. The level of difficulty cannot be predicted or controlled for real business negotiations and depends on the situation (Schmid and Schoop 2018). Business negotiators are expected to possess all relevant skills to effectively conduct negotiations of any level of difficulty. However, in the area of negotiation training the level of difficulty can be controlled, e.g. by starting with rather easy negotiation simulations at the beginning and continuously increasing their complexity and difficulty (Köszegi and Kersten 2003; Schmid and Schoop 2019).

Figure 2.10 The Dual Concern Model for Negotiation Strategies (Lewicki et al. 2010, p. 112)

With all the previously described inherent game characteristics of enegotiations in mind, the design of a gamified NSS used in e-negotiation training to support participants motivation, engagement, and learning appears to be a promising approach to evoke gameful and motivational experiences. The game elements deemed as suitable will be integrated in the previously presented

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Negoisst system, since it is the most sophisticated NSS available. The following methodology chapter explains the overall research process including the design and evaluation of the gamified system.

3

Design Science Research Methodology

The proposed research goal and research questions require a methodology supporting a design-oriented approach. For several decades, IS researchers created novel artefacts (e.g. constructs, models, methods, and software instantiations) to solve problems and provide utility to organisations (March and Smith 1995). The term design science research emerged with the seminal paper by Hevner et al. (2004), which integrated these early research streams and provided guidance for understanding, executing, and evaluating design science research. In the following two sections, the design science research methodology will be explained and the resulting approach for the present thesis will be described.

3.1

Design Science Research in Information Systems

Hevner et al. (2004) distinguish between two different research streams in IS. On the one hand, behavioural science is rooted in the natural science paradigm and seeks to find the truth, i.e. to develop and justify theories providing explanations or predictions for the socio-technical phenomena surrounding information systems. On the other hand, design science research follows a problem-solving paradigm by creating new IS artifacts or improving existing artefacts that are iteratively built and evaluated (Hevner et al. 2004). The goal of design science research is the creation of artefacts including constructs, models, methods, and software instantiations providing utility for identified problems (Hevner et al. 2004; March and Smith 1995). The IS research framework by Hevner et al. (2004) in Figure 3.1 combines both the behavioural science paradigm as well as the design science paradigm. IS research adheres to relevance by addressing problems or business needs stemming from the environment (i.e. people, organisations and technologies). IS research is © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_3

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further devoted to scientific rigour by integrating theoretical foundations (e.g. theories, frameworks, etc.) and applying scientific methodologies (e.g. data analysis techniques, formalisms, measures, etc.) to the research problem. To resolve the research problem, behavioural science develops and justifies theories, whereas design science builds and evaluates artefacts to resolve the identified problems (Hevner et al. 2004). Finally, the research results of both paradigms provide additions to the knowledge base and are applied in the environment. However, behavioural science is criticised for lacking relevance, being disconnected to the environment and providing little guidance for the design (Hevner et al. 2004). Design science emerged as an established methodology to solve relevant problems and ensures scientific rigour. The methodology differs from professional design as performed by for example business consultancies, since it resolves new or existing problems by creating new solutions or using existing solutions in new application areas, while professional design applies existing solutions to well-known problems in a routine way (Gregor and Hevner 2013).

Figure 3.1 Information Systems Research Framework (Hevner et al. 2004, p. 80)

Hevner (2007) extends the depicted IS research framework by introducing three cycles for design science research, namely, the relevance cycle, rigor cycle, and design cycle. Design science research projects start with the relevance cycle where problems are identified and objectives of the artefact are defined (Hevner

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2007; Peffers et al. 2007). When an artefact has been built and evaluated, the relevance cycle is closed by applying the artefact in the environment through field testing and, if necessary, problems and objectives are redefined (Hevner 2007). Within the rigor cycle, artefact creation and evaluation in design science research draws upon existing theories, frameworks, artefacts, and methodologies. According to Walls et al. (1992), kernel theories, which are borrowed from other research fields such as social science and mathematics, inform the meta-requirements for the design product of the artefact as well as its design process. However, the use of kernel theories is not always feasible and may impede creative solutions (Baskerville et al. 2018; Hevner 2007). Additionally, the rigor cycle encompasses any additions to the knowledge base, i.e. the artefact itself, design theories, and investigated effects of the artefact in its environment (Hevner 2007). Last, the design cycle includes all processes by which an artefact is rigorously built and evaluated. Different frameworks have been developed to justify an artefact’s metarequirements and its meta-components (Baskerville and Pries-Heje 2010) and to choose an appropriate evaluation strategy (Venable et al. 2016). To date, there are still debates on the expected research contributions (i.e. the additions to the knowledge base) of a design science research project (Baskerville et al. 2018; Iivari 2020; Peffers et al. 2018). In general, two types of design knowledge contributions of a design science research project are defined: the artefact itself and design theories (Baskerville et al. 2018; Gregor and Hevner 2013). Since design is both a process and a product, design theories can be both: theories about the product and about the process (Walls et al. 1992). Gregor and Hevner (2013) classify design science contribution types on three maturity levels: situated implementations of an artefact at the first level (e.g. software artefacts and processes), more abstract nascent design theories at level two (e.g. models, methods, and design principles), and well-developed design theories at level three. While a situated implementation is considered as a sufficient knowledge contribution if the artefact’s novelty is justified, design science researchers aim to generalise by constructing nascent design theories (Baskerville et al. 2018). Nascent design theories may generalise an understanding of how and why artefacts solve the relevant problems and achieve their objectives (vom Brocke et al. 2020). The term design theory is still somewhat ambiguous and differently conceptualised among researchers, as revealed by the critical review of design theories by Iivari (2020) depicted in Figure 3.2. The first design theory conceptualisation focusses on the relationship between kernel theory and the artefact (design theory 1), and a second conceptualisation encompasses the artefact’s internal structure, i.e. the relationship between meta-requirements, meta-components, and the

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instantiation (design theory 2). Walls et al. (1992) understanding of a design theory includes first relationship between kernel theory and the artefact and second the artefact’s internal relationship between meta-requirements and meta-design (Iivari 2020). The explanatory design theory by Baskerville and Pries-Heje (2010) is restricted to the meta-requirements and meta-design corresponding to design theory 2 (Iivari 2020). A completely different conceptualisation is provided by Venable (2006), who equates design theory with utility theory and focusses on the relationship between the artefact and its effects (design theory 3), i.e. the level of utility an artefact has in solving a problem. Last, a fourth conceptualisation of design theory is a union of the three other design theories (Iivari 2020). Consequently, a newly developed design theory can encompass a wide range of design knowledge surrounding the design of an artefact. It may include knowledge about the kernel theories informing the artefact’s design, the internal structure of the artefact, and, finally, the effect and utility of the artefact to solve a problem.

Figure 3.2 Design Theory Conceptualisations. (adapted from Iivari 2020, p. 507)

3.2

Applied Approach and Contribution

The present thesis follows the design science research methodology and aims at including game elements in an e-negotiation training to improve participants’ motivation, engagement, and their learning outcomes. The problems stemming from insufficient motivation and engagement have already been outlined in the introduction and define the relevant research problem from the environment. The problem environment of e-negotiation training is further characterised by using NSSs (Vetschera et al. 2006) and such training is often offered in university courses teaching negotiation theory and practice (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2016). Therefore, this research project

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uses an established NSS, which will be enhanced with selected game elements and evaluated within such university courses. The build and evaluate processes for the artefact to be designed look as follows. First, the relevant kernel theories have to be reviewed and contextualized to achieve the goal of a DSR based gamification project (Silic and Lowry 2020). In particular, motivation theories, learning methods, e-negotiation training, and e-negotiation processes are reviewed and analysed using an integrative literature review (Torraco 2005, 2016). Following the explanatory design theory (Baskerville and Pries-Heje 2010), general requirements for the class of gamified NSSs used in e-negotiation training are derived from the results of the literature review and a framework is proposed, thus, grounding the design on an established theoretical basis. The second step after defining the requirements is the actual design of the gamified NSS. The chosen game elements that are expected to fulfil the requirements are integrated in the NSS Negoisst, which is the most sophisticated NSS available. Negoisst includes communication and decision support as well as document management, and has been used for e-negotiation training since two decades (Schoop et al. 2003; Schoop 2010, 2020). Each chosen element is justified in terms of the expected psychological and behavioural outcomes, thus, connecting the kernel theories and the requirements with the artefact design. This addresses an important issue in gamification research, as oftentimes gamification studies do not provide justification for the incorporated elements (Dichev and Dicheva 2017) and more research is needed to investigate how the elements should be chosen for a certain task, how these elements interact among each other and create the desired psychological and behavioural outcomes (Liu et al. 2017). The present thesis follows an iterative design process, i.e. it reports on the design and evaluation of three versions of the artefact, which were investigated with regard to their effects (Hevner et al. 2004; Morschheuser et al. 2018). Venable et al. (2016) propose four different evaluation strategies that can be followed for an artefact’s evaluation. These strategies differ regarding the different evaluation episodes’ functional purpose (formative or summative) and the paradigm of the evaluation (artificial or naturalistic). Formative evaluations help to improve the efficiency or effectiveness of an artefact, whereas summative evaluations provide empirically based interpretations to create a shared meaning about the artefact and judge whether the outcomes match the requirements and expectations. Artificial evaluations are empirical or non-empirical and may for example include laboratory experiments or mathematical proofs. In a naturalistic evaluation, the artefact is tested in the real problem environment.

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The choice of an evaluation strategy depends on whether the primary risk of the design science research project is a technical or a social/user-oriented risk (Venable et al. 2016). Incorporating game elements in an information system is not a technically challenging task and can be performed quite easily. As outlined in Section 2.2, profound knowledge in human motivation is necessary for a successful gamification design (Morschheuser et al. 2018), and it is further required to consider both the context and its users (Hamari et al. 2014; Nicholson 2012). Consequently, it can be concluded that the primary risk of a gamification project is user oriented. For such a project, Venable et al. (2016) suggest the human risk & effectiveness evaluation strategy, which moves quickly from first formative and artificial evaluations to more naturalistic and summative evaluations. Following first artificial white-box tests ensuring that the game elements are correctly implemented, further evaluation episodes proceed with testing the artefact within its natural environment as part of university courses offering negotiation training. Using students as subjects, the iteratively developed versions of the artefact are evaluated regarding their effects on motivation, engagement, and learning outcomes. A first formative evaluation provides evidence whether the artefact works as intended and outlines possible improvements (Schmid et al. 2020, cf. chapter 5). The two subsequent studies following a randomised study design (Schmid 2021, cf. chapter 6) or including a control group (Schmid & Schoop 2022, cf. chapter 7) provide a summative evaluation and create a shared meaning about the artefact, a judgement of its effectiveness, and explain the effects of the game elements. Gregor and Hevner (2013) provide a framework of design science research knowledge contributions according to the two dimensions application domain maturity and solution maturity. The application domain maturity concerns the field of e-negotiation training and can be classified as high: Both the training’s content and the training’s methodology following experiential learning by engaging participants in negotiation simulations are grounded on a solid theoretical basis and have become established (Köszegi and Kersten 2003; Melzer and Schoop 2016). However, the solution maturity, i.e. using game elements to motivate participants and improve their learning in an e-negotiation training, can be classified as low. Gamification is still in its infancy (Koivisto and Hamari 2019) and there are no standard solutions to design gamified systems, since the success of a gamified system is to a large extent context-dependent (Hamari et al. 2014; Nicholson 2012). Thus, the knowledge contribution of the current thesis is classified as an improvement (see Figure 3.3). The contributions of this work are first of all the artefact itself, which represents the most concrete and least abstract contribution type among the three

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design science research contribution maturity levels described by Gregor and Hevner (2013). The artefact offers a valuable platform for the problem environment—i.e. institutions such as universities and practitioners—since it provides individuals with the means to acquire e-negotiation skills within a motivating envirnment.

Figure 3.3 Design Science Research Knowledge Contribution. (adapted from Gregor and Hevner 2013, p. 345)

As a natural sequence of design science projects, the design and evaluation of an artefact is followed by the development of nascent design theories (Baskerville et al. 2018), which are classified on the second maturity level and may include models, methods, and design principles (Gregor and Hevner 2013). After its evaluation, the present thesis will reflect about the artefact’s design (both in terms of the process and the product) and generate design principles to apply the embedded artefact knowledge to a wider range of applications (Baskerville et al. 2018). Design principles provide prescriptive knowledge to solve real world problems (Baskerville et al. 2018; vom Brocke et al. 2020). Since the results of gamified interventions are still somewhat inconclusive and there exists little guidance to design gamified systems or gamified learning interventions, the overall aim is

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to strive for a nascent design theory and to generalise how gamification can be employed in different contexts. Speaking in terms of the design theory conceptualisations by Iivari (2020) in Figure 3.2, the thesis aims for a design theory that includes all three design theory conceptualisations: First, it shows how kernel theories and justificatory knowledge can be contextualised to derive the artefact’s requirements (design theory 1). Second, the thesis explains the artefact’s internal nature, i.e. the embedded components to realise the requirements (design theory 2). Last, while performing three rounds of evaluations with different versions of the artefact, the thesis focusses to a large extent on the relationship between the different artefacts and their effects (design theory 3) and the utility these artefacts provide to solve a problem (Venable 2006).

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A Framework for Gamified Electronic Negotiation Training

Co-Author: Prof. Mareike Schoop, PhD Reprinted by permission from Springer Nature: Springer, Cham, Switzerland: A Framework for Gamified Electronic Negotiation Training. In: D. C. Morais, A. Carreras, A. T. de Almeida and R. Vetschera (eds.) Group Decision and Negotiation—Behavior, Models, and Support: GDN 2019. LNBIP 351, 2019, pp. 207–222, Andreas Schmid, Mareike Schoop, available at https:// link.springer.com/chapter/10.1007/978-3-030-217112 16

Abstract

The continual digitalisation of business processes requires individuals nowadays to learn to negotiate electronically. Negotiation trainings frequently use negotiation support systems (NSSs) to facilitate the development of electronic negotiation skills. Current NSSs offer a rich set of support functions but fail to provide constructive feedback to the learners regarding their negotiation performance, i.e. whether they reached a good agreement or how they can improve. To address this gap, the current paper suggests a novel approach for electronic negotiation trainings by including game elements in an NSS, thereby offering feedback and increasing the motivation and engagement of negotiators. The requirements for the design of such a gamified NSS are based on an integrative literature review on the state of electronic negotiation training, motivation theories, and gamification. Finally, we present a new framework

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_4

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for electronic negotiation training offering constructive feedback and motivational incentives as part of the NSS. Both elements are expected to enhance learners’ engagement and improve their learning outcomes when participating in such an electronic negotiation training.

4.1

Introduction

Negotiation skills are an important asset in today’s business life. Ideally, negotiators possess the required skills to reach optimal outcomes, save transaction costs, and maintain long-lasting relationships with important business partners. Negotiation skills involve communication, analysis and decision-making (Lewicki et al. 2010), which can be learned through years of experience or via dedicated negotiation trainings. The field of negotiation trainings rapidly emerged during the 1980 s (Lewicki 1997) and provoked several research studies until today on how to teach negotiations (Loewenstein and Thompson 2006; Melzer 2018). At the same time, ICT and the digitalisation of business process rapidly changed business practices. Nowadays, negotiations are often conducted using electronic media, especially using email (Schoop et al. 2008). Furthermore, the new technological possibilities gave rise to the development of electronic negotiation systems, which support the negotiators at least in their communication or decision-making tasks (Ströbel and Weinhardt 2003). In recent years, negotiation support systems (NSSs) as the predominant representatives of electronic negotiation systems have been frequently used in negotiation trainings to facilitate the development of additional skills for electronic negotiations (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2014; Vetschera et al. 2006). NSSs offer communication and decision support and might also facilitate conflict management and document management (Schoop 2010). Human negotiators are still in control of the negotiation process and decide whether to accept or reject an offer (Schoop et al. 2003). The NSSs employed in these trainings follow a structured, asynchronous bilateral negotiation protocol and include the exchange of textual messages (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2016). Representatives of the used systems are e.g. the Negoisst system (Schoop et al. 2003; Schoop 2010) or Inspire (Strecker et al. 2006). When participating in a negotiation training facilitated by an NSS, students’ development of negotiation skills crucially relies on reflections about one’s negotiation process and performance (Köszegi and Kersten 2003; Lewicki 1997). Reflections are often facilitated “offline” by structured debriefings (Köszegi and

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Kersten 2003) or journal entries (Melzer 2018). Obviously, the feedback mechanisms NSSs currently provide are not sufficient to facilitate such reflections. Students require feedback whether they perform well in their negotiations and what they can improve immediately or in subsequent negotiations. Besides learning from reflections on negotiation simulations, learning outcomes in general are positively influenced by students’ motivation and their resulting engagement in learning tasks (Kahu 2013; Reschly and Christenson 2012). As a distal consequence, engagement also positively impacts academic success (Kahu 2013). Therefore, fostering engagement to improve learning outcomes is one of the key challenges for instructors. One recent approach to facilitate engagement is gamification, defined as “[…] the use of game design elements in non-game contexts” (Deterding et al. 2011, p. 10). Gamification makes use of the powerful motivational elements of games whilst not turning the gamified context into a real game. In addition to their motivational power, several standard game elements such as points, badges, leaderboards, or performance graphs also include feedback on the users’ actions (Sailer et al. 2017). Gamification in the education area is especially employed in web-based and asynchronous systems (Buckley and Doyle 2016). Reviews on gamification approaches in general or specifically in the education area predominantly report positive effects on engagement (Dicheva et al. 2015; Hamari et al. 2014; Majuri et al. 2018; Seaborn and Fels 2015). We expect a gamified NSS used in negotiation trainings to lead to greater engagement and better learning outcomes. Furthermore, as an intensive exchange of offers leads to more integrative agreements (Gettinger et al. 2012a), we suggest students to reach better agreements in terms of individual as well as joint utility. However, gamifying an existing information system requires more than simply adding some game design elements such as points or badges. Literature and experts in the field of gamification highlight the need to analyse and understand the users’ needs, psychological processes, and the context of the implementation for the design process (Morschheuser et al. 2018). In this sense and as part of a design science research approach (Hevner et al. 2004), this paper follows the explanatory design theory (Baskerville and PriesHeje 2010) and presents the general requirements that have to be considered for gamifying an NSS in the context of negotiation trainings. In particular, we conducted an integrative literature review (Torraco 2005, 2016) focussing on general learning theories and the characteristics of learning to negotiate electronically (cf. Section 4.2) as well as the underlying motivational theories behind gamification and learning (cf. Section 4.3). In Section 4.4, we present and align our findings with a negotiation process model for electronic negotiation systems. We

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finally present a framework derived from the kernel theories for motivation, learning and negotiation, which forms the basis for the design of such a gamified NSS. The last chapter shortly discusses the results and presents our next steps.

4.2

State-of-the-Art in Electronic Negotiation Training

Negotiation trainings combine transmission of theoretical knowledge and principles with practical applications to develop negotiation skills, e.g. by engaging students in role plays, case studies, or simulations (Lewicki 1997). Loewenstein and Thompson (2006) provide a list of five teaching methods for negotiations: principle learning, trial-and-error learning, observational learning, learning via feedback, and analogy learning. All these methods emphasise the need to integrate theory and practice in negotiation trainings. Most negotiation trainings follow Kolb’s experiential learning methodology (Köszegi and Kersten 2003; Lewicki 1997; Melzer and Schoop 2016), which is rooted in the constructivist learning paradigm. Experiential learning is defined as “[…] the process whereby knowledge is created through the transformation of experience” (Kolb 1984, p. 41). The process includes the following four phases: (1) having an experience, (2) reflective observations on this experience, (3) drawing conclusions from the experience through abstract conceptualisation and (4) active experimentation in new situations. Because most people already possess limited negotiation knowledge from individual experiences like the fixed pie assumption, experiential learning is seen as a key method to challenge existing knowledge and integrate new concepts (Köszegi and Kersten 2003). Negotiation trainings often integrate NSSs to conduct negotiation simulations (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2016). Negotiating with an NSS requires additional knowledge and skills. As social cues such as mimics and gestures cannot be observed via the system, students have to learn to read between the lines and how to build up a positive relationship (Köszegi and Kersten 2003). They further learn to use the asynchronous mode of the system to their advantage, e.g. making efficient use of more preparation time as well as the use of time pressure tactics. Negotiating with an NSS also requires students to understand and utilise specific support features and gather experience in using such an NSS (Melzer and Schoop 2015). The course design for negotiation trainings, therefore, typically promotes face-to-face negotiation skills first, which are a necessary prerequisite before students can develop negotiation skills for electronic scenarios (Köszegi and Kersten 2003; Melzer and Schoop 2016).

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At the core of electronic negotiation training is the participation of a student in a negotiation simulation. The design of the training and of the negotiation simulations depends on the learning goals that should be achieved. To avoid excessive demands while using a complex NSS and gathering experience with its support functions, previous courses e.g. started with rather simple single-issue negotiations and proceeded with more complex, multi-issue negotiations (Köszegi and Kersten 2003). During the negotiation process, students perform different actions and requests in the NSS. The NSS provides different immediate feedback mechanisms for the actions, e.g. a utility value supporting the evaluation of offers. The use of negotiation simulations may either follow a trial-and-error learning approach or the learning via feedback method (Loewenstein and Thompson 2006). Simple participation in a negotiation simulation without reflecting on the negotiation process is not sufficient for in-depth learning and acquisition of negotiation skills (Köszegi and Kersten 2003; Lewicki 1997). Consequently, the experiential A Framework for Gamified Electronic Negotiation Training 209 learning phases of reflective observations and abstract conceptualisation are often facilitated by structured debriefings (Köszegi and Kersten 2003; Lewicki 1997) or journal entries (Lewicki 1997; Melzer and Schoop 2017) after a negotiation simulation has been concluded. However, the feedback supporting the reflection phase in electronic negotiations training is not a part of the NSS itself but provided by the surrounding training course. Students’ participation and engagement in the electronic negotiation training is driven by their motivation and the goals they pursue. If a student is not motivated at all—a state called a motivation (Ryan and Deci 2000a)—the student will not participate and engage in the electronic negotiation training and acquire any negotiation skills. However, when students are motivated to participate, their motivation may still greatly differ and impact their learning outcomes (Ryan and Deci 2000b). We will have a look at motivation and goals in the following section to explain why and how a student engages in certain tasks.

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Motivation Theories

Motivation is at the core of any action a person performs. If no motivation is present, a person will not carry out a task at all. Fundamental for all motivational theories is the distinction between extrinsic and intrinsic motivation. An intrinsically motivated person performs a task for the inherent satisfaction provided by the task itself (Ryan and Deci 2000a). An extrinsically motivated person performs a task to achieve a separable outcome (e.g. rewards) or to avoid negative consequences such as punishments (Ryan and Deci 2000a). In the following, relevant motivational theories in the area of learning and gamification will shortly be presented.

4.3.1

Self-Determination Theory

Self-Determination Theory (SDT) is one of the most discussed theoretical frameworks in gamification research (Seaborn and Fels 2015). SDT is based on several empirical studies and investigates social and environmental factors that foster or undermine motivation (Deci and Ryan 2012). According to SDT, an intrinsically motivated behaviour requires three basic psychological needs to be fulfilled, namely the need for competence, autonomy and relatedness (Ryan and Deci 2000a). An individual’s need for competence can be fostered by different contextual elements that cause feelings of competence for a task (Ryan and Deci 2000b), e.g. constructive feedback, rewards or optimal challenges. For an intrinsically motivated behaviour, however, it is also necessary that the individual perceives their action as self-determined and autonomous. Tangible rewards have been shown to have an undermining effect on intrinsic motivation, because they shift the perceived locus of causality for an action to be less self-determined (Deci et al. 2001). Similar effects have been found for deadlines, threats, directives, or imposed goals (Ryan and Deci 2000b). Relatedness, as the third factor, suggests that intrinsic motivation may additionally flourish if individuals have a feeling of being socially related with other individuals or perceive at least a secure relational base. Intrinsic motivation is the desirable type of motivation, as it results in enhanced performance, self-esteem, greater persistence, creativity, and highquality learning (Ryan and Deci 2000a, 2000b). However, most of an individual’s actions are not intrinsically motivated. Students completing an assignment for a course, which they value as beneficial for their career, still perform the task for an

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external outcome rather than for the inherent satisfaction of the task. On the other hand, a student completing the assignment because (s)he feels that otherwise (s)he will fail the course is also extrinsically motivated. Although both students are extrinsically motivated, their motivation differs in their relative autonomy (Ryan and Deci 2000b). SDT proposes a continuum of four different types of extrinsic motivation, ranging from externally driven motivation (i.e. external regulation, introjected regulation) to internally driven—yet still extrinsic—motivation (i.e. identified regulation, integrated regulation) (Ryan and Deci 2000b). Students with an externally regulated locus of causality for a task show less interest and effort than students with a more internal regulation. More autonomous extrinsic motivation positively influences, among other factors, engagement, enjoyment, performance and higher quality learning (Ryan and Deci 2000b). Over time, external regulations will often be internalised and integrated by an individual, i.e. external regulations are brought into congruence with the individual’s values, such that the motivation for a behaviour may shift from rather external to a more internal driven locus of causality (Ryan and Deci 2000b). The process of internalisation is again fostered by the three factors relatedness, competence and autonomy. Relatedness means, that the behaviour is valued or prompted by others whom an individual feels connected to. Individuals also need the relevant skills to succeed in the behaviour. Finally, in the most autonomous form of motivation, the regulation has been internalised, i.e. individuals understand its meaning and fully integrate it with their own goals and values.

4.3.2

Flow Theory

The notion of flow shares many similarities with intrinsic motivation and focusses on its subjective phenomenon (Nakamura and Csikszentmihalyi 2002). Flow is also referred to as an optimal experience, in which an individual is fully immersed and focussed on what is currently done, carrying out the activity for inherent satisfaction. Flow experiences can occur with any activity, whether it is playing a video game, doing sports, or writing a scientific paper (Chen 2007). Flow can be experienced when an activity is perceived as challenging but attainable by the individual (Nakamura and Csikszentmihalyi 2002). A balance of individual skills and the challenge is required, which stretches the individual’s skills without exceeding them. When skills and the challenge are not balanced, an individual might either face anxiety or boredom. Neither challenges nor skills are objective variables, as they depend on the subjective perspective of the individual.

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Besides the aforementioned balance of skills and challenges, flow theory provides several other implications for the design of games, gamified environments and learning environments (Jackson 2012; Kim et al. 2018; Nakamura and Csikszentmihalyi 2002; Sweetser and Wyeth 2005): First, clear goals help directing the attention towards the activity to perform. Second, unambiguous and immediate feedback on the progress towards reaching the goal is required. And third, a sense of control over one’s activity facilitates the occurrence of flow.

4.3.3

Achievement Goal Theory

Achievement goal theory, a theory originating in the late 1970s, explains the reasons for students’ engagement in specific tasks (Anderman and Patrick 2012). In general, goals drive performance through four mechanisms: (1) goals direct attention towards goal-relevant activities, (2) they have an energizing function, i.e. high goals produce greater effort than low goals, (3) they lead to greater persistence and (4) support the use of existing or the discovery of new task-related knowledge and strategies (Locke and Latham 2002). The core of the theory is formed by the distinction between mastery goals and performance goals (Murayama et al. 2012). In a mastery goal setting, students focus on the development of competence to successfully accomplish a task. Students following performance goals focus on demonstrating their ability for a task in comparison to others, i.e. they attempt to perform better than others. Within this distinction, mastery goals demonstrated several positive effects, including deep-process learning and self-regulated learning strategies. Due to mixed results on the effects of performance goals, a further distinction between approach and avoidance was introduced, leading to a 2 × 2 model of achievement goals (Murayama et al. 2012). Individuals with an avoidance-orientation focus on avoiding not mastering a task (mastery-avoidance) or avoiding performing poorly compared to others (performance-avoidance). Individuals with an approach-orientation possess a more positive attitude towards their goals, i.e. they would like to master a task (mastery-approach) or perform better than others (performance-approach). A few studies in the domain of negotiation training have investigated the differences between mastery and performance goals. Participants pursuing mastery goals showed greater transfer of learned negotiation skills to new negotiation scenarios than the performance-oriented ones (Bereby-Meyer et al. 2004; Gist and Stevens 1998).

4.4 Gamified Electronic Negotiation Training

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Gamified Electronic Negotiation Training

In the following section, we will integrate previously presented motivational theories and the characteristics of electronic negotiation trainings. First, we will derive the general requirements for a gamified NSS. Finally, based on our results, we present a framework for gamified electronic negotiation training.

4.4.1

General Requirements

The general requirements in this subsection will be aligned with a five-phase negotiation process model for electronic negotiation systems (Braun et al. 2006), which was developed to support a wider range of electronic negotiation scenarios than the original model proposed by Kersten (1997), that was based on Gulliver’s eight-phase model (Gulliver 1979). Each of the subsections represents one phase of the process model, which will shortly be described followed by our findings. Planning In the planning phase, negotiators determine their relevant issues to be discussed, aspiration and reservation levels for these issues and the best alternative to a negotiated agreement (BATNA) (Fisher et al. 1991). They decide about the overall approach (competing or collaborating) and the strategy and tactics used. From the point of view of negotiation trainings, it is important that negotiators are able to claim value as well as to create value (Loewenstein and Thompson 2006). For a negotiation simulation, this goal should be made explicit, e.g. through the description in a case study. Explicit goals help students to direct their attention and activities towards these goals (Locke and Latham 2002). At the same time, imposed goals may undermine students’ need for autonomy. By defining only a high level goal, i.e. a competing or collaborative approach, the students still have various strategic and tactical choices to reach the same goal. Consequently, students may experiment with various strategies, which should facilitate autonomy and motivation. Therefore, our first requirement is providing clear goals and freedom for strategic and tactical choices. Another challenge for the designers of gamified systems are the effects of the included game design elements. Several game design elements such as quests or leaderboards can be categorised as competitive elements. Including such elements in an NSS may affect negotiation behaviour in the planning phase and in the subsequent phases. In particular, for negotiators that should learn to claim value as well as to create value this may lead to an unbalanced focus on competitive

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strategies. Consequently, our second requirement is to balance competitive and cooperative game design elements. Agenda Setting and Exploring the Field In the next step of the negotiation, the negotiators discuss about the negotiation issues and their meanings (Braun et al. 2006). As a result, issues may be revised, added or deleted. Eventually, preferences and strategies have to be adapted. Among other factors, the complexity and difficulty of a negotiation depends on the number of negotiation issues. Single-issue negotiations are quite easy to handle, i.e. they do not necessarily require any decision support in the NSS. Especially for multi-issue negotiations, NSSs offer a utility value to evaluate offers (Schoop 2010). The cognitive burden for new NSS users is still very high. Therefore, and in-line with previous research (Köszegi and Kersten 2003), we propose to start with simple single-issue negotiations and add more complexity through multiple issues in subsequent negotiations. Further levels of complexity and difficulty may include variation for the zone of possible agreements or more competitive and/or aggressive negotiation behaviour of the negotiation partner (Schmid and Schoop 2018). Our third requirement can be summarised with providing increasing and optimal challenges. The provision of increasing challenges can be argued for from several perspectives: As a first step towards electronic negotiations, students may need to develop required skills to learn reading between the lines due to the absence of social cues. They further need to explore the system, get used to its support features and gather experience using an NSS. Imposing additional burdens through complex negotiation cases at the beginning might lead to excessive demands. In terms of flow theory, this unbalanced relationship between challenges and skills may lead to anxiety (Nakamura and Csikszentmihalyi 2002). Furthermore, SDT highlights the necessity for optimal challenges so students need for competence can be facilitated. When students successfully accomplish the provided challenges, they will feel more competent and intrinsic motivation is more likely to occur. Exchanging Offers and Arguments When the negotiation agenda has been defined, the negotiators start exchanging offers and arguments. The phase is characterised by a continual information exchange regarding issue preferences and priorities (Braun et al. 2006). Particularly for this phase, NSSs provide a rich set of support functionalities for communication, decision, conflict and document support. It is critical to

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remember, that these system functionalities only support the negotiator, i.e. the negotiator is always in control of the negotiation process (Schoop et al. 2003). Therefore, the use of support functionalities relies either on the user’s recognition of their benefits or on the user’s need for external help (Druckman et al. 2012). Consequently, support functionalities are sometimes not used, leading to less efficient negotiations. As a potential solution, a gamified NSS should offer incentives to use support functions. Gamification may not only foster motivation and engagement, but can effectively change user behaviour (Blohm and Leimeister 2013). When students face these extrinsic incentives first, they might perceive their behaviour for using the support functions as externally regulated (Ryan and Deci 2000b). They use support functions not as a result of own beliefs, but to fulfil an external demand. If, however, the students gained experience using the functions and finally recognised their benefits (Druckman et al. 2012), their motivation to use these functions will shift towards a more self-determined types of motivation. In particular, SDT proposes an internalisation process, where the regulated behaviour is positively valued and considered as personally important by the student (Ryan and Deci 2000b). Students will then experience greater autonomy in using the support functions. An intensified use of support functions should lead to more efficient outcomes in the negotiation simulations. Reaching an Agreement After an intensive exchange of offers, the negotiators may realise that they have successfully elaborated areas for an agreement (Braun et al. 2006). They develop joint proposals to settle an agreement. Alternatively, if both parties realise that there is no zone of possible agreements, they may decide to leave the negotiation without a deal. Especially novice negotiators tend to accept even bad and inefficient agreements, because they try to avoid a failing negotiation (Loewenstein and Thompson 2006). When the negotiators have settled such an inefficient agreement, they may engage in a post-settlement negotiation to improve their outcomes (Raiffa 1985). In electronic negotiations, the majority of post-settlement negotiations is rejected, leaving the negotiators with their initially negotiated, inefficient agreement (Block et al. 2006). Nevertheless, a post-settlement negotiation can be beneficial for complex negotiations where inefficient agreements are more likely to occur (Gettinger et al. 2016). Our intention is definitely not to scrutinise post-settlement negotiations, but to prevent students from accepting a bad agreement because they fear failing. Students must learn that negotiations can also end unsuccessfully, i.e. that no

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agreement is better than a bad agreement with respect to their BATNA. Negotiators avoiding a failing negotiation show the same behavioural patterns as described in the mastery-avoidance goal-setting (Murayama et al. 2012). Instead, it would be beneficial for students to follow a mastery-approach goal, where the negotiation simulation receives a positive valence for skill development. Previous research on the development of negotiation skills also demonstrated positive effects of mastery goals compared to performance goals. Students participating in a mastery-oriented training showed greater transfer of negotiation skills in stressful negotiations than participants in a performance-oriented training (Gist and Stevens 1998). Similarly, another study revealed that students in a performance-oriented setting were less successful in transferring negotiation skills learned in one negotiation scenario to a different negotiation scenario than their mastery-oriented colleagues (Bereby-Meyer et al. 2004). When students were faced with an identical negotiation scenario again, there were no differences between the two groups. These results are in-line with other studies on learning (see Murayama et al. (2012) for a summary), indicating that mastery goals facilitate in-depth learning. Consequently, our next requirement can be summarised with providing a mastery-approach goal-oriented setting. Concluding the Negotiation If the negotiators have successfully worked towards an agreement, they finally conclude the negotiation (Braun et al. 2006). The agreement is evaluated and might consider further improvements. In business negotiations, the negotiators settle their agreement in a contract (Schoop et al. 2003). To evaluate the achievement of the defined goals and support the reflection phase of the experiential learning cycle, students require feedback after the negotiation was concluded. Feedback should be provided in a constructive manner, highlighting positive actions and providing insights on improvements for future tasks. Similarly, negotiators concluding their negotiation unsuccessfully require feedback whether their decision to abort is comprehensible, e.g. because they experienced an impasse or could not find a fair compromise. Constructive feedback framed in a positive manner is especially important as novices tend to be more motivated by positive feedback whereas only experts can be motivated by negative feedback (Fishbach et al. 2010). Furthermore, providing constructive and encouraging feedback supports feelings of competence, which are likely to facilitate intrinsic motivation (Ryan and Deci 2000b). The feedback on a failing negotiation or possible improvements for an agreement provides another incentive for the students to repeat a negotiation simulation. In general, gamified learning maintains a positive relationship towards

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mistakes and failures and allows students to repeat their tasks until they succeed (Kim et al. 2018; Lee and Hammer 2011). Reflecting on their previous performance, students will derive their lessons learnt and employ them for future tasks (Kolb 1984). Indeed, previous research has shown that repeating the same negotiation scenarios enables people to logroll more effectively, leading to more integrative results (Bazerman et al. 1985). Logrolling behaviour also improves across different negotiation scenarios (Thompson 1990c). Furthermore, electronic negotiations provide incentives to experiment with different negotiation approaches (Köszegi and Kersten 2003). Therefore, and in order to maintain autonomy, we summarise our last requirement with allowing students to repeat challenges.

4.4.2

A Framework for Gamified Electronic Negotiation Training

The previously presented general requirements differ in several ways from the current utilisation of NSSs in electronic negotiation trainings. Starting with the elements of the NSS itself, our new artefact will include game elements which turns the original NSS into a gamified NSS (see Figure 4.1). The core of the training is still made up by negotiation simulations, in which the students participate. However, these simulations explicitly form different challenges, i.e. the students will “level up” by starting with rather simple negotiation simulations followed by more complex and difficult negotiation simulations. Our gamified NSS explicitly takes into account the motivation and goals that drive students’ engagement. The goal of students when participating in a negotiation training should be and probably mostly is mastery-oriented, i.e. becoming good negotiators and finding good agreements. For novice negotiators it is hard to evaluate whether they actually did a good job in their negotiation, or whether they e.g. failed to find more beneficial, integrative solutions and what they could have done better. Current NSSs do not provide such feedback to the learners. In contrast to state-of-the-art in electronic negotiation training, our new framework includes strengthened feedback provided by the gamified NSS. In addition to the feedback provided during the negotiation process, we extend it with constructive feedback about the negotiation performance, forming the basis for the students’ reflections on their negotiation. The constructive feedback replaces course activities such as journal entries (Melzer 2018) or debriefings (Köszegi and Kersten 2003).

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Figure 4.1 Framework for Gamified Electronic Negotiation Training

Furthermore, the game elements provide motivational incentives to the students. Our requirements especially highlight the satisfaction of the basic psychological needs for competence and autonomy, which according to SDT influence the probability for intrinsic motivation (Ryan and Deci 2000b). The incentives to use the support functions of the NSS obviously facilitate extrinsic behaviour but may shift over time to more internally-driven motivation and behaviour. Potential game design elements providing such incentives could be points or badges rewarding desirable and successful use of these support functions (Sailer et al. 2017). We can expect that the provision of strengthened, constructive feedback and additional motivational incentives will positively impact the students’ engagement in the negotiation simulations. Overall, the feedback and increased engagement will hopefully lead to better learning outcomes.

4.5 Discussion and Outlook

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Discussion and Outlook

Gamification is an innovative and promising way to foster motivation and engagement of students, and numerous studies in this domain have already been published (Hamari et al. 2014; Seaborn and Fels 2015). Especially qualitative studies often provide a mixed picture regarding the effects of gamified learning (Buckley et al. 2017; Majuri et al. 2018), therefore understanding the context of the gamification implementation is critical (Morschheuser et al. 2018). This paper presents a new approach for electronic negotiation trainings and proposes gamifying NSSs, the systems which are often used within these trainings (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2014; Vetschera et al. 2006). Following a design science research methodology, we conducted an integrative literature review to derive the general requirements for the design of such a gamified NSS and provide a framework for gamified electronic negotiation training. By synthesising the literature about negotiation training, motivation theories and gamification itself we derived seven general requirements. We cannot guarantee that these requirements and the presented framework are comprehensive, but they provide a well-founded theoretical basis for the design of the artefact. The core of the gamified electronic negotiation training is students’ participation in negotiation simulations. In line with Deterding’s method for gameful design (Deterding 2015), we design our gamified NSS around the inherent challenges of the students’ activities. Obviously, the inherent challenge in an NSS is to successfully negotiate an agreement with the negotiation partner. The negotiation simulations can have different levels of difficulty, e.g. the number of negotiated issues, the number of negotiation parties participating, varying zones of possible agreements and cooperative or competitive negotiation partners (Schmid and Schoop 2018). Challenges are one of the most used game elements in education (Majuri et al. 2018). The requirements especially highlight the need to provide constructive feedback within the gamified NSS, so the students can reflect on their negotiation performance. Current NSS implementations do not provide such feedback, and the reflection phase is supported by debriefings (Köszegi and Kersten 2003) or journal entries (Melzer 2018). The requirements further consider and facilitate the students’ motivation, another factor that positively impacts students’ engagement and their learning outcomes (Kahu 2013). Employing feedback elements is a common approach in several gamified learning interventions (Dicheva et al. 2015; Majuri et al. 2018). Feedback is efficient, if it is provided in a positive and constructive way, reflects students’

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performance and guides students to areas of improvement (Kim et al. 2018). To provide feedback, gamification literature in general suggests using points, leaderboards, levels, badges or performance graphs (Mekler et al. 2017; Sailer et al. 2017), which are frequently used in gamified learning (Majuri et al. 2018). With respect to the domain of electronic negotiations, further feedback elements should also include domain-specific feedback, e.g. in the form of a Pareto-efficiency graph (Tripp and Sondak 1992) to display whether integrative potential has been fully exploited. As the feedback emanates from the NSS itself, our framework for electronic negotiation training does not necessarily require any human negotiation trainer to support the reflection phase. In particular, a negotiation trainer or negotiation expert is only required to design the different negotiation challenges according to the learning goals. Therefore, the gamified NSS does not necessarily have to be embedded within a classical negotiation course but could also be offered as a Massive Open Online Course (MOOC) accessible to everyone interested in learning to negotiate. Besides the feedback that elements such as points, leaderboards, levels, badges, or performance graphs offer, these elements also provide various motivational mechanisms (Sailer et al. 2013; Sailer et al. 2017). Our requirements especially highlight fulfilling the psychological needs for competence and autonomy. However, whether the included game elements will promote extrinsic or intrinsic motivation is highly dependent on contextual factors and the individual student (Deci et al. 1999). While this paper primarily focusses on the contextual factors, effects of gamified learning also e.g. differ depending on personality traits and learning styles (Buckley and Doyle 2017). Furthermore, undergraduate and postgraduate students have different perceptions on gamified learning interventions (Buckley et al. 2017). Our next steps, therefore, include careful consideration of game elements that could fulfil our general requirements and might be part of our gamified NSS. Due to the various user characteristics influencing the perceptions and effects of gamification, the risk in designing such a system is user oriented and requires iterative design and formative evaluations (Morschheuser et al. 2018; Venable et al. 2016). We will report first findings regarding the effects of a gamified NSS on motivation, engagement and learning outcomes as soon as first prototypes have been developed and evaluated.

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Gamifying Electronic Negotiation Training—A Mixed Method Study of Students’ Motivation, Engagement and Learning

Co-Authors: Dr. Philipp Melzer Prof. Mareike Schoop, PhD The content of this chapter is already published as: Andreas Schmid, Philipp Melzer, Mareike Schoop, 2020, Gamifying Electronic Negotiation Training—A Mixed Method Study of Students’ Motivation, Engagement and Learning. In: Proceedings of the 28th European Conference on Information Systems (ECIS 2020), Research Papers, 131, available at https://aisel.aisnet. org/ecis2020 rp/131/

Abstract

Effective learning requires the learners’ motivation and engagement for the learning tasks. One recent approach to enhance learners’ motivation and engagement is gamification, the use of game design elements in non-game contexts. This paper will introduce a new gamification approach for an archetype of IS, namely negotiation support systems (NSSs) that are used in electronic negotiation training to facilitate the development of e-negotiation skills. E-negotiation training is an interesting area for gamification research, as social interaction through competition and collaboration are an inherent task of negotiations. We compared our gamified approach with a conventional training guided by a lecturer. Our results gathered via surveys and semi-structured interviews reveal positive effects of the gamified intervention on learners’

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_5

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intrinsic motivation and engagement. However, results for the learning outcomes are mixed, i.e. learners using the gamified system perceive themselves as more competent, but objective measures of learning outcomes reveal worse results than the non-gamified group.

5.1

Introduction

The digitalisation in the working environment demands employees to adapt to new technological channels for business interactions. In particular, the importance of conducting B2B negotiations electronically has grown (Schoop et al. 2008). Employees and students can obtain the demanded soft skills for e-negotiations by participating in dedicated negotiation training, teaching negotiation theory and practice. However, learners today face several problems concerning their motivation and engagement for learning tasks (Lee and Hammer 2011). The term “attention economy” describing attention as individuals’ rarest resource (Davenport and Beck 2001) has been transferred to the education domain (Buckley and Doyle 2017). Learning tasks are competing against distracting and more interesting activities. Sustaining students’ attention, facilitating their motivation and supporting their learning is one of the major challenges in education (Dichev and Dicheva 2017). Often-times, people voluntarily invest their free time on gamified platforms like Duolingo, where users learn a new language and obtain e.g. achievements and points for their progress. Using game elements such as achievements and points has been labelled as gamification, i.e. the use of game design elements in non-game contexts (Deterding et al. 2011). Recently, several studies in the education area have tried to transfer what works in the informal environment (i.e. free time) with platforms such as Duolingo to the formal environment at universities and schools (e.g. Hobert and Berens 2019). The motivational appeal of games and game elements are pre-dicted to enhance student’s engagement in gamified learning environments. Results regarding the effectiveness of gamification in the education area are predominantly positive (Dichev and Dicheva 2017; Dicheva et al. 2015; Sailer and Homner 2020). Therefore, gamification might solve some of the issues for today’s generation of students. In their recent work about knowledge retention in gamified workshops, Putz and Treiblmaier (2019) suggest to apply gamification to enhance social and practical skills such as problem solving, collaboration, and communication, which are relevant and important in the field of e-negotiations. Despite negotiators’

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participation in e-negotiation training, negotiators often end on inefficient agreements (Gettinger et al. 2016) and do not take advantage of negotiation system features (Druckman et al. 2012). A potential reason might be insufficient motivation and a lack of practicing e-negotiations, which result in inefficient system use and negotiation outcomes. Motivation impacts learning behaviour and consequently learning outcomes (Kahu 2013; Ryan and Deci 2000b). With several other inherently present game elements in e-negotiations such as scores or challenges (Schmid and Schoop 2018), gamification in e-negotiation training seems a promising solution to improve participants’ motivation, their engagement and their learning outcomes (Schmid and Schoop 2019). Following the design science research methodology (Hevner et al. 2004), we have designed a gamified negotiation system used in an e-negotiation training. This paper reports on the first design cycle (Hevner 2007), i.e. the construction of the system and its evaluation. Following the human risk & effectiveness evaluation strategy by Venable et al. (2016), we employ an early naturalistic evaluation with students as subjects. The evaluation in this paper focusses on the following research questions: 1. Which effect does a gamified e-negotiation training have on participants’ motivation? 2. Which effect does a gamified e-negotiation training have on participants’ engagement? 3. Which effect does a gamified e-negotiation training have on participants’ learning outcomes? To answer the research questions, we employ a mixed methods approach following a convergent design (Creswell and Clark 2018). We compared the gamified e-negotiation training using quantitative methods with a control group participating in a conventional e-negotiation training. We further answered our research questions qualitatively, i.e. through explorative interviews and qualitative survey data. Drawing on the results of both research streams, we aim to provide metainferences providing holistic answers to the research questions above (Venkatesh et al. 2013). The remainder of this paper is structured as follows: First, we review the central concepts and theories of gamification in the education area and describe the application domain of e-negotiation training. The existing negotiation system is then gamified by integrating selected gamification elements (Section 5.3). To evaluate the system, a mixed method evaluation scheme is designed and implemented. The results are discussed in Section 5.6 followed by the outlook to future research.

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Theoretical Background

The present paper is embedded in theories and concepts of gamification and psychological effects of gamification in the education area as well as in negotiation research.

5.2.1

Gamification & Education

Gamification is defined as using game design elements in non-gaming contexts (Deterding et al. 2011). Therefore, the non-gaming context (e.g. an information system) is enhanced with game design elements but is not transformed into a fully-fledged game. Werbach and Hunter (2012) categorise game elements according to their abstraction level as components, mechanics and dynamics. Dynamics are the most abstract form, relating to the objectives of the gamified system such as creating emotions and progression. Mechanics describe how the objectives are fulfilled, e.g. through competition, feedback or rewards. Finally, components are the most concrete form of game elements contributing to the mechanics, e.g. points, badges, leaderboards or avatars. Research in the area of gamified interventions in education report mixed, but predominantly positive effects of the use of such game design elements on students’ motivation, engagement and learning outcomes (Dichev and Dicheva 2017; Dicheva et al. 2015; Majuri et al. 2018; Sailer and Homner 2020). Explanations for the effects of gamification can be found in well-established psychological theories, especially in self-determination theory (SDT) (Ryan and Deci 2000b), which is one of the most adopted theoretical frameworks to study gamification (Seaborn and Fels 2015; Xi and Hamari 2019). According to this theory, the satisfaction of the three basic psychological needs (namely autonomy, competence and social relatedness) facilitates intrinsic motivation (Ryan and Deci 2000b). In contrast to extrinsic motivation (defined as performing an activity to achieve a separable outcome), intrinsic motivation is defined as performing an activity for its inherent satisfaction and is beneficial for high-quality learning (Ryan and Deci 2000a). Applying SDT as the theoretical framework, gamified interventions in education focus especially on the satisfaction of the three basic psychological needs and on providing game-like, engaging experiences. Among the most frequently used components in gamified education are points, quests, badges, leaderboards and levels (Majuri et al. 2018). These components do not only motivate the learners by their game-like appeal, but they also provide informational feedback due to

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clearly defined goals and feedback about progression towards these goals (Mekler et al. 2017). Game components with inherent feedback such as badges or leaderboards have been shown to affect the need for competence positively (Sailer et al. 2017). Feedback is a powerful mechanism for education, that helps learners to pursue their goals, informs them about how well they are currently working on these goals and what steps to perform next (Hattie and Timperley 2007). Consequently, it is no surprise why the majority of gamified learning interventions heavily build upon game components providing inherent feedback.

5.2.2

Negotiations & Electronic Negotiation Training

In recent years, business negotiations have been digitalised, leading to the conduct of electronic negotiations (e-negotiations) (Schoop et al. 2008). Such negotiations are conducted by at least two negotiators whose tasks are interdependent and who make decisions at each step of the negotiation and communicate throughout the negotiation; information technology supports the negotiators by means of communication, decision support, document and conflict management (Bichler et al. 2003; Schoop 2010). Individuals nowadays require negotiation and in particular e-negotiation skills in their workplace, which can be developed through dedicated negotiation training. The field of negotiation training is to date an ongoing research field, e.g. regarding new opportunities provided by e-learning (Melzer 2018). Negotiation training facilitates the development of negotiation skills related to the communication and decision-making tasks. During the negotiation process, negotiators should be able to behave competitively (i.e. claiming own positions) and/or collaboratively (i.e. searching for a mutual win-win solution) (Lewicki et al. 2010; Loewenstein and Thompson 2006). Therefore, competition and collaboration are inherent tasks of negotiations and important aspect of negotiation training. Electronic media for negotiations require additional negotiation skills, e.g. reading between the lines and building up a positive relationship without observing mimics or gestures or using the asynchronous mode to one’s advantage (Köszegi and Kersten 2003). To facilitate the development of e-negotiation skills, negotiation training in university courses often employs negotiation support systems (NSSs) for e-negotiation training (Köszegi and Kersten 2003; Melzer et al. 2012; Melzer and Schoop 2016). NSSs are web-based systems and provide support for the communication and decision making tasks of the negotiators, while not automating the process and leaving the control of the process with

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the negotiator (Schoop et al. 2003; Schoop 2010). These systems help to overcome the missing cues of electronic media and support finding agreements in different ways. For example, Inspire (Kersten and Noronha 1999) and SmartSettle (Thiessen and Soberg 2003) provide decision support; WebNS (Yuan et al. 1998) provides communication support; Negoisst (Schoop et al. 2003; Schoop 2010) provides decision and communication support in addition to document and conflict management. In this study, we focus on Negoisst as the most comprehensive system. Typically, a bilateral e-negotiation in Negoisst may look as follows: Each negotiator determines their preferred values for all negotiation issues and ranks these issues according to their importance. One of the negotiators initiates the negotiation and prepares their first request. To explicate the sender’s intention, NSSs require a message type such as a request, offer or counteroffer to be selected representing the mode of the message; some NSSs also offer informal message exchange including questions and clarifications (Schoop et al. 2003). The sender will then select the preferred values for the negotiation issues, thereby integrating communication and decision making. Once a first message was sent, it is the recipient’s turn to reply. Based on their own preferences, the recipient evaluates the selected values in the request using the utility value (which ranges from 0 to 100%) computed by the NSS. These utility values are important not only to evaluate an offer received but also to prepare one’s own counteroffer in assessing the concessions that are deemed appropriate (Schoop 2010). Both negotiators will engage in an intense exchange of messages, until one of them finally chooses to agree on a deal or to leave the negotiation without a deal. Learning to negotiate electronically, therefore, involves two tasks: First, an end-user training for the NSS to be used is required for users to gain the relevant skills to work with the system. Second, the development of the previously mentioned e-negotiation skills has to be facilitated (Melzer and Schoop 2016). The latter is implemented by engaging participants in bilateral negotiation case studies, where participants negotiate with each other or with a software agent (Köszegi and Kersten 2003; Melzer et al. 2012). Drawing upon the experiential learning meth-odology (Kolb 1984), the negotiation is part of the experience, and reflecting about the negotiation to derive conclusions for future negotiations is a crucial task for successful learning (Köszegi and Kersten 2003). In contrast to other research examining the effects of single game design elements using e.g. an order picking task (Sailer et al. 2017) or image tagging task (Mekler et al. 2017), e- negotiation training is a complex task to gamify due to its difficult communication- and decision-making tasks. Similar to other research studying gamification for complex tasks or systems such as a prediction market

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(Buckley and Doyle 2017), elicitating requirements (Lombriser et al. 2016) or full-day workshops (Putz and Treiblmaier 2019), several game design elements have been implemented for this study, which will be explained in the following section.

5.3

System Design

As gamification enhances a system with game design elements, we chose an existing system supporting electronic negotiations to integrate the game design elements. The chosen Negoisst system is the most comprehensive NSS providing communication and decision support, document management and conflict management (Schoop et al. 2003; Schoop 2010). Incidentally, it has been used for more than 15 years with several thousand students in international negotiation experiments facilitating students’ e-negotiation skills. In gamification research, one of the major identified weaknesses is typically the absence of justification for the choice of game design elements (Dichev and Dicheva 2017). To address this gap, our system design is based on previously derived requirements, which integrate theories about motivation, gamification and e-negotiation training (Schmid and Schoop 2019). Similar to the method of gameful design by Deterding (2015), the requirements for gamified e-negotiation training postulate adding game design elements around the inherent challenge of the activity, i.e. to find an agreement in a negotiation (Schmid and Schoop 2019). Relating to dynamics of the gamified system (Werbach and Hunter 2012), our overall objective is the encouragement of continuous learning through participation in realistic e-negotiation scenarios. Furthermore, participants should be encouraged to reflect about their negotiation performance, so that they can immediately adapt their behaviour or derive conclusions for future negotiations. Increased hands-on experience with e-negotiations and with the features of the NSS are expected to improve important negotiation skills such as preparedness, rationality, and strategic behaviour (Lewicki et al. 2010). At the level of mechanics, continuous learning is realised through increasingly more complex negotiation scenarios, which can be repeated in case of failure. As the component, we have chosen levels that provide clear goals to work for, visualise learners’ progress and become more difficult in higher levels (Kim et al. 2018; Mekler et al. 2017). A level in the NSS is connected to one bilateral negotiation that the learner has to complete in order to unlock the next level. Six levels of increasing difficulty were created. In the first two levels, users face simple single-issue negotiations and learn to exchange negotiation messages in the

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system. In level 3 the decision support with the utility value is introduced for the first more complex multi-issue negotiation. In level 4 the users receive an additional visualisation of the decision support and an enhanced communication support (Schoop 2010). In each of the first four levels a guided tour presents the new features of the system. All levels are connected by a continuous story, with the learner acting as a new employee in a purchase department, facing different negotiation scenarios. Our levels are tailored to the current skills of the learner, continually making the negotiation task and the system more complex, and, therefore, fulfill the requirement to provide increasing challenges without overburdening demands (Schmid and Schoop 2019). Regardless of whether the level was completed successfully, the users can start a level as often as they like, which creates a safe place for learning (Dichev and Dicheva 2017) and allows them to experiment with negotiation strategies. In bilateral negotiations, the negotiation tasks are interdependent. Therefore, one’s behaviour depends also on the negotiation partner’s behaviour (Schmid and Schoop 2018). To train behaviour, students will negotiate with an automated software agent called the Tactical Negotiation Trainer (TNT) (Melzer et al. 2012) on all levels to provide consistent negotiation challenges. The TNT follows a predefined strategy and generates text messages using a sentence recommender, which is based on the TNT’s own preferences, on the offers of the human negotiator, and on the previous offers made by the TNT (Melzer et al. 2012). The TNT will accept an offer when its goals are reached; it will terminate the negotiation if the human negotiator has not made any concessions for a longer period. At dynamic level, the TNT provides immediate feedback and may compete as well as collaborate with the learner. Besides immediate feedback for the negotiation messages, learners require feedback regarding their negotiation outcome to reflect on the quality of their agreement. As learners are engaged in identical and comparable negotiations, utility rankings were added to the system after the concept of utility values had been introduced in level 3. Rankings—also called leaderboards (Schöbel and Janson 2018)—provide informational performance feedback and can facilitate the need for competence (Mekler et al. 2017). Focussing on the individual performance alone would only promote competitive negotiation behaviour but a balance between competitive and collaborative behaviour is required in e-negotiations (Schmid and Schoop 2019). Therefore, two further metrics and their explanation were added, namely the joint utility (sum of both negotiators utilities) and the contract imbalance (absolute difference between the utilities), which are relevant for collaborative win-win agreements. The feedback by the rankings may motivate users to improve their negotiation outcome and repeat a level.

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Figure 5.1 Individual and Joint Utility Rankings

Experience points were added to the system as an immediate feedback, positive reinforcement and virtual reward for performed actions (Sailer et al. 2013) such as sending messages, analysing one’s preferences, analysing the history graph, using enhanced communication support or concluding the negotiation. A separate page is available, listing details of the received experience points for the last actions performed, thus providing transparency. Providing experience points offers incentives to make use of the NSS support functions and to increase the learners’ overall engagement in the system (Schmid and Schoop 2019). To supply further feedback, a ranking was included enabling learners to compare their experience points with others. Badges provide a clear goal to work for (Hamari 2017) and symbolise a user’s achievements. 20 badges were integrated in the system and awarded for two types of behaviour, namely (1) desirable system use, e.g. sending the 5th offer or repeating a level; (2) outstanding negotiation performance, e.g. achieving a fair agreement. Unlocking badges based on transparent unlock instructions can support feelings of competence and achievement (Sailer et al. 2013). Level, badges and experience points were displayed prominently on the home screen of the system (see Figure 5.2). In sum, the gamified NSS provides several game components with clear goals to work for, while leaving the choice of the negotiation strategy for each level to the learner (Schmid and Schoop 2019). Feedback is provided by the TNT itself, the levels, experience points, badges and utility rankings. A learning environment is provided focussing on mastery and skill development. The dramatic issue of failure, i.e. to have a negotiation getting rejected, receives a positive valence by giving experience points and even a badge for this. Learners could repeat each level as often as they like, so an overall fail-safe environment for learning is created.

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Figure 5.2 Home Screen of the Gamified NSS

5.4

Research Design

To answer the research questions, we performed a negotiation experiment in May 2019, which will be described in detail.

5.4.1

Participants & Procedure

The negotiation experiment itself included a training, in which the students learned how to negotiate using the NSS, followed by a five-day international e-negotiation. The evaluation of the training was conducted involving 158 undergraduate and graduate students from three European universities. All students attended a negotiation course teaching negotiation theory and practice at their home university. The vast majority of the participants studied management, economics or information systems. The students received credit points as an incentive for the participation in the negotiation training. Before the training, students filled in an online demographic survey. Two types of training were offered, namely the conventional training (c-training) and the gamified training (g-training). Estimated expenditure of time for both training types was about 90 minutes. To ensure the same effectiveness of the g-training, its contents was created using the slides from the c-training. Furthermore, the gtraining was evaluated by one lecturer of the c-training. Students had two weeks for their training. The g-training could be completed at any time during the two weeks. In the gtraining, the NSS includes all the game design elements presented in the previous chapter. Participants were required to successfully complete all levels up to level four.

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The c-training required the students to attend a face-to-face training. This established training followed an enactive method (Melzer and Schoop 2016), where students prepared for a given negotiation task, got familiar with the NSS that was presented by the lecturer first, and used the system to implement their prepared negotiation strategy in a negotiation following a trial-and-error approach. The results of the preparation and the final training negotiation were discussed in class with the lecturer, who acted primarily as a moderator (Melzer and Schoop 2016). In this training the students performed the negotiation with the TNT (Melzer et al. 2012), so the students received immediate responses to their negotiation messages. The students used a version of the NSS without any game design elements. The negotiation case in the c-training was the same as the one in level four for the g-training participants. The students were told to choose their preferred training type and also had the option to do both leading to three groups for the analysis, namely c-training participants, g-training participants and participants completing both. The participants completing both are a potentially interesting group, because they can directly compare the two types of training and help to detect weaknesses in either of the two. In both the conventional and the g-training, participants could work for two weeks. C-training participants could repeat the negotiation with the TNT as often as they liked; g-training participants could repeat any level as often as they liked or could continue with the levels five and six. After the training, the participants filled in a second online survey to assess their motivation for the training, the evaluation of the training and the learning outcomes, i.e. how well the participants understood the NSS features and how well they could perform e-negotiations. Additionally, voluntary free-text answers could be given to questions about participants’ reasons for training choice and reasons for repeating a negotiation (if they did). For analysis, the free-text answers have been summarised and similar answers were counted (Maxwell 2010). Students could volunteer to participate in an interview for four bonus exam points. In total, twelve students of one university completing the g-training participated in explorative interviews two weeks after their negotiations. The semi-structured interviews were conducted in German as the native language of all students. The participants were told that the interviews are conducted to improve the system. They could explain anything they liked and did not like about the system before being asked to reflect about their previous learning experiences in general and for the particular negotiation. Afterwards, they had to evaluate the training. Finally, detailed questions were asked regarding the game elements

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to gather information about participants’ motivation and perceptions during the training. All interviews were recorded and transcribed afterwards. For analysis we followed a deductive category assignment (Mayring 2014). The predefined categories are informed by the defined research questions, namely motivation, participants’ system use (relating to their engagement), and training evaluation (relating to learning outcomes and improvements). As the interviews were explorative, we also included participants’ perceptions as a fourth category for further analysis. The received responses were coded by one researcher into one of these categories.

5.4.2

Data Collection & Analysis

Students’ motivation after completing the training was measured using the Intrinsic Motivation Inventory (IMI) (Ryan et al. 1983), a well-established measurement suitable for motivation in gamified systems (Seaborn and Fels 2015). The used subscales of the IMI include interest and enjoyment (with 7 items), measuring students’ intrinsic motivation, as well as students’ perceived competence, pressure, and effort (with 5 items each). Effort was analysed to assess students self-report of engagement in the training task. All items were measured using a 7-point Likert scale. Cronbach’s α is 0.80 for pressure, 0.83 for effort, 0.86 for perceived competence, and 0.93 for intrinsic motivation. All of them exceed the recommended threshold of 0.70 (Hair et al. 2014). The large Cronbach’s α for intrinsic motivation can be explained by the large number of items increasing the α-value (Cortina 1993). Analysis of the IMI’s convergent and discriminant validity, as well as reliability are depicted in Table 5.1. Composite reliability (CR) is above 0.7 and maximum shared variance (MSV) smaller than average variance extracted (AVE). Thus, CR and MSV exceed the recommended thresholds of Hair et al. (2014). AVE values for effort and pressure are slightly below the recommended threshold of 0.5, but the values for these variables are quite close to this threshold and can be interpreted with care. The main diagonal depicts the square root of the AVE. According to SDT, perceived competence is a positive predictor for intrinsic motivation, whereas pressure is a negative predictor of intrinsic motivation (Ryan and Deci 2000b). Therefore, the correlations between the variables in Table 5.1 make sense. Learning outcomes for our scientific analysis were measured in the second survey using single choice and multiple-choice questions. Learning outcomes include

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Table 5.1 Convergent and Discriminant Validity and Reliability Analysis for the IMI CR

AVE

MSV

Pressure

Pressure

0.808

0.459

0.129

0.677

Intr. Mot.

Competence

Intrinsic Mot.

0.932

0.664

0.465

−0.207

0.815

Competence

0.866

0.567

0.465

−0.359

0.682

0.753

Effort

0.824

0.492

0.169

0.343

0.411

0.368

Effort

0.701

system skills, i.e. students’ knowledge and understanding of the features in the NSS, and e-negotiation skills. Each correctly ticked answer was awarded with one point, whereas wrongly ticked answer led to a subtraction of one point. In addition to the survey items above, students’ engagement was further assessed by analysing their behaviour during the training phase in the NSS. Both c-training and g-training participants could practice their negotiation skills by voluntarily conducting additional negotiations in the system. We counted all additionally concluded negotiations that were conducted by the students. Negotiations of g-training participants, who had to repeat a level negotiation in order to complete the training, were not counted. From the original data set of 158 participants several participants had to be removed from the statistical data analysis, e.g. if they did not answer the first and/or second survey or if they had not completed one training. In total, 123 participants remained for statistical analysis, which was conducted using IBM SPSS Statistics 25.

5.5

Results

In the following, the quantitative results (i.e. the two surveys) and the qualitative results (i.e. the free-text answers in the survey and the semi-structured interviews) will be presented.

5.5.1

Quantitative Results

63 participants visited the conventional training with 29 being female and 34 being male. 48 participants completed the g-training with 27 being female and 21 being male. 12 participants completed both types of training with 5 being

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female and 7 being male. They were between 18 and 36 years old with an average of 24.04 years (SD = 3.74). The three emerging groups participating in the c-training, e- training or both show similar average ages. The distribution for the two types of training is inconsistent among all of the university courses. Prior to the data analysis, we ran Kolmogorov-Smirnov test to assess normality distribution of the data. The null hypothesis assuming normality has to be rejected for pressure, the two skill variables and the number of additional negotiations. To compare these four variables, we will use a non-parametric independent samples Mann-Whitney U test. Results for the participants completing both types of training can be seen in Table 5.2 too, but we focus on the comparison between the c-training and g-training participants in the following. G-training participants report higher intrinsic motivation after the training (M = 5.10) than the c-training participants (M = 4.54). G-training participants’ perceived competence was also higher (M = 4.40) than in the c-training (M = 3.97). Regarding effort, g-training participants (M = 4.65) report a little more invested effort than in the c-training (M = 4.42). In contrast to that, g-training participants felt more pressured (M = 3.01). However, c-training participants e-negotiation skills (M = 3.81) were higher than those of g-training participants (M = 3.12). A similar pattern can be found forthe system skills, where c-training participants had a mean of 3.06 points and g-training participants a mean of 2.58 points. On average, participants of the g-training completed 2.60 additional negotiations. 64.58% of them voluntarily completed at least one additional negotiation. 66.66% participants completing both types of training completed additional negotiations with an average of 4.25 negotiations for all participants of this group. In the c-training group, 9.52% conducted additional negotiations, with an average of 0.21 negotiations across all participants. In a further analysis, we checked whether there were significant differences between the c-training and g-training groups. Intrinsic motivation was significantly higher in the g-training group than in the c-training group (t (109) = 2.493, p = 0.014). Furthermore, perceived competence was also significantly higher in the g-training group (t (109) = 2.327, p = 0.022). However, g-training participants reported significantly higher pressure (Mdn = 3.10) than c-training participants (Mdn = 2.40), U = 1122.50, z = −2.32, p = 0.020. No significant differences were found for effort (t (109) = 1.042, p = 0.300). Medians for enegotiation skills were the same (Mdn = 4) without significant differences (U = 1276.50, z =−1.47, p = 0.14). System skills had the same median too (Mdn = 3) again with-out significant differences (U = 1324, z = −1.13, p = 0.26). Last,

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Table 5.2 Descriptive Statistics. Learning Outcomes for E-Negotiation Skills Could Range between −6 and + 6 Points; Learning Outcomes for System Skills between −13 and + 13 Points Mean c-training (SD)

Mean g-training (SD)

Mean both (SD)

Total (SD)

Intrinsic motivation*

4.54 (1.17)

5.10 (1.14)

5.75 (0.79)

4.88 (1.19)

Perceived competence*

3.97 (0.95)

4.40 (1.01)

4.95 (0.79)

4.23 (1.00)

Effort

4.42 (1.16)

4.65 (1.11)

5.72 (0.70)

4.64 (1.16)

Pressure

2.58 (1.15)

3.01 (1.11)

2.45 (1.11)

2.77 (1.15)

E-negotiation skills

3.81 (2.78)

3.12 (2.77)

4.50 (2.28)

3.61 (2.74)

System skills

3.06 (4.02)

2.58 (4.15)

4.50 (3.43)

3.02 (4.02)

Additional negotiations***

0.21 (0.68)

2.60 (2.84)

4.25 (5.53)

1.54 (2.86)

Notes: * p < .05, *** p < .001.

additional negotiations were significantly higher for the g-training group (Mdn = 2) than for the c-training group (Mdn = 0), U = 637.50, z = —6.21, p < 0.001. In the c-training, contextual factors like the technical environment or the lecturer might have an impact on the measured variables. Therefore, we controlled for differences between the four university courses. We ran an ANOVA and found significant effects of the university courses on perceived competence, F(3, 59) = 4.19, p = 0.009, and on the self-reported effort, F(3, 59) = 5.59, p = 0.002. As post-hoc test for the c-training, we chose Gabriel’s pairwise procedure, which is recommended for different sample sizes (Field 2018). One of the university courses reveals significant differences for perceived competence when directly compared to the other courses. Therefore, we repeated the independent sample t-test excluding these participants. Intrinsic motivation was still higher in the gtraining group than in the c-training (M = 4.72, SD = 1.02), but the difference is no longer significant (t (92) = 1.659, p = 0.101). Also perceived competence in the c-training group is still smaller (M = 4.20, SD = 0.85), but no longer significantly different from the g-training (t (92) = 1.015, p = 0.313). Effort values for g-training and c-training (M = 4.67, SD = 1.19) are almost the same. To improve the designed system and detected weaknesses, we asked all participants of the g-training to evaluate the motivational power of the individual game design elements on a 5-point Likert scale from “not motivating” (1) to “extremely

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motivating” (5). Results from the 60 participants reveal that utility rankings had the strongest motivational power (M = 3.73, SD = 1.02), followed by the experience points ranking (M = 3.67, SD = 1.17) and the levels (M = 3.55, SD = 1.10). Experience points themselves (M = 3.32, SD = 1.21) and badges (M = 3.23, SD = 1.21) still scored quite well. Last, we asked all participants to evaluate the training on a 5-point Likert scale from “very poor” (1) to “excellent” (5). Evaluation results for the g-training were better (M = 4.15, SD = 0.63) than for the c-training (M = 3.65, SD = 0.71). Interestingly, when only analysing the 12 participants completing both, the evaluation results for the g-training (M = 4.42, SD = 0.51) and the c-training (M = 3.25, SD = 0.87) further diverged, although eight of the 12 participants started with the c-training first.

5.5.2

Qualitative Survey Results

In the second survey, participants had the opportunity to explain their training choice. The majority of participants in the g-training completed it for opportunistic reasons, e.g. because they did not have to travel to university and could do it at any time. Only five participants mentioned that they liked the practical tasks and that these better suit their learning styles. Likewise, c-training participants gave opportunistic reasons, e.g. because they were at university anyway. Many participants also stated that it is easier to ask questions in class, and only six went to the c-training because of the social interaction with other students. Furthermore, participants could explain why they performed negotiations more than once or performed additional levels. 31 responses were received for the gtraining and 14 for the c-training. In the c-training, three participants repeated a negotiation due to failure, i.e. “because we could not agree on a deal, so I had to repeat it in order to agree on a deal”. 13 participants also stated that they wanted to learn more and gather more experience. Learning more refers either to participants that wanted e.g. “to learn how the system works exactly”, or to participants improving their results and negotiation skills (e.g.: “Because in the first negotiation I had a bad solution. So I want to try to get a better one”, or: “I wanted to improve my skills and see what is the best result I can obtain”). One participant explicitly mentioned “fun” as a reason. In the g-training, 19 participants mainly performed additional levels to learn more, e.g. “learn a little bit more and try some different tactics in the negotiations” or “to gain more experience dealing with the NSS”. Improving their previously reached agreement motivated 6 participants, one of them explicitly

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mentioning the rankings as a motivational incentive. An additional number of 8 participants explicitly mentioned that they liked to master all levels, e.g. “because I wanted to finish all levels. I was also curious what new events await me”.

5.5.3

Qualitative Interview Results

Among the twelve interview participants were eight from the g-training and four from the group having done both types of training. We never used the term gamification during the complete experimental procedure, but three information system students had heard of it before and mentioned it in some of their answers. Among several other features of the NSS, a good user interface was mentioned by most participants. Three participants already mentioned they liked some of the gamification elements such as the complete gamification concept, the levels, and the ranking for the experience points. There were no gamified elements that were disliked. In particular, the levels were perceived positively by almost all the participants. “I liked it, because step by step more features were available, and you became familiar with them” (student 5). The negotiated case descriptions were sufficiently long, so the participants could understand the situation and their position in the negotiation quickly. Explanations in the guided tour were easily comprehensible, although a few participants would prefer another type of media (e.g. a video) to explain the features. For two participants, the explanations were sometimes too long, one of them admitted not having read everything carefully. Although only four levels were required to be completed, seven participants were motivated to complete all of the levels. The utility rankings were perceived positively by the majority of the participants. For three participants, the rankings were an incentive to repeat a level and improve their performance. Half of the participants were only interested in the ranking of (their) individual utility whilst the other half of the participants were interested in all three rankings. These participants did not perceive the ranking as a competition, but rather as an informative feedback on their performance. On the other hand, three of the participants viewed the rankings to be irrelevant for their behaviour as they negotiated with a bot (the TNT) or because they did not like the comparison with others. The responses obtained for the badges and experience points are mixed. Four participants revealed that they did not care about the badges or the points and ignored them. Three participants indicated that they had the goal to unlock some of the badges and thus used the system more intensively. Five participants did

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not work for the badges in particular, but when they unlocked a badge, they nevertheless felt happy and motivated. The details for the experience points obtained were unknown to four participants, although the button was prominently displayed on the home screen (Figure 5.2). Besides the participants ignoring the points and their ranking, participants were eager to collect more points, either for themselves only or to improve the position in the ranking. As a consequence, participants revealed to have used some of the support features more frequently and worked more with the system. Student 11 e.g. said: “I really liked the ranking as an incentive system to work more with the features, improve the skills or conclude a level. It was quite game-like”. Additionally, for one participant the experience points ranking was particularly interesting, because she could see how many peers were working with the system. We are aware, that the TNT cannot completely simulate human negotiation behaviour, especially regarding its communication behaviour. Asking for the participants’ perceptions of the TNT, answers greatly varied. Some criticised, that “[…] the agent does not care about one’s politeness” (student 10) and it “[…] is not comparable with a real human” (student 9). For two participants, the behaviour of the TNT and a real human as in the negotiation was quite the same and felt real. Four participants positively highlighted the immediate feedback by the TNT, e.g. whether their strategy and their arguments worked or not. Another participant liked to practice in these negotiations, as they allow to make mistakes. Confronted with the option to engage in additional negotiations with human negotiators during the training phase voluntarily, ten participants considered this to be useful and would use this option. A human negotiation partner would improve their communication behaviour, e.g. they could better elaborate conflicting positions. Only two participants would not use it and rather negotiate with the TNT, as the TNT provides immediate feedback or because they liked the experience to negotiate with an agent. All participants except one, for whom the explanations were too long, liked the training or had fun doing it. Participants e.g. liked to be introduced to new features and apply them directly afterwards in a negotiation. Almost everyone confirmed to have a good understanding of the system and its features. The participants completing both types of training went to the c-training first and were very well introduced to the system. These participants liked to practice further in the g-training. Regarding the obtained e-negotiation skills answers greatly differed. A few interviewees still felt insecure, how e-negotiations are conducted in reality. Others confirmed to have a good under-standing of the e-negotiation process, but

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still do not perceive themselves as good negotiators. One participant revealed to make more rational decisions after the training, and for another participant the training was important to gain self-confidence in negotiations. Five participants experimented with different negotiation strategies and tactics, to see whether a different approach results in different negotiation outcomes.

5.6

Discussion & Conclusion

In the present study, we gamified an existing NSS used for e-negotiation training to improve learners’ motivation, engagement and learning outcomes. Similar to several other studies in the education area recently reviewed in Sailer and Homner (2020), our approach was successful in enhancing participants’ intrinsic motivation and their engagement. Regarding the first research question and the effect on learners’ motivation, the quantitative results revealed significantly higher values for the gamified training participants’ intrinsic motivation. Perceived competence as a positive predictor of intrinsic motivation (Ryan and Deci 2000a) was significantly higher too, which could also explain our significant findings for intrinsic motivation. The effects of the game elements on participants’ motivation show satisfying results. The statistical analysis of each gamification component integrated in the system showed that on average each component was perceived as quite motivating. In addition, our qualitative interviews reveal that the participants are motivated by different game elements or combinations of game elements. In particular, the perceptions of the badges and experience points included in the system varied. They had the function to incentivise the use of the system and its features and provide additional competence-confirming feedback. These game elements scored quite well regarding their motivational appeal in the survey, however several participants chose to ignore them completely. Obviously, different personality traits have an impact on the perception and preferences of game elements, as it has been revealed e.g. by Tondello et al. (2016). Our interview participants did not indicate that the ignored game elements had a negative effect. Instead, these game elements can be seen as an additional motivational incentive which works for some participants, but which are not required to successfully complete the g-training. Besides the use of the levels, the use of every other gamification component was optional. Glover (2013) suggests to make the use of gamification optional to avoid negative effects on participants’ motivation. Based on the results of this study, we add that the provision of a set of different and optional game elements that the user chooses from can lead to motivating effects. In contrast to

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adaptive gamification (Böckle et al. 2017), such an approach leaves the decision to use a game element to the user. Our second research question was to investigate the effect of the gamified training on participants’ engagement. The quantitative results reveal a significantly higher number of additional voluntary negotiations in the g-training group. According to our quantitative and qualitative results, the combination of levels, i.e. different negotiation scenarios, and the utility rankings providing feedback on the negotiation outcome appear to be an effective mechanism to engage the participants in additional voluntary negotiations. The levels provide clear intermediate checkpoints for the participants for their progress and provide clear goals (Glover 2013). Reaching the next level is a form of competence-confirming feedback. Moreover, most of the interview participants saw the utility rankings not as a form of competition, but considered it as an informational feedback, which can enhance intrinsic motivation (Deci et al. 1999). As a consequence, several participants used the opportunity to experiment with different negotiation strategies and tactics, as it has been intended in the underlying requirements of Schmid and Schoop (2019). The g-training was a fail-safe environment, in which the participants could experiment and train to negotiate electronically. Finally, our third research question regarding the effects of the gamified training reveals the most interesting and controversial finding. In this study we found conflicting results between g-training participants’ perceived competence and their measured learning outcomes for system skills and e-negotiation skills. While their perceived competence was higher than for the c-training participants, their results were worse for the assessed learning outcomes. One possible explanation for the contrary findings is that there is a rich set of negotiation skills and e-negotiation skills, which can never be assessed completely within one survey. Such skills include e.g. rationality, effectiveness, strategic behaviour, self-confidence and many more (Lewicki et al. 2009; Lewicki et al. 2010). Two interview participants revealed to become more rational or self-confident by the training, while the survey did not measure e.g. self-confidence for the learning outcomes. Another issue which might explain the perceived competence values are related to the use of game elements themselves. In majority, the included game elements provide positive and competence-confirming feedback, e.g. by unlocking the next level, or receiving badges or points (Sailer et al. 2013). Furthermore, the utility rankings provide informational feedback on participants’ performance, e.g. they can evaluate whether they reached an integrative and fair agreement with their negotiation partner. However, these rankings are based on the performances of all participants and do not reveal any information on what could be obtained

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in the best case and whether they missed integrative potential. As a consequence, the inclusion of these predominantly positive feedback elements enhances perceived competence (Deci et al. 1999). Participants may thus overestimate their own competences. At least the differences between participants’ self-assessment of system skills in the interviews compared to the survey results would support this assumption. In summary, the gamified training and its competence-providing feedback had a positive impact on participants’ perceived competence. In line with the selfdetermination theory (Ryan and Deci 2000b), the increased perceived competence is reflected in increased intrinsic motivation. As a consequence, the participants in the g-training spent their time to engage voluntarily in additional negotiations. However, neither increased motivation nor increased engagement manifest themselves in better learning outcomes. Our study includes several limitations: First, the results of the comparison might be biased by the online vs offline setting. A more thorough comparison with an online control group should be investigated, which reduces the effect of contextual factors like the lecturer or the technical environments. Second, we did not assign the participants to the c-training or g-training group, but let the participants decide which of them they would like to visit. Whilst this choice enabled a third group (namely those students participating in both training) to compare both types of training, there might be differences affecting the results between participants of the g-training and those of the c-training in this study. Finally, our sample for the interviews includes only students from one university course. The results have revealed several strengths and weaknesses of the designed gamified NSS. We reported the first design cycle of such a gamified NSS including points, badges, rankings and levels, which are frequently used in gamified education (Dicheva et al. 2015). In the following design cycle iteration, we aim to improve participants’ learning outcome by providing further constructive feedback, which does not necessarily take the form of a game element (Schmid and Schoop 2019). A potential feedback might be a Pareto-efficiency graph (Tripp and Sondak 1992), in which the participants can clearly observe missed integrative potential. Furthermore, the TNT used in both types of training has clear limitations regarding its ability to imitate real human behaviour. As interview participants reacted positively towards a voluntary option to negotiate with other human participants, this might be an option to be considered in a revised system and could be offered for both training groups to enable comparison. This study contributes to organisations in two fundamental ways: First, level structures or any kind of increasingly more complex challenges to overcome

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seem to be an appropriate way for an efficient system training, which is favoured by the users. Such a system training might be useful for companies offering B2B cloud-based services like Salesforce to train their end-users. Using levels for system training has also been successfully applied in ERP training (Alcivar and Abad 2016). Second, our designed artefact paves the way for an extensive e-negotiation training. At any time, practitioners may train negotiation scenarios against a software agent, which has recently been evaluated as helpful by a majority of German managers (Voeth et al. 2019). With the artefact as a basis, more levels might be designed with immersive narrative game elements such as role-changes or storytelling. These elements could complement the continuous story in the level scenarios and increase the authenticity of the training. In a follow-up study, we will evaluate a modified version of the system with students from different European universities and an assignment to the c-training or g-training groups. From a human-computer interaction perspective, we aim to shed more light on participants’ interactions with the gamified and non-gamified system. Log-file analysis, as suggested by Sailer and Homner (2020), will help to analyse, whether incentives to use the support functions have a significant effect on actual system use, and to understand participants’ actions in the system in further detail.

6

Rankings or Absolute Feedback? Investigating Two Feedback Alternatives for Negotiation Agreements in a Gamified Electronic Negotiation Training The content of this chapter is already published as: Andreas Schmid, 2021, Rankings or Absolute Feedback? Investigating Two Feedback Alternatives for Negotiation Agreements in a Gamified Electronic Negotiation Training. In: Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021), pp. 1385–1394, available at http://hdl.handle.net/10125/ 70779

Abstract

The use of game elements in non-game contexts has gained popularity in the education domain to increase students’ motivation and engagement. Additionally, these elements provide feedback on students’ performance. Rankings are often applied to display performance feedback relative to others despite their potential negative effects, for example due to increased pressure. In this experimental study, we compare two types of gamified electronic negotiation training, each including the game elements levels, badges, and experience points. As the reflection on the negotiation performance is a central activity for negotiation training, we test two feedback alternatives for the negotiation agreements. One group received relative feedback through rankings, and the other group received a non-game and absolute feedback called Pareto graph. Our findings show similar intrinsic motivation and negotiation outcomes, but higher engagement for participants using the Pareto graph. Practitioners and researchers are encouraged to consider non-game feedback elements in their gamification design.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_6

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6.1

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Rankings or Absolute Feedback? …

Introduction

Lecturers nowadays often face problems regarding their students’ motivation and engagement (Lee and Hammer 2011), and as a consequence with students’ learning outcomes. Learning tasks compete against distracting and more interesting activities (Buckley and Doyle 2017). Therefore, sustaining students’ attention, increasing their motivation and engagement, and supporting their learning has become a major challenge in education (Dichev and Dicheva 2017). Gamification, defined as the use of game elements in non-game contexts (Deterding et al. 2011), has become a popular approach in education to mitigate the previously described problems (Dichev and Dicheva 2017; Hamari et al. 2014; Seaborn and Fels 2015). Reviews in the education domain predominantly report positive effects of gamification on motivation, engagement, and learning (Dichev and Dicheva 2017; Majuri et al. 2018; Putz et al. 2020). Gamification has a positive impact on knowledge retention (Putz et al. 2020) and is expected to improve social and practical skills such as problem-solving, collaboration, and communication (Kapp 2012; Putz et al. 2020). One area for practical and social skill development is the field of electronic negotiation (e-negotiation) training. Negotiations are nowadays often conducted via electronic media (Schoop et al. 2008) and one can expect their importance to grow further in times of COVID-19. Therefore, individuals are expected to gain the relevant skills for these negotiations. To facilitate the development of e-negotiation skills, university courses teaching negotiation theory and practice use web-based negotiation support systems (NSSs) (Köszegi and Kersten 2003; Melzer and Schoop 2016). Such an e-negotiation training follows Kolb’s experiential learning methodology (Kolb 1984), where participants engage in negotiations (the experience) and have to reflect about this experience for effective learning to take place (Köszegi and Kersten 2003). However, participants after a traditional e-negotiation training still settle on inefficient agreements (Gettinger et al. 2016), and one reason might be insufficient motivation and engagement to practice and reflect about negotiations (Schmid et al. 2020). In a prior study, a NSS used in such a training has been enhanced with the game elements levels, badges, experience points and rankings (Schmid et al. 2020). These elements demonstrated their effectiveness to increase motivation and engagement of participants compared to traditional e-negotiation training (Schmid et al. 2020). The rankings provide constructive feedback on the quality of the settled agreement supporting the crucial reflections and are expected to incentivise participants to experiment with different negotiation strategies (Schmid and Schoop 2019). While interviews confirmed this assumption (Schmid et al. 2020),

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the use of rankings is sometimes considered as problematic due to potentially negative effects such as social comparison (Huschens et al. 2019), induced competition (Sailer et al. 2013) and in consequence less motivation (Hanus and Fox 2015). Furthermore, in e-negotiations a ranking only provides feedback relative to others and includes no information about possible improvements beyond the first place (Schmid et al. 2020). An alternative feedback visualisation in the field of negotiations is the Pareto graph (Tripp and Sondak 1992), providing absolute feedback about one’s performance. Since in gamification design the synergy of different elements contributing to a motivating and interesting experience matters, this study follows a research stream to identify the best combinations of elements (Dicheva et al. 2019). In particular, we conducted an experiment in which participants in an e-negotiation training either received feedback by rankings or feedback through the non-game element Pareto graph, while not manipulating the other game elements. Our first research goal is to investigate the effects of relative and absolute feedback on participants’ motivational experiences and their engagement. In consequence, these effects may influence the achieved negotiation outcomes in the training. Prior research has focussed on the effects of a training for subsequent negotiations (Melzer and Schoop 2016), but not for the training negotiations themselves, which is therefore our second research goal. The remainder of the paper is structured as follows: First, we describe the application domain of NSSs and e-negotiation training, review the relationship between goals, feedback, and motivation, and describe the two investigated feedback mechanisms afterwards. Next, the hypotheses are derived, followed by the description the methodology, the results, and the discussion. Finally, the paper concludes with a summary, limitations of this study, and implications for researchers and practitioners.

6.2

Theoretical Background

6.2.1

Decision Support in E-Negotiations

For several years, business negotiations are conducted electronically (Schoop et al. 2008). In a negotiation at least two negotiators deal with interdependent tasks and continually engage in decision-making and communication tasks (Bichler et al. 2003). E-negotiations differ from face-to-face negotiations for example through missing social cues such as mimics and gestures (Köszegi and Kersten 2003). Negotiation support systems (NSSs) were developed for e-negotiations

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with the aim to find agreements of higher quality and to save transaction costs (Bichler et al. 2003). Therefore, these systems provide the negotiators with communication and decision support (Schoop 2010). To evaluate offers in complex negotiations with multiple negotiation issues, NSSs provide decision support by means of a utility value (Schoop 2010). In the preparation phase, the negotiators define their preferences, i.e. they determine their preferred values for all negotiation issues and rank the issues according to their importance. Based on these information and multi-attribute utility theory (MAUT) (Neumann and Morgenstern 2007), the NSS computes a utility value between 0 and 100 for each received offer or during the creation of a new offer. An offer with a utility value of 100 perfectly corresponds one’s preferences, whereas an offer with a value of 0 does not correspond one’s preferences at all. The negotiation ends when one negotiator agrees to accept a received offer or when a negotiator decides to leave the negotiation without an agreement. The negotiation outcomes are described by their effectiveness (i.e. agreement rate) and the agreements’ efficiency (Pruitt and Carnevale 1993). The efficiency of an agreement is usually described by the individual utility, the joint utility (sum of negotiators’ individual utilities), and fairness (Delaney et al. 1997). As both negotiators’ preferences may not be perfectly opposing, the joint utility may exceed a value of 100 (Delaney et al. 1997). The joint utility provides information on whether and to what extent the negotiators have exploited the potential for an integrative agreement (win-win solution). The contract imbalance (the difference between the two individual utilities) provides details about the agreement’s fairness (Delaney et al. 1997).

6.2.2

Gamified E-Negotiation Training

NSSs have been used for more than 15 years in university courses to facilitate students’ development of e-negotiation skills (Köszegi and Kersten 2003; Melzer and Schoop 2016). An e-negotiation training includes a system training for the NSS being used as well as a training for the development of e-negotiation skills (Melzer and Schoop 2016). Such negotiation training employs the experiential learning methodology (Kolb 1984), where participants are having an experience, followed by reflection, abstraction, and generalisation (Köszegi and Kersten 2003). Therefore, the participation in a negotiation scenario followed by a structured debriefing supporting reflections is essential (Köszegi and Kersten 2003).

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101

However, current forms of e-negotiation training insufficiently facilitate participants’ motivation and engagement (Schmid et al. 2020). Based on motivation theories such as self-determination theory (SDT) (Ryan and Deci 2000b) and goal-setting theory (Locke and Latham 2002), requirements for a gamified enegotiation training have been derived (Schmid and Schoop 2019). They were successfully realised by integrating game elements in a NSS used in such a training (Schmid et al. 2020). Repeatable levels are implemented, with each of them representing one negotiation scenario in which participants negotiate with a software agent. These levels become increasingly more challenging by making the negotiation scenario and the NSS used more complex. Experience points and badges award desirable system use. Badges also include difficult negotiation goals to reach. For a more detailed description we refer the interested reader to Schmid et al. (2020). Participants in the gamified training have the freedom to decide whether they would like to follow a competitive approach (i.e. maximising own profit) or an integrative approach (i.e. maximising joint profit). To facilitate the aforementioned reflections, feedback is required so participants can evaluate whether they reached their goals and settled on a good agreement (Schmid and Schoop 2019). In a prior study, utility rankings have been implemented, enabling comparison with others and providing feedback on negotiation agreements (Schmid et al. 2020). In general, feedback for agreements is expected to incentivise participants to experiment with different negotiation strategies in order to find a better agreement (Köszegi and Kersten 2003; Schmid and Schoop 2019). Higher engagement, i.e. when participants repeat negotiations, improves negotiation skills and outcome efficiency (Bazerman et al. 1985; Thompson 1990c). However, high levels of engagement require motivated participants.

6.2.3

The Relation between Goals, Feedback and Motivation

Goal-setting theory proposes that individuals are motivated to strive towards goals (Locke and Latham 2002). Furthermore, difficult goals lead to higher effort and performance than low goals (Locke and Latham 2002). Feedback informs individuals which goals to attain, how they currently perform towards these goals, and what steps to perform next (Hattie and Timperley 2007). Therefore, feedback is crucial for learning activities. When learning activities include rapid feedback cycles, learners perceive failure as an essential part of learning and experiment until they succeed (Lee and Hammer 2011). Besides passively receiving feedback e.g. by lecturers, Festinger’s theory of social comparison processes postulates an

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inherent drive in every individual to evaluate one’s opinions and abilities (Festinger 1954). One option to judge one’s abilities is the comparison with other individuals. Comparing oneself with others can make individuals aware of their lack of abilities, informs them about their status, and encourages competition (Garcia et al. 2006). A large discrepancy between one’s abilities compared to better individuals leads to a drive upward (Festinger 1954) and results in higher learning outcomes (Nebel et al. 2016). Depending on the task, individuals may also have the chance to evaluate their abilities to objective absolute standards. Festinger’s theory maintains that individuals will not compare their abilities with others when objective standards are available (Festinger 1954). This claim has been supported, showing that absolute feedback is more effective than comparative feedback (Moore and Klein 2008). Researchers have further argued that the relationship between high goals and high performance is moderated by an individual’s goal commitment, i.e. the determination to reach a goal (Klein et al. 1999). If there is no goal commitment, a goal cannot have a motivational effect. The goals of an individual influence its goal commitment, because goals are chosen based on one’s assumption that the goal can be reached (Locke and Latham 2002). Feedback enables them to set reasonable goals and once they have been reached to strive upwards to higher goals (Hattie and Timperley 2007). Goal commitment can be induced by feedback itself, e.g. by competition or rewards (Hattie and Timperley 2007). Feedback has also direct effects on individuals’ motivation. According to selfdetermination theory (SDT) (Ryan and Deci 2000b), the satisfaction of the basic psychological needs for autonomy, competence, and relatedness facilitate intrinsic motivation, which is the desirable type of motivation for learning. If feedback is perceived as controlling, autonomy is undermined and intrinsic motivation will be diminished (Ryan and Deci 2000b). Autonomy-supportive feedback facilitates intrinsic motivation (Ryan and Deci 2000b). Comparative feedback induces competition and can cause feelings of pressure (Huschens et al. 2019), which diminishes one’s perceived autonomy and in consequence intrinsic motivation (Reeve and Deci 1996). In gamification research, the use of achievement elements providing inherent feedback to the users such as badges, rankings and levels has been shown to satisfy the needs for competence and autonomy (Sailer et al. 2017; Xi and Hamari 2019).

6.2 Theoretical Background

6.2.4

103

Feedback for Negotiation Agreements

Gamified interventions often use rankings to provide relative feedback in comparison with others for simple performance metrics (Dichev and Dicheva 2017; Landers et al. 2017b; Majuri et al. 2018). In an e-negotiation training participants’ achieved individual utilities, joint utilities, and the contract imbalance can be ranked. Such a ranking allows the participants to evaluate their agreement and compare it with others negotiating the same scenario. In a prior study (Schmid et al. 2020), the rankings including these three metrics were available once a participant has settled on an agreement with the software agent (see Figure 6.1). Participants can switch between the three rankings via the tabs.

Figure 6.1 Screenshot of an Anonymised Joint Utility Ranking

Interviews with participants in a prior study revealed that the rankings were rather perceived as informational performance feedback than as a competition among them (Schmid et al. 2020). Nevertheless, the use of rankings can be problematic, as they induce social comparison resulting in pressure on participants (Huschens et al. 2019), can decrease motivation (Hanus and Fox 2015), and participants may not like competition at all (Schöbel et al. 2017). Furthermore, such

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a ranking does not provide information about the best possible negotiation agreement. Therefore, the use of utility rankings in e-negotiation training may not be the best choice to provide feedback on the agreements. The use of an alternative feedback mechanism to improve learning has already been suggested (Schmid et al. 2020): Participants may receive absolute feedback for their negotiation agreement through the Pareto graph (Tripp and Sondak 1992). The graph depicts one’s agreement in a diagram (see big blue dot in Figure 6.2), where the axes represent the individual utility values of both negotiation partners. The red points represent Pareto-optimal agreements and are often called Pareto frontier, i.e. agreements in which none of the negotiators can improve his/her result without the other one being worse off (Tripp and Sondak 1992). Participants can evaluate their agreement to an objective standard, i.e. whether the agreement is Pareto-optimal and whether they achieved a fair and integrative agreement. Therefore, the Pareto graph provides absolute feedback.

6.3

Hypotheses

In this study, we compare the two previously described feedback mechanisms for negotiation agreements and derive the following hypotheses from the literature. First, the use of comparative feedback in form of the rankings provides transparency about participants’ performances and can make participants aware of their lack of abilities compared to others (Garcia et al. 2006). Rankings induce social comparison and competition (Garcia et al. 2006; Huschens et al. 2019), which in turn increase perceived pressure (Huschens et al. 2019; Reeve and Deci 1996), even for well-performing individuals (Huschens et al. 2019). Since users of the Pareto graph compare their performance with objective standards and without competition, we propose: H1:

Users of the rankings will perceive higher pressure than users of the Pareto graph.

Without feedback negotiation training participants experience uncertainty about how well they performed and whether they exploited the negotiation potential. The type of feedback could influence beliefs about one’s e-negotiation competence. Mekler et al. (2017) found no effect of rankings on competence, whereas another study found a negative effect on competence (Bräuer and Mazarakis 2019). It is hypothesised that only for participants at the top of a ranking feelings

6.3 Hypotheses

105

Figure 6.2 Screenshot of the Pareto graph with Individual Utilities (0 to 100) as Axes

of competence can arise (Sailer et al. 2013). The Pareto graph provides feedback about individual performance compared to an objective standard. In another study including a task with uncertain outcomes, the use of absolute feedback has stronger effects on beliefs about ones abilities than comparative feedback (Moore and Klein 2008). Given the potentially negative effects of relative feedback, we hypothesise: H2:

Users of the Pareto graph perceive themselves as more competent to conduct e-negotiations than users of the rankings.

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The satisfaction of the basic psychological needs for autonomy, relatedness, and competence facilitates intrinsic motivation (Ryan and Deci 2000b). Rankings were found to reduce relatedness (Bräuer and Mazarakis 2019). In another study, the use of rankings did not impact intrinsic motivation (Mekler et al. 2017). However, when participants receive feedback through a ranking and have the choice to repeat and undo errors (similar to our gamified e-negotiation training design), participants experience autonomy and higher intrinsic motivation (Nebel et al. 2016). Furthermore, intrinsic motivation is negatively affected by pressure (Reeve and Deci 1996). On the one hand, rankings could increase pressure (H1), might not be beneficial for perceived competence (H2), and reduce relatedness. On the other hand, they could increase autonomy. Since the majority of factors influencing intrinsic motivation is supposed to have negative effects for rankings, we conclude: H3:

Users of the rankings are less intrinsically motivated compared to users of the Pareto graph.

Using rankings to provide feedback, individuals set different goals to work for: Some may strive for a top position, others for a place in the midfield and others focus on not ending up in last place (Landers et al. 2017b). Therefore, the goal commitment to reach good agreements in the training using rankings might highly differ. Clear and difficult goals are more effective than nonspecific goals (Hattie and Timperley 2007), and the Pareto frontier provides such a clear goal to work for (Gettinger et al. 2016). Consequently, we propose: H4:

Users of the Pareto graph are more committed to reach good agreements than users of the rankings.

Rankings have been shown to raise engagement levels of individuals compared to control groups (Bräuer and Mazarakis 2019; Mekler et al. 2017). However, individuals only tend to engage in a task again when they face a high discrepancy to the top position, whereas individuals at the top of a ranking are satisfied with their performance (Nebel et al. 2016). When people are provided with absolute feedback, they might still find potential for self-improvement. In a brain-training game absolute feedback increased future game play compared to relative feedback (Burgers et al. 2015). In the Pareto graph, the distance between one’s agreement and the Pareto frontier (see Figure 6.2) outlines clear improvements for the participants. It might further incentivise them to experiment with other negotiation approaches. Therefore, we hypothesise:

6.4 Research Methodology

H5:

107

Users of the Pareto graph show higher engagement compared to users of the rankings.

The results of negotiations are assessed by their effectiveness (i.e. agreement rate) and efficiency (i.e. quality of agreements) (Pruitt and Carnevale 1993). According to the so called negotiation dilemma, these two dimensions are conflicting objectives, since striving for a better agreement reduces the chances of reaching an agreement at all (Pruitt 1981). Prior research has not studied the effects of motivation and engagement on outcomes in training negotiations before. In general, participants with a low integrative agreement and a large distance to the Pareto frontier will seek to improve their agreement (Gettinger et al. 2016). In this training, participants have the chance to repeatedly experiment with different approaches without being judged for failure (Dicheva et al. 2019). On the one hand, experimenting with different negotiation approaches is a key element of enegotiation training (Köszegi and Kersten 2003). On the other hand, negotiation experience is an important factor for more efficient agreements (Loewenstein and Thompson 2006). Further, practicing negotiations enables participants to logroll more effectively, thus resulting in more efficient agreements (Bazerman et al. 1985; Thompson 1990c). Therefore, and in-line with H5, we propose: H6:

6.4

Users of the Pareto graph (a) achieve more efficient agreements and (b) negotiate less effective than users of the rankings.

Research Methodology

To test the hypotheses an experiment was conducted in May 2020 with 69 undergraduate students participating in an online course at a German university. The students were enrolled in management, economics, or information systems. As part of the course, students were taught negotiation theory and case studies, followed by the negotiation experiment described in the following.

6.4.1

Procedure

The experiment used the gamified e-negotiation training described in section 2.2, where the participants learned to use the NSS and to negotiate electronically. The training prepared them for a five-day international e-negotiation. Participants

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received fixed credit points for their participation in the training and the surveys, regardless of their performance in the training. They further received credit points depending on their performance in the international e-negotiation, but we will focus only on the training in the following. Participants were randomly assigned to one of the two training groups receiving feedback on their negotiation agreements either by the utility rankings or the Pareto graph. In a first online survey demographic data was assessed. Within a timeframe of four days, the participants had to complete the training. Estimated expenditure for the training are 90 minutes and participants had to complete the first three of five levels. Participants could practice further until the international e-negotiation started. The utility rankings (see Figure 6.1) or the Pareto graph (see Figure 6.2) were shown for the first time after level two, where the utility values were explained. Above both elements a textual description provided explanations about the graph or metrics respectively. After the training, participants had to fill in a second online survey measuring the variables described in the next section.

6.4.2

Data Collection & Analysis

After the training, participants’ motivational experiences were measured using the Intrinsic Motivation Inventory (IMI) by Ryan et al. (Ryan et al. 1983), an established measurement in gamification research (Seaborn and Fels 2015) based on the SDT. Five items measured participants’ intrinsic motivation (e.g. “I thought the training was very interesting”) and four items their competence for conducting e-negotiations (e.g. “I think I am pretty good at negotiating electronically”). Additionally, the IMI includes a subscale for pressure, from which three items were adopted (e.g. “I felt pressured while doing the training”). For relatedness, the three items used in (Sailer et al. 2017) have been adapted (e.g. “I felt socially related with others during the training”). Autonomy with regard to decision freedom was measured with three items (Sailer et al. 2017) (e.g. “During the training I could make my own decisions”). Four items assessed participants’ goal commitment (Hollenbeck et al. 1989; Landers et al. 2017b) (e.g. “I was strongly committed to reach good negotiation agreements in the training”). All items were measured using a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). One item for competence and one for goal commitment indicated standardised factor loadings below 0.4. Since they are below recommended thresholds (Stevens 2009) and the items could be interpreted as an evaluation of the software agent used instead of measuring the intended latent

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109

variable, it was decided to remove these items. The variables, the final item count, and Cronbach’s alpha are shown in Table 6.1, with each variable exceeding the typically recommended threshold for Cronbach’s alpha of 0.7 (Field 2018). In addition to the subjective measures, we complement our analysis with objective measures for participants’ engagement and the negotiation outcomes. Participants had the option to practice by conducting additional negotiations in the system, i.e. by proceeding further than level three or repeating a certain level. As a measure of voluntary engagement, each additional concluded negotiation is counted (Schmid et al. 2020) and we analyse up to which level participants proceeded. Furthermore, the negotiations with the software agent provide a comparable data set. For each level, negotiation effectiveness is operationalised by the agreement rate (Melzer and Schoop 2016). Negotiation agreement efficiency is measured using individual utility, joint utility, and contract imbalance (Delaney et al. 1997). Table 6.1 Variables and Cronbach’s Alpha

Variable

Items

Cronbach’s α

Intrinsic motivation

5

.88

Pressure

3

.76

E-negotiation competence

3

.81

Autonomy (decisions)

3

.75

Relatedness

3

.82

Goal commitment

3

.86

Participants were excluded from the analysis if they had not answered the surveys or did not participate in the training. Two participants were excluded due to prior experiences with the system. In total, 53 participants remained for statistical analysis, which was performed using IBM SPSS Statistics 26.

6.5

Results

After filtering the data, 27 participants using the Pareto graph and 26 participants using the utility rankings remain for the analysis. Gender is almost equally distributed among the groups (rankings: 13 females; Pareto graph: 12 females). On average, the students are 22.18 years old (SD = 3.07). To account for the small sample size and non-normal distribution of the data, non-parametric independent samples Mann-Whitney U test will be used to compare the two groups

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(Field 2018). The medians, means, standard deviations, and Mann-Whitney U test statistics for all survey and engagement variables are shown in Table 6.2. H1 postulated that users of the rankings perceive higher pressure. Pressure values are below the average of the scale and higher for the ranking group than for the Pareto graph group, but this difference is statistically not significant (p = .17, one-sided). Thus, H1 cannot be supported. H2 assumed that users of the Pareto graph perceive themselves as more competent to conduct e-negotiations than users of the rankings. There is indeed a tendency that the Pareto graph users perceive themselves as more competent than ranking users. However, this difference is close to but still not statistically significant (p = .056, one-sided). Consequently, H2 cannot be supported. Furthermore, H3 assumed that ranking users are less intrinsically motivated than Pareto graph users. Intrinsic motivation is in general quite high and higher for Pareto graph users than for ranking users, but no significant effect is found (p = .23, one-sided). Relatedness as one factor for intrinsic motivation is below the scale’s average and slightly worse in the ranking group, but no significant effect is found (p = .30). Autonomy in regard to decision freedom as another factor is quite high in both groups. The ranking group reports higher autonomy than the Pareto graph group, but again no significant effect is found (p = .39). Since there are neither significant differences for the basic psychological needs nor for intrinsic motivation, H3 has to be rejected. H4 predicted higher goal commitment of participants using the Pareto graph to reach good negotiation agreements. Goal commitment for the Pareto graph group is in fact higher than in the ranking group. However, there is no statistically significant difference, so H4 is rejected (p = .075, one-sided). H5 assumed higher engagement in the Pareto graph group. Participants were required to complete at least level 3 and could proceed further. Analysing the additional negotiations conducted and the level reached, the Pareto graph group reveals clearly higher engagement. Pareto graph users perform more additional negotiations. There is a statistically significant difference (p = .014, one-sided) and a medium effect (r = -0.30). Pareto graph users also proceed further within the levels compared to the ranking users. This also results in a medium effect (r = -0.34) and a statistically significant difference (p = .007, one-sided). Thus, H5 can be confirmed. H6 postulated (a) more efficient agreements but also (b) fewer effectiveness for users of the Pareto graph. Table 6.3 shows the agreement rates revealing the effectiveness and the individual utilities, joint utilities, and contract imbalance for the efficiency of the agreements. For the latter three variables, the average values achieved for all performed negotiations of a participant are given as well as the

6.5 Results

111

Table 6.2 Descriptive and Test Statistics for Survey and Engagement Variables Utility ranking

Pareto graph

Mann-Whitney U test

Variables

Median

Mean (SD)

Median

Mean (SD)

U

z

p

r

Pressure

3.83

3.68 (1.09)

3.33

3.40 (1.00)

298

−0.95

.17 1

−.13

E-negotiation competence

5.00

4.79 (0.78)

5.33

5.15 (0.85)

217.5

−1.44

.056 1

−.22

Intrinsic motivation

5.40

5.28 (0.92)

5.60

5.42 (0.97)

309

−0.75

.23 1

−.10

Relatedness

3.33

3.27 (1.23)

3.67

3.56 (1.12)

293

-0.95

.30

−.14

Autonomy (decisions)

5.33

5.32 (0.72)

5.00

5.19 (0.77)

303.5

-0.85

.39

−.12

Goal Commitment

5.13

5.26 (1.00)

5.67

5.65 (0.88)

217.5

-1.44

.075 1

−.20

Additional negotiations*

1.50

2.69 (3.78)

5.00

5.67 (7.80)

228

−2.22

.014 1

−.30

Level reached**

3.00

3.62 (0.85)

5.00

4.26 (0.94)

226

−2.45

0.007

−.34

1

Notes: * p < .05, ** p < .01. 1 denotes one-sided p-value.

extreme (max) scores that a participant has achieved within a level. Level 1 did not include decision support and the feedback yet and is therefore not analysed as well as level 5, which only 6 participants of the ranking group completed. For all variables only two statistically significant differences are found. First, Pareto graph users’ individual utilities in their best level 2 negotiation (M = 54.28) differs from the best result of the ranking users (M = 51.13), U = 236, z = −2.05, p = .04. Second, in level 3 Pareto graph users’ extreme score for contract imbalance is higher (M = 17.46) than for ranking users (M = 12.61), indicating that they achieved unfairer agreements (U = 239.5, z = −1.19, p = .047). Since individual utilities in other levels are slightly higher for ranking users and the other variables provide an inconclusive picture, we conclude that H6a and H6b cannot be confirmed.

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Table 6.3 Negotiation Outcomes for the Levels Showing Agreement Rates, Achieved Individual Utilities (IU), Joint Utilities (JU) and Contract Imbalance (CI) Level

Variables

Utility ranking

Pareto graph

Level 2 Participants Agreement rate Mean avg. IU (SD), Mean max. IU* Mean avg. JU (SD), Mean max. JU Mean avg. CI (SD), Mean max. CI

26 81.41% 50.94 (5.63), 51.13 121.97 (3.82), 121.97 20.18 (11.23), 20.57

27 74.22% 53.96 (6.01), 54.28 122.01 (3.80), 122.25 15.52 (10.01), 16.54

Level 3 Participants Agreement rate Mean avg. IU (SD), Mean max. IU Mean avg. JU (SD), Mean max. JU Mean avg. CI (SD), Mean max. CI*

26 85.44% 59.57 (8.76), 60.67 122.94 (6.27), 123.82 11.62 (8.56), 12.61

27 83.51% 55.78 (10.44), 56.72 120.72 (6.88), 121.07 15.89 (10.20), 17.46

Level 4 Participants Agreement rate Mean avg. IU (SD), Mean max. IU Mean avg. JU (SD), Mean max. JU Mean avg. CI (SD), Mean max. CI

10 58.34% 50.46 (3.76), 51.12 112.05 (3.43), 112.35 11.13 (4.32), 12.22

18 55.00% 49.65 (6.09), 48.87 113.83 (5.49), 114.12 14.53 (8.77), 14.79

Notes: * p < .05.

6.6

Discussion

Although rankings increase pressure (Huschens et al. 2019), the reported pressure in this study is quite low and there are no significant differences between the groups. One reason might be that there was no plain condition with rankings only, but the rankings were presented in combination with other game elements. Another reason might stem from the perceptions of the rankings, which previously were found to provide rather informational feedback than induce competition among the participants (Schmid et al. 2020). Feedback perceived as informational can enhance intrinsic motivation (Deci et al. 1999). Intrinsic motivation does not differ between the two groups, although a tendency exists that users of the Pareto graph are a little more intrinsically motivated. Absolute feedback exerts stronger effects on affective reactions than comparative feedback (Moore and Klein 2008). However, the choice to repeat improves motivation when a ranking is present (Nebel et al. 2016). Further analysing the basic psychological needs facilitating intrinsic motivation (Ryan and Deci 2000b), no significant differences are found. Relatedness is lower in the ranking group and therefore in line with prior research (Bräuer and Mazarakis 2019). Autonomy in regard to decision freedom is slightly higher for the participants in the ranking

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113

group, which might be related to the choice of the users for which position in the ranking they want to strive for (Landers et al. 2017b). There is however an insignificant tendency that users of the Pareto graph perceive themselves as more competent than the ranking users. The distance between one’s agreement and the objective standard of the Pareto frontier might be a more effective evaluation of an agreement than the distance between one’s position and the top position in a ranking. The use of the Pareto graph also results in a non-significant but still higher goal commitment compared to the ranking users. On the one hand, the Pareto frontier provides a clear goal to work for, and the graph’s visualisation clearly outlines possible improvements. On the other hand, rankings provide an opportunity for users to set their own goals, which might be a top position in the ranking but might also result in goals such as not ending up in last place (Landers et al. 2017b). The strongest and statistically significant effect of the feedback provided is found for participants’ engagement. Participants using the Pareto graph conducted more voluntary negotiations than participants using the ranking. The majority of the Pareto graph users was further motivated to proceed up to the fifth level. In the context of serious games, the provision of absolute feedback also increased future game play compared to relative feedback (Burgers et al. 2015). Furthermore, the Pareto graph clearly shows areas for possible improvements, which could motivate participants to improve their agreement and try out different negotiation approaches. For rankings however, participants at the top position are usually satisfied with their performance (Nebel et al. 2016). This could explain the large discrepancy regarding participants’ engagement between the two groups. The differences regarding engagement and goal commitment are not mirrored in the negotiation outcomes during the training. A key element in e-negotiation training is the experimentation with different negotiation approaches (Köszegi and Kersten 2003), and as such sometimes worse agreements for approaches that turn out to be unsuccessful are possible. Nevertheless, prior research has shown that repeating negotiation scenarios improves negotiation skills (Bazerman et al. 1985; Thompson 1990c). Given the self-reported competence for e-negotiations and the engagement results, we assume that users of the Pareto graph gained more e-negotiation skills.

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6.7

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Rankings or Absolute Feedback? …

Conclusion & Outlook

In the present study, we analysed the effects of two elements providing essential feedback for the agreements in a gamified e-negotiation training. Feedback was provided either by comparing one’s agreements with others in the utility rankings, or through the Pareto graph providing absolute feedback compared to objective standards. In summary, this study provides some interesting insights on how the modification of one feedback element in a holistic gamification design can result in different outcomes. Regarding the first research goal, the study provides partial evidence for Festinger’s claim that absolute feedback is more influential than relative feedback (Festinger 1954). This influence manifests in significantly higher engagement. No significant differences were found for self-reported motivational experiences. Negotiation outcomes investigated as the second research goal were not affected, thus showing that experimentation with different negotiation approaches in such a training is important (Köszegi and Kersten 2003). This study includes some limitations. First, the sample size is relatively small and future research may be needed to validate the findings. Due to the sample size, we could not analyse potentially moderating effects between the variables. Second, although participants received credit points independent from their answers given in the surveys, we cannot completely rule out a social desirability bias. Last, the effect on negotiation outcomes might be limited due to the strategy pursued by the software agent in these negotiations and by the negotiation cases themselves. Finally, we recommend that the usage of rankings in a gamification design should be considered carefully. For a motivating experience, individuals should be given the option to undo errors (Nebel et al. 2016) as in this study. Nonetheless, individuals may not like the induced competition at all (Schöbel et al. 2017). When different feedback alternatives are available, the provision of absolute feedback should be preferred (Moore and Klein 2008). This study reveals that the use of a non-game feedback element results in a motivational experience too. We encourage researchers and gamification designers to look beyond the typically implemented elements such as points, badges, and rankings and consider the use of domain-specific and non-game feedback elements. In the context of this study, we might analyse the negotiation approaches applied in detail, i.e. whether they differed and which approaches were used. Furthermore, an analysis of negotiations conducted after the training could show whether there are effects on actual negotiation competence. The relationships and potentially mediating effects between the variables could be analysed in a

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115

larger study. An analysis of personality traits or player types could further reveal, whether all participants benefit from absolute feedback, or whether the provision of both types of feedback—leaving the choice which of them to use to the participants—exerts greater effects.

7

Gamification of Electronic Negotiation Training: Effects on Motivation, Behaviour and Learning

Co-Authors: Prof. Mareike Schoop, PhD The content of this chapter is already published as: Andreas Schmid, Mareike Schoop, 2022, Gamification of Electronic Negotiation Training: Effects on Motivation, Behaviour and Learning. In: Group Decision and Negotiation 31 (3), pp. 649–681, available at https://link. springer.com/article/10.1007/s10726-022-09777-y

Abstract

Organisations are involved in various types of negotiation. As digitalisation advances, such business negotiations are to a large extent electronic negotiations. Consequently, dedicated training for such electronic negotiations is important for mastering negotiation skills. We designed a gamified negotiation system used in e-negotiation training to increase participants’ motivation, engagement, use of the system’s negotiation support features and to improve their decision making. The quantitative evaluation using students as subjects shows higher motivation, engagement and better system and decision-making skills for participants in the gamified training compared to a conventional training. Furthermore, female participants show higher engagement in the gamified training than males. An analysis of the individual elements in the system provides insights into participants’ perceptions and shows that the inclusion of a domain-specific feedback element yields motivational results that are almost similar compared to those using traditional game elements.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_7

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Organisations can employ the designed artefact for fundamental and effective e-negotiation training.

7.1

Introduction

Communication processes in business organisations have become increasingly digitalised shaping various forms of social interactions. Negotiations as one important form of business interaction including communication and decisionmaking, are nowadays conducted electronically via asynchronous media such as email or negotiation systems (Schoop et al. 2008). The required skills for electronic negotiations (e-negotiations) can be obtained by participating in dedicated negotiation training, involving negotiation theory and practical tasks. In e-negotiation training, participants engage in realistic negotiation simulations and use web-based negotiation support systems (NSSs) that provide several features to support communication and decision-making tasks (Köszegi and Kersten 2003; Melzer and Schoop 2016; Schoop 2020; Vetschera et al. 2006). However, despite the participation in e-negotiation training, the negotiators still settle on inefficient agreements (Gettinger et al. 2016) and the features of NSSs are not always used to their fullest extent (Druckman et al. 2012). Potential reasons for the observed problems are the participants’ lack of motivation to engage deeply with the practical negotiation tasks and the NSS, and a lack of feedback in current forms of negotiation training (Schmid and Schoop 2019). In recent years, the need to sustain learners’ attention, facilitate their motivation and support their learning has been recognised as a major challenge in education (Dichev and Dicheva 2017). Game-based approaches have become popular to improve students learning due to their motivational power and feedback provided. In the area of negotiation training, game-based approaches have been introduced by means of agents as virtual characters (Gratch et al. 2016; Kim et al. 2009), by means of virtual reality training (Ding et al. 2020), or by means of full-fledged games such as Merchants or Reign of Aquaria. These approaches target a face-to-face negotiation training and simulate real-time interactions with the negotiation partner, whereas e-negotiations conducted via asynchronous media do not use virtual characters but involve the (written) exchange of formal offers and longer text messages. Another promising approach to enhance learners’ motivation is gamification, defined as the use of game design elements in non-game contexts (Deterding et al. 2011). When gamification is applied to an information system, all of the system’s functionalities are retained and game elements are additionally

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incorporated (Liu et al. 2017). To date, the results of gamified learning interventions on learners’ motivation, engagement and cognitive outcomes are mixed but predominantly positive (Dichev and Dicheva 2017; Sailer and Homner 2020). Gamification also seems suitable to improve social and practical skills such as problem-solving, decision-making, communication and collaboration (Putz et al. 2020). These skills and tasks are particularly important for e-negotiations. Since NSSs are often used for e-negotiation training (Vetschera et al. 2006), gamifying of an NSS is a promising solution for the afore-mentioned problems. It has been shown that e-negotiations have several inherent game-like elements such as feedback scores (i.e. utility values) and negotiations of varying difficulty (Schmid and Schoop 2018), and, therefore, provide an interesting basis for adding game design elements. Our research goal is to design a gamified NSS to be used in e-negotiation training to improve participants’ motivation, engagement and learning outcomes. A first evaluation demonstrated the effectiveness of the designed artefact in enhancing motivation and engagement; however, learning outcomes could not be improved (Schmid et al. 2020). This paper reports the first large-scale quantitative evaluation of the revised artefact and also investigates effects beyond the training phase. In particular, we have chosen students as subjects (for reasons to be discussed later) to answer the following research questions: 1. Which effect does a gamified e-negotiation training have on participants’ motivation? 2. Which effect does a gamified e-negotiation training have on participants’ engagement? 3. Which effect does a gamified e-negotiation training have on participants’ learning outcomes? 4. What is the participants’ perception of the integrated elements regarding their support for motivation and learning? 5. Which effect does a gamified e-negotiation training have on the participants’ use of an NSS in a follow-up negotiation? 234 students from three universities participated in this study. We compare our gamified e-negotiation training with an established, conventional e-negotiation training (Melzer and Schoop 2016). The central theories and concepts of motivation, gamification and e-negotiation training are reviewed in the following section. The designed system (Section 7.3) and the research design (Section 7.4) are introduced, followed by the results of the evaluation in Section 7.5. Finally, we discuss

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our results, limitations and contributions to practice and provide an outlook on future research directions.

7.2

Theoretical Background

The current work integrates theories and concepts of motivation, feedback, gamified education and training and negotiation research, which will now be described.

7.2.1

Motivation & Feedback

Motivation can be intrinsic (i.e. performing an activity for its inherent satisfaction) or extrinsic (i.e. performing an activity for a separable outcome or to avoid negative consequences) (Ryan and Deci 2000a). In education, intrinsic motivation is the desirable motivation and results in high-quality learning (Ryan and Deci 2000a). According to self-determination theory (SDT)—a macro-theory of motivation—intrinsic motivation flourishes once an individual’s basic psychological needs for autonomy, competence and social relatedness are fulfilled (Ryan and Deci 2000b). Autonomy is defined as the extent to which an individual perceives an action as self-determined. Furthermore, individuals master an activity and thereby feel competent. Finally, individuals require a secure social basis and/or need to feel connected with others (Ryan and Deci 2000b). Individuals are further motivated to strive towards goals (Locke and Latham 2002). Feedback is an essential part of learning, as it helps individuals to pursue their goals, informs them about their progress and shows which steps to perform next (Hattie and Timperley 2007). The way feedback is presented and formulated has direct effects on the motivation of an individual. Feedback perceived as controlling undermines autonomy, whereas feedback perceived as informational positively affects autonomy and intrinsic motivation (Deci et al. 1999). Furthermore, positive and constructive feedback and challenges that are feasible for the individuals facilitate their feelings of competence and in turn increase intrinsic motivation (Ryan and Deci 2000b).

7.2 Theoretical Background

7.2.2

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Gamification in Education & Training

Deterding et al. (2011) define gamification as the use of game design elements in non-game contexts, while not transforming the non-game context into a fullyfledged game. In addition, other researchers explicitly highlight the goals of gamification. Landers (2014) defines gamification of education as a means to facilitate learning and related outcomes. Gamification studies report predominantly positive effects on the motivation, engagement and cognitive outcomes of learners (Dichev and Dicheva 2017; Sailer and Homner 2020). Research is often based on SDT (Ryan and Deci 2000b) to explain the effects of gamification (Tyack and Mekler 2020; Xi and Hamari 2019), and therefore gamified interventions focus on integrating game elements that are expected to fulfil a learner’s basic psychological needs. Still, the results of a gamified intervention depend on the context and the perception of its users (Hamari et al. 2014) and further depend on the combination of and interaction between the game elements (Dicheva et al. 2019; Liu et al. 2017). The game elements used in gamification can be categorised according to their abstraction level into components, mechanics and dynamics (Werbach and Hunter 2012). The most abstract form are dynamics, representing the overall objectives of the gamified intervention such as creating emotions or progression. Mechanics represent the means to realise these objectives, e.g. in the form of competitions, challenges, rewards or by providing feedback. The most concrete form are the components, which contribute to the mechanics. Examples of components are avatars, badges, levels, and quests (Werbach and Hunter 2012). In the education domain, points, quests, badges, rankings, and levels are frequently used (Majuri et al. 2018). Such elements provide clear goals to work for and provide feedback about the progression towards these goals (Mekler et al. 2017). However, the frequent use of points, badges, and rankings has been criticised because these components work as extrinsic motivators by inducing and rewarding an activity (Liu et al. 2017; van Roy and Zaman 2017). Crucially, it depends on whether an individual perceives a reward as controlling, which undermines autonomy and intrinsic motivation, or as informational feedback towards the achievement of a goal (Deci et al. 1999). The context-dependent effects of gamification (Hamari et al. 2014) have been demonstrated in a number of studies. Using badges and rankings led to less motivation and lower exam scores in Hanus and Fox (2015). Sailer et al. (2017) found that badges, rankings, and performance graphs positively affect participants’ autonomy and competence need satisfaction, which facilitate intrinsic

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motivation. A study by Xi and Hamari (2019) showed that badges, points, status bars, and rankings positively affect all three basic psychological needs. Gamified learning interventions should implement game elements that set challenging but attainable goals and satisfy all three basic psychological needs in order to engage the learners (van Roy and Zaman 2017) and facilitate intrinsic motivation in the long run (Sailer and Homner 2020). To choose appropriate game elements, an analysis of the application context is necessary (Morschheuser et al. 2018).

7.2.3

Electronic Negotiation Training

A negotiation is conducted by at least two negotiation parties dealing with interdependent tasks, who continually engage in communication and decision-making tasks to search for a consensus (Bichler et al. 2003). For several years, business negotiations have been conducted electronically, especially using asynchronous media such as e-mail (Schoop et al. 2008). Electronic negotiations are conducted with the primary objective of saving transaction costs, finding agreements in less time, and reaching agreements of higher quality (Bichler et al. 2003). Negotiators must not only possess the required negotiation skills and the required ICT skills but also specific digital negotiation skills for conducting electronic negotiations. For example, communication in electronic negotiations differs from communication in face-to-face negotiations due to missing cues such as mimics, gestures, and tone of voice. Instead, electronic negotiation communication must convey semantics by different patterns through communication quality (Schoop et al. 2010; Schoop 2021), e.g. ensuring grounding and coherence. These skills require extensive training. Dedicated negotiation training is offered in organisations such as companies and universities to facilitate the development of the relevant communication and decision-making skills. Negotiations require individuals to claim their own positions behaving competitively as well as acting collaboratively in search of win-win agreements (Lewicki et al. 2010). As discussed above, e-negotiations require dedicated negotiation systems to improve communication and decision making (Köszegi and Kersten 2003; Schoop 2010). The asynchronous mode of enegotiations allows for more preparation time to define and implement negotiation strategies and tactics. E-negotiation training facilitates the development of these skills and often uses negotiation support systems (NSSs) such as Inspire (Köszegi and Kersten 2003; Vetschera et al. 2006) or Negoisst (Melzer and Schoop 2016).

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NSSs provide various types of support for communication and decisionmaking whilst leaving the final decision with the negotiation party (Schoop et al. 2003). A bilateral e-negotiation process may look as follows: In the preparation phase, both parties separately define the preferred values for each of the negotiation issues as well as a ranking of these issues according to their importance. One party initiates the negotiation and prepares a first message, which looks similar to an email. NSSs such as Negoisst require the selection of a message type such as a request, offer, or counteroffer to explicate the sender’s intention (Schoop 2010, 2020). Some NSSs also offer informal message exchange through means of questions and clarifications (Schoop 2010). Once a message type and consequently the mode of the message is determined, the sender’s communication and decision-making skills are required to select the preferred values for the negotiation issues, provide reasonable arguments for their selection, and establish a relationship with the negotiation partner (Schoop et al. 2010). When the first message was sent, the recipient can evaluate the received request using a utility value computed by the NSS ranging between 0 and 100%. This value represents the extent to which the received request or the own offer under construction corresponds to the negotiator’s preferences and helps to assess appropriate concessions for one’s own next step (Schoop 2010, 2020). The message composition is exemplified in Figure 7.1, where the user is sending a reply to a negotiation partner with the fictitious role name “Alexander Iwanow”. The user has chosen a counteroffer as the message type and selected the values for the three negotiation issues about the printing ink procurement in the agenda on the right. The currently selected values result in a utility value of 86% for the user. In addition, the user writes a textual message in natural language to present own arguments to the negotiation partner, who has sent a counteroffer that yields a utility value of 25% for the current user. The negotiation ends once a party agrees to accept a received offer or once a party decides to finally reject the offers and thus to end the negotiation without a deal. Consequently, an e-negotiation training requires (1) an end-user training for the NSS to be used and (2) a training for the development of e-negotiation skills (Melzer and Schoop 2016). Negotiation training usually follows the experiential learning methodology by Kolb (1984). As part of an e-negotiation training, participants engage hands-on in bilateral negotiation simulations either with another participant or with a software agent as part of the experience (Köszegi and Kersten 2003; Melzer et al. 2012). For actual learning to take place, participants need feedback to reflect on their experience and draw conclusions for future behaviour. A recent study shows that participants want to receive particular

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feedback to improve their negotiation skills such as preparedness, effectiveness, rationality, strategy and problems-solving (Meyer et al. 2020). Current e-negotiations training supports negotiators with their individual reflections solely offline through debriefings or in-class discussions (Köszegi and Kersten 2003; Melzer and Schoop 2016). However, the use of gamification and of gamified feedback elements integrated in an NSS might facilitate the reflection phase and could improve participants’ motivation, engagement, and negotiation skills (Schmid and Schoop 2019). In the following section, we will present our system design for a new e-negotiation training.

Figure 7.1 Writing a Message in Negoisst

7.3 System Design

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System Design

Improving e-negotiation training with game design and feedback elements requires choosing an existing NSS used in e-negotiation training and integrate these elements into the NSS. The NSS chosen for this study is Negoisst (Schoop 2010, 2020) due to the following reasons. Negoisst is a research prototype that can be used to conduct business negotiations, has been used for almost two decades to train future negotiators, has been used to conduct international negotiation experiments, and is one of the most comprehensive NSSs including communication and decision support as well as document management and conflict management (Schoop 2010, 2020). We will use the negotiation process as described in section 7.2.3 and present the newly added feedback and gamification components briefly and justify their choice. Based on previously derived requirements (Schmid and Schoop 2019), our system design is in line with the method for gameful design by Deterding (2015) stating that game elements are centred around the inherent challenge of a user’s pursued action. This inherent challenge in the context of e-negotiation training is to reach a good agreement with the negotiation partner. Its difficulty depends to a large extent on the complexity of the negotiation and on the behaviour of the negotiation partner (Lewicki et al. 2010). Various tactical and strategic actions can be performed to finally establish an agreement. Our overall learning goal is to engage participants in continuous learning through participation in realistic bilateral e-negotiation simulations. Dichev and Dicheva (2017) emphasize the necessity for safe learning places, in which participants can gain experience without fearing negative consequences. Therefore, all tasks (in this case all e-negotiation simulation tasks) can be repeated during the training phase. During the training negotiation, the participant requires feedback for the performed actions and will need additional feedback once the agreement was settled, i.e. whether a distributive, integrative and/or fair agreement was achieved. The latter feedback could serve as an incentive to repeat a negotiation simulation, to experiment with different negotiation strategies and to improve logrolling behaviour (Schmid and Schoop 2019). The provision of feedback is essential as it serves as a trigger for the reflection phase in the experiential learning methodology. We expect increased hands-on experience and feedback on negotiation performance to facilitate the development of important e-negotiation skills such as preparedness, rationality, and strategic behaviour (Lewicki et al. 2010).

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To avoid overburdening demands and tailor the e-negotiations to the current skills of the participants we chose the mechanics of increasing challenges. These are realised through the implementation of five levels, each corresponding to one bilateral negotiation simulation that the user needs to complete successfully in order to unlock the next more difficult level (see Figure 7.2). The levels are connected through a continuous story, with the participant being the responsible negotiator for a procurement department. In the first level, participants face a simple single-issue negotiation about the price of a product and learn to exchange messages in the system. In level two, the decision support by means of the utility value and a visualisation of one’s preferences is introduced and helps the participants in their first multi-issue negotiation. Level three includes the history graph as additional visualisation supporting decision-making and an enhanced communication support feature called semantic enrichment (Schoop 2010). Level four is designed as a complex negotiation with several issues and level five represents a competitive negotiation. The features in the first three levels are presented using a guided tour before participants start negotiating the corresponding case allowing them to apply these features. The increasing challenges and their implementation using levels is, therefore, realised by making the negotiation simulations and the NSS more complex. Furthermore, levels provide the learners with clear goals to work for and visualise the progress towards these goals (Mekler et al. 2017).

Figure 7.2 Level Overview with Levels 1 to 3

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Negotiation tasks are always interdependent and a negotiator’s behaviour depends on the negotiation partner (Bichler et al. 2003). In order to keep the difficulty in the levels consistent and to train communication behaviour, the participants negotiate with an automated software agent called Tactical Negotiation Trainer (TNT) (Melzer et al. 2012). Based on predefined preferences and a strategy, the TNT creates a new counteroffer corresponding to its concession strategy and generates a matching text message presenting its arguments and requests using a sentence recommender. Since the TNT replies within a few seconds, immediate feedback is provided to the human participant on whether the negotiation behaviour, i.e. strategies and tactics, turn out to be successful or not. In all levels, the preferences of the human negotiator are already defined and cannot be modified. Therefore, once the trainee is familiar with the case and the given preferences, they initiate the negotiation and send a first message. When an agreement has been found, utility rankings provide feedback. Three rankings display the individual performance as well as the sum of the negotiators’ individual performances (i.e. the joint utility) and the contract imbalance of the agreement. Rankings provide informational feedback (Mekler et al. 2017); however, this feedback is relative and depends on the performances of others. A top position in the ranking does not necessarily mean that an excellent agreement has been found; there might still be potential for improvement on both negotiation sides. Whilst the joint utility ranking over all participants shown in Figure 7.3 suggests that the maximum joint utility that was reached is 121.50%, this value is not necessarily the highest possible utility; joint utility could thus be further maximised. Therefore, a classic feedback visualisation from the negotiation literature was added in this study, namely the Pareto graph (Tripp and Sondak 1992). In contrast to the Pocket Negotiator by Jonker et al. (2017), that displays the graph during the negotiation based on the assumed preferences of the negotiation partner, our graph is displayed after an agreement has been found. The graph has two axes which display the real utilities of partner 1 and of partner 2 (both represented by their role names in the negotiation and not by their real names), each ranging between 0 and 100 (see Figure 7.4). It displays all Pareto-optimal agreements as small red points indicating the Pareto frontier; the settled agreement is displayed as a big blue dot. Consequently, the user can clearly see how close the settled agreement is to the Pareto-optimal agreements. In contrast to the utility rankings, the graph offers absolute feedback about one’s performance. Although the graph is not a typical game design element at the component level in terms of the game element hierarchy by Werbach and Hunter (2012) and certainly domainspecific, the provision of absolute feedback at the level of mechanics comparing

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Figure 7.3 Anonymised Joint Utility Ranking

a user’s performance to an absolute standard (in this case: the Pareto frontier) is a common game element (Burgers et al. 2015). A discrepancy between the settled agreement and the Pareto-optimal agreements can be expected to motivate users to repeat the negotiation simulation and to find a better agreement. Based on the individual utility in the utility rankings, participants are provided with information about how well they are currently performing during their ongoing negotiation. To this end, the current individual utility is compared to the utility values of all other participants that are currently negotiating or have already reached an agreement. A small textual information was added which we call process feedback, that gives the participants the information that they are currently among the top 10%, the top 25%, the upper or the lower half of all negotiators in this level. The process feedback is immediate and is refreshed each time a new message is received. Last, two reward components are present to induce positive emotions and to lead to intensified system use. Experience points serve as an immediate feedback for performed actions (Sailer et al. 2013). Several actions are rewarded, e.g. sending messages, viewing one’s preferences during the negotiation, or using other system features. Users can compare their obtained experience points in a ranking. Furthermore, a page displaying the recently earned experience points is available to ensure transparency about the point rewarding mechanism. The

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Figure 7.4 Pareto Graph Display

second implemented reward mechanism are badges (see Figure 7.5). A separate badge page lists all 25 badges and their corresponding unlock instructions since badges should have a clear goal setting function to be motivating (Hamari 2017). On the one hand, the badges award desirable and intensified system use, e.g. the “Process Analyser (Bronze)” for analysing the history graph (Schoop 2010) for the first time. On the other hand, the badges also include more difficult goals to work for, such as “The Maximiser” for finding an agreement which is very close to the maximum joint utility that can be achieved in the negotiation. Users receive a notification on their screen once they unlock a new badge. Rewards are considered to be effective for short-term or intermittent system use facilitating

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extrinsic motivation (Liu et al. 2017). In our context, we expect them to increase in-depth negotiation training and desirable system use.

Figure 7.5 Badge Page Showing all Unlocked Badges

The gamified Negoisst provides several components for the participants with goals to work for; most of them prominently displayed on the home screen (see Figure 7.6). All components show either immediate or delayed feedback. Whilst the rewards and rankings may be perceived as extrinsically motivating components first, intrinsic motivation might also be facilitated on the long run: A participant has the freedom to define the negotiation strategy for each level and can repeat a level, e.g. when the negotiation was unsuccessful or the outcome turned out to be inefficient. Freedom of strategic choices, feedback components and the option to repeat levels reduces thoughts about negative consequences and can incentivise participants to experiment with different negotiation approaches. Participants’ autonomy is further facilitated through the freedom to decide which goals they would like to pursue (e.g. which badge to unlock, which position in the ranking to strive for).

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Figure 7.6 Home Screen of the Gamified Negoisst System

7.4

Research Design

To answer the research questions and to evaluate our artefact, we performed a quasi-experimental study in November 2019.

7.4.1

Participants & Setting

The evaluation of the training was conducted involving 234 students from four universities in Austria, the Netherlands and Germany, 218 of whom were graduate students. Each university offered a negotiation course for their students participating in the study. The courses taught negotiation theory and practice. 81 students participated in course 1 (Germany), 24 students participated in course 2 (Austria), 54 students participated in course 3 (Austria) and 75 students participated in course 4 (Netherlands). 221 of the participants studied management, business administration, information systems, or business communication and digital media. All students gave their consent before participating in the study. Regardless of their training performance, the students received credit points for the participation in the training and for completing all online surveys, which are described in section 7.4.2. As part of the negotiation lecture course at each university, the students participated in an international negotiation simulation. To prepare, all students took part in an e-negotiation training with Negoisst once they had gained fundamental face-to-face negotiation skills. After the students were trained to negotiate electronically using the Negoisst system, they conducted bilateral negotiations with students from the other universities. The data obtained during the training phase

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and the international negotiation have been used to answer important research questions over many years (e.g. Filzmoser et al. 2016; Melzer and Schoop 2016) as this setting has been in place for over ten years. The current paper reports on the 2019 experiment. The conventional e-negotiation training (c-training), i.e. the non-gamified existing training, is conducted by the same instructors that also teach their students during the regular negotiation lecture course. All instructors have decades of experience in conducting such a training and frequently exchange the contents and the pedagogical methods in their courses, thus, ensuring that all students obtain the same knowledge in an identical way. The c-training is compared to the new gamified training which was developed as part of the research reported on in this paper.

7.4.2

Experiment Procedure

The experiment included the training to be evaluated, in which the students learned to use the NSS and to negotiate electronically, followed by the five-day international e-negotiation. In the international e-negotiation participants negotiated in a bilateral setting. We focus on the training phase and will not analyse the negotiation outcomes of the five-day e-negotiations, as these are strongly influenced by the negotiation partner and their behaviour, which might interfere with our experimental manipulation. However, we can analyse the participants’ system use during these e-negotiations, which is less dependent on and influenced by the negotiation partner as the negotiations had to be conducted via the system. The negotiation courses were assigned a-priori to the control group or to the gamified group using the designed system. We chose this approach instead of a randomised assignment, as students of the same university could become aware about the different system features within their course, i.e. the included game components, which would confound our experimental setting and results. Students in courses 1 & 2 were assigned to the gamified training group (g-training) and students in courses 3 & 4 were assigned to the control group participating in a conventional training (c-training). In a first online survey before the training, students’ demographics and their intrinsic motivation for the overall negotiation course were assessed. Afterwards, the two types of training took place within a timeframe of nine days. The c-training was conducted during the regular lectures of the courses by their instructors. G-training participants could choose their preferred time to complete the training. Both types of training are expected to require about 90 minutes for each student.

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In the g-training participants had to complete the first three levels successfully. The slides from the c-training were the basis for the contents of these levels presented in the guided tours, ensuring the same effectiveness. Its effectiveness and contents were furthermore evaluated by one instructor of a c-training. In the g-training all previously described game components were present. The ctraining was conducted in a face-to-face setting and followed an enactive method (Melzer and Schoop 2016). The instructor presented the NSS and its features first followed by the students implementing their chosen negotiation strategy in a negotiation using a trial-and-error approach. During that training, students prepared for and completed a multi-issue negotiation task. In the present experiment, this task was identical to the third level in the g-training. With the instructor acting as a moderator, students discussed their results after the preparation phase and after the negotiation(Melzer and Schoop 2016). Similar to the g-training, the participants negotiated with the TNT (Melzer et al. 2012) and received immediate responses. However, their version of Negoisst did not contain any of the gamified components. In both training settings, participants could continue to work with the system and practice e-negotiations as often as they liked during the allotted nine days. As a measure for voluntary engagement all concluded negotiations were counted. After the training was conducted, participants had to fill in a second online survey measuring their intrinsic motivation for the training. The survey further included quiz questions to assess their learning outcomes regarding their understanding of the NSS and their ability to perform e-negotiations, as well as an evaluation of the training and the integrated components.

7.4.3

Data Collection & Analysis

The students’ intrinsic motivation was measured using the Intrinsic Motivation Inventory (IMI) by Ryan et al. (1983), which is rooted in the self-determination theory (Ryan and Deci 2000b) and is an established measurement for intrinsic motivation for gamification (Seaborn and Fels 2015). We used the IMI’s subscales “interest/enjoyment” and “perceived competence”. The interest/enjoyment subscale is the self-report of intrinsic motivation. Perceived competence is a positive predictor of intrinsic motivation (Ryan and Deci 2000b) and particularly interesting for gamification research, as several game elements provide competence-confirming feedback (Sailer et al. 2017). We assessed intrinsic motivation and perceived competence before the training (i.e. their motivation and competence for the negotiation lecture course) and after the e-negotiation training.

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The analysis follows a repeated measure design, which allows to adjust for any differences in motivation between the training groups and—since instructors of the c-training are also the instructors of their negotiation lecture course—enables analysing motivational changes for the training. All variables were measured using five items and a 7-point Likert scale from “Strongly disagree” to “Strongly agree”. Cronbach’s alpha shows good reliability for the four measured variables “intrinsic motivation for the negotiation course” (α = .86), “perceived competence for the negotiation course” (α = .86), “intrinsic motivation after the training” (α = .90) and “perceived competence after the training” (α = .86). Learning outcomes were measured using multiple-choice quiz questions with four answers each in the second survey. In the following, we distinguish between learning outcomes for the system (LO system), i.e. participants’ knowledge about the NSS, and learning outcomes for e-negotiations (LO e-negotiation) referring to their ability to conduct e-negotiations, especially relating to their decision making (Schmid et al. 2020). Four questions measured LO system and three questions LO e-negotiation (see appendix). Each correct answer per question was awarded with one point, whereas wrong answers led to a subtraction of one point. Additionally, participants evaluated their training using a 5-point Likert scale with scores from “1 very good” to “5 poor”. They were asked to evaluate how well the training helped them to get used to the system, how well it helped them to learn to negotiate electronically and how they would evaluate the overall feedback gathered during the training. Finally, the students’ engagement was analysed using one objective and one subjective measure. As objective measure, we use the participants’ voluntary engagement to further their skills. Participants in both types of training had the option to practice by conducting additional negotiations in the system. Therefore, all additional negotiations were counted. If participants in the g-training failed to pass one of the first three levels and had to repeat the negotiation again, this negotiation was not counted. As a subjective measure, we included the effort subscale of the IMI (Ryan et al. 1983). This variable was measured using five items and the same 7-point Likert scale used for the previously presented IMI variables. Cronbach’s alpha reveals good reliability (α = .83). To evaluate our design in detail, the participants were asked about their perceptions of the integrated game components. We used seven items for each component which cover a broad spectrum of desirable and undesirable gamified education. We asked participants, whether they felt generally motivated by the element, whether the element made them strive to be the best or better themselves, and whether they consider its use to be enjoyable. For effective learning we have argued for the importance of feedback. Two questions assessed whether participants perceive the element to have helped them in their learning tasks and

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to have provided valuable feedback. To identify potentially negative effects of the elements, the participants stated whether they perceived the element to be demotivating and distracting (Blohm and Leimeister 2013), which e.g. could potentially occur for the badge notifications. Answers were given on a 7-point Likert scale from “Strongly disagree” to “Strongly agree”, and also included “not applicable” if participants could not answer. We complement and improve our data analysis using log file analysis. Every HTTP-request in Negoisst has been tracked with a timestamp. The collected data allows us to analyse which participant has used which feature or component in the system and how often these were used. This enables us to exclude ratings of components that were not used at all. Furthermore, while negotiation outcomes in any negotiation are highly dependent on the negotiation partner, we will analyse the impact of the training on the use of three NSS features in the international e-negotiation. The features were presented in both types of training and are expected to improve participants’ rationality, decision-making, and communication quality, namely the visualisation of their preferences, the history graph, and semantic enrichment (Schoop 2010). Statistical analysis was performed using IBM SPSS Statistics 27. From the original data set of 234 participants several participants had to be removed from the data analysis. Participants were excluded from analysis if they had not answered both surveys or did not participate in the training. Three participants were excluded due to contradictory answers for the reverse items in the IMI, i.e. for strongly agreeing on finding the training very interesting and very boring at the same time. In total, 201 participants remained for the analysis.

7.5

Results

After cleansing, 91 participants in the g-training and 110 in the c-training remained for statistical analysis. Participants in the c-training group are slightly older (M = 24.62, SD = 2.72) than the g-training participants (M = 24.01, SD = 2.20). Gender distribution is unequal between the groups: While female (43) and male (48) participants in the g-training are almost balanced, the c-training includes more female (78) than male participants (32). To ensure that there is no selection bias and that the groups have similar relevant characteristics for an electronic negotiation training (Shadish and Cook 2009), participants were asked about their how often they use electronic devices within a month (from “1— once a month” to “5—several times a day”) as well as their average duration of daily use (from “1—less than 2 hours” to “5—more than 8 hours”). Table 7.1

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shows the mean values per group and the test statistics, revealing that there are no significant differences between the groups. Table 7.1 Descriptive and Test Statistics for Participants’ Use of Electronic Devices G-Training

C-Training

Mann-Whitney U test

Variable

Median

Mean (SD)

Median

Mean (SD)

U

z

p

Use Frequency

5.00

4.95 (0.23)

5.00

4.97 (0.16)

4866.5

−0.996

.319

Duration of Use

4.00

3.57 (1.07)

3.00

3.37 (1.03)

4462.5

−1.375

.169

7.5.1

Impact on Participants’ Motivation

To answer the first research question regarding the impact of the training on participants’ motivation, their intrinsic motivation and perceived competence for the negotiation course before the training (pre-score) and their intrinsic motivation and perceived competence after the training were measured. The means, standard deviations, and correlations for the four variables are shown in Table 7.2. Before the training, participants in the c-training reported slightly higher intrinsic motivation. In both types of training, intrinsic motivation decreased, with the g-training participants reporting slightly higher intrinsic motivation after the training. Perceived competence was higher in the c-training group before and after the training. However, in both types of training the perceived competence slightly improved. Participants’ pre-score of intrinsic motivation is significantly correlated with the intrinsic motivation after the training (r = .30). According to SDT, perceived competence is a positive predictor of intrinsic motivation. Therefore, the significant correlations between perceived competence after the training and intrinsic motivation after the training (r = .42) and between perceived competence (pre-score) and intrinsic motivation (pre-score) (r = .38) make sense. Additionally, perceived competence (pre-score) significantly correlated with the perceived competence after the training (r = .33). To analyse the effect of the training on participants’ intrinsic motivation, we conducted a repeated measures ANOVA. Our experimental design can be described as a “one between” and “one within” factor design (Stevens 2009), where time is the within-subjects factor and the participation in the training the between-subjects factor. Since we only compare two settings at two points of

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Table 7.2 Descriptive Statistics and Correlations for Motivation Variables G-Training Mean (SD)

C-Training Mean (SD)

Total Mean (SD)

1

2

3

Intrinsic Motivation after the training

5.17 (1.05)

4.93 (1.11)

5.04 (1.09)

–

–

–

Intrinsic Motivation (pre-score)

5.22 (0.92)

5.31 (0.75)

5.27 (0.83)

.30**

–

–

Perceived Competence after the training

4.55 (0.92)

4.72 (0.98)

4.64 (0.95)

.42**

.10

Perceived Competence (pre-score)

4.42 (0.85)

4.67 (0.84)

4.56 (0.85)

.09

.38**

–

.33**

Notes: ** p < .01.

time, the assumption of sphericity is not relevant (Field 2018). Applying the repeated measure ANOVA, the analysis of the effect on intrinsic motivation revealed a significant effect of time, F(1, 199) = 6.79, p = .010. On average, the reported intrinsic motivation decreased for participants of both types of training. However, intrinsic motivation in the c-training decreased more drastically than in the g-training. The main interaction effect between time and training had a significant effect on intrinsic motivation, F(1, 199) = 4.05, p = .045. Consequently, participants in the g-training could maintain their intrinsic motivation level over time, whilst participants in the c-training report lower intrinsic motivation after the training. No significant time or interaction effects between time and training were found for perceived competence.

7.5.2

Impact on Engagement and Learning

The self-reported effort and the voluntary engagement of participants to conclude additional negotiations were analysed to answer research question 2; and the learning outcomes of participants were analysed for the multiple-choice questions to answer research question 3. According to an a priori conducted KolmogorovSmirnov test, normality distribution cannot be assumed for the learning outcome

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(LO) variables and the additional negotiations. We will, therefore, use a nonparametric Mann-Whitney U test for comparison between the groups. Regarding the effect on engagement, participants in the g-training reported higher effort than c-training participants (see Table 7.3). An independent samples t-test revealed no significant differences (t (199) = −1.333, p = .18). The number of additional completed negotiations was higher in the g-training group (Mdn = 2) than in the c-training (Mdn = 0). This difference was highly significant (U = 2443.50, z = −7.12, p < .001) and resulted in a large effect (r = −.50). The learning outcomes for the system could range between −16 and + 16 and were higher in the g-training group (Mdn = 6) compared to the c-training group (Mdn = 2). System learning outcomes differed with a high significance (U = 2088.50, z = −7.19, p < .001) and also resulted in a large effect (r = −.51). The learning outcomes for e-negotiations could range between −12 and + 12. Again, the gtraining group performed better (Mdn = 10) than the c-training group (Mdn = 8), resulting in a significant difference and a small effect (U = 3816, z = −3.00, p = .003, r = −.21). Table 7.3 Descriptive Statistics for Engagement and Learning Outcomes G-Training Median

C-Training Mean (SD)

Median

Mean (SD)

Effort

5.20

5.14 (1.00)

5.00

4.96 (0.94)

Additional negotiations ***

2

2.98 (3.51)

0

0.41 (1.23)

LO system ***

6

6.51 (3.98)

2

2.27 (3.29)

LO e-negotiations **

10

9.32 (3.13)

8

7.73 (3.92)

Notes: ** p < .01, *** p < .001.

Since gender distribution is unequal between the two training groups, gender differences were assessed. While gender had no significant impact on participants’ intrinsic motivation, some interesting results exist for participants’ engagement and learning outcomes. For both types of training, females reported higher invested effort (see Table 7.4). In the g-training group, there was a significant difference between male and female participants (t (89) = 2.112, p = .038). Female participants in the g-training conducted more additional negotiations than males, also resulting in a significant difference (U = 778.5, z = − 2.07, p = .038). However, male participants in the c-training were more likely to perform additional negotiations than females. Regarding the learning outcomes, male participants achieved better system and e-negotiation learning outcomes in

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both types of training. These differences were significant for the system learning outcomes in the g-training (U = 766, z = −2.14, p = .032) and the c-training (U = 740.5, z = −3.41, p = .001). Table 7.4 Mean Values per Gender for Engagement and Learning Outcome Variables Females (G-Tr.) Males (G-Tr.) Females (C-Tr.) Males (C-Tr.) Effort

5.37

4.93

5.05

4.73

Additional negotiations 3.51

2.51

0.24

0.81

LO system

5.67

7.25

1.69

3.69

LO e-negotiations

8.79

9.79

7.44

8.44

The participants also evaluated the training regarding its subjective impact on their learning using scores from “1 very good” to “5 poor”. G-training participants evaluated their training to have helped them getting used to the system significantly better than c-training participants (see Table 7.5). The same pattern with slightly lower scores can be found for the question whether the training helped to negotiate electronically: G-training participants rated their training better than the c-training participants, but no significant effect was found. The overall feedback received during the training was rated the worst in both types of training but was significantly better evaluated in the g-training than in the c-training. Table 7.5 Participants’ Evaluation of the Training using Scores G-Training

C-Training

Mann-Whitney U test

Median Mean (SD) Median Mean (SD) U

z

p

r

1

1.52 (0.58) 2

1.92 (0.90) 3811.5 −3.20 .001 −.23

E-Negotiation 2 Training Evaluation

1.96 (0.67) 2

2.23 (0.94) 4321

Feedback Evaluation

2.27 (0.83) 2

2.49 (0.93) 4248.5 −1.98 .048 −.14

System Training Evaluation

2

−1.86 .063 −.13

140

7.5.3

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Evaluation of Integrated Components

To evaluate our design in detail and answer the fourth research question, the participants were asked about their perceptions of the included components. The means for each of the components and the measured seven items are shown in Table 7.6. Note that not every participant evaluated every component. Furthermore, we removed ratings for the utility rankings and the Pareto graph from the analysis, if the log file showed that the participants had not viewed the component at all. The use of the other components was either obligatory (i.e. the levels) or could be seen on the start-page or as notifications in the system. In sum, the levels as the most central part of our gamified system were perceived most positively. They were perceived as motivating, enjoyable and helpful for learning tasks. The utility rankings also revealed motivational power, and their feedback on negotiation performance was considered to be valuable. Interestingly, the Pareto graph as a negotiation specific feedback component still possesses much motivational power and made participants strive to better their outcome. The graph’s feedback was perceived as the most valuable feedback component. The most controversial component was the process feedback. It received the lowest scores in five of seven categories. The high standard deviations further indicate that this component was perceived differently. The two reward components, i.e. experience points and badges, revealed quite similar results and were considered to be particularly motivating and enjoyable, and scored only average regarding their feedback and impact on learning. In general, the badges were perceived more positively than the experience points. Overall, this analysis confirms large parts of our system design. Splitting the participants by gender reveals some interesting but not significant tendencies regarding the perceptions. The levels were more motivating for females (Mfemale = 5.54; Mmale = 5.36) and made them strive harder to better their performance (Mfemale = 5.58; Mmale = 5.29). The Pareto graph was more enjoyable to females (Mfemale = 5.03; Mmale = 4.52) and made them strive harder to better their performance (Mfemale = 5.28; Mmale = 5.09). In contrast, males perceived the utility rankings to be more enjoyable (Mfemale = 4.81; Mmale = 5.15) and helped them strive harder to be the best (Mfemale = 5.71; Mmale = 5.96). Male participants also found the badges to be more motivating (Mfemale = 5.40; Mmale = 5.70) and enjoyable (Mfemale = 5.05; Mmale = 5.34) than female participants.

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Table 7.6 Mean Values for the Evaluation of the Integrated Components Level (n = 87)

Utility Rankings (n = 54)

Pareto graph (n = 53)

Process Feedback (n = 89)

Experience Points (n = 86)

Motivating

5.44 (SD = 1.09)

5.54 (SD = 1.53)

4.96 (SD = 1.25)

4.88 (SD = 1.78)

5.21 5.56 (SD = 1.47) (SD = 1.40)

Demotivating

2.60 (SD = 1.32)

2.89 (SD = 1.57)

2.51 (SD = 0.95)

3.49 (SD = 1.67)

2.70 2.34 (SD = 1.31) (SD = 1.13)

Enjoyable

5.29 (SD = 1.32)

4.98 (SD = 1.55)

4.81 (SD = 1.36)

4.09 (SD = 1.51)

5.12 5.21 (SD = 1.42) (SD = 1.54)

Try to be the best/better oneself

5.42 (SD = 1.11)

5.83 (SD = 1.08)

5.20 (SD = 1.30)

5.07 (SD = 1.55)

5.08 5.03 (SD = 1.31) (SD = 1.55)

Helps Learning

5.61 (SD = 1.09)

4.40 (SD = 1.65)

4.90 (SD = 1.43)

4.13 (SD = 1.45)

4.23 4.45 (SD = 1.56) (SD = 1.55)

Valuable Feedback

5.08 (SD = 1.28)

5.22 (SD = 1.49)

5.66 (SD = 1.32)

5.02 (SD = 1.53)

4.44 4.95 (SD = 1.52) (SD = 1.59)

Distracting

3.13 (SD = 1.33)

3.15 (SD = 1.39)

2.68 (SD = 1.36)

3.90 (SD = 1.69)

3.43 3.11 (SD = 1.48) (SD = 1.50)

7.5.4

Badges (n = 89)

Impact on Subsequent System Use

After the training, all but three participants engaged in a five-day international e-negotiation. The participants continued to use the gamified or non-gamified system they used in the training. We only analysed the participants’ individual system use by analysing our log files and the messages exchanged to answer our fifth research question for reasons discussed before. First, we assessed how often the participants analysed their preferences, which is useful for the preparation and offer construction. The second feature is the history graph (Schoop 2010). Both features are expected to increase participants’ rationality and improve their decision-making. Since participants’ need to grasp the displayed information, only those data sets were considered that showed a time of use of at least 10 seconds. Finally, the exchanged messages and how often the communication

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feature semantic enrichment were analysed (Schoop 2010). The semantic enrichment is used in the textual messages to avoid misunderstandings and to improve communication quality. The results in Table 7.7 show that on average participants of the gamified training used all of the system features more often. Since there is a non-normal data distribution, a non-parametric independent samples Mann-Whitney U test was conducted. In general, the participants of the g-training analysed their preferences more often, resulting in a statistically significant effect (p < .001) and a medium effect (r = −.38). Also, g-training participants analysed the negotiation process more often (which is statistically relevant with p < .001) which resulted in a small effect (r = −.27). Last, participants of the g-training also used the semantic enrichment feature more frequently, which yields another statistically significant difference (p < .001) and a medium effect (r = −.41). Table 7.7 System Feature Use after the Training

Feature Use

G-Training (n = 89)

C-Training (n = 109)

Mann-Whitney U test

Median Mean (SD)

Median Mean (SD)

U

z

p

r

Preferences Analysed

4

5.43 (5.14)

1

2.01 (2.85)

2722 −5.42 < .001 −.38

Negotiation Process Analysed

3

3.56 (3.15)

1

2.14 (2.34)

3331 −3.84 < .001 −.27

Semantic 21 Enrichment Used

7.6

24.17 (18.01) 0

10.94 (15.01) 2582 −5.77 < .001 −.41

Discussion

In the present study, we report on the design of a gamified NSS used in e-negotiation training to improve participants’ motivation, engagement and resulting learning outcomes. Using a quantitative study, we evaluated our designed artefact by comparing it with a conventional e-negotiation training (Melzer and Schoop 2016). We further provided an analysis of the included components on how they support participants’ motivation and learning. For the first research question, the effects of the gamified NSS on participants’ motivation were analysed. The participants’ intrinsic motivation before

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143

and after the training was measured. A repeated measure ANOVA revealed a significant interaction effect between time and training. While participants’ intrinsic motivation in the c-training decreases, intrinsic motivation for g-training participants is on an almost constant level. After the training, the c-training participants report lower intrinsic motivation than g-training participants. In general, negotiations as a soft-skill topic build upon intrinsic motivation to learn about it (Melzer 2018). Traditional negotiation training creates large involvement with the learners through role plays, cases, and discussions (Lewicki 1997). Prior research assumes that intrinsic motivation of participants before and during an e-negotiation training is also very high and facilitates self-regulated learning (Melzer and Schoop 2015). However, negotiation training systems have recently been criticised for neglecting the importance of facilitating motivation for the training tasks (Ding et al. 2020). The results support our assumption that these conventional forms of e-negotiation training do not facilitate intrinsic motivation sufficiently, as intrinsic motivation decreases during the training. Using the gamified artefact, participants’ intrinsic motivation for the course could be maintained throughout the e-negotiation training. According to the results and in line with SDT, perceived competence is a strong predictor of intrinsic motivation (Ryan and Deci 2000b). However, in this study perceived competence was not affected by the participation in the training. On the one hand, this is surprising, as several components implemented in the NSS such as badges, points, and rankings provide competence-confirming feedback (Sailer et al. 2017). On the other hand, the controversial process feedback might have diminished perceived competence. Potential reasons for the difference in intrinsic motivation between the groups might be related to the satisfaction of the psychological needs for autonomy and relatedness, which have not been measured in this study. Regarding our research questions two and three, we were interested in the effects on participants’ engagement and their learning outcomes. Although participants in the g-training already had to complete the first three level negotiations, they were motivated to further engage in more additional negotiations than those in the c-training group. Qualitative interviews in a previous study revealed that the relative feedback on their negotiation performance through the utility rankings motivated them to repeat a level and experiment with other negotiation strategies (Schmid et al. 2020). In this study, we additionally included the Pareto graph to provide an absolute feedback, allowing to assess missed negotiation potential, and to facilitate the crucial reflections for the experiential learning methodology (Kolb 1984). In general, participants’ need for social comparison to assess

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one’s performance differs (Schöbel et al. 2017). Offering both absolute and relative feedback through rankings and the Pareto graph provides motivating and informative feedback for all participants. Significantly better learning outcomes—relating to participants’ decisionmaking and important negotiation skills such as preparedness, rationality, and strategic behaviour—were obtained by the participants in the g-training. Whilst negotiators so far often agree on inefficient agreements (Gettinger et al. 2016), we expect our participants in the g-training to settle on better or less inefficient agreements. An even stronger effect on participants’ learning outcomes exists for the system skills. The participation in increasingly more complex levels in combination with the feedback provided through game elements and an improved intrinsic motivation appear to be an effective mechanism for participants to be deeply engaged in the system. For novice users of an NSS, the cognitive burdens are very high (Schmid & Schoop 2019). Intrinsic motivation is likely to occur when the task at hand is considered to be both challenging and attainable, matching the current skills of an individual (Csikszentmihalyi 1990). This is done in our artefact by providing increasingly more complex levels and system features on each level. The c-training, presenting all of the features at once and starting with multi-issue negotiations, might overwhelm the participants and is less effective for learning according to the results. An analysis of the game elements in this study confirm that participants liked learning with the levels. While Urh et al. (2015) recommend to divide the main learning task into smaller sub-tasks, Alcivar and Abad (2016) and the results of this study particularly show the effectiveness of structuring the tasks using levels for system training. Within the g-training a significant impact of gender was found. While females and males reported similar intrinsic motivation, females’ subjective and objective engagement was higher than for males. So far, prior research revealed only perceptual differences regarding social benefits of gamification (Koivisto and Hamari 2014) or different perceptions of the game components (Codish and Ravid 2017). The higher engagement of females might stem from the perception of levels to be more motivating for female participants than for their male colleagues. The role of gender remains to be explored in more detail, as our findings also suggest the badges to be more motivating for males, which contradicts the findings of Codish and Ravid (2017). Differences between the implementations and goal setting of the badges in these two studies might cause different perceptions, which need to be further investigated. To further analyse research questions two and three, the participants assigned scores for their perception of the system training, e-negotiation training, and overall feedback. These scores reflect the results of the learning outcomes very well,

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145

i.e. the participants perceived the g-training to be very good (which is the highest score) for system training and much better than their c-training counterparts. The impact on e-negotiation training was also evaluated better for the g-training than for the c-training but the difference is smaller. The overall feedback in both types of training was characterised as satisfactory and better in the g-training, but still improvable. We acknowledge, that most of the feedback in the gamified artefact is centred around the participants’ learning progress and their decision-making in e-negotiations and less around their communication behaviour. The TNT replying to the messages of the human negotiator is limited in its ability to reflect human communication behaviour, e.g. to detect and reflect emotions or different levels of politeness. One potential technology to improve the TNT is bot technology, which is capable of realistically imitating human behaviour (Ferrara et al. 2016) and might improve realism in e-negotiations (Schmid et al. 2021). A recent study on feedback in e-negotiation training shows that participants want additional features such as an expert review or the possibility to set and track their negotiation goals, all of which improve negotiation skills such as preparedness, effectiveness, goal-orientation, rationality, strategic and problems-solving (Meyer et al. 2020). Including such elements could further improve the acquisition of e-negotiation learning outcomes in this complex application domain. In its current form, the artefact might also be used by business organisations for e-negotiation training. When the TNT is capable of answering in a more realistic way, further levels including different negotiation simulations can be added for a more extensive e-negotiation training. The current components included in our gamified system and their perceptions of the participants have been investigated as part of research question four. Similar to other gamified learning interventions (e.g., Buckley and Doyle 2017; Putz et al. 2020) we have integrated several game elements contributing to different parts of the learning experience. According to Dicheva et al. (2019) it is important to focus on the holistic learning experience provided by the use and combination of different components. These different components contribute to different objectives of a gamified learning intervention, i.e. to pedagogical objectives and/or the facilitation of psychological needs. Our results do not allow to draw direct conclusions regarding their individual impact on the measured variables. However, they provide an evaluation for each component, i.e. whether they are perceived as intended or need to be revised, and give a rough estimation on how they might contribute to the results. The levels—the only component whose use was enforced—were very positively perceived among all categories, i.e. they are perceived as being very helpful for learning while motivating the participants through clear intermediate checkpoints and feedback on their progress

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(Glover 2013). The rewards and the feedback by badges and experience points were perceived as less helpful for learning but make the learning experience more enjoyable. The utility rankings together with the badges are the most motivating component. Utility rankings provide informational performance feedback, motivated the participants to do their best and to improve their performances. As another performance feedback visualisation, the Pareto graph was included as a domain-specific feedback component. Surprisingly, the Pareto graph was perceived to be motivating in general and less demotivating than the utility rankings. Furthermore, the Pareto graph was evaluated as being more helpful for learning than the utility rankings and scored as the best component regarding its valuable feedback. In fact, absolute feedback can be more powerful than relative feedback (Moore and Klein 2008) and evaluating a score against an absolute standard is also common in several games. The review by Koivisto and Hamari (2019) shows that points, badges, and leaderboards are still most frequently used in gamified systems. They also observe that several studies incorporate other gamification elements as well. Our results suggest that using a domain-specific feedback component can also yield motivational power. We would, therefore, encourage other gamification designers to broaden their point of view by searching for such feedback components, as they can serve as an important addition for a gamification design. The most controversial component was the process feedback displayed during the negotiation process. It was perceived to be the least motivating and most demotivating component. If comparative feedback is present, participants are more oriented towards social comparison behaviour and will experience greater pressure (Huschens et al. 2019). In contrast to the informational feedback providing utility rankings, the process feedback might be perceived as much more comparative and competitive. Another issue explaining the rather negative evaluation of this component might be the design of the process feedback messages themselves, which confront low performers with the coarse feedback that they belong to the lower half. More detailed feedback as to whether these low performers belong to the third quarter or the last quarter could be more informative and motivating. Based on the results, we will question the use of this component in a further design iteration. Additionally, we encourage other researchers designing and evaluating complex gamified learning interventions to adapt the seven suggested items, helping them to detect weaknesses and strengths in the design. Mixed-method studies in early design stages might further help to improve the gamification design and gather insights into participants’ perceptions.

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147

Finally, the g-training also had a positive effect on participants’ subsequent use of the system in an international negotiation (RQ 5). All three system features improving participants’ decision making or communication behaviour had a significantly higher frequency of use by participants of the g-training. However, all participants used the system only for the duration of three weeks which is rather short. Two reward components, i.e. the experience points and the badges, awarded desirable system use. The use of rewards is often debated, as they lead to rather extrinsically motivated behaviour and may undermine intrinsic motivation (Deci et al. 1999). Liu et al. (2017) consider the use of rewards to be effective for short-term or intermittent system use. At least for short-term usage, we successfully demonstrated that employing rewards as an incentive for the use of support features of the NSS works. The insufficient use of NSS features might, therefore, be solved by the use of game elements instead of designing these systems more proactively (Druckman et al. 2012). Based on the internalisation process in SDT, Schmid and Schoop (2019) suggest that the originally extrinsically motivated behaviour (i.e. through rewards) will become more self-determined, as soon as the benefits of the features for one’s negotiation performance are recognised. However, the effects of gamification in NSS for the long-term remain unknown, and, therefore, need to be investigated as one important area for future gamification research (Nacke and Deterding 2017). Our study includes several limitations. First, we conducted a quasiexperimental study and no randomised study to evaluate our artefact. While it was necessary to run a quasi-experimental study to avoid confusion among the students within the same course about the presence of game elements, there might be differences between the two training groups affecting our results. The results might be further biased by the online vs offline setting and due to the asynchronous vs. synchronous setting, leading to effects that cannot be attributed to the gamification design only. However, a prior study showed that given the choice between an asynchronous and synchronous training, students choose their training for opportunistic reasons. Furthermore, the students participating in the asynchronous training particularly liked the game elements (Schmid et al. 2020). Nonetheless, a bias cannot be completely ruled out. Last, our learning outcomes for e-negotiations cover only the decision-making skills of the participants. An additional, more detailed analysis of their communication behaviour is required to retrieve an overall picture regarding the training’s effectiveness.

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7.7

Conclusion and Future Research

The present study reports the design and evaluation of a gamified negotiation support system, which is commonly used in e-negotiation training (Melzer and Schoop 2016). Our evaluation using a quasi-experimental design with a conventional training as control group reveals a positive effect of gamification on participants’ intrinsic motivation, enhanced engagement, and better learning outcomes. The artefact was particularly beneficial for system training, which is also manifested in improved system use by the participants of the gamified training. The effect on the acquisition of e-negotiation skills was also positive. However, we have also seen that the feedback during the training for participants’ e-negotiation skills could be further improved, as this complex task includes problem-solving, decision-making, communication, and collaboration. Overall, we successfully applied gamification in the domain of e-negotiations to provide an artefact that can be used for a motivating and engaging e-negotiation training. For both system and negotiation training, we recommend the use of increasingly more challenging tasks (e.g. in the form of levels), as they present clear proximate sub-goals to attain (Glover 2013; Urh et al. 2015) and match participants’ current skills (Lee and Hammer 2011). Our study provides several opportunities for future research. First, our research shows the effects of the individual game elements without measuring their direct impact on motivation and learning, different combinations of game elements can be tested and their effects measured (Dicheva et al. 2019). For example, the Pareto graph and the utility rankings are two competing feedback alternatives for participants’ negotiation performance. It would be interesting to see whether the use of a domain-specific feedback element such as the Pareto graph, which was evaluated as being motivating, yields the same effects as the use of the utility rankings. Second, the artefact itself provides various opportunities to include new negotiation feedback and further improve the learning process as well, as has been investigated by Meyer et al. (2020). Last, having collected more data and log files of users, we might derive certain usage patterns and game element preferences of the participants. Classifying participants according to their gamification user types (e.g., Tondello et al. 2016) and deriving usage patterns and preferences for these user types might reveal interesting results for the area of adaptive gamification research, and might also help to explain the higher engagement of females in greater detail. Adaptive gamification might be used to provide suitable game elements for each type of user and to avoid negative effects on their motivation.

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Appendix System Learning Outcome Quiz Questions: 1) Which of the following statements is true regarding the negotiation agenda for multiple issues when writing a formal message? a. For each issue a value has to be defined. b. For several or all issues the values can be left empty. (R) c. One cannot select values for the issues while writing a formal message. d. Each time the value of an issue is changed, the utility value is updated. (R) 2) What kinds of issues are represented in the Negoisst preferences? a. Numeric issues (R) b. Compatible issues c. Non compatible issues d. Categorial issues (R) 3) Which of the following statements is true for the negotiation protocol? a. I can send a Reject to end the negotiation at any time. b. I can only send a message when it’s my turn to do so. (R) c. I can send an informal message, if I’d like my negotiation partner to clarify something. (R) d. I can only accept my negotiation partner’s counteroffer, if he/she defined values for all negotiation issues. (R) 4) What is NOT a purpose of semantic enrichment? a. Clearly indicating the intention of the negotiation message. (R) b. Improving English grammar. (R) c. Consistency between the written message and the values in the agenda. d. Suggesting optimal counteroffers. (R) E-Negotiation Learning Outcome Quiz Questions:

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1) Given the preferences above, which negotiation issue or which negotiation issues are your most important ones? a. Price b. Guarantee c. Delivery (R) d. 4 years 2) Given the preferences above, which statements are correct regarding the best cases? a. The best case for the issue price is 5000. (R) b. The best case for the issue guarantee is 3 years. c. The best case for the issue delivery is overnight. (R) d. The best case for the issue guarantee is 2 years.

3) Which of the statements are true for the History Graph depicted above? a. My last offer had a utility of 50% and my partner should have a utility of 25%. b. My last offer had a utility of 50%, my partner’s utility value is unknown. (R) c. My partner’s last offer had a utility of 25% for me, so my partner should have a utility value of 75%. d. My partner’s last offer had a utility of 25% for me, my partner’s utility value is unknown. (R)

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Note: Answers marked with (R) are right answers. The quiz questions included the following instruction: “Note that for each question one or multiple answers can be correct”.

8

Discussion & Outlook

The following chapter summarises the findings of the thesis concerning the design and evaluation of the gamified e-negotiation training first. The second section of this chapter discusses the contributions of this thesis, i.e. the software artefact itself and – as a natural sequence of a design science project (Baskerville et al. 2018) – the development of design principles on how to design gamified systems and learning interventions. Finally, section three describes the limitations of this thesis, whilst the last section discusses future research avenues for a holistic e-negotiation training and for gamification research.

8.1

Summary

The overarching research goal of this thesis is to improve participants’ motivation, engagement, and learning outcomes in an e-negotiation training employing gamification. In order to reach this goal, this thesis followed a design science research approach (Hevner et al. 2004) in order to design, implement, and evaluate a gamified e-negotiation training. Game design elements are incorporated in the NSS Negoisst, which has been used in e-negotiation training for almost two decades (Schoop 2020). Overall, the design cycle, including the activities of building and evaluating an software artefact (Hevner 2007), is performed three times, resulting in different versions of the artefact and the creation of new knowledge concerning the effectiveness of those artefacts to improve participants’ motivation, engagement, and learning outcomes (Venable 2006). As a first step towards achieving the overarching research goal, the first research question “How can gamification be applied to the context of enegotiation training?” needs to be answered. Since the effects of gamification are context dependent (Hamari et al. 2014), a careful analysis of the context, its © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 A. Schmid, Gamification of Electronic Negotiation Training, Gabler Theses, https://doi.org/10.1007/978-3-658-38261-2_8

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users, and profound knowledge in game design and human motivation is necessary (Morschheuser et al. 2018). Chapter 4 presents the result of an integrative literature review investigating the application context of e-negotiation training, motivation theories, and gamification design. This theoretical basis represents the kernel theories and justificatory knowledge that is used to derive seven general requirements for the design of a gamified e-negotiation training. The seven requirements are: 1) to provide clear goals and freedom for strategic and tactical choices; 2) to balance competitive and cooperative game design elements; 3) to provide increasing and optimal challenges; 4) to provide incentives to use the support functions of the NSS; 5) to provide a mastery-approach oriented learning environment; 6) to provide constructive feedback; and 7) allow students to repeat the challenges. Finally, a framework for gamified e-negotiation training is presented, which postulates to add the game design elements around the inherent challenge of an e-negotiation training, namely, to settle on an agreement with the negotiation partner. Game design elements and other elements provide constructive feedback for the actions and requests that the participant performs within the negotiation in the gamified NSS. This may, additionally, provide motivational affordances for the participants. The framework and the seven general requirements provide first answers to research question 1 and grounds the design on a solid theoretical basis, which enables the choice of game design elements that are expected to fulfil these requirements. Chapter 5 presents a first design of a gamified NSS with game design elements that are deemed to satisfy the requirements, and thus, also answers the first research question. However, this is with a focus on the actual artefact implementation. Using the game element hierarchy by Werbach and Hunter (2012), the game design elements and their overarching mechanics and dynamics, to which these elements contribute, are explained and justified. At the core of the design are repeatable levels. The levels are connected by a continuous story, in which the participant acts as a new employee in a purchase department. Each level represents a bilateral negotiation scenario and must be solved in order to unlock the next more difficult level. The levels become increasingly difficult with regard to the available features in the NSS and the cognitive complexity of the negotiation in terms of the issues to be negotiated. Initially, a guided tour presents the new features of a level, before the participant can utilise them in the follow-up negotiation scenario. To ensure a consistent behaviour of the negotiation partner and provide rapid feedback to the participants’ latest messages, the participants negotiate with the Tactical Negotiation Trainer (TNT). The TNT follows a predefined strategy and generates text messages using a sentence recommender (Melzer et al. 2012). Since the levels can be infinitely often repeated, the participants

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do not have to fear negative consequences in the event of failure, may experiment with other negotiation approaches, and a mastery-approach oriented learning environment for skill acquisition is provided. In addition to the implementation of increasingly more challenging negotiations in the form of levels, different game design elements are included around these levels providing further motivational affordances. First, utility rankings provide feedback on the settled negotiation agreement and enable comparison with others. For each level, three rankings for the achieved individual utility, joint utility, and contract imbalance are displayed. These enable participants to compare themselves with others regarding how well they negotiated and whether they achieved an integrative and fair agreement. Second, experience points serve as an immediate feedback, positive reinforcement, and virtual reward for the performed actions in the NSS (Sailer et al. 2013). The experience points reward the use of support features and their engagement in the negotiations. A separate page provides transparency for the received experience points and a further page displays a ranking that enables participants to compare their achieved experience points with others. Last, badges are implemented and award for desirable use of the system’s features as well as outstanding negotiation performance. A page lists all available badges and their corresponding unlock instructions, which provide clear goals to work for (Hamari 2017). Participants receive a notification once they have unlocked a badge. Subsequently, the different versions of the artefact are evaluated with regard to research question 2: “Which effect does a gamified e-negotiation training have on participants’ motivation, engagement, and learning outcomes?”. In this vein, chapter 5 additionally presents the very first evaluation of the artefact using students as subjects. Employing a mixed method evaluation including quantitative and qualitative survey data as well as semi-structured interviews, the artefact is compared with an established e-negotiation training (Melzer and Schoop 2016). Participants could attend their preferred training or both, which enabled a third group to attend and compare both types of training. The quantitative data shows that participants’ motivation and perceived competence in the gamified training is significantly higher than for the conventional training. These participants also perform significantly more voluntary negotiations than the participants in the conventional training. No significant differences are obtained for the learning outcomes. These findings are reflected in the semi-structured interviews with the gamified participants: Almost every interview participant enjoyed the training and they confirmed that they gained a good understanding of the NSS. However, their opinions regarding the gained negotiation skills differed: While several participants stated that they had a good understanding of the negotiation processes,

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they still felt insecure about real negotiation scenarios and did not perceive themselves as good negotiators. Others gained self-confidence or revealed making more rational decisions. Furthermore, chapter 5 also provides first answers for the third research question, namely “How do the incorporated elements contribute to the learning experience?”. The interview participants perceived the levels very positively, as the participants continually learned something new and could subsequently apply it in a negotiation scenario. The participants completing both types of training highlighted the practically oriented approach of the gamified training. The feedback provided by the utility rankings motivated several participants to experiment with different negotiation strategies, tactics, and approaches. The interview results further revealed that the perception of the badges and experience points greatly differed: Some worked explicitly towards obtaining more points or badges, others simply liked collecting or receiving them while not explicitly working towards their achievement, whereas further participants did not care about these elements at all. In summary, the first evaluation demonstrates the effectiveness to motivate and engage participants within a gamified training, but the learning outcomes can still be improved. The study in chapter 6 investigates two different feedback alternatives for the negotiation agreements in the gamified training in order to identify the best combination of game design elements (Dicheva et al. 2019; Schöbel et al. 2020a). As an alternative to the previously implemented utility rankings, the Pareto graph is introduced as a negotiation specific feedback element. Rankings are often seen critical in gamification designs, since they lead to social comparison, increased pressure (Huschens et al. 2019), induce competition (Sailer et al. 2013), and can decrease motivation (Hanus and Fox 2015). While rankings provide relative feedback, the Pareto graph provides absolute feedback for the settled negotiation agreement. In a randomised study using students as subjects, the participants’ psychological experiences, their engagement, and their negotiation outcomes in the training are assessed in order to evaluate the effectiveness of the two artefacts (Venable 2006). The study addresses the research questions two and three. It is the only study assessing all three basic psychological needs of SDT (Ryan and Deci 2000b). Results reveal that participants’ experienced relatedness is relatively low, whereas their autonomy with regard to the decision freedom during the training is quite high. Intrinsic motivation is quite high in both groups, but does not differ significantly. There are insignificant tendencies that participants receiving absolute feedback perceive themselves as more competent to conduct e-negotiations and are more committed to settle on good agreements during the training as opposed

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to participants using the rankings. A significant difference between the two groups is found for participants’ engagement, revealing that participants using the Pareto graph are more engaged than those using rankings. Despite the higher engagement, a mixed picture is obtained from the negotiation outcomes during the training. This supports the finding that an e-negotiation training incentivises participants to experiment with different negotiation strategies, approaches, and styles (Köszegi and Kersten 2003), which may also turn out as unsuccessful and lead to worse agreements. The final study in chapter 7 presents a large-scale quantitative evaluation of the revised gamified training, comparing it once again with a conventional training (Melzer and Schoop 2016). Additionally, the study includes a detailed evaluation of the integrated elements, and, therefore, provides answers to the research questions two and three. All participants in the gamified training had access to the utility rankings and the Pareto graph. Additionally, an element called process feedback is implemented and evaluated, which informs the participants about how well they perform in their ongoing training negotiations, in terms of their individual utility compared to others. Students of four university courses participated in this study. In contrast to the study in chapter 5, these courses were assigned to the gamified or conventional training group a-priori to any data collection. Regarding research question three, the findings of the last study reveal a significant effect of the training on intrinsic motivation. Compared to the intrinsic motivation before the training and assessed for their negotiation course, the intrinsic motivation of participants in the conventional training decreased, whereas participants in the gamified training could maintain their level of intrinsic motivation from prior to the training. Learning outcomes for system and e-negotiation skills are significantly better for participants in the gamified group. However, no significant effect is found for perceived competence. Participants in the gamified training show significantly higher engagement, as they conducted more voluntary negotiations. Interestingly, female participants in the gamified training conducted significantly more voluntary negotiations than the male participants. In a followup negotiation after the training, participants’ system feature use was assessed. The findings reveal that all features are used significantly more often by the gamified training participants than by the conventional training participants. In an overall evaluation, the gamified training was rated as significantly better than the conventional training concerning the system training and the overall provided feedback. However, the findings also reveal that the overall feedback can be further improved. Although the training of e-negotiation skills was also, on average, rated better for the gamified training, no significant effect is found.

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The last study provides further interesting insights regarding research question three. In line with the study in chapter 5, levels are perceived to have a positive impact on participants’ motivation and learning. The utility rankings provide valuable feedback and motivate the participants to do their best. Partly confirming the results of the study in chapter 6, the Pareto graph – although not a game design element – is perceived as motivating too and as the most valuable feedback element. Experience points and badges mainly contribute to a motivating and enjoyable learning experience. The newly introduced element called process feedback, showing immediate feedback about their current negotiation performance relative to others, is perceived as both the least motivating and most demotivating element. The results of this thesis show that gamification of an e-negotiation training is an effective solution to improve participants’ motivation, engagement, and learning outcomes. The following section elaborates on the contributions of this thesis, i.e. the artefact itself and generalisations concerning the design of the artefact for other contexts – both in terms of design as a noun (i.e. product) and a verb (i.e. process).

8.2

Integrative Discussion of Contributions

Following a DSR approach (Hevner et al. 2004), the present thesis designs and evaluates a gamified e-negotiation training. Surprisingly, few gamification studies explicitly follow a DSR approach to rigorously develop and evaluate a gamification design. Recently, the use of such a DSR approach has been suggested to develop design theories which accumulate design knowledge related to the effects of gamification design elements (Schöbel et al. 2020a) and to derive design principles (Silic and Lowry 2020). The DSR approach applied in the current thesis aims for two major contributions. First, a new software artefact for e-negotiation training is designed and evaluated, which solves the problems concerning participants’ motivation, engagement, and learning outcomes by gamifying an NSS. Such an artefact is a situated implementation and instantiated for a particular context. It represents a specific and the least matured contribution type of DSR (Gregor and Hevner 2013), but is nonetheless a sufficient contribution to the knowledge base. Second, the thesis aims for a nascent design theory, presenting more abstract and generalised knowledge about the artefact design (Gregor and Hevner 2013). A nascent design theory can be represented in the form of constructs, methods, models, technological rules, or design principles (Gregor and Hevner 2013). Reflecting

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both on the design process and the design product, design principles are derived that are applicable to a wider range of gamification designs (Baskerville et al. 2018). The following two subsections elaborate on the knowledge contribution in the form of the software artefact first, followed by the derived design principles.

8.2.1

Gamified E-Negotiation Training as an Innovative Artefact

At the level of a specific instantiation, a DSR knowledge contribution includes artefacts which are, for example, represented as innovative software artefacts or new processes (Gregor and Hevner 2013). A DSR artefact solves identified problems within a particular environment in an innovative way. The design knowledge is often implicitly represented in the description of the form and functions of the artefact (Baskerville et al. 2018). This knowledge is limited and often less matured, but at the same time very specific and fitting to solve real-world problems within a particular problem environment (Gregor and Hevner 2013; vom Brocke et al. 2020). This thesis presents an e-negotiation training improvement by using gamification. In particular, the developed software artefact – represented by a gamified NSS – aims to improve the participants’ motivation, engagement, and learning outcomes. The methods and contents for an e-negotiation training are established (Köszegi and Kersten 2003; Melzer and Schoop 2016), but using gamification to solve these problems has not yet been attempted, which characterises the present contribution as an improvement (Gregor and Hevner 2013). At the instantiation level, the present contribution includes more than a simple description of the artefact’s form and function. This thesis contains explicit design knowledge about the kernel theories and justificatory knowledge used to formulate the artefact’s general requirements (cf. chapter 4). These requirements are derived from motivation theories that have proven to provide valuable explanations for the motivation and behaviour of learners and players, namely SDT (Ryan and Deci 2000b), flow theory (Csikszentmihalyi 1990), goal-setting theory (Locke and Latham 1990, 2002), and achievement goal theory (Elliot and McGregor 2001). To derive the requirements for the present gamified artefact, these theories are integratively reviewed together with e-negotiation processes, the experiential learning methodology used within e-negotiation training, and gamification design (Torraco 2005, 2016). Many gamification studies do not provide justification for the incorporated game design elements (Dichev and Dicheva 2017). In the present thesis, explicit

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Discussion & Outlook

justification for the selected game design elements is given through the derived requirements as well as the overarching mechanics and dynamics (Werbach and Hunter 2012) that these game design elements contribute to. Thus, the artefact’s internal structure with regard to the requirements, its components, and its specific instantiation as a gamified NSS is explained in detail and, therefore, represents explicit design knowledge for the problem environment of e-negotiation training. Regarding the effects of the artefact, the final evaluation reveals that the overall research goal is achieved, i.e. the gamified training has a positive effect on participants’ intrinsic motivation, engagement, and learning outcomes. Intrinsic motivation flourishes if the basic psychological needs for autonomy, competence, and relatedness are fulfilled (Ryan and Deci 2000b). According to the results of the last study in chapter 7, the significant differences between participants’ intrinsic motivation in the conventional and gamified training do not stem from participants’ perceived competence. Instead, the differences might be caused by fulfilment of the needs for autonomy and relatedness. The study in chapter 6 assesses all three psychological needs in the gamified training and shows that relatedness is relatively low, whereas autonomy with regard to decision freedom during the training is very high. In line with the requirement in chapter 4 to provide clear goals and freedom for strategic and tactical choices, the findings in chapter 6 suggest that the artefact supports autonomy-supportive learning, which facilitates participants’ intrinsic motivation. Both studies in chapters 5 & 7 – including an evaluation and comparison of the gamified training with a conventional training – reveal significantly higher engagement for participants in the gamified training. Participants in the gamified training conduct more additional training negotiations on a voluntary basis than participants in the conventional training. Often, participants use an e-negotiation training to test different negotiation strategies, tactics, and styles in their negotiations (Köszegi and Kersten 2003). The qualitative results in chapter 5 support the assumption that the gamified training provided the participants with affordances to experiment with different negotiation strategies and tactics. This assumption is further confirmed by the inconclusive picture regarding participants’ negotiation outcomes in chapter 6, where participants obviously experimented with different negotiation strategies and tactics to achieve a better negotiation outcome, but sometimes their chosen approach failed to achieve a better outcome. Such an experience, and reflections on this experience, facilitate the development of negotiation skills (Kolb 1984; Köszegi and Kersten 2003). Conducting more training negotiations is crucial for training participants to gain negotiation experience. Negotiation experience manifests in better negotiation behaviour and outcomes, especially in integrative negotiations (Thompson 1990a; Thompson 1990c). In

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general, spending more time in a negotiation training positively influences the participants’ skill acquisition (ElShenawy 2010). In addition to improved intrinsic motivation and engagement, the gamified training also improves learning outcomes. The effectiveness of a negotiation training can be measured on different levels, e.g. by participants’ reactions on the first level, learning on the second level, behaviour change and application on the third level, and, on the final level, the training’s impact in an organisation (Movius 2008). On the first level, participants’ reactions are often poor predictors of the training’s impact on their skill acquisition (Movius 2008). The final study indicates that participants in the gamified training are satisfied with the training’s impact for their system skills and e-negotiation skills. However, a significant difference between the gamified and conventional training group is only found for system skills. Participants’ perceived competence did not significantly differ between the two types of training. Therefore, the feedback for e-negotiation skills can be improved, as indicated by the findings in chapter 7, further examined by Meyer et al. (2020), and discussed in the outlook Section 8.4.1. Regarding participants’ learning on the second level, the participants in the gamified training achieved significantly better outcomes for the quiz questions measuring system skills and e-negotiation skills than the conventional training participants. Finally, participants’ use of the system features are measured in the last study, reflecting the third level of the training’s effectiveness. After the training, participants in the gamified training used the system features significantly more often than participants in the conventional training. Consequently, the presented artefact is an improvement for the current state-ofthe-art in e-negotiation training, as it improves participants’ intrinsic motivation, their engagement, and – at least in part – their learning outcomes without negative effects. Incorporating game design elements in an NSS is an effective solution for the identified problems, but the design of the artefact does not “reinvent the wheel”. Instead, inherent game characteristics of e-negotiations (Schmid and Schoop 2018) as well as the concept of increasing training negotiations’ level of difficulty (Köszegi and Kersten 2003) are already known and provide the foundation for the design. In the present artefact, they are reframed to fulfil the derived requirements to support participants’ motivation, engagement, and learning and implemented in the form of explicit game design elements. Another example is the Pareto graph (Tripp and Sondak 1992), which has frequently been used to evaluate and improve agreements, but whose motivational power and effectiveness to provide constructive feedback to participants in an e-negotiation training

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was not investigated yet. Additionally, badges and experience points are implemented to provide incentives for intensified system use and to create an enjoyable training experience. Contrary to the game-based negotiation training approaches that have been developed in the past, which provide little to no fit for an e-negotiation training (Dzeng et al. 2014; Gratch et al. 2016; Kim et al. 2009), the present artefact using gamification in an NSS provides several advantages. First, the incorporation of game design elements in an information system is less cumbersome than the development of a serious or educational game. Second, such games fulfil a non-entertainment purpose, such as learning, but still require a holistic game design that creates an enjoyable and entertaining experience (Deterding 2015). Often, aspects of the real-world are sacrificed to maintain the game’s entertainment value (Liu et al. 2017). The present artefact retains and offers all the digital support features of an NSS, which the participants might also use in their realworld e-negotiations. Third, it incorporates game design elements only to create game-like experiences instead of restructuring the negotiation activities to afford the entertaining value of a game. Fourth, the gamification design can be easily adapted. As an example, new badges or new levels including additional negotiation simulations can be implemented to motivate participants and facilitate their skill acquisition. In contrast to serious games, these additions do not affect the gameplay nor require restructuring the gameplay. Finally, the artefact can be used by university courses and companies for a fundamental and effective e-negotiation training. The NSS itself, i.e. the Negoisst system in which the game design elements in the present thesis are integrated, can still be used to conduct complex business negotiations (Schoop et al. 2003; Schoop 2010, 2020). Now it additionally enables individuals to complete an e-negotiation training at any time. For a holistic e-negotiation training future research is needed to extend and improve the current artefact, as outlined in the subsection 8.4.1.

8.2.2

Design Principles for Gamified Interventions

Design principles are prescriptive statements about how artefacts can be designed, implemented, and evaluated (Baskerville et al. 2018). They are an important DSR contribution in three ways. First, design principles summarise and communicate essential design knowledge for an artefact’s design (Chandra et al. 2015). Second, they allow to abstract from specific contexts and instantiations by formulating more generalised descriptive knowledge (Chandra et al. 2015). Third, they are

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an important advancement towards more comprehensive bodies of knowledge and design theories (Gregor and Hevner 2013). The second point especially is often highlighted, as design knowledge should prescribe how to solve a class of problems when they occur, rather than solving a single, situated problem (vom Brocke et al. 2020), and generalise an understanding how and why an artefact solves the issues in the problem environment (Venable 2006). However, each design principle only works under a certain boundary condition or context, which must be explicitly formulated (Chandra et al. 2015). In the following, design principles for gamified interventions are derived through reflections about the artefact’s design process as well as the design product and its effects on the users. The present thesis already adheres to several design principles that have guided the design process (Morschheuser et al. 2018). Additional design principles for the design process are presented as well as design principles for the design product are derived, i.e. how a particular design can solve a problem in other environments. Like every good software design process, the objectives of a gamification design and its requirements are defined first (Morschheuser et al. 2018). While deriving the requirements from the kernel theories, the present thesis uses and integrates several motivation theories as a solid basis for the design. According to a design principle by Morschheuser et al. (2018), profound knowledge in human psychology is necessary to design a gamified system. SDT (Ryan and Deci 2000b, 2017) is a frequently used psychological theory in gamification research (Seaborn and Fels 2015; Xi and Hamari 2019). The derived requirements for the present artefact stem not only from SDT. Instead, flow theory (Csikszentmihalyi 1990), goal-setting theory (Locke and Latham 1990, 2002), and achievement goal theory (Elliot and McGregor 2001) are analysed too and hold important explanations for what motivates individuals and why they behave in a certain manner. Similarly, van Roy and Zaman (2019) urge researchers to broaden their theoretical perspective beyond SDT. The different psychological theories used in the present thesis are not contradicting, but rather shed light on the individuals’ motivation and behaviour from different perspectives, see e.g. Deci and Ryan (2000) for a comparison of the theories and how they relate to each other. Integrating the implications from various theories of an application context can hold important requirements for the design of an artefact. The present work uses, for example, achievement goal theory, which explains learners’ behaviour as a result of their goals. In consequence, the design avoids imposing performance goals and focusses on providing a learning environment for skill acquisition and mastery. Therefore, the following design principle (DP) is proposed:

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DP 1:

8

Discussion & Outlook

Designers deriving the requirements for the design should consider different psychological theories beyond SDT that explain how users are motivated and behave in a certain context.

The framework and the requirements in chapter 4 highlight the need to provide constructive feedback. In terms of the game element hierarchy by Werbach and Hunter (2012), shown in Figure 2.6, feedback is a mechanic and can be provided through various game elements at the component level such as points or badges. Using a top-down approach enables identification of several suitable game elements for the desired mechanics and dynamics. In the present thesis, the top-down approach identified different elements at the component level providing the same feedback. The study in chapter 6 evaluates two alternatives to provide feedback for the negotiation agreements, i.e. the utility rankings and the Pareto graph. Design alternatives must be evaluated to identify the best combinations of game design elements (Dicheva et al. 2019; Schöbel et al. 2020a). Top-down approaches enable such design alternatives to be identified. Therefore, for a successful gamification design, the following design principle claims to focus on the overarching dynamics and mechanics first before designers choose which concrete game design elements at the component level are selected to realise them. DP 2:

Designers should follow a top-down approach, i.e. they should focus on the overarching dynamics & mechanics they seek to create first and, subsequently, choose and evaluate game design elements to realise them.

Using a top-down approach also facilitates creative gamification designs instead of implementing only the most-frequently used game design elements. The studies in chapters 6 and 7 reveal that a traditional feedback element from the application context (i.e. the Pareto graph) can yield motivational power too. In general, designers are required to analyse the application context (Morschheuser et al. 2018). This analysis should also include the identification of already present feedback elements in this context. Such feedback elements might be modified and can be integrated in a gamification design. Besides using the Pareto graph, the present thesis also uses the inherently present scores, which are represented as utility values (Schmid and Schoop 2018) to display rankings. It can be argued that such feedback elements are also present in other application contexts. For instance, in the software development domain developers use collaborative platforms for managing their software projects, such as GitHub or GitLab. These platforms transparently track the actions of a user, e.g. when they

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edit code (Dabbish et al. 2012). Additionally, they provide a contribution chart in the user profile, as displayed in Figure 8.1. The chart may have a goal-setting function, i.e. it may motivate developers or computer science students to engage in a project on a daily basis. Since these user profiles are often public, their work can get recognised and acknowledged by others, which should facilitate their feelings for competence and relatedness (Ryan and Deci 2017). Similar inherent feedback elements may be present in other application contexts. The question remains, whether such elements should be regarded as a game design element and characterise a game design. As outlined in the theoretical background, this decision is often a subjective one. In strategy games and simulation games, such as Anno 1800 or the Football Manager series, various graphs can be found sharing similarities with the Pareto graph and the contribution graph. Even if such elements are still not regarded as game design elements, they can provide an important addition to a gamification design. Therefore, such elements should be identified and their motivational power should be investigated. DP 3:

Designers should consider using or modifying traditional feedback elements from the application context in their gamification design.

Figure 8.1 GitLab Contribution Chart

For the design of the gamified negotiation training. a balance between competitive and collaborative elements is postulated to facilitate the development of distributive and integrative negotiation skills (cf. chapter 4). Negotiations are

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inherently competitive as even integrative negotiations include tactics to claim value (Lewicki et al. 2010). Within this inherently competitive application context, the present thesis reveals mixed effects regarding additional competitive game design elements. Relative feedback is, for example, provided by the utility rankings and such rankings induce competition among the users (Garcia et al. 2006; Sailer et al. 2013). Although the utility rankings are perceived by many participants to provide informational feedback (cf. chapter 5), these rankings exert weaker desirable psychological and behavioural effects than the absolute feedback not inducing such competition (cf. chapter 6). Furthermore, the process feedback (cf. chapter 7), informing participants about how well they are currently performing compared to others, induces competition too and received quite bad ratings. Prior research suggests that not every user of a gamified system likes competition (Schöbel et al. 2017). Within an inherently competitive application context, such as the one in the present thesis, additional competitive game design elements appear to be less effective to motivate the majority of the users. DP 4:

Designers should analyse whether the application context is already inherently competitive and use additional competitive game design elements with care.

The effects of a gamification design emerge from the chosen game design elements and the interaction among these elements (Liu et al. 2017). Additionally, there is a large consensus that these effects vary greatly among the users, i.e. an user’s individual traits, personality, interests, and demographic factors impact the gamification experience (Klock et al. 2020). Moreover, users have preferences for certain game design elements (Tondello et al. 2016). Therefore, many gamification approaches do not rely on a single game design element, but implement multiple game design elements (e.g. Buckley and Doyle 2017; Putz et al. 2020; Silic and Lowry 2020). A recent study confirms that more game elements lead to greater persistence and motivation (Groening and Binnewies 2021). The present work supports previous findings regarding the differences among the users concerning their perceptions and use of the gamified system. It also demonstrates the effectiveness of using different game design elements that each provide clear goals to work for or support users in defining their own goals. First, the badges used in the gamified training include clear unlock instructions to work for (Hamari 2017). Second, the implemented experience points are virtual rewards, where the first goal is to collect them (Landers et al. 2017b; Sailer et al. 2013). Additionally, the ranking provided for these points enables users to define an additional goal to pursue (Landers et al. 2017b). Third, the utility rankings and

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the Pareto graph support users in their goal setting to improve their negotiation agreement and experiment with other negotiation strategies, tactics, and styles (cf. chapter 6). The following design principle argues to implement different game design elements which support users in their goal setting and in pursuing their goals. Moderate and difficult goals are effective to increase user’s engagement and performance (Locke and Latham 1990). Furthermore, providing different game design elements accounts for the different users and the users’ characteristics when interacting with the system. Except for the levels, the users are free to decide which game design elements offer meaningful goals to them and which goals they would like to pursue. Last, allowing users to choose the goals that they would like to pursue facilitates feelings of autonomy and increases their intrinsic motivation (Nicholson 2012). DP 5:

Designers should employ different game design elements supporting individuals’ autonomous goal setting in order to motivate users.

Badges are among the most frequently used game design elements (Dichev and Dicheva 2017; Koivisto and Hamari 2019; Majuri et al. 2018) and are also integrated in the gamified e-negotiation training. However, badge implementations can highly differ and, consequently, the motivational impact resulting from the badges varies based on the rules to obtain them, together with their psychological value (Dichev et al. 2020). Badges have a goal setting function (Hamari 2017; Sailer et al. 2013) which requires designers to present clear unlock instructions to the users. In the current work, badges are an optional game design element, i.e. users were notified when they achieved a badge and could also view all achieved badges and all locked badges plus their unlock instructions on a separate page. Different badge types are implemented concerning their system use or negotiation performance, and these badges also differed in their level of difficulty. Diverse badge types are an appealing game design element for users of gamified systems (Hsu et al. 2013). The final study (cf. chapter 7) reveals that participants perceive the badges in the gamified e-negotiation training as the most motivating, least demotivating, and very enjoyable game design element. Furthermore, the interviews in chapter 5 show that several participants explicitly worked towards achieving the badges, whereas other participants did not work for the badges explicitly, but still enjoyed receiving them. In general, obtaining a badge can foster users’ perceived competence and give them a sense of accomplishment (Sailer et al. 2013). When badges with different levels of difficulty are present,

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they provide either moderate or difficult goals for the users to work for leading to intensified effort, or they provide at least an enjoyable experience for the participants obtaining the, comparatively, easy ones. DP 6:

Designers implementing badges should provide clear unlock instructions and include easy as well as moderate and hard achievements to earn badges.

The following two design principles explicitly focus on learning environments and how intrinsically motivated learning can be facilitated. Motivation of learners is one of the most investigated domains of SDT (Ryan and Deci 2017). Furthermore, this theory is often used in gamification research, but only a few papers deal with SDT beyond descriptive accounts (Tyack and Mekler 2020). The theory itself provides valuable implications on how to design a motivating gamified system, i.e. by satisfying the users’ basic psychological needs (Ryan and Deci 2000b). Depending on the application context, certain needs are more cherished by the users than others, e.g. learners cherish the need for competence more than the other two needs (van Roy and Zaman 2019). Nonetheless, intrinsic motivation requires an autonomous form of motivation (Ryan and Deci 2000b). As part of the analysis of the application context, the present thesis identifies opportunities for an autonomy-supportive learning environment and further identifies actions of the training participants, for which constructive feedback should be provided (cf. chapter 4). For example, participants could implement their own negotiation strategies, tactics, and styles. The use of several game design elements, such as the badges and experience points, is not obligatory (van Roy and Zaman 2017). The game design elements should further provide constructive feedback that is meaningful for the learning task at hand. When the learning environment and its game design elements are autonomy-supportive, and elements providing constructive feedback are present, intrinsic motivation is likely to be induced (Ryan and Deci 2000b). Autonomy in the present work is found to be very high (cf. chapter 6). Furthermore, the evaluations in the present thesis confirm that the gamification design has a positive impact on participants’ intrinsic motivation. DP 7:

For gamified learning environments, the application context should be analysed to identify opportunities for autonomy-supportive learning as well as actions for which the learners require constructive feedback.

8.2 Integrative Discussion of Contributions

169

The central component of the gamified e-negotiation training are the levels. These levels split the overall learning goal to obtain the necessary system and e-negotiation skills into smaller learning chunks. Such an approach is in line with prior research, suggesting to divide the overall learning goal into smaller sub-goals which inform learners about their progress and give them a sense of accomplishment when they have reached a sub-goal (Glover 2013; Urh et al. 2015). Clear goals which set the focus on the mastery of new learning content are particularly effective (Landers et al. 2017a). Challenging, but attainable, goals can spark the learner’s interest for the learning activity (Malone 1981) and, therefore, a balance between the current skills of the learner and the challenge imposed by the goal is needed, where the current skills are slightly stretched (Csikszentmihalyi 1990). In the present work, levels are not just an progress indicator for the points a learner has collected (Mekler et al. 2017). Instead, each level is connected with a specific learning task (Kim et al. 2018), namely, to perform an e-negotiation and use the support features of the NSS. The levels become increasingly more complex with regard to the complexity of the e-negotiation and the features available. Levels in the current thesis are very positively perceived by the interview participants in chapter 5, and as shown by the quantitative analysis in chapter 7. By allowing the learners to repeat these levels in the case of failure and success, a clear focus on the mastery of learning content is set (Landers et al. 2017a) and a fail-safe learning environment is created. This environment offers the learners the freedom to experiment with other approaches, without fearing negative consequences. The concept of using levels to provide increasingly more difficult learning content can be adapted in almost every kind of learning environment and learning methodology. For example, they have also been used to train users of an ERP system, where they were found to be effective (Alcivar and Abad 2016). Using levels can also be applied to other courses following an active learning approach, such as in computer science courses, and can also be applied to learning courses following a behaviourist approach such as operant conditioning (Landers et al. 2015). It can be argued that such levels are generally effective for several reasons: First, levels support the definition of clear but challenging sub-goals that do not overburden the learners (Glover 2013; Lee and Hammer 2011). Second, in contrast to other game design elements such as badges, levels have a clear order in which they should be completed and, therefore, help learners to structure their learning activities (Alcivar and Abad 2016). Last, levels form a clear connection with the acquisition of new learning content, i.e. when learners complete a level

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they are told that they have successfully “levelled up”, giving them a sense of accomplishment. DP 8:

For gamified learning environments, learning content should be provided in the form of repeatable levels, which become increasingly more challenging.

All aforementioned design principles provide guidance towards implementing a good gamification design. For gamification projects following a DSR approach, rigorously building and evaluating an artefact is a mandatory task (Hevner et al. 2004). All gamification projects should follow an iterative design process, which requires performing evaluations whether the defined objectives have been achieved (Morschheuser et al. 2018). Since the risk of a gamification project is user oriented, i.e. the success depends on the user’s perception rather than on the technical complexity of a gamified intervention, naturalistic evaluations with the actual users are desirable (Venable et al. 2016) and gamification design ideas should be tested as early as possible (Morschheuser et al. 2018). The evaluations in the present thesis used established measures such as the IMI (Ryan et al. 1983) to assess, for example, participants’ intrinsic motivation as one objective for improvement. Nonetheless, such quantitative data delivers only metrics on how well the gamified intervention improves participants’ motivation, engagement, and learning outcomes, but it does not provide insights into how the gamified intervention and its elements are perceived by the users and what can be improved. The qualitative data obtained in chapter 5 reveal, for example, that the participants performed additional negotiations due to the informational feedback and from incentives to improve their negotiation outcomes provided by the utility rankings. Furthermore, the interviews show that elements such as the badges are perceived and used differently. A mixed-method evaluation design improves the understanding of users’ perceptions of the gamified intervention and helps to detect weaknesses in the design (Alsawaier 2019). DP 9:

Mixed-method evaluation designs should be used – at least in early design stages – to capture and understand participants’ perception of the design.

Prior gamification studies have often not used properly validated psychometric measurements to evaluate the outcomes of a gamified intervention (Dichev and Dicheva 2017; Seaborn and Fels 2015). The IMI (Ryan et al. 1983) used in this thesis is an established measurement for gamified interventions (Seaborn and Fels

8.3 Limitations

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2015) and grounded in SDT. Using such established self-report measurements is a valid approach and enables comparison between gamification studies. However, the results of self-report measurements are always prone to the social desirability bias. While the effort subscale of the IMI reveals no significant differences in the studies in chapter 5 & 7, these studies show significant differences for the voluntary conducted negotiations. In chapter 6, where two versions of the gamified training are compared, no significant differences are found for the selfreport measurements, but significant effects are revealed regarding behavioural measures of engagement. These findings are in line with a recent study by Groening and Binnewies (2021), who found stronger effects on behavioural measures than for self-report measures. Using only self-report measures, studies, such as in chapter 6, would reveal no effect at all although there is an evident effect on behavioural measures. DP 10:

The evaluation should include self-report measures as well as behavioural measures to get a holistic picture concerning the effects of the gamification design.

All previously derived design principles, including their boundary conditions, i.e. to which contexts they are generalisable, are summarised in Table 8.1. These principles form a nascent design theory that aims to overcome the lack of empirically validated guidance for selecting the right game design elements for a given application context (Liu et al. 2017) and support others in designing and evaluating gamified interventions. However, the design theory is still nascent, and the presented list of design principles derived from the current work does not claim for completeness but may be further extended by other researchers. The presented design principles also extend a list of design principles for the design process of a gamified intervention by Morschheuser et al. (2018). They are an important contribution towards developing more matured and theoretically guided gamification designs.

8.3

Limitations

This thesis is subject to several limitations, which stem from the design or evaluation processes of the applied design science research methodology and need to be taken into account for the interpretation and generalisation of the results. First, the presented artefact provides only an electronic negotiation training and assumes that all participants attended a fundamental negotiation training in

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Table 8.1 Design Principles for the Design of Gamified Interventions #

Design Principle

1

Designers deriving the requirements for the design should consider different psychological theories beyond SDT that explain how users are motivated and behave in a certain context

2

Designers should follow a top-down approach, i.e. they should focus on the overarching dynamics & mechanics they seek to create first and, subsequently, choose and evaluate game design elements to realise them

3

Designers should consider using or modifying traditional feedback elements from the application context in their gamification design

4

Designers should analyse whether the application context is already inherently competitive and use additional competitive game design elements with care

5

Designers should employ different game design elements supporting individuals’ autonomous goal setting in order to motivate users

6

Designers implementing badges should provide clear unlock instructions and include easy as well as moderate and hard achievements to earn badges

7

For gamified learning environments, the application context should be analysed to identify opportunities for autonomy-supportive learning as well as actions for which the learners require constructive feedback

8

For gamified learning environments, learning content should be provided in the form of repeatable levels, which become increasingly more challenging

9

Mixed-method evaluation designs should be used – at least in early design stages – to capture and understand users’ perception of the design

10

The evaluation should include self-report measures as well as behavioural measures to get a holistic picture concerning the effects of the gamification design

advance of this, which teaches negotiation theory and applies the theory practically through case methods, role plays, and other forms of practical experiences (Lewicki 1997). The participants are already familiar with fundamental negotiation concepts, such as distributive bargaining and integrative negotiation, and, in consequence, have acquired the necessary skills to effectively conduct faceto-face negotiations. The presented training does not teach these fundamental negotiation concepts and expects that the participants gained prior negotiation knowledge and skills. Therefore, the e-negotiation training only focusses on the development of specific e-negotiation skills, i.e. the necessary skills to negotiate via an asynchronous electronic media and to utilise the digital support tools to the negotiator’s advantage.

8.3 Limitations

173

Second, the training contents are restricted to the essential parts of an enegotiation, namely, the phases of planning, exchanging offers and arguments, reaching an agreement, and concluding the negotiation. Like other forms of enegotiation training, participants negotiate about a given case with already defined preferences for the negotiation issues (Köszegi and Kersten 2003; Melzer and Schoop 2016). Since preferences are already elicited, the gamified training does not span across the preference elicitation process (Schoop 2010, 2020) and the preferences cannot be adapted during the negotiation. Due to incomplete information in the planning phase, negotiators usually adapt their preferences when they communicate with their negotiation partner and receive more information (Lenz and Schoop 2019). Furthermore, NSSs provide negotiators with the means to negotiate about and agree on changes for the agenda, i.e. to add or remove negotiation issues during the negotiation process (Fernandes 2016). Adding new negotiation issues to the agenda is a creative skill and a tactic to create mutual gain in an integrative negotiation (Lewicki et al. 2010). However, the current training neither includes nor gamifies the elicitation of preferences, adaption of preferences, and changes to the negotiation agenda, but may be expanded to these tasks in the future. The evaluations performed in the chapters 5–7 used students as subjects that participated in a negotiation course teaching negotiation theory and practice at their university. Negotiation training is usually offered as corporate training (ElShenawy 2010) or in university courses (Köszegi and Kersten 2003; Melzer and Schoop 2016). Using students as subjects is, therefore, a valid approach to evaluate the training within its application environment (Hevner et al. 2004). However, the findings should be interpreted and generalised with care, as the students are enrolled in European universities and form a rather homogenous group of participants. The age of managers – presenting another target group for an e-negotiation training – highly differs (e.g. Voeth et al. 2019), and playing games is far less prevalent among older adults than among students (Bitkom 2020). A study shows that the ease of use of gamification declines with age (Koivisto and Hamari 2014). Cultural differences regarding the dimension of individualism versus collectivism (Hofstede et al. 2010) might also affect the impact of the game design elements (Klock et al. 2020). Therefore, the findings and implications of the present thesis need to be confirmed with more heterogenous samples. In chapters 5 and 7 the artefact evaluation compared the gamified training with an established e-negotiation training (Melzer and Schoop 2016). Both forms of training included identical learning contents, but game design elements were either present or absent in the training. However, the two types of training differ in another fundamental aspect: the traditional training was conducted offline

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facilitated by an instructor, whereas the gamified training was purely online. Participants in the traditional training were required to attend their regular lecture, whereas participants in the gamified training could complete it at their preferred time. The differences between participants’ motivation might therefore not only be attributed to the gamified training but can also arise due to the offline versus online setting. However, the qualitative interview results in chapter 5 confirm that it was especially the game design elements that created a motivating learning experience, however a bias cannot be completely ruled out. The learning outcomes were measured using quiz questions and by assessing their perceived competence. Quiz questions targeted to measure their understanding of the system and to measure their e-negotiation skills, especially their decision-making skills and whether they make comprehensible and rational decisions. Communication skills were not directly assessed, since the TNT (i.e. the NSA with whom the participants negotiate with), in its current form, is limited in its communication behaviour, and consequently not sufficient for an effective communication training (Schmid et al. 2021). Participants’ transfer of negotiation skills could have been assessed in follow-up negotiations, but the participants only negotiated once after the training was completed. Consequently, their negotiation processes and outcomes are strongly influenced by their negotiation partners and the incentives (e.g. bonus points or grades) they receive for their participation in these follow-up negotiations. Instead, participants were asked whether they perceive themselves as competent, addressing a plethora of negotiation skills, as presented in section 2.3.3. Finally, the participants’ interaction with the gamified system was relatively short. This was also considered in the design, since for short-term system use reward-based gamification is deemed as suitable (Liu et al. 2017). The effectiveness of a longer and continual gamified training remains to be explored. Koivisto and Hamari (2014), for example, found that the enjoyment of a gamified system declines with use. Consequently, if the system is intended to be used for a recurring e-negotiation training, the gamification design might have to be adapted.

8.4

Outlook

The present thesis aimed to improve the motivation, engagement, and learning outcomes of participants in an e-negotiation training. The findings reveal that the incorporation of game design elements in an NSS used within an e-negotiation training has a positive impact on these variables and can indeed improve enegotiation training. Nonetheless, important questions that are beyond the scope

8.4 Outlook

175

of the present thesis remain unanswered, and future research is needed to further improve the field of e-negotiation training and to contribute to the developing field of gamification research.

8.4.1

Towards a Holistic E-Negotiation Training

The developed artefact paves the way for a holistic and motivating e-negotiation training. First, new levels with new negotiation scenarios can be added quite easily. In its present form, the e-negotiation training provides the participants with the means to utilise all of the NSS’s support functions and includes negotiation scenarios with different complexity levels (i.e. number of negotiation issues) and negotiation partners using different strategies (i.e. collaborating vs. competing). For instance, new levels that focus on negotiations in a multilateral setting or include cultural differences can be added to facilitate the development of further e-negotiation skills. Second, the inherent feedback offered by the system enables individuals to complete the training at any time and eliminates the necessity of offline debriefings with an instructor. Individuals can develop their negotiation skills whenever it fits their schedule, regularly train and reinforce their gained knowledge, and maintain their skills (ElShenawy 2010). The feedback that can be provided by NSSs to participants during an e-negotiation training calls for several future research avenues. Constructive feedback is at the core of the presented framework for a gamified e-negotiation training and can be offered in the form of game elements or by using nongame elements (Schmid and Schoop 2019). Feedback in e-negotiations is offered in various forms, including the feedback by the NSS itself as well as the reactions of the negotiation partner (Schmid and Schoop 2018). The present thesis focusses to a large extent on the incorporation of feedback provided by game design elements, however, other forms of feedback can be additionally provided to facilitate the skill development. A study by Meyer et al. (2020), which was simultaneously conducted with the study in chapter 5 by Schmid et al. (2020), has derived, developed, and investigated additional feedback mechanisms that can be offered to the participants for their actions. The three top ranked mechanisms requested by the participants are a preparation quiz before the negotiators start to exchange offers and counteroffers, a feature to set and track one’s goals throughout the negotiation process, and an expert review of one’s negotiation behaviour and performance (Meyer et al. 2020). Participants rate the developed features as particularly useful for their decision-making skills, but also for their communication skills, i.e. to prepare effectively by setting one’s goals and defining strategies

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and tactics, behave in a rational and goal-oriented manner, and to solve problems (Meyer et al. 2020). However, these features have not been ranked against the newly incorporated game design elements in order to examine the most preferred features and game design elements. Therefore, and since only mock-ups were presented to them, little is known whether these features will actually be used by the participants. The proposed feedback mechanisms by Meyer et al. (2020), and other feedback alternatives that are deemed to be suitable, may be actually implemented and field-tested regarding their acceptance, use, effect on motivation, and impact on skill acquisition of the participants. These feedback mechanisms can further improve an e-negotiation training without requiring an instructor to be present. The suggested preparation quiz is relatively easy to implement and asks training participants about the basic facts of the negotiation, i.e. the negotiation parties, issues, and individual goals and preferences (Meyer et al. 2020). Such a quiz can help participants to follow a standard procedure for the most relevant activities in the planning phase of a negotiation and to better prepare themselves for the following negotiation with their partner. Additionally, answering the questions correctly can be rewarded with game elements such as points and badges. The feature to set and track goals connects the planning phase with the actual conduct of the negotiation, by enabling participants to define goals and constraints for specific issues as well as the overall negotiation outcomes in terms of individual and joint utilities (Meyer et al. 2020). The extent to which these goals are reached, or constraints are violated, can be tracked throughout the negotiation process. Allowing participants to define their own goals makes their learning experiences more meaningful and intrinsically motivating (Nicholson 2012), and can further incentivise them to experiment with different negotiation strategies and styles. The final top-ranked feature in the study by Meyer et al. (2020) is an expert review, which can be requested for certain topic areas such as one’s preparation, decision-making, and communication skills, once the negotiation is concluded. In its suggested form, the expert review requires an instructor or negotiation expert to formulate constructive feedback about a participant’s negotiation performance. However, this has two major disadvantages: First, such feedback will be provided with a certain delay, and second, the analysis of the negotiation and the formulation of the feedback is cumbersome for the expert. An alternative approach to offer such feedback is to generate it automatically within the NSS. As a starting point, the logrolling behaviour can be analysed, including information about the negotiation partner’s utilities. Such information is useful to conclude whether one’s concessions were rational and appropriate, whether these were also useful concessions for the negotiation partner, and whether additional

8.4 Outlook

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value was created for the negotiators or not. Further feedback can be offered based on already developed pro-active negotiation support systems, which monitor the negotiation behaviour and intervene when necessary (Kersten and Lai 2007a). While these systems offer the necessary capabilities to intervene during the negotiation process, their analytical functionality can be used ex post. Such analysis further includes the communication behaviour to detect impasses and escalating conflicts (Druckman et al. 2012), and could be performed using sentiment analysis (Körner 2019). Pattern recognition, analysing both utility and communication data, is another useful approach to provide advice (Kaya and Schoop 2019). However, systems that include the communication perspective are still scarce and future research is necessary to create constructive feedback and advice for e-negotiation training participants. A last area for improvements of e-negotiation training are the NSAs, with whom the participants negotiate with. These NSAs present direct feedback through their reply on the participant’s last offer or counteroffer. A negotiation partner provides feedback on whether the participant’s recently applied tactics and arguments have been successful or not, whether the participant successfully worked towards an agreement, or whether they need to adapt their tactics and behaviour. Chapter 5 shows that the training participants particularly like the fast replies and the immediate feedback offered by such an agent. However, such agents are criticised for lacking real human communication behaviour (Schmid et al. 2020), for their applied strategies and concession behaviour (most follow a tit-for-tat strategy), and for a discrepancy between the concessions made and the textual message sent (Schmid et al. 2021). These findings call for several future research directions and developments: an improved communication behaviour of the agents, a better alignment of their communication behaviour with their strategies, the use of appropriate and different negotiation strategies, and a training of participants with agents following these strategies. Current NSAs conducting negotiations with humans underpin their offers with text messages generated from a priori defined sentence templates, which fit to the current negotiation situation (Melzer et al. 2012; Vahidov et al. 2017a). Since these NSAs lack real human communication behaviour and do not care about the partner’s sentiments (Schmid et al. 2020), participants in an e-negotiation training are likely to put less emphasis on training their communication behaviour. The present thesis has therefore focussed, to a large extent, on the decision-making skills of the participants. However, communication skills are vital to implement negotiation strategies, resolve conflicts, and develop a mutual understanding with the negotiation partner (Schoop et al. 2010). Negotiators constantly adapt their

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behaviour according to the behaviour of the negotiation partner and use different communication elements to reach their individual and joint goals (Schoop et al. 2010). Such communication behaviour must also be trained. Consequently, improving an NSA’s communication behaviour is a significant milestone towards a holistic e-negotiation training. A possible solution to improve NSAs is to adapt the technologies used for bots (i.e. social bots and chatbots) that have been developed in the last few years, which employ human-like behaviour (Varol et al. 2017). A recent study developed a first prototype and found that more participants perceived the agent using bot technology as human-like as opposed to the TNT, but there are still issues regarding the agent’s argumentation behaviour (Schmid et al. 2021). Since research has currently drawn attention towards creating more personalised bot messages (Bowden et al. 2019; Thomaz et al. 2020), and towards establishing social relationships between humans and bots (Przegalinska et al. 2019), future research should further investigate the emerging technological solutions, adapt them for NSAs, and continuously improve the developed NSA by Schmid et al. (2021) for a more human-like communication behaviour. Another NSA-related research direction to improve e-negotiation training is the appropriate use of negotiation strategies. The strategy of a negotiator depends on the importance of the negotiator’s own outcome and goals, and the importance of the relational outcome (Lewicki et al. 2010). Different tactics are applicable to implement a strategy, e.g. integrative and distributive tactics can be used at the same time or are predominant in different negotiation phases (Pruitt and Carnevale 1993). For NSAs, behaviour-dependent, resourcedependent, or time-dependent concessions are suggested as tactics (Faratin et al. 1998). Behaviour-dependent and time-dependent strategies are quite common: The TNT used in this thesis utilises behavioural tactics following a tit-for-tat strategy (Melzer et al. 2012), and time-dependent tactics are also frequently studied (Vahidov et al. 2017a; Vahidov et al. 2017b). Incorporating new scenarios with NSAs following different real-world negotiation strategies, an e-negotiation training can improve the participants’ choice and adaption of negotiation strategies and tactics. Ideally, the strategies and tactics are not only behavioural dependent regarding the participant’s concessions, but also take into account and depend on the communicative tactics of the participants. Similar to human negotiators, the NSA would constantly adapt its behaviour to the behaviour of the negotiation partner (Schoop et al. 2010), thus, enabling an even more realistic e-negotiation training. In summary, the field of e-negotiation training would benefit from several future research directions that aim to provide further constructive and realistic feedback to the participants. Feedback provided by the NSS or by the NSAs,

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with whom the participants negotiate with, can further enhance participants’ skill acquisition. The presented artefact in this thesis enables such a holistic enegotiation training within a motivating environment. New negotiation scenarios can be easily integrated. The used game elements might be further adapted or extended (e.g. new badges) to match with the learning goals of such a training.

8.4.2

Future Research Avenues for Gamification Research

According to the results of the present thesis, gamification can create enjoyable and motivating learning experiences that facilitate the learning outcomes of participants in an e-negotiation training. These results are based on an evaluation using students as subjects enrolled in a negotiation course at a university, which represents a typical application context for an e-negotiation training (Köszegi and Kersten 2003; Melzer and Schoop 2016). However, demographic differences can affect the outcomes of a gamified intervention (Koivisto and Hamari 2014) and a meta-analysis found, for example, significantly better cognitive learning outcomes for school children than for students in higher education as well as participants in informal settings (Sailer and Homner 2020). Since e-negotiation training is also highly relevant for experienced and older employees in business companies, future research needs to investigate the effects of the gamified e-negotiation training with more diverse samples. The game design elements may be adapted for conducting actual business negotiations in an NSS, i.e. the gamified NSS is adapted for its use beyond the training phase. This leads to another future research area for both gamification and negotiation researchers, namely, to investigate the impact of gamification on the intention to use a gamified system. Negotiation research has a long tradition to examine the perceived ease of use, perceived usefulness, and the use of an NSS and its features, by using the Technology Acceptance Model (TAM) (Davis et al. 1989; Venkatesh and Bala 2008; Venkatesh and Davis 2000) as a theoretical basis (Kersten and Lai 2010). However, the user’s assessment of an NSS and their intention to use it differ from other information systems in an important way: These constructs are heavily influenced by the achieved negotiation outcomes and the affective aspects of their interaction with the negotiation partner (Etezadi-Amoli 2010; Vetschera et al. 2006). The third version of the TAM includes perceived enjoyment as a determinant of perceived ease of use (Venkatesh and Bala 2008). Since gamification should create enjoyable experiences, an important research question is to examine how a gamified system can enhance perceived ease of use, and, in consequence, the perceived usefulness

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and use of such a system (Treiblmaier et al. 2018). First empirical results show that the user’s conception of the system, i.e. whether the gamified system is perceived as rather utilitarian or fun-oriented, has a moderating effect between the constructs perceived enjoyment, perceived ease of use, and perceived usefulness and the use intentions (Köse et al. 2019). For example, the more fun-oriented a gamified system is perceived, the more enjoyment positively affects continued use (Köse et al. 2019). Future research should investigate whether these findings hold true for the continued use of an NSS, which is heavily influenced by the negotiation outcome and affective reactions towards the negotiation partner, and whether these findings are also applicable for other gamified systems. Despite advancing knowledge in gamification design and the effects of particular game design elements, the creation of a holistic gamified system with several game design elements remains a challenging tasks due to the interaction among the elements (Liu et al. 2017). The study in chapter 6 demonstrates that even the replacement of one game design element, while retaining all the other game design elements, leads to different psychological and behavioural outcomes. Future research is needed to identify effective combinations of game design elements (Dicheva et al. 2019; Schöbel et al. 2020a). A shortcoming of the present research project is that the evaluation participants’ interaction with the gamified system was relatively short, which is a general shortcoming of many gamification studies (Dichev and Dicheva 2017; Nacke and Deterding 2017). Depending on the length of interaction with a gamified system, other game design elements providing different motivational affordances may be used (Liu et al. 2017). However, little is known about the long-term effects of gamified systems and whether certain combinations of game design elements are more beneficial for long-term interactions than others. Future research is needed to investigate whether the benefits of gamification are maintained, decreased, or amplified in the long run (Schöbel et al. 2020a). A last direction for future research is to investigate the potential benefits when personalising a gamified system to the user’s needs. Similar to players preferring certain game genres, users of a gamified system prefer particular game design elements over others, and may even become demotivated by certain game design elements. When interacting with a gamified system, individual traits, one’s personality and interests, and demographic factors can influence the experience (Klock et al. 2020). As a result, the research stream of adaptive gamification (also referred to as tailored gamification) emerged, which adapts the presented game design elements according to the user’s profile and which is especially applied in educational contexts (Klock et al. 2020). To adapt the game elements, approaches either rely on tracking behaviour and dynamically adapt the game

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design elements, or rely on user profiles (Hallifax et al. 2019). User profiles are often based upon one’s personality or upon player types, such as the ones suggested by the Hexad typology (Tondello et al. 2016). Prior research in educational contexts is predominantly positive regarding the effects of adaptive gamification (Hallifax et al. 2019). However, the field is still emerging and guidance to design an adaptive gamified system is scarce. Since, for example, the Hexad typology is not very well suited for learners’ motivation (Dichev et al. 2020), future research is needed to identify the most effective adaption methods for educational contexts and other application contexts.

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