Current and Future Trends on Intelligent Technology Adoption: Volume 1 (Studies in Computational Intelligence, 1128) 3031483960, 9783031483967

Table of contents :
Preface
Contents
Log Out or Stay Connected? Unveiling the Intention for Continuous Use in the Metaverse
1 Introduction
2 Literature Review and Hypotheses Development
2.1 Theoretical Foundation: S-O-R Model
2.2 Telepresence of Metaverse
2.3 Interactivity of Metaverse
2.4 Social Presence of Metaverse
2.5 The Relationship Between Perceived Enjoyment, Value Co-creation, and Continuous Use Intention
2.6 Perceived Trust as a Moderator
3 Research Methodology
4 Research Findings
4.1 Assessment of Measurement Model
4.2 Structural Model Assessment and Hypotheses Testing
5 Discussion and Implication
5.1 Theoretical Implication
5.2 Practical Implication
6 Conclusion
7 Limitations and Future Studies
References
Virtual Influencers as the Next Generation of Influencer Marketing: Identifying Antecedents and Consequences
1 Introduction
2 Literature Review and Research Hypotheses
2.1 Anthropomorphism
2.2 Interactivity
2.3 Mediating Role of Parasocial Interaction
2.4 Brand Credibility
2.5 Brand Attachment
2.6 Research Framework
3 Methodology
3.1 Data Collection
3.2 Measures
4 Data Analysis and Results
4.1 Common Method Bias (CMB)
4.2 Measurement Reliability and Validity
4.3 Hypotheses Testing
5 Discussion
5.1 Summary of Major Findings and Contributions
5.2 Limitations and Suggestions for Future Research
References
Modeling the Continuous Intention to Use the Metaverse as a Learning Platform: PLS-SEM and fsQCA Approach
1 Introduction
2 Literature Review
3 Theoretical Background
3.1 Technology Acceptance Model (TAM)
3.2 Self-determination Theory (SDT)
4 Research Model and Hypotheses Development
4.1 Autonomy
4.2 Relatedness
4.3 Competence
4.4 Perceived Usefulness and Perceived Ease of Use
5 Methods
5.1 Population and Sample
5.2 Instrument
5.3 Data Analysis
6 Findings
6.1 Measurement Model Assessment
6.2 Structural Model Assessment
7 Asymmetric Analysis
7.1 Calibration
7.2 Identifying the Configurations
8 Discussion
8.1 Theoretical and Practical Implications
9 Conclusion
10 Limitation and Future Work
References
Are You Ready for Tapping into the Metaverse in Higher Education? Integrated by Dual PLS-SEM and ANN Approach
1 Introduction
2 Literature Reviews
2.1 E-Learning and Metaverse
2.2 Unified Theory of Acceptance and Use of Technology
3 Hypothesis Development
3.1 Performance Expectancy (PE)
3.2 Effort Expectancy (EE)
3.3 Social Influence (SI)
3.4 Facilitating Condition (FC)
3.5 Self-efficacy (SE)
3.6 Behavior Intention (BI)
4 Methodology
4.1 Questionnaire Structure
4.2 Sampling Method and Sample Size
4.3 Respondent Profile
4.4 Common Method Bias
4.5 Assessing the Outer Measurement Model
4.6 Inspecting the Inner Structural Model
4.7 The Predictive Relevance
4.8 ANN Analysis
5 Finding Discussion
5.1 Theoretical Implication
5.2 Managerial Implication
6 Conclusion and Further Research
References
Investigating the Influence of Technology Anxiety on Healthcare Metaverse Adoption
1 Introduction
2 Research Model and Hypotheses Development
2.1 Perceived Usefulness
2.2 Perceived Ease of Use
2.3 Technology Anxiety
3 Methodology
3.1 Survey Design
3.2 Sample
3.3 Measures
3.4 Data Analysis
4 Results
4.1 Measurement Model
4.2 Hypotheses
5 Discussion and Conclusion
5.1 Practical Implications
5.2 Theoretical Contributions
5.3 Limitations and Future Research Directions
Appendix: Constructs and Items
References
The Metaverse: A New Frontier for Learning and Teaching from the Perspective of AI
1 Introduction
1.1 Background of the Study
1.2 Justification for the Problem
2 Novel Ways to Reimage and Democratize Education Through Metaverse
2.1 Interacting with the Metaverse in Education from the Perspective of AI
2.2 Machine Learning and Metaverse in Education
3 Managerial Applications of Metaverse in Education
4 Limitations of Metaverse in Education
5 Theoretical Contribution
6 Conclusion
References
Understanding the Effects of Social Media Advertising on Purchase Intention Through Metaverse
1 Introduction
2 Literature Review and Development of Hypotheses
2.1 The Stimulus-Organism-Response (SOR) Model
2.2 Purchase Intention Through the Metaverse
2.3 Perceived Uniqueness and Affective as Well as Cognitive Involvements
2.4 Perceived Vividness and Affective as Well as Cognitive Involvements
2.5 Perceived Interactivity and Affective as Well as Cognitive Involvements
2.6 Credibility and Affective and as Well as Cognitive Involvements
2.7 Affective and Cognitive Involvements as Well as Purchase Intention
3 Methodology
4 Results and Discussion
4.1 Common Method Bias
4.2 Respondents’ Profiles
4.3 Assessment of the Measurement Model
4.4 Assessment of the Structural Model
5 Discussion
6 Conclusion
References
The Impact of Environmental Factors on Social Selling Intention via Virtual Reality Technology and Social Selling Performance: The Mediating Effect of Self-efficacy
1 Introduction
2 Literature Review
2.1 Social Commerce and VR Technology
2.2 Underpinning Theory: Theory of Planned Behaviour
2.3 Social Selling Performance
2.4 Social Selling Intention via VR Technology
2.5 Environmental Factors
2.6 The Mediator Role of Self-efficacy
2.7 Hypotheses Development
3 Research Method
4 Findings
4.1 Assessment of the Measurement Model
4.2 Assessment of the Structural Model
4.3 Discussion
5 Conclusion
5.1 Theoretical and Practical Implications
5.2 Limitations and Direction for Future Research
References
Reshaping Sport with Extended Reality in an Era of Metaverse: Insights from XR the Moroccan Association Experts
1 Introduction
2 Related Work
2.1 Extended Reality (XR)
2.2 XR in Sport
3 SWOT-AHP for XR in Sport
3.1 SWOT
3.2 AHP
3.3 SWOT-AHP
4 Results
4.1 SWOT Analysis
4.2 AHP Results
5 Discussion
5.1 Theoretical Implications
5.2 Practical Implications
6 Conclusion
References
Unveiling the Influence of Social Technologies on Online Social Shopping in Malaysia
1 Introduction
2 Literature Review
2.1 Online Social Shopping Purchase Intention
2.2 Model of Goal-Directed Behaviour (MGB)
3 Hypotheses Development
3.1 Social Desire as Mediator
3.2 Commercial Desire as Mediator
3.3 Trust as Mediator
4 Methodology
4.1 Instruments
4.2 Data Collection Method and Procedure
4.3 Common Method Bias
4.4 Data Analysis
5 Results and Discussion
5.1 Profile of Respondents
5.2 Measurement Model Assessment
5.3 Structural Model Assessment
6 Discussions
7 Theoretical and Practical Implications
8 Conclusion
9 Research Limitations and Future Research
References
Mobile Payment Adoption in Vietnam: A Two-Staged SEM-ANN Approach
1 Introduction
2 Literature Review
2.1 The Unified Theory of Acceptance and Use of Mobile Technology, UTAUMT
2.2 Prospect Theory
2.3 Flow Theory
2.4 Hypotheses Development and Proposed Conceptual Framework
3 Research Methodology
4 Results and Discussion
4.1 Profile of Respondents
4.2 Assessing Outer Model
4.3 Examining Inner Structural Model
4.4 ANN Analysis
4.5 Discussion
5 Conclusion and Practical Implications
References
Millennials Fintech Services Adoption: What Matters Most?
1 Introduction
2 Literature Review
3 Research Method
4 Analysis
4.1 Demographic Profile
4.2 Data Analysis and Results
5 Discussion and Conclusions
References
Perceived Risk of Users’ Intention to Use Cryptocurrency in Malaysia: A Multi-analytic Approach
1 Introduction
2 Literature Review
2.1 Overview of Cryptocurrency
2.2 Related Research
2.3 Related Research Summary
3 Conceptual Model Development
4 Research Hypothesis Development
4.1 Performance Expectancy (PE)
4.2 Effort Expectancy (EE)
4.3 Social Influence (SI)
4.4 Perceived Risk (PR)
5 Methodology
6 Finding and Analysis
6.1 Descriptive Analysis
6.2 Measurement Model Using Confirmatory Factor Analysis
6.3 Structural Model Assessment
6.4 Neural Network Analysis
6.5 Hypothesis Discussion
7 Conclusion
References
Prediction of Consumer Repurchase Intention with Food Delivery Apps: The Mediating Role of Prior Online Experience Using PLS-SEM-ANN Approach
1 Introduction
2 Related Literature and Hypotheses Development
2.1 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Constructs
3 Research Method
4 Data Analysis
5 Results
5.1 Non-response and Common Method Bias
5.2 Measurement Model Assessment
5.3 Structural Model Assessment
5.4 Artificial Neural Network Analysis
6 Discussions and Conclusion
References
Effects of Platform Values on Consumers’ Willingness to Continue and Subscribe to Metaverse Live-Streaming: With a Moderating Effect of Digital Literacy
1 Introduction
2 Literature Review
2.1 Metaverse Live Streaming
2.2 Continuance Intention Theory
3 Hypothesis Development
3.1 Platform Values of the Metaverse Live-Streaming
3.2 Moderate Effect of Digital Literacy
3.3 Customer’s Immersion in the Metaverse Live Streaming
4 Research Methodology
4.1 Data Collection
4.2 Measurement Development
4.3 Profile of Respondents
5 Data Analysis
5.1 Statistical Analysis
5.2 Common Method Bias Analysis
5.3 Assessing the Outer Measurement Model
5.4 Structural Model
5.5 Predictive Relevance and Effect Size
6 Research Implications
6.1 Discussions
6.2 Theoretical Implications
6.3 Practical Implications
7 Limitations and Recommendations for Future Research
References
Organizational Adoption of Blockchain Based Medical Supply Chain Management
1 Introduction
2 Methodology
2.1 Research Design
2.2 TOE Framework
2.3 AHP Analysis
3 Systematic Literature Review
3.1 Research Questions
3.2 Search Strategy
3.3 Inclusion and Exclusion Criteria
3.4 Primary Pool
4 Proposed Research Model
4.1 Technological Dimension
4.2 Organizational Dimension
4.3 Environmental Dimension
5 Analytic Hierarchy Process
5.1 Data Collection
5.2 Data Analysis
6 Discussion
7 Conclusion
References
Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech
1 Introduction
2 Related Works
2.1 Speech as Objective Indicator
2.2 Features Indicators of Acoustic Signal Classification
2.3 Speech Signals Classification
3 Analysis of Feature Selection for Malay Language
4 Conclusion
References
Enhancing User Acceptance of E-Exam Systems: A Comprehensive Model and Empirical Analysis
1 Introduction
2 Research Model and Hypotheses Development
2.1 Actual Usage (AU)
2.2 Behavioral Intention (BI)
2.3 Perceived Usefulness (PU)
2.4 Perceived Ease of Use (PEOU)
2.5 Subjective Norms (SN)
2.6 Security (SEC)
2.7 Experience (EXP)
2.8 Practicality (PRAC)
2.9 Computer Self-efficacy (CSE)
2.10 User Interface Design (UID)
2.11 Computer Anxiety (CA)
3 Methodology
4 Results
5 Discussion and Conclusion
5.1 Theoretical and Practical Implications
5.2 Limitations and Future Work
Appendix. The Constructs and Scale Items
References
Investigating the Relation Between Learning Analytics and Academic Performance at the International Modern Arabic School: A Qualitative Study
1 Introduction
2 International Modern Arabic School (IMAS)
3 Methodology
4 Findings and Discussion
4.1 Benchmarking and Comparison
4.2 Early Intervention
4.3 Improved Teaching Strategies
4.4 Efficient Resource Allocation
4.5 Data-Driven Decision Making
4.6 Quality Assurance
5 Future Works Recommendations
6 Contribution
6.1 Theoretical Contributions
6.2 Practical Implications
7 Conclusion
References
The Role of Accounting Information Systems in Strengthening Organizational Resilience: An Empirical Investigation Using the SEM-ANN Approach
1 Introduction
2 Theoretical Background
2.1 Flexible AIS and Organizational Resilience
2.2 Complementary Business Intelligence System and Organizational Resilience (OR)
2.3 AIS-Related Human Resource Competency and OR
3 Methdology
4 Results
4.1 Assessment of the Reflective Measurement Model
4.2 Assessment of the Structural Model Hypotheses Testing
4.3 Artificial Neural Network Analysis
5 Discussions and Implications
5.1 Theoretical Implications
5.2 Practical Implications
6 Conclusion
6.1 Limitations and Future Research
References
Deep Dive into the Augmented Reality Customer Experience and Adoption Research: A Bibliometric Study
1 Introduction
2 Research Method
3 Results and Discussion
3.1 Research Trend and Impact
4 Conclusion
4.1 Theoretical Contribution
4.2 Practical Implication
References
Blockchain and IoT in the Modern Digital Age
1 Introduction
2 Blockchain and IoT Fundamentals
3 Types of Blockchain
4 Blockchain and IoT
5 Security Aspects in Blockchain and IoT
6 Discussion
7 Trends
8 Conclusions
References
How Does Gamification in an E-Commerce Platform Drive Customer Experience? The Mediating Roles of Perceived Enjoyment and Brand Engagement
1 Introduction
2 Literature Review
2.1 Theoretical Framework
3 Methods
3.1 Participants of the Study
3.2 Research Instrument
3.3 Data Analysis
4 Results
4.1 Measurement Model Assessment
4.2 Structural Model Assessment
5 Discussion
6 Conclusions
7 Practical Implications
8 Theoretical Implications
9 Limitations and Future Research Direction
References

Citation preview

Studies in Computational Intelligence 1128

Mohammed A. Al-Sharafi Mostafa Al-Emran Garry Wei-Han Tan Keng-Boon Ooi   Editors

Current and Future Trends on Intelligent Technology Adoption Volume 1

Studies in Computational Intelligence Volume 1128

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, selforganizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

Mohammed A. Al-Sharafi · Mostafa Al-Emran · Garry Wei-Han Tan · Keng-Boon Ooi Editors

Current and Future Trends on Intelligent Technology Adoption Volume 1

Editors Mohammed A. Al-Sharafi Universiti Tenaga Nasional (UNITEN) Selangor, Malaysia

Mostafa Al-Emran The British University in Dubai Dubai, United Arab Emirates

Garry Wei-Han Tan UCSI University Kuala Lumpur, Malaysia

Keng-Boon Ooi UCSI University Kuala Lumpur, Malaysia

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-031-48396-7 ISBN 978-3-031-48397-4 (eBook) https://doi.org/10.1007/978-3-031-48397-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

In an era of rapid technological advancement, understanding and embracing new tools and platforms has become crucial to societal progression. The digital transformation journey has reshaped numerous industries, enhanced global connectivity, and offered untold potential for the future. The first volume of this edited book dives deep into this captivating evolution, providing readers with a comprehensive overview of current practices and future prospects in intelligent technology adoption. The motivation behind this book stems from the urgent need to bridge the understanding between emerging technological solutions and their real-world applications. By investigating and documenting the trends, challenges, and opportunities within this digital revolution, we aim to provide scholars, practitioners, and enthusiasts with a roadmap to navigate the intricate landscape of modern technology. The exploration does not stop at adopting new technologies but delves into the human side of the equation, how individuals and organizations perceive and interact with these innovations. This book serves not just as a repository of research but as a beacon for the future, where technology is interwoven with our daily lives, shaping our experiences, decisions, and aspirations. It involves insights grounded in empirical research, robust methodologies, and review studies. These empirical and review studies will aid researchers in undertaking subsequent studies in the field and recognizing emerging technologies’ potential future advancements and adjustments. Graduate students can likewise understand the latest progress in emerging technologies and their uses. This book is designed to showcase cutting-edge research on present and upcoming trends in adopting intelligent technology. It successfully garnered 45 submissions from various global locations. Of these, 23 were accepted, reflecting a 51.1% acceptance rate. This book’s chapters are featured in the Studies in Computational Intelligence Series by Springer, known for its significant SJR impact. Selangor, Malaysia Dubai, United Arab Emirates Kuala Lumpur, Malaysia Kuala Lumpur, Malaysia

Mohammed A. Al-Sharafi Mostafa Al-Emran Garry Wei-Han Tan Keng-Boon Ooi

v

Contents

Log Out or Stay Connected? Unveiling the Intention for Continuous Use in the Metaverse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. M. Chong, Tat-Huei Cham, Evan Lau, B. K. M. Wong, and S. N. Syed Annuar Virtual Influencers as the Next Generation of Influencer Marketing: Identifying Antecedents and Consequences . . . . . . . . . . . . . . . Shuzhen Liu, Eugene Cheng-Xi Aw, Garry Wei-Han Tan, and Keng-Boon Ooi Modeling the Continuous Intention to Use the Metaverse as a Learning Platform: PLS-SEM and fsQCA Approach . . . . . . . . . . . . . Mohamed Soliman, Reham Adel Ali, Jamshed Khalid, Imran Mahmud, and Muhammadafeefee Assalihee

1

23

41

Are You Ready for Tapping into the Metaverse in Higher Education? Integrated by Dual PLS-SEM and ANN Approach . . . . . . . . Tri-Quan Dang, Phuc-Thien Tran, and Luan-Thanh Nguyen

63

Investigating the Influence of Technology Anxiety on Healthcare Metaverse Adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seckin Damar and Gulsah Hancerliogullari Koksalmis

85

The Metaverse: A New Frontier for Learning and Teaching from the Perspective of AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Anjali Munde and Jasmandeep Kaur Understanding the Effects of Social Media Advertising on Purchase Intention Through Metaverse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Chee-Hua Chin, Winnie Poh-Ming Wong, Tat-Huei Cham, Ek-Tee Ngian, Siew-Chen Sim, and Jill Pei-Wah Ling

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Contents

The Impact of Environmental Factors on Social Selling Intention via Virtual Reality Technology and Social Selling Performance: The Mediating Effect of Self-efficacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Melvin Sin-Yon Tie, Winnie Poh-Ming Wong, Tat-Huei Cham, and Chee-Hua Chin Reshaping Sport with Extended Reality in an Era of Metaverse: Insights from XR the Moroccan Association Experts . . . . . . . . . . . . . . . . . . 165 El Mostafa Bourhim and Oumayma Labti Unveiling the Influence of Social Technologies on Online Social Shopping in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Kin Leong Tang, Hon Tat Huam, Tat-Huei Cham, and Boon Liat Cheng Mobile Payment Adoption in Vietnam: A Two-Staged SEM-ANN Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Luan-Thanh Nguyen, Tien-Thao Cong Phan, Duc-Viet Thi Dang, and Thuy-Thanh Thi Tran Millennials Fintech Services Adoption: What Matters Most? . . . . . . . . . . 229 Mosharrof Hosen, Ai-Fen Lim, Taslima Jannat, Naveed R. Khan, and Chuen-Khee Pek Perceived Risk of Users’ Intention to Use Cryptocurrency in Malaysia: A Multi-analytic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Redhwan Al-amri, Shuhd Al-shami, and Gamal Alkawsi Prediction of Consumer Repurchase Intention with Food Delivery Apps: The Mediating Role of Prior Online Experience Using PLS-SEM-ANN Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Fadi Herzallah, Amer J. Abosamaha, and Mohammed A. Al-Sharafi Effects of Platform Values on Consumers’ Willingness to Continue and Subscribe to Metaverse Live-Streaming: With a Moderating Effect of Digital Literacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Zhiying Hou, Yet-Mee Lim, and Yu Zhang Organizational Adoption of Blockchain Based Medical Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Murat Tahir Çalda˘g and Ebru Gökalp Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Mohana Shanmugam, Nur Nesa Nashuha Ismail, Pritheega Magalingam, Nik Nur Wahidah Nik Hashim, and Dalbir Singh Enhancing User Acceptance of E-Exam Systems: A Comprehensive Model and Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Gulsah Hancerliogullari Koksalmis, Pasa Ciceklidag, and Ibrahim Arpaci

Contents

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Investigating the Relation Between Learning Analytics and Academic Performance at the International Modern Arabic School: A Qualitative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Muaadh Mukred, Waleed Abdulkafi Ahmed, Umi Asma’ Mokhtar, and Burkan Hawash The Role of Accounting Information Systems in Strengthening Organizational Resilience: An Empirical Investigation Using the SEM-ANN Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Ahmed Saleh Al-Matari, Mohammed A. Al-Sharafi, and Mohammed A. Hajar Deep Dive into the Augmented Reality Customer Experience and Adoption Research: A Bibliometric Study . . . . . . . . . . . . . . . . . . . . . . . 413 Prio Utomo, Tat-Huei Cham, Chuen-Khee Pek, and Shukhrat Mamatkulov Blockchain and IoT in the Modern Digital Age . . . . . . . . . . . . . . . . . . . . . . . 435 Reinaldo Padilha França, Rodrigo Bonacin, Ana Carolina Borges Monteiro, and Rangel Arthur How Does Gamification in an E-Commerce Platform Drive Customer Experience? The Mediating Roles of Perceived Enjoyment and Brand Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Jean Paolo G. Lacap, Mark Ivan Leo Ricafrente, Jude Caponpon, Rannie Medina, Ruby Anna Raneses, Zarah Centeno, and Sharifah Nurafizah Syed Annuar

Log Out or Stay Connected? Unveiling the Intention for Continuous Use in the Metaverse K. M. Chong , Tat-Huei Cham , Evan Lau , B. K. M. Wong , and S. N. Syed Annuar

Abstract Grounded on the S-O-R framework, this paper examines the impact of telepresence, interactivity, and social presence on users’ perceived enjoyment and value co-creation, which in turn, leads to continuous use intention. Moreover, the moderating effect of perceived trust is also examined in the interrelationship between perceived enjoyment, value co-creation, and continuous use intention. In this research, 400 responses were collected using a quantitative survey method with 352 valid responses, which were analyzed and interpreted. Structural equations modelling and bootstrapping were used to verify the hypotheses testing for the proposed model. The results showed that telepresence, interactivity, and social presence positively affected users’ continuous use intention in the metaverse. Perceived enjoyment and value co-creation have demonstrated a significant relationship with users’ continuous use intention in the metaverse. In addition, the moderating effect of users’ perceived trust has a significant relationship with perceived enjoyment and value co-creation K. M. Chong Graduate School of Business, SEGi University, Kota Damansara, Petaling Jaya, Selangor, Malaysia T.-H. Cham (B) UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia e-mail: [email protected] Tashkent State University of Economics, Tashkent, Uzbekistan Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia E. Lau Faculty of Economics and Business, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia B. K. M. Wong Faculty of Business, Design and Arts, Swinburne University of Technology Sarawak Campus, Kuching, Sarawak, Malaysia S. N. Syed Annuar Faculty of Business and Management, Universiti Teknologi MARA Sabah Branch, Kota Kinabalu Campus, Sabah, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_1

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to users’ continuous use intention in the metaverse. The study is one of the few that examines the impact of telepresence, interactivity, and social presence in addressing users’ perceived enjoyment and value co-creation through the metaverse. Future studies could consider using a qualitative approach to complement the quantitative findings and offer a more in-depth understanding of the continuous use intention. Keywords Perceived enjoyment · Value co-creation · Perceived trust · Continuous use intention · Metaverse

1 Introduction With a combined audience of 3.7 billion users who log in regularly on Facebook, Instagram, and WhatsApp, the CEO of Facebook, Mark Zuckerberg, announced the idea of the metaverse and made it public by renaming Facebook as Meta, which completely changes how we interact with the outside world. The belief of the next generation of the internet is the metaverse, and the current social media will fall under the metaverse, subsequently combining virtual and real-world [1–6]. The metaverse enables people to socialize, work, play, and relax in a single virtual community. Its meaning is still evolving as it’s in early development. The word “Meta” is a Greek prefix meaning post, after, or beyond, whereas “Verse” is the universe, making the closed compound word “metaverse” [7]. The metaverse is a multiuser system that continuously and persistently merges physical and virtual worlds. It offers 3D layers, authentic user interactions, improved services, products, and information access. In the virtual world, the term “metaverse” refers to a digital setting where reality and virtuality are interconnected to provide a transcendent experience [8]. With the first-ever property metaverse platform in Malaysia, Matrix Concepts Holdings Bhd has enabled their customers to interact with one another online and in real time. Users can use the microphone and camera on their device to speak verbally with anyone in the metaverse by creating their own ‘avatars’ [9]. The metaverse is more than merely a 360-degree spectacle; it teleports potential customers to the appropriate area while allowing them to communicate and converse with salespeople and providing numerous sales professionals. It is similar to live visiting and touring a sales gallery and show units. Many cutting-edge use cases have arisen as businesses continue to invest millions of dollars in the metaverse market and are expected to grow by 39.6% to reach $2.278 billion annually by 2023 [10]. Innovation is expanding in the Malaysian metaverse sector, from online gaming to online retail. Additionally, from an education perspective, SEGi University is positioned to fundamentally and exhilaratingly alter how students learn, interact, and connect as the first university group in Malaysia to build a campus in the metaverse, Meta Campus. Despite the benefits and widespread support for the metaverse, Southeast Asian nations have not yet wholly entered the metaverse [11]. These nations may find launching many commercial prospects in the metaverse motivating. The popularity of the metaverse has begun to benefit the global audience. Southeast Asian (SEA)

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nations are at various stages of development inside the metaverse. Acknowledging how far along the metaverse is in these nations and the sustainability of adopting metaverse usage in Southeast Asia is crucial. In this aspect, we have identified two research gaps. Given the above scenario, practitioners must gauge the economic and social effects of the metaverse accurately. However, many of them are still in the observing phase due to a lack of evidence on their outcomes and users’ expectations. Thus, we aim to narrow this perceptual gap using the findings from this study. From a practical perspective, the metaverse is still in its infancy, and investors and business entrepreneurs are hesitating to venture into this new world. Nonetheless, innovators and early adopters in the business and other domains want to gain a first-mover advantage by capitalizing on the metaverse’s competitive edge at its advent. On this issue, it is undeniable that a huge investment is needed for investors to fully embrace the digital advantages of the metaverse and stay ahead of the game. Therefore, this study is expected to provide precious insights for practitioners with essential and relevant data to strategize their business plans. The second research gap is the development of literature on the metaverse and its acceptance, which is still in the early stages. From an academic standpoint, the metaverse can be a concept and an innovative model that involves altering consumer behavior. Limited literature and research exist on its antecedents of acceptance and intention to use. One of the main reasons is the need for a more prevalent presence. Unlike social media on the internet or mobile phones, the metaverse is conceptualized as the virtual world of the future, where consumers and businesses interact and carry out their day-to-day activities [12]. This presents a challenging task for academics, as such a concept can be highly abstract for users to imagine and adopt [13, 14]. Therefore, this study intends to set the antecedents for the continuous use of the metaverse among early adopters. The findings of this research are thus expected to shed light on the aforementioned gaps by providing statistical evidence for further literature development. As such, this study aims to contribute to the practical and literature domains of the metaverse by investigating the continuous use intention of the metaverse in the retail industry in Malaysia.

2 Literature Review and Hypotheses Development 2.1 Theoretical Foundation: S-O-R Model The Stimulus-Organism-Response Model (S-O-R Model) is the paradigm or theoretical framework created in the cognitive approach that studies consumer behavior. It was created in an effort to comprehend how customers make decisions on their own, with others, or inside organizations. The major purpose of the model is to assess how consumer behavior and stimulation interact. The stimulus (S) denotes elements of the environment that may have an impact on a person’s internal states [15, 16], whereas the organism (O) refers to the internal processes and structures intervening

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between the stimuli and the final actions, reactions, or responses [17]. Depending on their emotional state, a person transforms the stimulus into information at this level, which they then use to analyse their environment [18]. In addition, according to [19], response (R) is conceptualised as a continuum of consumer behaviour, ranging from approach to avoidance. There is a plethora of literature related to the S-O-R model in the business and marketing domains, especially with the emerging trends in social media; more scholars are applying this model to explain the effect of intention and technology adoption. Nonetheless, as the metaverse is a nascent concept with a limited digital presence, it is imperative to define the operating environment of the S-O-R model in the metaverse. First, the stimulus (S) refers to the virtual environment in the metaverse, which can entice the users’ emotions and behavior. The environment stimulus for the metaverse can be the metaverse design, atmosphere in the metaverse (including the vibe, music and verbal communication, etc.) and the user experience while interacting with the avatars in the metaverse. Such environmental interaction will lead to the response of the emotions or feelings of the users in the metaverse. These responses are considered the results of the organism (O) of the users expressed by their emotional behavior and reactions. The responses (R) can be either positive or negative experiences in the metaverse, which are essential for this study as the outcomes of the metaverse interaction will determine the intention to use the metaverse continuously. As users enter the metaverse, the first encounter will be the surroundings built by the business. If the users’ experience upon entry is less pleasant and counterintuitive, without doubt, such experience will lead to reduced intention to continuous use of the metaverse. The user experience in the metaverse ideally should be enjoyable and fun. This perception naturally is connected to the enjoyment domain of this study. Moreover, the behavior and responses of the users in the metaverse will inevitably involve value co-creation in their interaction with the environment and other users. This study, therefore, aims to provide important information on the expectations of the users towards the metaverse experience related to enjoyment and value co-creation.

2.2 Telepresence of Metaverse Telepresence is the virtual presence with sound, vision and touch techniques taking place at a remote location by creating a sense of physical presence [20]. Moving from one’s actual location to another without physical movement is also the technique of telepresence [21, 22]. Metaverse technologies have drawn more attention in recent years because they are quite comparable to service forms in the real world and provide users with a sense of enjoyment [7]. There was a value of USD 1.79 billion in the global telepresence equipment market in 2020 [23] and is expected to grow at a compound annual growth rate of 3.1% during the forecast period until 2028. The emergence of modern technology has fostered an enhanced sense of presence within virtual environments. As a result, users are increasingly inclined to engage

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in self-disclosure within virtual reality settings. Moreover, the feeling of presence significantly contributes to the development of user motivation [24]. The spatial and social presence of a virtual reality platform influenced users’ relatedness, selfexpansion, and satisfaction favorably, according to [25]. Arguably the telepresence in the metaverse has not been verified by scholars at a wider scale as the notion is nascent. As such, the following research hypothesis was developed as this study believes that the telepresence provided by the metaverse will positively impact enjoyment and value co-creation. H1a: The telepresence of the metaverse has a positive relationship with the user’s perceived enjoyment. H1b: The telepresence of the metaverse has a positive relationship with the user’s value co-creation.

2.3 Interactivity of Metaverse Interactivity is defined as the ability of technology to make it easier for users to interact with and participate in content [26]. Interactivity maximizes the shopping experience by allowing users to spend their time voluntarily to achieve shopping efficiency [27]. Encouraging interactivity in virtual worlds ensures users can impact the virtual environment. In virtual environments, interactivity refers to the extent to which users can actively and synchronously modify the digital space’s structure and content [28, 29]. The interactivity of the metaverse is encouraged to ensure the user can cause an effect on the virtual environment. Hwang et al. [30] confirmed that by influencing the user’s incentive to use it, the feeling of space and conversation exchange offered by the metaverse similar to the real world could raise the intention of continuous use. Aljukhadar et al. [31] also pointed out that the virtual world’s interaction and interactivity would directly impact consumers’ perception of hedonism and utilitarian value in the virtual environment. However, as the metaverse is a new business model and interactivity in the metaverse has not been effectively proven, the concept of interactivity should be approached with care. We believe that the seller, physically or virtually, should build a meaningful and valuable relationship with the target market through interaction. Tsai and Men [32] further pointed out that sellers and consumers obtained interaction and participation through virtual communication, and both parties obtained common values. The following research hypothesis was developed as this study believes that the interactivity provided by virtual worlds will positively impact user enjoyment and value co-creation, so the following research hypotheses are proposed. H2a: The interactivity of the metaverse has a positive relationship with the user’s perceived enjoyment. H2b: The interactivity of the metaverse has a positive relationship with the user’s value co-creation.

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2.4 Social Presence of Metaverse Ou et al. [33] pointed out that social presence emphasizes an online design element, which promotes repeated transactions with sellers by establishing fast relationships and trust through the interaction and presence of sellers and consumers, and is a relationship including mutual understanding, reciprocity and harmony. In a computermediated environment, social presence is thought to create a “being with other” insight [34]. By conveying a sense of interpersonal interaction, sociability and sensitivity, an increased social presence of a medium leads to a more focused perception of the person it is interacting [35–37]. Even though social presence will influence consumers’ purchase intention by influencing network traffic in the virtual world [38], we posit that in the metaverse the social presence factor may play a more crucial role in influencing the purchase intention in the virtual world, unlike the physical world, required more pro-active interaction from the users. That said, if the users are not keen to go online, the social presence factor will have less effect in the metaverse. On the other hand, Baker et al. [39] argued that consumer trust is influenced by both perceived remote presence and social presence. The more capable individuals are of generating social presence, the more likely they are to form meaningful and satisfying relationships with others [40–42]. Hassanein et al. [43] studied that practitioners manipulate website design elements to increase consumers’ perception of social presence, thus affecting usefulness, enjoyment and trust. Hew et al. [44] found that service quality, perceived mobility, and the system’s social presence have direct and indirect effects on tourists’ perceived enjoyment and usefulness. These factors contribute to an increased willingness to shop in mobile social tourism. Based on the discussion above, it is anticipated that social presence will have a positive impact on users’ enjoyment and value co-creation, so the following research hypotheses are proposed. H3a: The social presence of the metaverse has a positive relationship with the user’s perceived enjoyment. H3b: The social presence of the metaverse has a positive relationship with the user’s value co-creation.

2.5 The Relationship Between Perceived Enjoyment, Value Co-creation, and Continuous Use Intention Per previous studies, we posit a relationship between perceived enjoyment, value cocreation, and intention to continue usage in the metaverse experience. For example, in a virtual retail shop within the metaverse, users can immerse themselves in an interactive shopping experience that combines elements of real-world shopping, social interaction, and virtual technologies. They begin by creating and customizing their virtual avatars to reflect their personal style and preferences [45, 46]. Navigating through a virtual store, users can explore different product categories, aisles, and

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shelves, just as they would in a physical store. They can interact with virtual product displays, zoom in on items, and access detailed information such as descriptions, images, customer reviews, and pricing. Furthermore, users may have the opportunity to virtually try on clothing, accessories, and cosmetics using augmented reality or virtual reality technologies, enabling them to see how the products would look and fit on their avatars. Such an enjoyable experience happens in the virtual world without having to go through the hassle of commuting to the physical store. In addition, they can also engage with virtual products by picking them up, examining them, and experiencing interactive features or animations that showcase their functionality. According to research, entertainment directly influences usage intentions [47, 48] and indirectly influences continuation intentions [30]. Similarly, according to [49], enjoyment perception, which is related to entertainment uses and gratification, is associated with the intent to buy virtual products. Moreover, enjoyment is supported by social interaction. In this context, we argue that if the metaverse is unable to provide the level of entertainment needed to attract the users and entice their purchase intention, the business model of the metaverse will be less successful. In addition, social interaction is facilitated within virtual retail shops, allowing users to communicate with other shoppers, seek recommendations, and share their thoughts on products through voice or text chat, fostering a sense of community and enabling a social shopping experience [50]. Purchases are made using virtual currencies or digital tokens specific to the metaverse, with options to add items to a virtual shopping cart, proceed to checkout, and complete transactions within the virtual retail shop. After purchase, users can choose to have the products delivered to their physical address or access them virtually within the metaverse, where virtual items can be instantly available for use or customization in the virtual inventory. The virtual retail shop experience aims to provide users with a seamless, interactive, and personalized shopping journey, leveraging the metaverse’s capabilities to create a unique and engaging environment for users to explore, discover, and make purchases [51]. People’s perceptions of the activity as a good experience indicate that they were absorbed in its flow [52, 53]. Interestingly, Pace [54] discovered that users’ curiosity was increased to the point that they forgot what they had intended before when they felt telepresence in a threedimensional virtual world (3D VR). Onum and Han [55] demonstrated that users with high levels of telepresence had higher levels of subjective enjoyment, demonstrating a causal link between telepresence and enjoyment. Overall, the metaverse’s virtual retail shop experience offers a dynamic and innovative way for users to engage with products, connect with others, and enjoy a rich shopping experience that blurs the boundaries between the physical and virtual realms. While users are experiencing the digital journey in the metaverse, they will eventually co-create values in this virtual world. More importantly, value co-creation with the metaverse is a collaborative process that brings together users and businesses to enhance the value of products, services, and experiences in the virtual environment [56]. As such, we believe that it is vitally important for the users to be able to respond and interact with the sellers and encouraged to co-create values in the metaverse through innovative means. Users play an active role by contributing user-generated content, such as virtual fashion items and digital art, enriching the metaverse ecosystem and providing unique experiences

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for others. According to [57, 58], a company’s competitive advantage mostly stems from how a product and service given may maximize consumer value. Businesses can collaborate with users in virtual commerce, involving them in designing and developing virtual products and services [59]. This collaboration allows businesses to gather feedback and ideas, leading to the co-creation of innovative and personalized offerings. Furthermore, the metaverse enables collaborative design, where users and businesses work together to customize virtual spaces, products, and events, ensuring that the end results cater to user preferences and enhance overall value. In the metaverse, personalization and customization are critical aspects of value co-creation. Users can personalize their virtual experiences, avatars, and possessions according to their preferences. At the same time, businesses can offer customization options to facilitate the co-creation of products that meet users’ unique tastes and requirements. From the standpoint of customers, value co-creation comprises a procedure that boosts their confidence and well-being and gives them a practical way to lessen anxiety [60]. This tendency happens as a result of customers having a lot of opportunities to contribute and exchange ideas as part of the interactive value co-creation process to jointly develop a product that satisfies their demands [61]. In addition to the above, the metaverse also fosters social interaction and the formation of communities, enabling users to connect with like-minded individuals, share ideas, and collectively contribute to the development and improvement of the metaverse ecosystem. This social interaction contributes to the co-creation of social value within the virtual environment. In addition, businesses actively seek user feedback and incorporate it into the iterative development and refinement of their offerings in the metaverse, ensuring that products, services, and experiences continuously evolve to meet user needs and preferences. Since consumers frequently look forward to participating actively in the creation of goods and services, the success of value co-creation is crucial in the context of Internet marketing. More recent research discovered that value co-creation fostered relationships with online users, resulting in high user happiness in the social media environment [62, 63]. Overall, value co-creation in the metaverse empowers users and businesses to collaborate, innovate, and enhance the overall value proposition, delivering personalized and meaningful experiences while allowing users to shape the development and evolution of the metaverse ecosystem actively and consistently benefit from using metaverse in their daily convenient [64, 65]. Following the studies highlighted above, this study suggests that the greater the user’s enjoyment when using metaverse, the higher the intention to continue using metaverse. In addition, the greater the user’s ability to co-create value in the metaverse, the higher the intention of the users to continue to embrace the metaverse. Therefore, the following hypotheses are synthesized: H4: The user’s perceived enjoyment has a positive relationship with the continuous use intention. H5: Value co-creation has a positive relationship with the continuous use intention.

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2.6 Perceived Trust as a Moderator Trust is defined as consumers’ willingness to further interact with a virtual shopping environment [66]. The psychological state of customers who are prepared to engage with the retail environment further to accomplish a desired goal is referred to as the trust outcome [39, 67, 68]. On the other hand, Teoh and Cyril [69] looked at the constructions of these two outcomes, whereby the relationship between shopping attitudes and customer trust developed while shopping, which was said to be dually affected by perceived telepresence and social presence. As such, we posit that perceived trust is of utmost importance in influencing the factors that lead to continuous use intention. H6: There is a moderation effect of perceived trust between the relationship of perceived enjoyment and continuous use intention. H7: There is a moderation effect of perceived trust between the relationship of value co-creation and continuous use intention.

3 Research Methodology This study targeted individuals who have previously used metaverse technology. To collect data from 400 participants, a self-administered survey questionnaire was utilized through the online-survey platform, Qualtrics. Invitations to participate in the survey were sent to potential respondents via WeChat, Facebook, WhatsApp, and email. A purposive sampling technique was employed to ensure reliable responses, Perceived Trust

H6

Telepresence

H7

H1a

Perceived Enjoyment

H1b H2a

Continuous

H4

Use Intention

Interactivity H2b

Value Co-creation H3a

Social presence H3b

Fig. 1 Research model

H5

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which involved implementing two screening criteria to determine the suitability of the respondents before they could participate in the study. These criteria included being a user of metaverse technology and being at least 18 years old. Following the data cleaning process, only 352 observations among the collected responses were usable for data analysis and hypotheses testing. The sample size for the present study is considered sufficient from the theoretical and statistical point of view [70, 71]. In terms of constructs’ operationalization, the present study used existing scales to measure constructs, with some modifications to better fit the study’s context. All the measurement items were rated on a six-point Likert scale, with 1 indicating strongly disagree and 6 indicating strongly agree. The questionnaire was pre-tested and pilot tested to assess face validity. The measurement items for the constructs used in this study were adapted from the existing literature, with slight modifications made to align them with the research purpose [72–74]. As for the demographic aspect, this study’s respondents comprised 54.5% female respondents, and the remaining were male. The majority of them were married (65.9%) and fell within the age group of 26–35 (46%), followed by the 18–25 age group (34.7%). As for metaverse usage, it is interesting that most respondents used the platforms at least four times a week. Furthermore, the current study does not exhibit common variance bias (CMB), as the model with all item indicators as a single factor was not a good fit. Harman’s single-factor test indicated that the variance explained by the first factor was only 27.33%, well below the threshold of 40% [75, 76].

4 Research Findings 4.1 Assessment of Measurement Model The present study employed confirmatory factor analysis (CFA) to evaluate the measurement items’ reliability. CFA was used to assess the measurement model’s adequacy by examining the convergent and discriminant validity of the constructs. Low et al. [77] proposed various indices to evaluate model fit, including the Goodness of Fit (GFI), Normed Chi-square (χ2 /df), Root Mean Square Error of Approximation (RMSEA), Parsimony Normed Fit Index (PNFI), and Tucker-Lewis Index (TLI). To consider a model as fit, it needs to satisfy the following criteria: GFI ≥ 0.90, χ2 /df < 3, RMSEA < 0.08, PNFI > 0.50, and TLI ≥ 0.90. The assessment of the measurement model in the present study revealed the following values: GFI = 0.935, χ2 /df = 1.195, RMSEA = 0.024, PNFI = 0.791, and TLI = 0.918, suggesting that the measurement model is indeed fit. The present study assessed the convergent validity of the constructs based on the criteria proposed by Hair et al. [77]. These criteria include: (1) all measurement items of the constructs should have factor loadings exceeding 0.60, (2) the composite reliability for each construct should be equal to or greater than 0.70, and (3) the average

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Table 1 The outcome of convergent validity Items

FL

CR

AVE

Continuous use intention

3

0.698–0.870

0.816

0.598

Interactivity

3

0.738–0.898

0.851

0.657

Telepresence

4

0.749–0.786

0.852

0.589

Social presence

4

0.692–0.845

0.867

0.621

Value co-creation

4

0.733–0.801

0.857

0.600

Perceived enjoyment*

4

0.673–0.820

0.840

0.569

Perceived trust

3

0.723–0.863

0.816

0.599

Notes FL represents factor loadings, CR represents composite reliability, AVE represents average variance extracted, and *represents an item being dropped from further analysis

variance extracted (AVE) for each construct should be higher than 0.50. However, an item related to perceived enjoyment was dropped from further analysis due to its low loading value. After removing this item, the results in Table 1 demonstrate that the measurement model satisfies the requirements for convergent validity. Therefore, it can be concluded that convergent validity was established in this study. In addition, the discriminant validity was assessed based on the criteria proposed by Fornell and Larcker [78]. This criterion involves comparing the square root of the Average Variance Extracted (AVE) values against the variances shared between different constructs. According to [78], discriminant validity is established when the square root of AVE values is greater than the variances shared between any two constructs. The discriminant validity results in Table 2 indicate that the square root of AVE values (in brackets along the diagonal entries) is greater than the variances shared between any two constructs (shown in bold in the off-diagonal entries). Therefore, it can be concluded that the constructs in this study exhibit discriminant validity. After evaluating the measurement model, the next phase will involve analyzing the fit of the structural model and presenting the results of hypothesis testing.

4.2 Structural Model Assessment and Hypotheses Testing The structural model’s fit indicators indicate that the GFI = 0.878, χ2 /df = 1.295, RMSEA = 0.029, TLI = 0.960, and PNFI = 0.774, suggesting the model is reasonably well-fitted. Given the satisfactory fit of the structural model, the researchers can proceed with further analysis of the formulated hypotheses. The outcomes of the path analysis for all the hypotheses in this study are presented in Table 4. The fit indices for the structural model indicate that the GFI = 0.938, χ2 /df = 1.286, RMSEA = 0.026, PNFI = 0.800, and TLI = 0.902. These values collectively indicate a good model fit. With the structural model fitting satisfactorily, the researchers can now proceed to conduct a more in-depth investigation of the formulated hypotheses. The study’s

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Table 2 The outcome of discriminant validity 1

2

3

USG

(0.773)1

IT

0.3052

(0.811)

TP

0.311

0.431

(0.768)

SOC

0.199

0.366

0.325

4

5

6

7

(0.788)

VC

0.395

0.504

0.410

0.423

(0.775)

EJY

0.349

0.403

0.309

0.302

0.414

(0.754)

TRT

0.257

0.125

0.080

0.152

0.258

0.115

(0.774)

Notes USG continuous use intention, IT interactivity, TP telepresence, SOC social presence, VC value co-creation, EJY perceived enjoyment, TRT perceived trust, 1 The diagonal entries (in bracket) represent the squared root average variance extracted by the construct, 2 The off-diagonal entries (in bold) represent the variance shared between constructs

findings are presented in Table 3, which summarizes the results of the path analysis conducted to test all proposed hypotheses. Specifically, the findings indicated that the attributes of the metaverse, such as telepresence, interactivity, and social presence, were positively linked to users’ perceived enjoyment and value co-creation. Furthermore, the results of the path analysis demonstrated that both perceived enjoyment and value co-creation influenced users’ continuous use intention towards metaverse. Therefore, it can be concluded that H1a, H1b, H2a, H2b, H3a, H3b, H4, and H5 received support. The moderating effect of perceived trust on the relationship between perceived enjoyment → continuous use intention (H6) and value co-creation → continuous use intention (H7). To test the moderating effect, SPSS PROCESS was adopted. It was suggested that a moderation effect occurs when the interaction term in the regression model is significant. The results in Table 4 show that only the interaction term in hypothesis 6 was statistically significant, as indicated by the 95% confidence interval (lower bound − 0.284; upper bound − 0.111), which spelt out that perceived trust moderates the relationship between perceived enjoyment and continuous use intention. Similarly, the same output was also sighted in hypothesis 7 (lower level − 0.261; upper level − 0.106) whereby it was found that perceived trust moderates the relationship between value co-creation and continuous use intention. Moreover, the graphs in Figs. 2 and 3 show that the interpolation lines for metaverse users who trust the platform are steeper than those with less trust. Specifically, this output indicates that perceived enjoyment and value co-creation are strongly associated with continuous use intention among metaverse users who trust the platform.

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Table 3 Result of path analysis Critical ratio

Hypothesis

H1a: telepresence → perceived 0.141 enjoyment

0.063*

Yes

H1b: telepresence → value co-creation

0.059*

Yes

H2a: interactivity → perceived 0.299 enjoyment

0.067**

Yes

H2b: interactivity → value co-creation

0.343

0.064**

Yes

H3a: S.presence → perceived enjoyment

0.154

0.065*

Yes

H3b: S.presence → value co-creation

0.238

0.063**

Yes

H4: perceived enjoyment → continuous use intention

0.236

0.078**

Yes

H5: value co-creation → continuous use intention

0.314

0.076**

Yes

Hypothesized path

Standardized estimate (β)

0.194

Notes S.presence social presence, ** and * denote significance at 99% and 95% confidence level respectively

Table 4 Testing moderating effect of perceived trust C.I Variable

β

SE

L.L

U.L

Model 1: Perceived trust moderates the relationship between perceived enjoyment and continuous use intention H6: interaction (EJY X TRT)

− 0.191

0.044

− 0.284

− 0.111

Model 2: Perceived trust moderates the relationship between value co-creation and continuous use intention H7: interaction (VC X TRT)

− 0.183

0.039

− 0.261

− 0.106

Note EJY perceived enjoyment, TRT perceived trust, SE standard error, β co-efficient beta, C.I confidence intervals, VC value co-creation, at 95%, L.L lower limit, U.L upper limit, **p-value < 0.001, *p-value < 0.05, ns not significant

5 Discussion and Implication With the advent of the metaverse, the business model has gradually adopted the online model to enhance user experience, particularly in the retail industry, enabling more creativity and flexibility. The younger generation, driven by the desire for instant gratification in their daily activities, is drawn to the metaverse in a similar manner to how they embrace social media. However, the metaverse offers a distinct level of engagement and intrigue for its users. Despite numerous studies [74, 79] on social media and its adoption and continuous usage determinants, the metaverse as a

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6

Continuous use intention

5.5 5 Low Trust

4.5

High Trust

4

3.5 3 Low perceived enjoyment

High perceived enjoyment

Fig. 2 Interaction plot of perceived enjoyment × perceived trust on continuous use intention

Fig. 3 Interaction plot of value co-creation × perceived trust on continuous use intention

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virtual reality platform in the retail business remains relatively unexplored and underdiscussed. While the social presence and interactivity factors may appear common in most social media literature, telepresence emerges as a crucial aspect within the metaverse. Research has shown that telepresence significantly influences continuous usage of the metaverse, with avatars playing a crucial role in facilitating users’ comfortable and free interactions. Furthermore, telepresence acts as a precursor to interactivity. Additionally, it is important to highlight that perceived enjoyment of the metaverse plays a vital role in its continuous usage. This notion closely relates to hedonic motivation, where users experience positive interactions and emotions while utilizing the metaverse. Such experiences reinforce users’ intention to return to the metaverse and immerse themselves in the virtual world while engaging in real-world activities within the metaverse. Another crucial factor is value co-creation, enabling users to contribute to content proliferation in the virtual world. This function is essential for users to enjoy the interactive process, as it prevents activities and communication from becoming monotonous and one-way. By having the ability to contribute, create, and communicate with others in the metaverse, users are motivated to continue using it. Value creation leads to satisfaction, enjoyment, and reinforces their online behavior. These findings emphasize that continuous usage intention in the metaverse is influenced by factors such as telepresence, perceived enjoyment, value co-creation, and others. This underscores the significance of self-experience in the metaverse, where users evaluate its attractiveness through their involvement. It is crucial to note that respondents who have experienced the metaverse exhibit predominantly positive and encouraging reactions and responses. This suggests that creating and maintaining a successful market or business in the retail industry relies heavily on providing compelling first-time experience. Users who have experienced enjoyment and value co-creation are more likely to return to the metaverse and become increasingly engaged. Therefore, practitioners should focus on delivering a gratifying experience to users by meeting their expectations while they are in the metaverse rather than solely after they exit. Moreover, this implies that users’ engagement with the metaverse intensifies with each subsequent use, as the principles of the hedonic treadmill influence their experiences, driving them to desire more each time they engage with the metaverse.

5.1 Theoretical Implication This study adds to the literature impact of telepresence, interactivity and social presence by conducting empirical research on metaverse platforms, reflecting the continuous use intention of metaverse platforms and technologies. The current study expands the body of marketing research in several ways. Consumer behavior has been emphasized in this research, with the theories validated under the real-world context to explain the consumer behaviors of virtual consumption. Jung et al. [80] further explained that the designs of behavioral research are vital to benefit the research

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community by drastically lowering the heavy workload of data gathering and organization in the research process. Besides, these designs can help the research community by freeing up the researchers to concentrate on more intellectually demanding activities. In addition, the continuous use intention of the metaverse is essential towards the sustainability of the virtual economy. Therefore, paying attention to virtual consumption should be encouraged as such envision is still far from reality [66]. Secondarily, by examining the underlying determinants that affect the continuous use intention of the metaverse, this study contributes to the marketing literature by utilizing the S-O-R model. As such, we found that telepresence, interactivity and social presence are the important stimuli that lead to perceived enjoyment and value co-creation [39, 72, 74]. Thus, future studies need to consider the effects of perceived enjoyment and value co-creation when understanding the continuous use intention of metaverse platforms and technologies. Thirdly, the moderating effect of perceived trust between perceived enjoyment and continuous use intention and the moderating effect of perceived trust between value co-creation and ongoing use intention has brought the insight of metaverse by utilizing the interconnectivity of real-world and virtual and a new form of enjoyment in electronic commerce. A trust-building factor promotes confidence in their virtual experience, especially when buying and selling virtual products, goods and properties, including trading in and out of virtual world currencies per se [44, 81, 82].

5.2 Practical Implication The retailers can create a virtual shopping experience for the customers in the metaverse by offering them immersive and engaging shopping interaction, which can be more personalized than the physical store. As the customers enter the virtual world, their personal data will be captured by the retailers, and thus, products and services can be tailored to their preferences and past purchase records. Their social presence can be enhanced using virtual reality (VR) and augmented reality (AR) technologies. Such technologies will lead to better-perceived enjoyment, which is significant to their continuous usage of the metaverse. Through the customers’ engagement and involvement in the products or services customization, they will enjoy the process of value co-creation, which eventually enhances their continuous usage as personal involvement and creation will improve satisfaction and interaction. The users’ interactivity in the metaverse can be enhanced by involving them in the virtual community where customers can meet each other to exchange ideas and experiences and provide recommendations for certain brands. If managed smoothly and successfully by the business, this interactivity can create a better branding atmosphere for retailers and generate positive electronic word-of-mouth for its brand. The study’s findings also imply that the practitioners could utilize the customers’ data to make accurate and strategic decisions based on their interactivity in the metaverse. Businesses could use big data collected in the metaverse to arrive at some predictive decisions, such

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as the likelihood of the next purchase, event celebrations, etc. Such decisions can be measured by assessing the level of customer satisfaction and enjoyment, as it has been proven that perceived enjoyment leads to continuous usage intention. Similarly, if businesses offer customers greater freedom in value creation, such as designing their own apparel or personalizing products with engravings, and opportunities to become brand ambassadors in the virtual world, it can enhance both customer satisfaction and the likelihood of repurchase.

6 Conclusion This study has achieved the research objectives and answered the research questions by providing statistical evidence and relevant literature support in studying the relationships of the variables towards the continuous usage intention of the metaverse in the retail industry. These variables under study are telepresence, social presence, interactivity, perceived enjoyment and value co-creation, which have shown significant relationships towards the continuous usage intention of the metaverse. The findings might shed some light on the ever-developing digital business domain related to the metaverse and consumer behavior in the virtual world. Academics can benefit from these findings and include perceived enjoyment and value co-creation as essential variables in their research model as independent or moderating variables. The moderating role of perceived trust is fundamental in the virtual world, as logging into the metaverse may mean exposing personal data to the business or the community. Therefore, the credibility and reliability of the virtual platforms are inevitably important in creating a safe and engaging virtual environment in the metaverse. To attract business and retain their customers in the metaverse, the company must understand these factors and their interrelated effects in influencing the consumers’ decisions, involvement and continuous usage intention.

7 Limitations and Future Studies Despite the aforementioned contributions and implications, the current study has a few limitations. The flow experience of customers on the metaverse platform could be an important factor influencing the continuous usage intention of users. Other characteristics of the metaverse platform, such as economic flow and creativity, can be included as antecedents of ongoing usage intention, thereby emphasizing users’ flow experience in the metaverse [83]. The addition of a creation tool to enhance the creativity of digital objects and personalize users’ virtual world environment and experience would add value to the enjoyment and value of co-creation for metaverse users. Therefore, future research should delve deeper into each factor influencing metaverse continuous usage intention and, subsequently, the purchase of metaverse products. Next, the use of a survey questionnaire with closed-ended questions to

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gather the necessary data constitutes a second limitation of this study. The collection of more in-depth information and opinions, which may require open-ended questions, might have been restricted by the limited options and absence of further qualitative approaches related to the survey questionnaire. Third, future research should consider categorizing preferences among different age groups and socioeconomic classes actively participating in the metaverse platform. Focusing on different generations, such as Generation X, Y, or Z, could lead to different findings and shed light on the factors influencing their continuous usage intention in the metaverse. Given the above, future studies should enhance and re-test the existing study model using a more varied sample. This is suggested in the account for potential effects as proven by demographic aspects such as age, experience, culture, belief, and religion on one’s perception regarding the continuous use of metaverse technologies and platforms. Additionally, to ensure a better understanding of the potential impact of background and upbringing on the subject matter, the study can be conducted towards comparing responses obtained from various cohorts. Future studies could also consider using a qualitative approach to complement the quantitative findings and offer a more in-depth understanding of the continuous use intention.

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Virtual Influencers as the Next Generation of Influencer Marketing: Identifying Antecedents and Consequences Shuzhen Liu, Eugene Cheng-Xi Aw, Garry Wei-Han Tan, and Keng-Boon Ooi

Abstract Virtual influencers have gained substantial popularity among millennials and Generation Z, emerging as a notable phenomenon in brand endorsement marketing. This study aims to examine the influence of anthropomorphic and interactive attributes of virtual influencers on the formation of parasocial interactions and their implications for branding outcomes. A total of 283 valid responses were collected through online questionnaires. The data was analyzed using PLS-SEM. The findings reveal that the anthropomorphism and interactivity of virtual influencers positively affect the development of parasocial relationships. Furthermore, parasocial interactions play a crucial role in enhancing brand credibility and brand attachment. These brand-related factors contribute to the growth of electronic word-of-mouth (e-WOM) for the brand. The insights from this study offer valuable guidance for marketers on leveraging virtual influencers to increase consumer engagement and achieve favorable brand outcomes. Additionally, it enriches both the theoretical and practical dimensions of virtual influencer research. Keywords Influencer marketing · Virtual influencers · Artificial intelligence · Brand trust · Purchase intention · Consumer behavior

S. Liu · E. C.-X. Aw (B) · G. W.-H. Tan · K.-B. Ooi UCSI Graduate Business School, UCSI University, 56000 Cheras, Malaysia e-mail: [email protected] G. W.-H. Tan School of Business, Faculty of Business, Design, and Arts, Swinburne University of Technology Sarawak Campus, Kuching, Malaysia E. C.-X. Aw · G. W.-H. Tan Department of Business Administration, IQRA University, Karachi, Pakistan G. W.-H. Tan College of Business Administration, Adamson University, Manilla, Philippines © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_2

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1 Introduction Social media has revolutionized the way brands connect with consumers, and virtual influencers have emerged as a prominent phenomenon within this dynamic landscape [1, 2]. A virtual influencer is a virtual person or avatar who is designed to have human appearance characteristics and personality [3]. Social media platforms such as Instagram, YouTube, and TikTok serve as active and influential channels for virtual influencers, amassing a significant number of followers and exerting a profound impact on their audience [4–6]. Researchers showed that social media, especially Instagram, has a positive impact on teenagers and college students in terms of obtaining information, providing learning, and purchasing [7]. Other studies indicated that utilizing social media for delivering health messages and promoting nutrition education can significantly influence followers’ perceptions and behaviors [8, 9], and even boost college students’ general election votes [10]. Recognizing the potential of virtual influencers, brands, and companies have increasingly shifted their focus toward leveraging their influence to enhance marketing strategies [11]. According to a survey conducted in March 2022 among American consumers, 75% of respondents aged between 18 and 24 indicated that they follow at least one virtual influencer. Similarly, among respondents aged between 25 and 34, as well as 35 and 44, 67% expressed their engagement with virtual influencers [12]. Virtual influencers have become a dominant force in Chinese internet marketing, with computer-generated characters on social media platforms expected to quadruple by 2022. More than four-fifths of active avatars congregate primarily on the social media platforms Weibo (31%), Bilibili (28%), and Douyin (22%), where their content captivates millions. This trend not only reshapes the marketing landscape but also creates possibilities for brands to engage with their intended consumers [13]. Many well-known companies have begun to use virtual influencers as brand ambassadors to promote products and services through their huge social media online attention and fan base [14]. On social media platforms, it can be seen more virtual influencers are appearing every week, these virtual influencers (e.g., Lil Miquela) have gained a lot of attention and followers, and are also used as endorsers by many brands [15]. Virtual influencers interact with fans and establish close ties by posting information, pictures, videos, and animations. Their uniqueness and appeal allow them to quickly amass a large following on social media platforms and create a powerful influence [16]. Despite the established positive impact of virtual influencer marketing on social media, concerns raised by consumers have been noted [17]. These concerns stem from the perception that virtual influencers are inauthentic, triggering a sense of unease known as the uncanny valley effect. Consequently, their lack of authenticity and limited resemblance to followers hinder their ability to cultivate relationships with consumers, ultimately diminishing their persuasive influence on purchase intent [18]. In contrast, interactive engagement and communication with audiences are key attributes of social media influencers that aid in fostering brand trust [19]. However, the role of virtual influencers in brand marketing and the associated underlying

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mechanisms remains largely unexplored [11, 20]. Hence, this study aims to analyze virtual influencer characteristics and their impact on consumers’ psychological and behavioral intentions by investigating (i) the influence of virtual influencer traits, namely anthropomorphism, and interactivity, on brand credibility, brand attachment, and electronic word-of-mouth (e-WOM), and (ii) the mediating role of parasocial interaction.

2 Literature Review and Research Hypotheses 2.1 Anthropomorphism Anthropomorphism is the tendency to infuse human-like characteristics, motives, intentions, or emotions into non-human subjects’ real or imagined behavior [21]. Anthropomorphism is arguably the essential feature of all virtual characters, including virtual influencers. Preliminary evidence suggests that the more anthropomorphic the appearance of a virtual person, the more trustworthy they can be [22]. Anthropomorphic characteristics are important characteristics of virtual characters that determine human empathy and interaction quality [23]. In the context of perceptual control, users generally expect virtual humans to have a more anthropomorphic design and prefer highly humanized artificial intelligence [24]. Recent findings indicate that consumers may develop a more intimate relationship with a voice assistant when it exhibits human-like characteristics. Which contributes to their persuasive abilities [25]. Additionally, virtual influencers exhibiting a high degree of anthropomorphism provide a comparable level of personalization to human influencers, thus providing experiences akin to interactions with human influencers, fostering human-like relationships [26]. Therefore, virtual influencers with a higher degree of anthropomorphism will bring more user interaction and a potential parasocial relationship will also be established between them [27]. Based on the above findings, we propose the following hypothesis: H1: Anthropomorphism contributes positively to parasocial interaction.

2.2 Interactivity The theory of parasocial interaction is particularly well suited to explain the relationship between consumers and influencers established through social media communication [28]. Previous studies examining interactions between media users and human representatives featured in media (such as presenters, actors, and influencers, often referred to as “media personalities”) have explored these connections through the lens of parasocial relationships [29]. Maintaining parasocial interactions between influencers and their followers is a key factor for the success of influencer marketing [30].

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In social media marketing, researchers believe that parasocial interaction increases users’ favorability for brands social media influencers endorse [31]. A further investigation indicates that the digital content of influencers has a positive effect on the establishment of parasocial relationships with consumers, but the role of interactivity appears to play a more important role [32]. Similar to social media influencers, virtual influencers also maintain communication with users to enhance their parasocial interaction. Some studies have compared the parasocial relationships of human influencers and virtual influencers through experiments and found that there is no significant difference in the parasocial interaction between the two [27]. The user interaction with 3D animation virtual influencers is more prevalent than with 2D animation virtual influencers. The explanation for this is that designers use 3D technology to create virtual Internet influencers with human characteristics, which enhances the authenticity and interaction of users interacting with 3D animation virtual influencers. Thereby, parasocial relationships are boosted [6]. Likewise, another study shows that high perceived interactivity and openness constitute important predictors of parasocial interactions [33]. Accordingly, virtual influencers use AI technology and image technology to enhance interaction with users, thus establishing this kind of parasocial relationship interaction. Based on the above findings, our study proposes the following hypothesis: H2: Interactivity contributes positively to parasocial interaction.

2.3 Mediating Role of Parasocial Interaction Social media marketing academics have extensively utilized parasocial interactions theory in the interaction between digital media users and influencers [2, 20, 32, 34]. The researchers predicted the first definition of parasocial relationship, which was a fictitious, unilateral, realistic-appearing psychological relationship between individuals and influencers [35]. Social media influencer forms parasocial relationship with followers to promote consumer trust in brands [36]. Research even verified that this type of relationship consumers establish with their voice assistants can have a beneficial impact on their brand perceptions [37]. The parasocial relationship between the friend robot and the consumer performed as an obvious positive intermediary [38]. While brand credibility has a positive impact on followers’ attitudes towards advertisements and products, parasocial relationships have a significant positive impact on followers’ attitudes and behavioral intentions [39]. In addition, the interactive and innovative forms of virtual influencers can also attract the attention of audiences and prompt them to participate in brand-related activities and discussions. Studies have shown that consumers who interact with virtual people experience stronger parasocial relationships with virtual people, and the type of relationship affects brand credibility through parasocial relationships [40]. Based on the above research results, we put forward the following research hypotheses:

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H3: Parasocial interaction contributes positively to brand credibility. Many studies indicate brand attachment as the strength of a brand’s bond to itself. Attachment theory underlies brand attachment. A rich and accessible psychological profile shows how customers feel about the brand and how it relates to itself [41]. Virtual influencers share their experiences, use brand items, and offer professional advice to create an emotional connection with users and promote brand attachment [42]. According to study, virtual influencers’ personalized images and interactive approaches increase brand attachment by creating emotional relationships with users. Another study found that social-like, low-transaction-cost, and effective interactions between customers and fictional personalities in media are enjoyable and thrilling and improve consumers’ emotional links to brands [43]. Users’ emotional connection and identification with virtual influencers leads to brand loyalty and investment [44]. Virtual influencers’ persona characteristics, engagement with users, and attentiveness to user demands increase brand attachment by making users deeply emotionally attached. The emotional connection established between consumers and social virtual media influencers constitutes the parasocial relationship [32]. Thus, we propose the following research hypotheses based on the preceding research findings: H4: Parasocial interaction contributes positively to brand attachment.

2.4 Brand Credibility Previous studies have determined that there are three distinct phases of eWOM: creation, exposure, and evaluation [45, 46], this calls for a deeper comprehension of e-WOM from a variety of perspectives. Moreover, likes, comments, tweets, photos, and blog posts—customer input as well as views on items and services—are crucial to electronic word of mouth [47]. Specifically, trustworthiness is the most critical component of credibility. It influences the attitudes and intentions of Ewom [48]. As consumers’ application scenarios shift from traditional media to digital media [20], they provide feedback on products or services and display their opinions on digital media (websites, social platforms, blogs, etc.), such as likes, comments, ratings, comments, video recommendations, tweets, pictures, and blog posts, these are all different forms of eWOM [45]. It has been shown that the credibility of a tweet promotes the intention of distributing eWOM [46]. Consumers have trust in the brand and are more willing to actively participate in likes, comments, and share with others. Research shows that the credibility of brand information can not only affect consumers’ acceptance of brand eWOM, but also affect the spread and effect of brand eWOM [49]. In light of the foregoing research findings, we therefore propose the following research hypotheses: H5: Brand credibility contributes positively to brand eWOM.

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2.5 Brand Attachment In accordance with prior studies, brand attachment can be characterized as the psychological connection between consumers and brands [41]. Further research displays that consumers’ affection, enthusiasm, and closeness to brands build this emotional connection [50]. Emotional attachment in a brand may motivate consumers to engage in voluntary behavior, such as posting brand message [50] and enhances consumers’ more active and frequent attention to brand information and communication (blogging, reposting, likes, etc.), all of which belong to eWOM. By the same token, there is an emotional bond between customers and service providers [51], implying that users’ attachment to brands significantly impacts eWOM. Recent research suggests that attachments is emotional connection between ardent consumers and sports team brands that impact brand loyalty and positive eWOM [52]. In addition, there is a positive correlation between brand attachment and brand eWOM, which is based on brand marketing strategy and is a long-term brand building [53]. When consumers have a strong attachment to the brand, they are more motivated to share brand-related content on social media and actively participate in the communication of brand eWOM [54]. Accordingly, we put forward the research hypothesis: H6: Brand attachment contributes positively to brand eWOM.

2.6 Research Framework On the assumption of the parasocial interaction theory and the hypotheses that are suggested, the following research framework is developed to validate consumers’ perceived characteristics of virtual influencers and their influence on endorsement brand credibility and brand attachment, as well as the eventual effect of brand electronic word-of-mouth impact (Fig. 1).

Fig. 1 Research model

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3 Methodology 3.1 Data Collection PLS-SEM which is widely used in business, is an excellent forecasting tool that can additionally be utilized for theoretical testing. This approach works well when the sample size is small, do not conform to the distributional assumption, the model is complex, and the research purpose is experimental. Consequently, this study employed PLS-SEM [55]. Therefore, SmartPLS4 was utilized in this investigation to validate our construct. Thus, the hypothetical model is determined through the data analysis of Factor Loading, Cronbach’s alpha, composite reliability (CR), average variance extraction (AVE), convergence validity and discriminant validity, [56] standard and Q2 Predictive relevance, etc. We conducted an online poll among an accurate representation of Chinese adolescents who frequently engage with social media and who show an overwhelming preference for following virtual influencers to evaluate the hypotheses presented here. Survey questionnaires were disseminated via social media, and data was collected from May 28 to June 2, 2023, using the reputable online survey platform “Sojump.” “Sojump” is a professional survey platform in China, and its pool of voluntary participants can deliver respondents who have been thoroughly vetted and who come from a wide range of locations across the country [57]. Referring to the preceding method of influencer research [20], this questionnaire consisted of two sections. In the first section, we asked potential participants some basic questions to weed out those who wouldn’t be a good fit. Participants were asked to identify which social media sites they frequent and how much they know about “virtual influencers” on such sites. It was determined not to neglect individuals who did not possess experience in either sector. Participants were required to figure out the brands attributed with the virtual influencers they follow on social media and provide the names of the corresponding influencers. Valid responses were obtained in the first section prior respondents proceeded on to the survey questions in the second section. At foremost, 283 valid questionnaires were submitted for statistical analysis, fulfilling the set threshold. Table 2 displays respondents.

3.2 Measures Four items were used to measure anthropomorphism, as suggested by Noor et al. [58]. Five items from [59] were utilized to measure interactivity. Three items formulated by Mohart et al. [60] were used to evaluate brand credibility. Ten items were used to measure brand attachment [41]. The prior researchers propose measuring brand eWOM using three variables [61]. Nine items adopted parasocial interaction [28]. Except for anthropomorphism items and Brand attachment, all queries implemented a seven-point scale ranging from strongly disagree (1) to strongly agree (7).

30 Table 1 CMB: full collinearity assessment

S. Liu et al.

Variable

VIF values for random-number dummy variable

ANT

1.862

INT

1.845

BC

1.309

BA

2.175

BE

1.622

PI

1.452

Notes ANT anthropomorphism, INT interactivity, BC brand credibility, BA brand attachment, BE brand eWOM, PI parasocial interaction

4 Data Analysis and Results 4.1 Common Method Bias (CMB) To examine the possibility that our study investigates whether common method bias is inflated between the constructions of the reported data to the model. We adopted Harman’s single-factor test [62], and the results confirmed that the single-factor variance did not exceed the 50% threshold, which proved that the questionnaire was not impacted by CMB. Furthermore, we also use random numbers to create dummy variables for the fully collinear model, refer to the procedure of [63], and point all variables in the theoretical model to dummy variables for calculation. The variance inflation factor (VIF) values (see Table 1) were all well below 3.3, indicating the absence of CMB in this study [55]. According to the statistical data presented in Table 2, the distribution of respondents based on gender reveals that females constitute 42.76% of the sample, while males account for 57.24%. The largest age group among the respondents falls within the 25–29-year-old category, making up more than one-third of the total population. Interestingly, none of the respondents were above the age of 45. Furthermore, 39.93% of the respondents reported being currently employed. In terms of monthly income, the majority (48.76%) fell within the range of 3000–8000 RMB. The majority of respondents (48.41%) possessed a bachelor’s degree, representing nearly half of the total sample size.

4.2 Measurement Reliability and Validity Table 3 displays the Cronbach alpha values for all the structures, which are reported to be above 0.819. This value is significantly higher than the critical value of 0.7, as suggested by Hew et al. [64, 65], indicating strong internal consistency. This finding is in line with the recommendation by Hair et al. [55]. Furthermore, Table 3 reveals

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Table 2 Demographic profile Characteristics

Description

Count

Percentage

Gender

Male

121

42.76

Female

162

57.24

Age

18–24 years old

77

27.21

25–29 years old

97

34.28

Personal total income (per month)

Professions

Marital status

30–34 years old

62

21.91

35–39 years old

41

14.49

40–44 years old

6

2.12

45–49 years old

0

0

50 years old and above

0

0

Less than RMB 1500

15.55

RMB 1501-RMB 3000

42

14.84

RMB 3001-RMB 5000

75

26.5

RMB 5001-RMB 8000

63

22.26

RMB 8001-RMB 12000

28

9.89

RMB 12001-RMB 15000

15

5.30

Above RMB 15001

16

5.65

113

39.93

Student

Employed

93

32.86

Freelance

56

19.79

Unemployed

13

4.59

Others

8

2.83

Dating

71

25.09

Single

Education background

44

90

31.80

Married

102

36.04

Others

20

1.41

4

7.50

High school

Primary/middle school

25

8.83

College degree

64

22.61

Bachelor degree

137

48.41

38

16.30

Master/Ph.D. degree

that all factor loadings range between 0.620 and 0.896. Convergent validity was established by [2020] as they reported mean variance values exceeding 0.5 for all constructs. Additionally, Table 3 demonstrates that the average variance extracted (AVE) values for all constructs surpass the square requirement of their correlations with other constructs, further supporting their validity (Table 4).

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Table 3 Measurement model evaluation Construct

Measurement item

Factor loading

Cronbach’s alpha

Composite reliability

Average variance extracted

Anthropomorphism

ANT1

0.813

0.832

0.888

0.664

ANT2

0.816

ANT3

0.807

ANT4

0.823

INT1

0.813

0.891

0.920

0.697

INT2

0.811

INT3

0.875

INT4

0.872

INT5

0.800

BC1

0.825

0.819

0.892

0.734

BC2

0.896

BC3

0.848

BA1

0.783

0.896

0.914

0.518

BA2

0.794

BA3

0.724

BA4

0.752

BA5

0.736

BA6

0.709

BA7

0.620

BA8

0.714

BA9

0.866

BA10

0.862

BE1

0.896

0.862

0.916

0.784

BE2

0.898

BE3

0.863 0.909

0.926

0.581

Interactivity

Brand credibility

Brand attachment

Brand eWOM

Parasocial interaction PI1

0.711

PI2

0.769

PI3

0.806

PI4

0.772

PI5

0.770

PI6

0.728

PI7

0.683

PI8

0.800

PI9

0.809

Notes ANT anthropomorphism, INT interactivity, BC brand credibility, BA brand attachment, BE brand eWOM, PI parasocial interaction

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Table 4 Discriminant validity (Fornell and Larcker criterion) Construct

ANT

1. Anthropomorphism

0.815

BE

BC

INT

2. Brand eWOM

0.568

0.886

3. Brand credibility

0.699

0.629

0.720

4. Interactivity

0.556

0.602

0.618

0.857

5. Parasocial interaction

0.608

0.460

0.606

0.525

PI

0.835

Notes ANT anthropomorphism, INT interactivity, BC brand credibility, BA brand attachment, BE brand eWOM, PI parasocial interaction

4.3 Hypotheses Testing To validate the proposed hypotheses, we implemented guiding procedures determined by the previous analysis. The results indicate that anthropomorphism (β = 0.459; p < 0.05) and interactivity (β = 0.342; p < 0.05) have a significant positive effect on parasocial relationships. Moreover, parasocial relationships positively affect brand credibility (β = 0.571; p < 0.05) and brand attachment (β = 0.748; p < 0.05). Both factors have a positive effect on brand electronic word-of-mouth (eWOM), with the former producing a value of 0.345 and the latter producing a value of 0.415, both with p-values less than 0.05. In conclusion, the data analysis provides strong support for all hypotheses (H1–H6) advocated in this study (Table 5) (Fig. 2). All variables accounted for had Q2 values greater than zero, ranging from 0.278 to 0.509. This suggests that the structural model possessed a high degree of predictive ability. The Q2 value is a measure of the model’s predictive ability, with greater values indicating more accurate predictions. On the basis of these results, we can conclude that the structural model’s predictions are reliable and accurate. These results strongly support further research and deployment of this model [66].

Fig. 2 Structural model

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Table 5 Hypotheses testing Hypotheses

Relationship

Path coefficient

H1

ANT → PI

0.459

T-Value

p-Value

Remark

8.320

0.000**

Supported

H2

INT → PI

0.342

6.069

0.000**

Supported

H3

P I → BC

0.571

9.929

0.000**

Supported

H4

P I → BA

0.748

16.623

0.000**

Supported

H5

BC → BE

0.345

5.507

0.000**

Supported

H6

BA → BE

0.415

5.514

0.000**

Supported

Notes: **p ≤ 0.01, ANT anthropomorphism, INT interactivity, BC brand credibility, BA brand attachment, BE brand eWOM, PI parasocial interaction

5 Discussion 5.1 Summary of Major Findings and Contributions Social media influencers, particularly virtual influencers, have gained popularity among the millennial and Gen Z generations and are recognized as a significant marketing channel by brand companies. They possess a large following and increasing social influence, which can bring substantial benefits to brand promotion. However, there is still a lack of research in the field of virtual influencer marketing in social media [11]. With advancements in AI-generated technology and rendering techniques, the emergence of virtual influencers as the latest marketing tool has received great admiration from brand marketers, yet research on this phenomenon remains limited. To gain a better understanding of the endorsement impact of social media virtual influencers and their influence mechanisms on brands, we have constructed and tested a relevant model that explores the interrelationships among social media virtual influencer characteristics (i.e., anthropomorphism and interactivity), parasocial interactions, brand marketing (i.e., brand credibility and brand attachment), and brand electronic word-of-mouth (eWOM). The findings of this study reveal that social media virtual influencer characteristics, particularly anthropomorphism and interactivity, play a significant role in establishing parasocial interactions. Highly anthropomorphic virtual influencers foster stronger parasocial interactions with consumers. Likewise, interactivity among social media virtual influencers positively facilitates the development of parasocial interactions, although its influence is comparatively weaker. This is because consumers tend to perceive highly anthropomorphic virtual influencers as another self or a mirror image, leading to greater inclination to imitate and feel understood by them [67]. Most AI-driven virtual influencers communicate with consumers primarily through immobile social media postings or images, and only certain individuals have involvement in actual conversations and circumstances [68]. The limited influence of interactivity is attributed to technical constraints that impede continuous interaction

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among virtual influencers. Another possible explanation for this finding is the exclusivity associated with reputation, which may create a feeling of detachment amongst consumers towards virtual influencers, ultimately extending to the endorsed brands. The parasocial interactions formed between virtual influencers and consumers have a moderating effect on brand credibility and brand attachment, with brand attachment exhibiting a particularly significant influence. Finally, we find that both brand credibility and brand attachment significantly contribute to brand eWOM. The research findings indicate that the attributes of virtual influencers, specifically their personification and interactivity, have a positive impact on the formation of parasocial interactions. This reveals the importance of consumers’ identification and emotional connection with virtual influencers. The results provide valuable insights for marketers, suggesting that by enhancing the personification and interactivity of virtual influencers, brands can establish stronger parasocial interactions with consumers, ultimately leading to increased brand trust, brand dependence, and electronic word-of-mouth (eWOM) promotion. From a theoretical perspective, these findings contribute to the understanding of the role of virtual influencers in brand marketing. The study fills a research gap by shedding light on the influence of virtual influencers as emerging marketing tools in the realm of social media. Additionally, the research highlights the significance of parasocial interactions in shaping consumer attitudes towards brands and eWOM. Practically, the findings have implications for brand marketing strategies. Marketers can strategically collaborate with virtual influencers who possess high levels of personification and interactivity to foster stronger emotional connections with consumers and establish parasocial interactions. This, in turn, can enhance brand trust, brand dependence, and generate positive eWOM.

5.2 Limitations and Suggestions for Future Research The study involves limitations despite its significant contributions. First, the research sample is restricted to a particular population, limiting its generalizability. Future research could increase sample size to enhance the external validity and dependability of the findings. Second, this study recruited only Chinese participants. Given that virtual influencer endorsement and marketing have become an international trend and that cultural factors can influence an individual’s perception of anthropomorphism [69], it is suggested that the study be replicated with consumers from various cultural backgrounds [70]. Future research could also investigate the effects of other virtual influencer characteristics (such as algorithms), virtual influencer content, and parasocial interactions on brand endorsements for a deeper understanding. Future research could further explore the impact of other attributes of virtual influencers on brand eWOM, thus enriching the understanding of this domain. Furthermore, as virtual influencer technology continues to advance, future studies can investigate their deeper impact on consumer behavior and brand effect [20]. Lastly, researchers can consider conducting scenario comparative studies to explore variations in the

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influence of virtual influencers on consumers and brands across different scenarios (e.g., livestreaming commerce and metaverse), industries (e.g., fashion, food, and hospitality), and product types (e.g., luxury products) [13, 71–73].

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Modeling the Continuous Intention to Use the Metaverse as a Learning Platform: PLS-SEM and fsQCA Approach Mohamed Soliman , Reham Adel Ali , Jamshed Khalid , Imran Mahmud , and Muhammadafeefee Assalihee

Abstract The current study explores Metaverse adoption among higher education institutions in the light of a theoretical framework to empower future perspectives of the Metaverse as a learning platform. Few attempts have been made to assess the impact of this technology despite its recent launch in the higher education sector. This study considers integrating the technology acceptance model (TAM) and selfdetermination theory (SDT) to investigate the factors influencing the continuous intention to use the Metaverse as a learning platform. In order to collect the data on the suggested model, a questionnaire was devised and administered to private university students. The effect of continuous intention (CI) to use the Metaverse as a learning platform is investigated using a hybrid approach consisting of partial least squares structural equation modeling (PLS-SEM) as symmetric assumptions and a fuzzy-set qualitative comparative analysis (fsQCA) method as asymmetric configurations. This method is designed to give a deeper understanding of the complicated relationships between the model’s antecedents and its targeted output. It takes into account how diverse configurations of exogenous constructs utilize a distinguished influence on an endogenous construct. The empirical evidence suggested that autonomy and perceived usefulness (PU) are significant factors in elucidating the CI to use the Metaverse as a learning platform in the Egyptian context. Perceived ease of use (PEOU), on the other hand, had no effect on the CI. In addition, theoretical and practical ramifications are addressed. From the configurational analysis, research findings indicate that none of the conditions alone is sufficient to explain M. Soliman (B) · M. Assalihee Prince of Songkla University (PSU), Pattani Campus, Pattani, Thailand e-mail: [email protected] R. A. Ali Ahram Canadian University (ACU), 6th of October City, Egypt J. Khalid Nanjing University of Aeronautics and Astronautics, Nanjing, China I. Mahmud Daffodil International University, Dhaka, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_3

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a high level of Metaverse CI on their own. Instead, the study found three different configurations leading to an improved CI. This research was carried out in Egypt and hence added a piece of empirical evidence regarding the Metaverse as a learning platform in a developing country. Also, conclusions and suggestions for further study and practice are provided. Keywords Metaverse · Adoption · Technology acceptance model · Self-determination theory · PLS-SEM · fsQCA · Fuzzy-set · Qualitative comparative analysis

1 Introduction Metaverse refers to the online virtual world [1]. This new frontier is a potential game-changer for companies all across the world [2]. It is a mystical zone where real life and virtual reality meet to create an immersive, engaging environment for multiple users [3]. Researchers have previously linked the term “Metaverse” with technologies like augmented reality (AR), extended reality (XR), virtual reality (VR), and mixed reality (MR) that use a three-dimensional (3D) virtual world to provide an immersive and collaborative experience for users [2]. The years leading up to the COVID-19 pandemic saw its foundation in the virtual world as a means of overcoming limitations such as those posed by individual needs and extracurricular commitments [4]. In addition, the impact of online education may have been better, but it was hindered by a variety of problems, including inadequate technological conditions, infrastructure, software applications, and others. There are many issues that have surfaced that need to be addressed immediately in online teaching, such as a lack of context, limited engagement, and a weak sense of participation [5]. The rapidly expanding Metaverse technology, however, has presented remarkable benefits to online education in recent years [6]. People born into Generation Z (those after 1995) are particularly receptive to the idea because of the striking differences between their generation and those that came before them [7, 8]. The Metaverse can be used as a teaching tool in a variety of contexts [9]. Many companies, including Meta, Roblox, Zepeto, and others, have started developing products for the Metaverse classroom. They incorporate the Metaverse during the course of curriculum development, making the experience more rewarding for students. While Metaverse technology has great potential in the classroom, its adoption is still in its infancy. The educational quality of free mobile platforms is considerably lower rated [5]. Consequently, users can be attracted to the Metaverse’s education ecosystem by investigating the aspects that affect their intentions when using the application’s platform. It also has the potential to bridge the gap between the availability of and demand for specific types of educational technology. To that end, due to its relative novelty, research into the acceptance and implementation of the Metaverse system by users in industrialized countries is limited. Metaverse appears to be valuable to its users as a means of facilitating better classroom administration [10]. As a result,

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the potential for this study to shed light on the factors that influence CI adoption of the Metaverse technology is substantial. In this study, we hope to fill in these gaps by contributing in three ways to the literature on educational technology application platforms in formal educational environments. Firstly, there have been prior studies on the intent to adopt educational technology platforms. Most studies focus on e-learning [11, 12], mobile learning [13–15], learning management systems [16], wearables in education [17], AR/VR technologies [18–20], and social media services [21, 22]. However, research findings based on the influencing factors of other educational technology users’ intention to use technology are not transferable to the field of Metaverse educational technology due to differences in technical equipment, educational content, educational purpose, technical support, application fields, and user groups [23]. Consequently, there is a persistent need for more investigation into the predictors that stimulate users to engage with the Metaverse. Second, to gain a more nuanced understanding of the complicated causal relationships between the antecedent and the main target output, a fuzzy-set qualitative comparative analysis (fsQCA) method is combined with partial least squares structural equation modeling (PLS-SEM) as symmetric assumptions [24]. Therefore, this research greatly enhances our theoretical understanding of Metaverse applications in educational settings and provides valuable insight for practitioners. Thirdly, this study contributes to the literature by integrating the technology acceptance model (TAM) and self-determination theory (SDT). It introduces a clear understanding of factors affecting Egyptian students’ continuous intention to use Metaverse. In conclusion, the specific problem addressed in this study is the lack of robust theoretical understanding and empirical research investigating the factors of continuous intention to use Metaverse as a learning platform. Hence, the original research question guiding this study is what are the significant factors and different configurations of exogenous constructs influencing students’ continuous intention to use Metaverse as a learning platform in Egypt?

2 Literature Review Scholarly interest has been sparked by the unique benefits of the Metaverse in the education setting classroom. Firstly, some research mainly focuses on the Metaverse definition in the education sector. It is best described as a new learning setting fostered by Metaverse technology [6], which incorporates four distinct types of AR, VR, life logging, and a mirror world [1]. Secondly, the Metaverse’s characteristics in the classroom are illuminated by other investigations. It was proposed by Go and Jeong [25] that the Metaverse possesses the following 5C features: currency, continuity, canon, creator, and connectivity. With its strong sociability, virtual identity, immersive experience, open and accessible creation, strong interaction, and comprehensive teaching evaluation, the Metaverse is a powerful tool for education that offers a great deal

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of flexibility and few restrictions in time and space when compared to face-to-face instruction, screen-based remote instruction, and Metaverse-based instruction [5]. Thirdly, Key educational technology and resources of the universe are discussed in some research. Wearable devices, immersive, modeling and simulation, AI (artificial intelligence), education, mobile, sensors, and game applications are just some of the technologies and techniques that Tlili and Huang [10] categorized. Besides, Contreras and González [26] emphasized the supporting role of cloud computing, artistic intelligence, graphics processing units (GPUs), 5G, augmented reality (AR), photographic 3D engines, blockchain, brain–computer interfaces (BCI), virtual reality (VR), and extended reality (XR) in the development of Metaverse education. Fourthly, another direction explains how the Metaverse helps and how it changes the classroom. Lin and Wan [27] discuss the Metaverse’s positive impact on education from seven different angles, including its ability to aid in the better visualization of learning content, increase learning efficiency, free up previously restricted educational resources, and decrease educational costs and risks. Also, the Metaverse software’s educational application has given students to think creatively and greater autonomy [28]. Fifthly, the educational scenario and impact assessment of the universe were investigated. A learning platform, an immersive curriculum, a virtual school, a twin campus, and an open university are just five of the case-based educational application possibilities detailed by Hu and Ni [29]. Through user analysis of the usage effect of the spatial system learning platform, Wang and Shin [6] highlight the Metaverse advantages in terms of usability, simplicity, interactivity, immersion, and interest in educational applications. Jovanovi´c and Milosavljevi´c [30] looked into the impact of virtual teamwork on education. Finally, some scholars concern that the Metaverse could pose a threat to education. According to Wang and Shin [6], using Metaverse technology in the classroom would pose moral and privacy risks. Concerns include a lack of computing resources, problems in implementing the technology, and hazards to students’ physical and mental health that should be considered [29]. The TAM model, or the TAM model paired with other models (i.e., ECM, TPB, IDT, TTF, UTAUT, etc.), has been utilized in past studies of educational technology [22, 31, 32]. TAM and SDT models are strongly consistent with studies of e-learning technology adoption [33]. Furthermore, the SEM is used by the vast majority of empirical studies investigating the goals of educational technology. However, the integration of SEM and fsQCA has been discovered in a few studies. FsQCA is typically used to shed light on potential configurations in complex causal interactions [6]. Finally, according to the literature, integrating TAM with SDT to examine Metaverse as a learning platform is unknown. This study contributes by predicting students’ continuing intention to use Metaverse through TAM and SDT.

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3 Theoretical Background 3.1 Technology Acceptance Model (TAM) Davis [34] proposed this information systems theory to explore the impact of external variables (i.e., new system or technological characteristics) on a user’s internal beliefs, attitudes, and intentions [35]. It claims that PEOU and PU explain a user’s technology acceptance of a new system or technology. PEOU is the notion that using technology will be easy, while PU is the notion that technology will improve performance [34]. PEOU and PU affect attitudes and behavioral intentions [36]. They mediate external variables and technology usage probability. However, the components of TAM and their relationships are not powerful enough to predict the technology adoption behaviors of students with different [37]. Some students may be good with technology. Others are unwilling to use technology due to technological fear. In recent attempts to explain students’ technological acceptance, researchers have integrated PEOU and PU into the TAM [38]. In the same vein, Wang and Tan [39] attempt to investigate antecedents of consumers’ acceptance of behavioral targeting advertising services by extending technology acceptance Model 2 (TAM2). TAM links PU and PEOU. Davis [34] and Lin and Fofanah [40] corroborated the causation of these variables and behavioral intentions. PU and PEOU determine students’ e-learning intentions [41, 42]. PU and PEOU show that those who find new technology easy to use also find it valuable [34]. External variables in the TAM help researchers predict technology uptake. It also identifies specific reasons for choosing proper technology, causing scholars and practitioners to take corrective steps [34].

3.2 Self-determination Theory (SDT) SDT promotes development, motivation, and well-being. SDT focuses on autonomous and controlled motivation as performance, relationships, and well-being determinants. The type or quality of an individual’s motivation was more important than the aggregate amount in predicting psychological health and well-being, successful performance, inventive problem-solving, and deep or conceptual learning. Autonomous motivation improves psychological wellness, long-term persistence, and heuristic performance [43]. Moreover, SDT considers humans to be active entities with evolved psychological growth tendencies [44]. Intrinsic motivation manifests from birth as a desire for challenges, novelty, and learning chances. Ryan [44] adds that it is also obvious in internalization, or people’s lifetime predisposition to take up and integrate societal practices and ideals. Therefore, SDT describes basic psychological requirements as those needed for growth, integrity, and wellness. It recognizes competence, autonomy, and relatedness [43].

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4 Research Model and Hypotheses Development The current research develops a model by integrating TAM and SDT. From TAM’s perspective, PU and PEOU affect the continuous intention to use the Metaverse platform. In SDT, it has been proposed that autonomy, relatedness, and competence significantly affect PU and ease of use. In addition, the proposed research model suggests that the continuous intention to use the Metaverse platform is influenced by autonomy.

4.1 Autonomy Autonomy is self-regulation, not an external force. To be autonomous, one must control one’s actions instead of relying on others. It means pupils are responsible for their learning. Students have learning autonomy [37]. Students feel internal control over learning goals and achievements [45]. Student satisfaction affects autonomy. Autonomy-based motivation increases satisfaction [46]. Kye and Han [28], Racero and Bueno [33], Nikou and Economides [37], and Roca and Gagné [47] found a positive association between perceived autonomy, PU, and PEOU in a variety of ICT scenarios. Also, Ali and Arshad [48] proved the existence of a significant relationship between autonomy and e-learning acceptance. These findings led to the following hypotheses: H1: Autonomy has a positive effect on PU. H2: Autonomy has a positive effect on PEOU. H3: Autonomy has a positive effect on continuous intention.

4.2 Relatedness Being “related” means wanting to be seen as a part of a larger group. In the context of education, “relatedness” refers to a student’s ability to cooperate and interact with their peers [49]. SDT also asserts that students can benefit from social relationships and interactions [45]. As a result, relatedness can help alleviate anxieties and enhance the willingness to share information. Research by Racero and Bueno [33] found that relatedness was an important predictor of PEOU and PU. Therefore, the following hypotheses have been proposed: H3: Relatedness positively affects PU. H4: Relatedness positively affects the PEOU.

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4.3 Competence Competence is the desire to feel competent in achieving values. Competent people are able to achieve their goals and master tasks [50]. Competence has been related to the concepts of PU and PEOU in previous educational studies to the concept of competence [47, 51]. To acquire excellent grades, we require pupils to have a higher competency in using e-learning [52]. H5: Competence has a positive effect on PU. H6: Competence has a positive effect on the PEOU.

4.4 Perceived Usefulness and Perceived Ease of Use PEOU is the extent to which a person has a belief in a system to be safe and simple to use [34]. PU, on the other hand, relates to an individual’s belief that implementing a specific system will improve job performance [34]. PEOU and PU have been found to be important factors in determining technology adoption in multiple studies [33, 47, 53–55]. H8: PEOU positively affects students’ continuous intention to use the Metaverse platform. H9: PU positively affects students’ continuous intention to use the Metaverse platform. H10: PEOU positively affects students’ PU of the use of the Metaverse platform.

5 Methods 5.1 Population and Sample This study was conducted using a quantitative research method using a survey to collect data during the academic year 2022/2023. The population of this research was private university students enrolled at an information technology college. They were, by default, aware of virtual environment platforms (i.e., Augmented reality (AR), virtual reality (VR), extended reality (XR), and mixed reality (MR)). It is possible they learned about Metaverse from friends or social media. Also, we attached a YouTube video link in the survey to the targeted respondents about “Discover a Metaverse Built for Education” in order to acquaint them with the Metaverse Learning Environment’s overall structure. Ethical approval for the study was granted by the chosen institution. The participation of the respondents was voluntary. The instrument used was a web-based survey developed with Google Forms. The subject participants were selected through a purposive sampling technique. The G*Power tool was

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employed [56] to determine the minimum required sample size. The G*Power parameters include the values of 0.15 as a medium effect size, 0.05 as the error type (α), 0.80 as the power, and five as the number of predictors. Consequently, the minimum required sample size was found to be 92 cases. Students got the data via WhatsApp groups, including the objective of this research and the access link to the concerned survey. The questions have mandatory answers to avoid missing data; 145 students completed the online survey. Approximately 58% of the whole sample consists of male students. Females make up 42% of the total sample size. Their ages range from 18 to 23 years. Participants gave their time and effort voluntarily, which can improve the quality of the data collected and the validity of the study’s findings. Accordingly, students’ feedback on Metaverse technology is more trustworthy since they participate voluntarily.

5.2 Instrument Survey participants’ demographic data are collected in the first section of the survey. The constructs (i.e., PEOU, PU, autonomy, relatedness and competence, and continuous intention) of the conceptual model were measured in the second section. These constructs were measured using a “7-point Likert scale”. Items to measure autonomy and competence were adopted from Nikou and Economides [37], Lee and Lee [57]. Relatedness items were adopted from Lee and Lee [57] and Sørebø and Halvari [58]. To measure PU and PEOU, their items were adopted from Venkatesh and Morris [59] and Nikou and Economides [37]. Items of continuous intention were adopted from Bhattacherjee [60].

5.3 Data Analysis This study adopted the Partial Least Squares (PLS) analysis technique to analyze the proposed research model using the SmartPLS 4.0 software tool [61]. According to Anderson and Gerbing [62], analytical procedures included two phases: measurement model evaluation, which includes the measures’ validity and reliability, and structural model evaluation, which embraced testing the hypothesized relationships. The main reason to use PLS-SEM is that numerous researchers have analyzed their research models using this software with a robust community [63–66]. When generating and testing hypotheses, PLS-SEM allows for the evaluation of models’ causal-predictive relationships [67]. Optimizing the prediction of causal relationships augments the variance of a target construct explained by predictive constructs [68]. Additionally, it offers researchers more statistical power than factor-based SEM [69]; researchers can expect their model to have high predictive accuracy, bridging the gap between explanation and prediction [70]. Through a sequence of ordinary least squares regressions, it is able to examine aggregate indicator scores [71]. Our conclusion that PLS

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is the best approach for this research is supported by the arguments given above. Additionally, a fuzzy-set qualitative comparative analysis (fsQCA) method is used on top of PLS-SEM to combine asymmetric configurations and symmetric assumptions [72, 73]. PLS-SEM is predicated on symmetric assumptions; in contrast, fsQCA generates many paths by tracing the intricate and asymmetrical connections between causal factors and outcomes [74]. With this method, we hope to gain a deeper understanding of the interplay between the antecedent and some carefully chosen outcome variables. It takes into account the varying effects of various configurations of the output and input constructs [75, 76].

6 Findings 6.1 Measurement Model Assessment Outer models need to be tested when the suggested model is constructed, according to Hair and Hult [77]. Composite reliability (CR), discriminate validity, factor loading, and the average variance extracted (AVE) are all indicators used to evaluate the outer model. AVE (above 0.50) and CR (above 0.70) have already met their thresholds since all outer loadings are above the recommended value of 0.708 [78], except for PU1 (0.494) and RLT4 (0.629), which have been afterward removed (Table 1). Discriminant Validity Each construct’s AVE is larger than the squared correlation of other constructs or their square root, according to Fornell and Larcker [79] criterion for discriminant validity. Due to the diagonal including values bigger than the corresponding row and column values, Table 2 presents discriminant measures. Moreover, Henseler and Ringle [80] suggested choosing the HTMT to assess discriminant validity. As such, this study tested the discriminant validity using this newly suggested method by Ramayah et al. [81]. The results are shown in Table 4. Since HTMT values are less than the HTMT.90 value of 0.90 [82], then there is not any problem with discriminant validity.

6.2 Structural Model Assessment The next step is analyzing the inner model. According to Hair and Hult [77], this study analyses the inner model by estimating the multicollinearity, coefficient of determination (R2 ), PLSpredict , path coefficient, goodness of fit (GoF), model fit measures, and NCA procedure.

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Table 1 Factor loading, CR, and AVE Constructs

Items

Loadings

CR

AVE

Autonomy

AUT1

0.870

0.895

0.741

AUT2

0.911

AUT3

0.797

CI1

0.784

0.883

0.716

CI2

0.908

CI3

0.842

COM1

0.751

0.832

0.624

COM2

0.852

COM3

0.764

EOU1

0.830

0.891

0.731

EOU2

0.878

EOU3

0.855

PU2

0.899

0.916

0.785

PU3

0.899

PU4

0.847

RLT1

0.767

0.871

0.694

RLT2

0.794

RLT3

0.898

CI

Competence

PEOU

PU

Relatedness

Table 2 Discriminant validity Discriminant validity (Fornell and Larcker) Autonomy Autonomy

0.861

CI

0.725

CI

Competence

PEOU

PU

Relatedness

0.790

Competence

0.693

0.615

0.846

PEOU

0.495

0.478

0.416

0.855

PU

0.531

0.398

0.515

0.541

0.886

Relatedness

0.667

0.639

0.738

0.456

0.611

0.833

CI

PEOU

PU

Relatedness

Discriminant validity (HTMT) Autonomy

Competence

Autonomy Competence

0.836

CI

0.847

0.804

PEOU

0.581

0.631

0.488

PU

0.621

0.503

0.623

0.626

Relatedness

0.818

0.856

0.830

0.540

0.742

Note Values on the diagonal (bold) represent the square root of the AVE, while the off-diagonals are correlations

Modeling the Continuous Intention to Use the Metaverse as a Learning … Table 3 Coefficient of determination

Endogenous variables

R Square

CI

0.511

PEOU

0.290

PU

0.485

51

Multicollinearity Test (VIF) Collinearity must be checked to ensure that it does not skew the regression findings before the structural model can be evaluated [78]. Variance inflation factors (VIFs) are computed from the latent variable scores of the predictor constructs in the partial regression. Multicollinearity happens once two or more predictors are correlated, providing redundant information. VIF measured it. Small VIF values indicate a low correlation among variables. The VIF value is recommended if it is less than 3.3 [83]. Regarding the research model, all VIF values are less than 3.3 (see Table 5). As a result, no multicollinearity issue was found in the research model. Coefficient of Determination (R2 ) Regarding R2 , the highest effect level is one, while the lowest is 0. Cohen [84] defined R2 values of 0.26, 0.13, and 0.02 on endogenous variables as substantial, moderate, and weak. Table 3 in this study has a high degree of predictive power. Twenty-nine percent of the variance in PEOU was accounted for by autonomy, relatedness, and competence as predictors. Also, they account for 48.5% of the variance in PU. Lastly, PEOU and PU account for 51.1% of the variance in CI. Path Coefficient In order to study path coefficients in the proposed research model, t values were estimated using the bootstrap approach with 5000 resamples [85]. Path coefficient values start at − 1 through + 1. A strong positive relationship will be achieved when the value is closer to + 1, and the relationship becomes negative when the value is closer to − 1. Therefore, the path coefficients’ most minor significance level should be 0.05. In the one-tailed test, the critical values for a significance level of 10%, 5%, and 1% are 1.645, 1.96, and 2.33, respectively [86]. Among the ten hypotheses proposed for this study, H4, H6, and H9 were not supported (see Table 4). Students’ continuous intention to use the Metaverse platform was supported by 7 of the 10 hypotheses in the PLS structural model, which lends overall validity to the proposed model. Predictor variables of CI were shown to be favorably affected by autonomy (β = 0.577, p < 0.01) and PU (β = 0.195, p < 0.05). However, there was no significant relationship between PEOU and CI (β = 0.026, p > 0.05). Autonomy was positively related to PEOU (β = 0.238, p < 0.05) and PU (β = 0.220, p < 0.05). The relationship between competence and PU (β = -0.202, p < 0.01) was shown to be statistically significant, while it was not significant to PEOU (β = 0.196, p > 0.05). Similarly, there was a substantial positive association between relatedness and PU (β = 0.445, p < 0.1), whereas there was no significance between relatedness

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Table 4 Path coefficient and hypothesis test result Std error

t-value

p value

f2

Effect size

VIF

Decision

0.577

0.115

5.033

0.000

0.45

Large

1.52

Supported

Autonomy → PEOU

0.238

0.143

1.661

0.048

0.03

Small

2.47

Supported

H3

Autonomy → PU

0.220

0.117

1.875

0.030

0.04

Small

2.55

Supported

H4

Competence → PEOU

0.196

0.154

1.273

0.101

0.02

Small

2.32

Not supported

H5

Competence → PU

− 0.202

0.108

1.864

0.031

0.03

Small

2.38

Supported

H6

PEOU → CI

0.026

0.103

0.247

0.403

0.00

No effect

1.54

Not supported

H7

PEOU → PU

0.325

0.145

2.240

0.013

0.15

Medium

1.41

Supported

H8

PU → CI

0.195

0.108

1.813

0.035

0.05

Small

1.63

Supported

H9

Relatedness → PEOU

0.173

0.129

1.338

0.090

0.02

Small

1.98

Not supported

H10

Relatedness → PU

0.445

0.127

3.512

0.000

0.19

Large

2.02

Supported

No

Relationship

H1

Autonomy → CI

H2

Std. beta

Table 5 Testing model fit

Parameter

Saturated model

Estimated model

SRMR

0.064

0.070

NFI

0.915

0.910

and PEOU (β = 0.173, p > 0.05). Finally, the relationship between PEOU and PU (β = 0.325, p < 0.05) was positively substantial. Cohen’s effect size estimation was used to calculate the effect size [84]. If the values are 0.02, 0.15, or 0.35, the effect sizes are categorized as small, medium, and large, respectively. Table 5 shows that autonomy and relatedness have large effect sizes on CI and PU. PEOU has a medium effect size on PU, while other factors have small effect sizes. However, PEOU had no effect size on CI. Model Fit Before proceeding to test the model, we first tested model fit using a pair of adjusting aspects: one is the standardized root mean square residual (SRMR), and the second is the normed fit index (NFI). An acceptable SRMR value of a good fit model is less than 0.08, which is calculated by subtracting the model-implied correlation matrix from the observed correlation matrix [87]. Henseler and Dijkstra [88] proposed the SRMR to help prevent model misspecification in PLS-SEM as a goodness-of-fit

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Fig. 1 The research model (symmetric model)

metric. The second gauge of model fitness is the normed fit index (NFI), which is an incremental fit metric that uses the chi-square value of the proposed model to compare it to a meaningful standard [81]. In general, the NFI value greater than 0.9 indicates a good fit [89]. Due to the lack of free paths in the saturated (measurement) model, values obtained by fitting the measurement model and the values obtained by fitting the structural (estimated) were quite similar. We calculated the SRMR to be 0.075 (< 0.08), and the NFI was 0.927 (> 0.90), indicating that the data fit the model well. The test results are presented in the final research model in Fig. 1.

7 Asymmetric Analysis In order to provide a different perspective than that provided by PLS-SEM, which is predicated on symmetric assumptions and the discovery of the net effect between competing variables in a model, fuzzy-set qualitative comparative analysis (fsQCA) was employed [74]. fsQCA, in contrast to the SEM method, generates many paths by tracing the intricate and asymmetrical connections between causal factors and outcomes [90]. The fsQCA method is adaptable to theory testing, construction, and elaboration since it may be used with either inductive or deductive reasoning [91]. We believe fsQCA can help us better grasp the underlying reasons for the outcome, given the complicated patterns of causal interrelationships between independent factors and the dependent variable [24, 75].

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7.1 Calibration Calibration, the initial stage of fsQCA, was carried out according to established procedures by Ragin and Fiss [92]. Ordinal or interval scales are transformed into membership score sets using the set membership theory underlying fsQCA. By utilizing the calibrating function accessible from the fsQCA 4.0 software, fuzzy set membership scores were generated by transforming latent variable scores from a PLS-SEM model [75]. The scores on the fuzzy-set scale go from 0 (fully non-member of any fuzzy set) to 1 (fully member within any fuzzy set). The fuzzy-set scores range from 0 (fully non-member of a fuzzy set) to 1 (full of a fuzzy set), and the mid-point (0.50) is treated as a cross-over point (intermediate set) [72]. We applied the threshold values of 0.95 as a guarantee for full membership, 0.50 as the cross-over point, and 0.25 as a foundation for full non-membership for a 7-point Likert scale [74, 93].

7.2 Identifying the Configurations Prioritizing necessity over sufficiently was a suggestion made by Ragin [72]. However, when we examined each potential causal condition separately, we discovered that the highest consistency is 0.83, which is lower than the threshold value of 0.90. As a result, we were unable to pin down any necessary conditions. The fsQCA 4.0 program was then used to implement the fuzzy set technique, and a truth table with 2k rows was generated as a result (k is the number of independent variables, and each row represents a unique combination of those variables). Subsequently, the rows in the truth table that did not reach the minimum required frequency of 2 are then detached. According to Fiss [94], a threshold of 2 would be appropriate for small samples (100), whereas a threshold of 3 or higher would be appropriate for large samples (>150). The three main outcomes from the fsQCA findings are parsimonious, complex, and intermediate [72]. Because it is thought to be preferable in terms of interpretability and completeness, the intermediate solution was picked [75]. Three different configurations were found to result in a high CI. When trying to make sense of the fsQCA results and assess the model’s value, consistency and coverage are the two most crucial criteria to look at. Consistency, like correlation, measures how well a collection of observed configurations predicts an event, and coverage, like R2 in regression, assesses the practical importance of that set [72]. Any settings that had a consistency score lower than 0.90 are taken out. A high degree of consistency (> 0.75), as well as coverage (> 0.20), was demonstrated by every configuration and the overall solution we retained [75]. Since the overall solution coverage was 0.726, it was identified that the three determined configurations adequately explain the observed results (see Table 6).

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Table 6 Intermediate solution results (i.e., combinations that lead to high CI) Path

CQD_

PU_*Rlt_ *Aut_

●

PU_*PEOU_ *Rlt_ *~Com_ ~PEOU_ *Rlt_*Com_ *Aut_

●

Com_

Rlt_

PU_

●

●

◯

●

●

●

●

PEOU_

Raw coverage

Unique coverage

Consistency

0.668688

0.399839

0.965276

●

0.210894

0.0234487

0.928096

◯

0.242571

0.0343643

0.923077

Solution coverage: 0.726501 Solution consistency: 0.961734 Note Black circles (●) denotes the presence of a condition, circles with “x” (◯) denotes its negation. Blank spaces indicate the condition may be either present or absent

8 Discussion The primary goal of this research was to analyze the variables affecting the continuous intention to use the Metaverse as a learning platform by Egyptian university students. This research has been accomplished by integrating two theoretical models, including TAM and SDT. The discussion section presents the results obtained from the empirical survey. We found support for seven out of ten added hypotheses in the proposed model. Autonomy and relatedness positively impact PU and PEOU. Thus, autonomy and relatedness are considered significant predictors. This result was consistent with the study of Wang and Shin [6] and Racero and Bueno [33]. This finding identifies that the Metaverse as a learning platform becomes useful and easy to use if it has the characteristics of autonomy and relatedness. By contrast, competence has no impact on PU and PEOU. Hence, competence is an insignificant predictor. This discrepancy may be attributed to cultural differences between Egypt and other countries. For example, students in Egypt are dependent on teachers, parents, or private tutors in their studies. As a result, they may not have confidence in using Metaverse as a learning platform. Additionally, autonomy and PU are considered significant predictors. Students are more willing to continue using the Metaverse as a learning platform when they feel autonomous and useful. These results are consistent with the findings of Kye and Han [28] and Racero and Bueno [33]. However, the unexpected finding of this study shows that PEOU was not a significant predictor in influencing the continuous intention to use the Metaverse as a learning platform. Most students have no knowledge or experience using the Metaverse, as this is the first time they have been asked to use it. By clarifying how the application of fsQCA may be superior to the linear-based PLS-SEM, we propose three configurations as solutions in dealing with causal complexity to achieve Metaverse CI. Solution 1 proposes that the effect of the concurrent presence of PU, relatedness, and autonomy can advance CI with the presence

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or absence of competence or PEOU. Likewise, solution 2 reveals that PU, PEOU, and relatedness can yield high CI when combined with the presence or absence of autonomy during the negation of competence. Significantly, solution 3 indicates that high CI can still be achieved through the presence of relatedness, competence, and autonomy, although the negation of PEOU. In conclusion, fsQCA’s three configurations with the combination of content attributes support the “general tendency” findings of PLS-SEM to achieve high Metaverse CI in the education context. These novel findings imply that the emphasis should not be placed on isolating the one thing that has the greatest impact on producing the best results; alternatively, numerous efficient avenues exist. In the end, there is no simple way to say whether or not a certain quality should be highlighted; rather, each trait’s relative importance depends on the surrounding circumstances. The results motivate a “mix and match” strategy that produces optimal pattern combinations.

8.1 Theoretical and Practical Implications Numerous theoretical advances can be attributed to the research presented in this paper. A new hybrid model, including TAM and SDT, is developed in the present study, which improves the existing knowledge body. This model was used to predict continuous intention to use the Metaverse as a learning platform in Egypt, where very few studies have discussed that. Second, this study introduces several key findings that autonomy was observed to be more reliable than PU about the continuous intention to use the Metaverse as a learning platform. Third, this study introduces several key findings regarding the factors affecting PU and PEOU. These key findings are autonomy and relatedness. Autonomy has the most significant effect on PU than relatedness. However, relatedness has a more robust effect on PEU than autonomy. Also, this research improves upon previous efforts by developing a PLS marker variable technique for mitigating bias introduced by common method variance (CMV) in the PLS structural path. The findings demonstrate convincingly that the PLS marker variable is an effective method for eliminating any CMV issues. We also argue that the hypothesized variables exist in a variety of configurations and asymmetric relations, which adds to the study’s contribution (i.e., CI). Both PLS-SEM and fsQCA focus on making predictions, although their model estimates can be seen from various perspectives (symmetric vs. asymmetric). These supplementary viewpoints provide for more nuanced understandings of the interplay of variables, allowing for deeper managerial implications to be drawn. In addition to generating construct scores via nomological linkages, PLS-SEM is particularly promising because its estimations explicitly account for the measurement error intrinsic to the indicators [69]. This distinguishes it from implementations of fsQCA and related methods that have traditionally used sum scores of multi-item measures, including seminal presentations of these methods [95]. Simultaneously, by determining all potential necessary combinations of antecedents that yield an outcome, fsQCA broadens and reinforces the PLS-SEM results at the individual level.

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Although most of the empirical evidence pertaining to Metaverse typically focuses on the “net effect” of different factors, it fails to account for the potential interactions among these various antecedents. Complexities in students’ CI to use Metaverse as a learning platform are disrupted since the conventional regression-based method is inadequate for elucidating more nuanced relationships. In particular, we identified three possible configurations that include PU, PEOU, relatedness, autonomy, and competence variables in a multifaceted causal form, leading to an increased CI. As a result, we demonstrate that there are numerous solutions relevant to the formation rather than a single best one, contributing to the painting of a more complex and comprehensive image of the Metaverse CI model in the context of education. According to these key findings, a set of practical implications can be recommended. First, the Metaverse as a learning platform should have techniques that support relatedness features, such as virtual chat rooms, virtual discussion rooms, instant messaging, and virtual message boards. These ideas can increase the usefulness and ease of use, enhancing the continuous intention to use Metaverse as a learning platform. Second, autonomy is also a critical factor and can be enhanced by adding tools that enable students to control the pace of learning and develop a sense of choice and freedom. Finally, PU is crucial in increasing the continuous intention to use the Metaverse as a learning platform. It can be bolstered by the addition of valuable features that can save time, boost learning, and make information more accessible. Additionally, using fsQCA results, Metaverse learning service providers or developers can combine antecedents (i.e., PU, PEOU, relatedness, autonomy, and competence) in a way that meets the requirements (i.e., causal recipes) for optimal results of CI [96]. Moreover, researchers can evaluate their models’ prediction potential from symmetric and asymmetric viewpoints with greater depth when fsQCA and PLS-SEM are used together.

9 Conclusion The current research augments the knowledge body by conceptualizing and empirically testing a model that integrates TAM and SDT theories. University students were asked to complete a survey using a questionnaire. The PLS-SEM method was used to examine the data. Autonomy and PU were major indicators of students’ decision to continue using the Metaverse as a learning platform in the future. In contrast, PEOU was an insignificant determinant of continuous intention. Besides, the study revealed that autonomy and relatedness are considered significant predictors to explain PU and PEOU, whereas competence is an insignificant predictor. Furthermore, the dual analysis approach (i.e., PLS-SEM and fsQCA) was applied to enhance and add new insights to the results. The symmetric (PLS-SEM) assumptions and asymmetric (fsQCA) configurations were assessed using the strength of both approaches to help us learn more about the factors that led to the result. This method takes into account the various configurations in which an exogenous construct can influence an endogenous one [95, 97]. Also, there is a consistent pattern of equifinality discernible in

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the data obtained using the fsQCA method. Based on the results of the configuration analysis, we can conclude that none of the conditions alone are sufficient to explain a high-level of Metaverse CI as a learning platform. As an alternative, the study indicated six different configurations leading to an improved CI to use Metaverse as a learning platform.

10 Limitation and Future Work This work has a few limitations that should be acknowledged. The current study is cross-sectional because of time constraints, so it measures intentions at a given time. The perceptions of individuals change over time as they gain experience. Therefore, it is best to use the longitudinal approach. The contributors in the study were taken from only one private university without public ones. Therefore, the results of this study cannot be generalized to all universities in Egypt. As a result, future work could include students in both public and private universities to explore the difference between two different situations and generalize the results. The interests of students and the variables that influence their adoption of new technologies can be better understood in future research if additional theories of technology acceptance are included. Other significant factors, such as cognitive capacity, social interactions, and security concerns, might be included in future studies. Delivering connectivity can help develop the virtual learning system even more since a reliable and robust internet connection is vital for online learning. Failure to provide enough infrastructure support can negatively impact the functionality of electronic gadgets. This deduction means that internet services must be of the highest quality. Finally, evaluating the model using the qualitative approach in future work is recommended. Using an interview or focus group with participants might discover more factors. Acknowledgements The authors would like to greatly thank Prince of Songkla University (PSU) for providing any facilities for this study under the research project number ISL6602068S.

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Are You Ready for Tapping into the Metaverse in Higher Education? Integrated by Dual PLS-SEM and ANN Approach Tri-Quan Dang, Phuc-Thien Tran, and Luan-Thanh Nguyen

Abstract In the context of ever-evolving technology and the post-Covid-19 pandemic, it is essential to utilize the metaverse of modern technology to guide university students. The purpose of this study adopts the UTAUT theory and selfefficacy to investigate metaverse adoption in higher education learning. Utilizing quantitative analysis, the study find out the factors that influence the adoption of metaverse for learning. The students who possess digital devices and have experience with online learning in order to accomplish the study’s objective, which is to examine the behavior of using Metaverse in education. PLS-SEM and ANN were then utilized to analyze the data. The results confirmed the objectives of proposing model UTAUT and self-efficacy to investigate the adoption of metaverse for learning, and the model explained 77% of the intention to use metaverse for learning and 69.3% of its adoption. All factors of model UTAUT have an influence on behavior in the metaverse context. In addition, the non-linear outcome of the ANN provided additional insight into the significance of social influence and self-efficacy in the adoption metaverse for learning. This study contributed to the existing literature by highlighting the initial metaverse adoption by students in higher education, particularly in developing nations such as Vietnam. Keywords UTAUT · Metaverse · Online learning · Technological self-efficacy · Higher education · PLS-SEM · ANN

T.-Q. Dang (B) · P.-T. Tran · L.-T. Nguyen Ho Chi Minh City University of Foreign Languages—Information Technology, Ho Chi Minh City, Vietnam e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_4

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1 Introduction Because of the pandemic’s long-term impact, world culture has altered, and face-toface interactions are no longer a prioritized compulsion for spreading knowledge and culture. We have shown that cultural dispersion may have occurred because of digital technology to reduce time and space constraints [1]. The COVID-19 pandemic has significantly influenced several students and professors, online forms of learning have grown in popularity [2–4]. For example, in the classroom, the usage of virtual reality and augmented reality technologies can aid to improve students’ ability to study and their concentrate on when virtual reality can satisfy their interest [5]. Therefore, the combination of traditional education and online learning has created a new door for the education field [6]. Human life is inextricably linked to information technology, and the educational sector is likewise influenced by it, so online learning can increase teaching quality while encouraging student innovation [7]. Students applying reciprocal electronic subjects to perform mathematical computations as well as reserve material are better than traditional learners [8]. Online education might be the future tendency because of its advantage, allowing users to modify their study schedule based on their own plans, granting permission to access current academic content at their convenience, as well as maximizing students’ potential [9]. Soroko et al. [10] explained that the use of educational electronic platforms can aid instructors in cultivating students’ learning motivation, educational projects, and problem-solving tasks while encouraging them to associate with the use of information technology to acquire their own skills and competencies. Even, students can expect a more individualized learning experience thanks to the integration of AI into online education [11]. However, online learning also brings many shortcomings mentioned in previous studies such as minimizing the interaction between lecturers and students, students lose motivation to study because they are not in a good learning environment [12]. Therefore, Metaverse was born as a tool to overcome the weaknesses of online learning on the Internet. Users can create their virtual environments using the Metaverse World Platform. They are free to roam the metaverse and converse with others (virtual characters). Many group activities can serve as stand-ins in real and fantastic worlds. Using the 3D virtual space, students and teachers can communicate in real-time. Compared with traditional teaching, teaching through the metaverse world can overcome some limitations including space limitations. Higher education institutions that adopt metaverse technology will have a leg up on their competitors who are constrained to more conventional settings. It is worth noting, however, that educational use of the metaverse is still in its infancy, and its full potential has not yet been realized. Therefore, The goal of the current study is to develop a methodology for assessing the integration of metaverse world technologies into university-level education. This study proposes the Unified Theory of Acceptance and Use of Technology (UTAUT) as a fundamental paradigm to address the adoption of the metaverse in

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the context of higher education. This theory is an important and original theory in the acceptance of new technology that has been applied in many different contexts [13, 14]. In addition, the study is carried out to assess if metaverse could be used in curriculum development and assess the desire of learners for using the metaverse. Therefore, To achieve the fully comprehend the strength of each component in the level of acceptance of the metaverse. The studied combined the PLS-SEM and ANN analysis to measure linear and non-linear relationship between the variables to evaluate the strenght of each factor. This approach was approximately in the previous studies in different contexts such as mobile payment, online shopping, or mobile learning [15–17].

2 Literature Reviews 2.1 E-Learning and Metaverse Cloud-based e-learning is being integrated with more traditional classroom methods to better meet the growing demands of today’s students [18]. Integrating simulation technology with blockchain, cultivating proficient engineers, and combining university education with metaverse, virtualization, as well as augmented reality are all critical for the sector. Users can carry on learning and expand their study opportunities in the metaverse by using virtual reality technology, which accommodates a protected and productive environment for not only business but also education [19]. For example, students can demonstrate better writing abilities, overcome their timidity of speaking and writing in English, along with becoming more prepared to either voice their opinions or share their ideas in English by using online learning in English classes [20]. The best method to motivate students to persist their studies the outside of influencing their academic achievement is through online learning [21, 22]. In the face of the epidemic, online education has emerged as an integral aspect of the educational system [9, 23]. Online classes facilitate both learners and faculty in the learning process, and at the same time, a variety of online education methods and e-learning methods can be used to improve the quality of education by applying online learning [24]. In today’s classrooms all around the world, ICTs are used, and students employing online study tools express superior levels of cognitive capacity. Online learning and in-person instruction are increasingly being combined in higher education institutions [25]. Furthermore, the combination of metaverse and learning allows participants to experience the presence of learning better [1]. As a result, using the metaverse to create virtual classrooms is critical in the age of technology.

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2.2 Unified Theory of Acceptance and Use of Technology The Unified Theory of Acceptance and Use of Technology (UTAUT) clarified how individuals used technology as well as their intended objectives [26]. These four significant factors are described as follows: perfomance expectancy is “the degree to which an individual believes that using the system will help him or her to attain gains in job performance” [16] effort expectancy as the “degree of ease associated with the use of the system” [16], social influence as “the degree to which an individual perceives that important others believe he or she should use the new system” and facilitating conditions as “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system” [27]. Despite these limitations, it has integrated eight important theories and is frequently employed as a theoretical framework to explain the acceptance, adoption, deployment, and efficient use of technology. The UTAUT is widely applied in the fields of mobile payment [14], mobile learning [28], blockchain [15, 29], online shopping [30]. As a result, the UTAUT model served as the study’s theoretical foundation.

3 Hypothesis Development 3.1 Performance Expectancy (PE) The level of confidence that a person has in the system they are utilizing to benefit them is known as their performance expectancy [26]. In other words, it refers to how much individuals can use technology to accomplish objectives. According to Al-hujran et al. [31], the performance expectancy of students points to how much learning by mobile benefit their educational objectives. It gauges how confident students are that using mobile devices to learn would help them achieve better learning outcomes [32]. Users’ desire to use online banking may rise if they are given high-performance expectations [33]. As performance, user productivity, and healthcare quality all increase in the medical area, users’ goodwill to make use of the network also rises [34]. Therefore, it can be supposed that: H1: Performance expectancy (PE) positively affects behavioral intention (BI) to adopt metaverse for learning.

3.2 Effort Expectancy (EE) “Effort expectancy” refers to how easy it is expected that the new system will be to learn and utilize [26]. For the aged people, the degree of effort expectancy indicates their support and ease with digital technology [35]. It is the degree to which students

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find it simple to utilize the services of studying with mobile [31]. Customers’ intention to adopt mobile banking technology is significantly influenced by their effort expectancy because it requires certain knowledge and abilities [36]. Thongsri et al. [37] discovered in a study on student interest in using mobile learning services that students’ social needs positively affected their effort expectations. Therefore, it can be hypothesized that: H2: Effort expectancy (EE) positively affects behavioral intention (BI) to adopt metaverse for learning.

3.3 Social Influence (SI) According to research by Venkatesh et al. [26], “social influence” describes the extent to which a user’s actions are influenced by people around them. When discussing online learning, the degree to which students feel compelled to use mobile learning resources from teachers to family members to friends-is referred to as social influence [31]. Adoption of mobile government services is initially driven by social influence in developing countries [38]. Social influency may impact on people’s readiness to use e-government services [39]. From the foregoing overview of the literature, it is clear that social influence is a crucial factor in shaping participants’ behavioral intentions. Therefore, it can be assumed that: H3: Social influence (SI) positively affects behavioral intention (BI) to adopt metaverse for learning.

3.4 Facilitating Condition (FC) The level of trust that an individual has in the existence and accessibility of devices and technologies is known as the “facilitating condition” [26]. Facilitating conditions exist when students have a positive perspective on the resources and assistance they receive when using mobile learning services [31]. Therefore, it can be hypothesized that: H4: Facilitating condition (FC) positively affects behavioral intention (BI) to adopt metaverse for learning.

3.5 Self-efficacy (SE) The concept of self-efficacy was born very early and is defined as the ability to generate self-motivation and self-regulation in terms of cognition and behavior of an individual to meet a particular situational requirement [40]. It is the main factor in creating the intrinsic motivation of the individual consumer. Previously, researchers

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have relied on the argument of Compeau and Higgins [41] about self-efficacy structure to conceptualize and measure technological self-efficacy in various aspects such as internet self-efficacy, online technological self-efficacy, and mobile technological self-efficacy [42, 43]. Likewise, for this study, the author refers to technological self-efficacy as confidence in customers’ ability to use AI chatbots. Besides, Akour et al. [44] verified that digital technological self-efficacy has a positive impact on the intention to use technology in the Fintech context and technological self-efficacy has a positive effect on the intention to continue using technology in e-learning environments [44]. Hence, the present study hypothesizes as follows: H5: Technological self-efficacy (SE) positively affects behavioral intention (BI) to adopt metaverse for learning.

3.6 Behavior Intention (BI) The intention to change one’s behavior because of using e-government services is known as behavior intention [39]. The term “behavior intention” is used to describe the degree to which an individual makes deliberate preparations for their actions in the future [45]. Individuals’ intentions, which can foretell future technological acceptance, are based on their evaluations of a technology’s usefulness and simplicity of use. Because of their optimistic outlook, students are more likely to follow through on their intentions to implement the online learning platform [46]. The influence of others significantly influences how students intend to behave when using mobile devices for learning [47]. The previous studied also find that people’s behaviors when utilizing new technologies affect their behavior intentions in a multicultural setting [48, 49]. Therefore, it can be assumed that: H6: Behavior intention (BI) positively affects use behavior (UB) to adopt metaverse for learning (Fig. 1).

4 Methodology 4.1 Questionnaire Structure The survey approach, which is a popular way to obtain primary data, was employed in the study [50]. Instead of focusing on an individual, this method collects information from a large cross-section of the population [51]. This study used Google platform to survey. The questionnaire was created using a variety of items that were changed to be used in a metaverse environment and based on metrics that have been validated and used in earlier studies. Additionally, the survey was initially written in English before being translated into Vietnamese so that it would be appropriate for the situation and the subject of the survey [52]. Furthermore, the study surveyed people who

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Fig. 1 Conceptual framework

fit two criteria: (1) students who have experiences on online learning, and (2) they were available and willing to participate in the survey. A seven level Likert scale (ranging from 1 for “completely disapprove” to 7 for “completely agreement”) was also utilized for each survey item to learn about participants’ perceptions of an issue of interest [53]. The variable performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating condition (FC), behavior intention (BI) and user behavior (UB) accepted from the study of Yang et al. [13], technological self-efficacy (SE) adopted from Chen et al. [43].

4.2 Sampling Method and Sample Size Thus, non-probability with judgmental sampling method was chosen as the sampling method. The students who have digital devices and have experience on online learning to fulfill the objective of study, which is to investigate the behavior to use Metaverse in education. This method has also been used in peer-reviewed studies [29, 54, 55]. Moreover, sample size is critical for estimation and interpretation. The require sample size based on G*Power 3 software [56] analysis for this study is 128 [57–59]. Consequently, 365 samples were used in this investigation, which is thought to be the appropriate amount for analysis in the following phase.

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4.3 Respondent Profile The survey from 365 respondents reveals that males account for 41.64% of participants, while females account for 58.36% of the total. Freshmen account for 33.42% of students, sophomores account for 34.25%, juniors account for 20.27%, and seniors account for the remaining 12.05%. The majority of students study business administration, with 26.58% studying information technology. While 17.53% of respondents are interested in international relations, just 15.62% are interested in linguistics and literature.

4.4 Common Method Bias The presence of common technique bias is probable due to the collection of data for both exogenous and endogenous variables from a single source. In order to evaluate the potential risk posed by CMB and address this concern, the researchers employed procedural and statistical methodologies for data analysis and utilized a cross-sectional research design [60, 61]. According to Ooi et al. [62], the statistical analysis conducted using Harman’s single factor analysis reveals that the Kaiser– Meyer–Olkin (KMO) measure and Bartlett’s Test both exceed the minimum threshold of 0.5, with a value of 0.95 [63, 64]. Additionally, the analysis indicates the presence of a single component that explains 38.89% of the total variation, which falls within the acceptable range of less than 50%. Hence, this CMB issue is not likely to occur for the dataset.

4.5 Assessing the Outer Measurement Model During the quantification phase, each model’s reliability and validity must be determined and assessed [65, 66]. To begin with, the study focused on building reliability applying composite reliability (CR) and Dijkstra-rho Henseler’s rh0_A (rh0_A) [17, 67–69]. According to prior studies (CR), and rho_A values greater than 0.7 suggest a high degree of reliability [67–69]. In Table 1, (CR), and rho_A values all surpass the minimum value of 0.7, showing that the measurement model is highly reliable. The study next tested the convergent validity using the average variance extracted (AVE) and individual factor loading (FL). According to the basic rule of thumb, external loading should be at least 0.7 [70], while AVE should be greater than 0.5 to be deemed good [71, 72]. Table 1 also shows that all factor loadings are greater than 0.7 and AVE values exceeded the 0.5 thresholds. As a result, the model’s convergent validity is strong. Additionally, the study examined discriminant validity by using the FornellLacker Test and cross loading [73, 74]. The square root of AVE for all components

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Table 1 Reliability and convergent validity Latent constructs

Items

Loadings

(rh0_A)

(CR)

(AVE)

PE

PE1

0.856

0.840

0.904

0.758

0.842

0.897

0.743

0.823

0.894

0.738

0.773

0.867

0.685

0.831

0.898

0.746

0.841

0.903

0.757

0.835

0.898

0.747

EE

SI

FC

SE

BI

UB

PE2

0.886

PE3

0.869

EE1

0.842

EE2

0.902

EE3

0.840

SI1

0.851

SI2

0.864

SI3

0.861

FC1

0.865

FC2

0.815

FC3

0.801

SE1

0.851

SE2

0.892

SE3

0.848

BI1

0.837

BI2

0.893

BI3

0.879

UB1

0.867

UB2

0.888

UB3

0.837

on the diagonal was larger than the correlation coefficients with other constructs, as shown in Table 2, demonstrating discriminant validity. In addition, according to the cross-loadings test, each item loading should be larger than its associated construct, according to the literature, and item loadings are also regarded a threshold [71]. The item loadings and cross-loadings for all linked variables are shown in Table 3. The cross-loadings requirements are satisfied because the item loadings of the factors are greater than the cross-loading values of the other latent factors [73, 74]. As a result, discriminant validity has been met.

4.6 Inspecting the Inner Structural Model Before the inner structural model analysis, the collinearity test was carried out to find the presence of components that were firmly related. Variance inflation factors were present for all constructs (VIF) less than 2.266, which is less than the cutoff value of 5.0 [65], indicating that multicollinearity did not occur. Additionally,

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Table 2 Fornell-Lacker BI

EE

FC

PE

SE

SI

UB

BI

0.870

EE

0.222

0.862

FC

0.765

0.150

0.828

PE

0.732

0.151

0.615

0.870

SE

0.816

0.216

0.767

0.708

0.864

SI

0.788

0.177

0.781

0.666

0.747

0.859

UB

0.832

0.182

0.735

0.738

0.765

0.759

0.864

FC

PE

SE

SI

UB

Table 3 Cross-loading factor BI

EE

BI1

0.837

0.228

0.612

0.618

0.734

0.625

0.679

BI2

0.893

0.188

0.669

0.651

0.703

0.712

0.747

BI3

0.879

0.166

0.713

0.641

0.695

0.717

0.744

EE1

0.181

0.842

0.107

0.139

0.192

0.114

0.142

EE2

0.217

0.902

0.154

0.151

0.217

0.190

0.175

EE3

0.172

0.840

0.124

0.094

0.143

0.148

0.152

FC1

0.670

0.151

0.865

0.561

0.700

0.686

0.648

FC2

0.582

0.127

0.815

0.481

0.569

0.626

0.585

FC3

0.642

0.094

0.801

0.483

0.628

0.626

0.589

PE1

0.624

0.074

0.520

0.856

0.603

0.606

0.639

PE2

0.636

0.159

0.535

0.886

0.631

0.577

0.66

PE3

0.651

0.158

0.551

0.869

0.615

0.558

0.629

SE1

0.678

0.166

0.681

0.604

0.851

0.679

0.656

SE2

0.712

0.184

0.652

0.663

0.892

0.635

0.669

SE3

0.723

0.208

0.656

0.568

0.848

0.624

0.657

SI1

0.669

0.163

0.677

0.542

0.622

0.851

0.653

SI2

0.695

0.114

0.677

0.569

0.65

0.864

0.663

SI3

0.666

0.181

0.659

0.607

0.652

0.861

0.638

UB1

0.752

0.239

0.676

0.648

0.686

0.672

0.867

UB2

0.740

0.129

0.663

0.664

0.687

0.690

0.888

UB3

0.662

0.097

0.559

0.599

0.605

0.600

0.837

the indices support the approximate model fit according to the standardized root mean square residual (SRMR) criterion. Any model with a decent fit would be supported by an SRMR score of less than 1 [71]. The saturated model and estimated model value is 0.051 and 0.059 (less than 1) respectively in this study indicates a good fit for PLS path model. The structural model is the next step after confirming

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Table 4 Result of structural model Pathway PE → BI

0.214

T Statistics 4.042

p Values

2.50%

97.50%

Remark

0.000

0.113

0.324

Support

EE → BI

0.047

2.134

0.033

0.006

0.092

Support

SI → BI

0.254

4.605

0.000

0.141

0.358

Support

FC → BI

0.174

2.818

0.005

0.053

0.295

Support

SE → BI

0.331

5.271

0.000

0.206

0.453

Support

BI → UB

0.832

43.002

0.000

0.792

0.868

Support

the measurement model. The results showed that PE (β = 0.214, ρ_value < 0.05), EE (β = 0.047, ρ_value < 0.05), SI (β = 0.254, ρ_value < 0.05), FC (β = 0.174, ρ_value < 0.05), SE (β = 0.331, ρ_value < 0.05) impact on BI and BI (β = 0.614, ρ_value < 0.05) influence to UB. Therefore, the all the hypothesis are supported. These findings above also supported by the confidence interval contains a zero value through the bias-corrected confidence interval of 2.5% and 97.5% (Table 4).

4.7 The Predictive Relevance The blindfolded technique was used to figure out the Q2 value, which represents the predictive accuracy of the structural model. Table 5 indicates the predictability of the research model because the Q2 values are greater than zero. Additionally, the R2 values must be high enough to guarantee that the model has at least some explanatory power [75]. R2 should be larger than or equal to 0.1 order to variance. The minimal R2 value in this situation is 0.693 (higher than 0.1), which is a significant value [59, 76]. As a result, it can help explain why a certain dependent variable is regarded sufficient. In a similar vein, the study assessed the effect size for each of the exogenous constructs using the Cohen f2 values to observe the impact of each exogenous construct on the R2 value of an endogenous construct [77]. Accordingly, the Cohen f2 values will produce small, medium, and large effects with values of 0.02, 0.15, and 0.35, respectively [78]. As shown in Table 6, with the effect sizes ranging from 0.185 to 0.462, The external construct influences the intrinsic construct in a minor to significant way. Table 5 Predictive relevance Q2 and R2 Q2 (=1-SSE/SSO)

Predictive relevant

BI

0.575

Q2

>0

0.770

UB

0.510

Q2 > 0

0.693

Dependent variable

R2

74 Table 6 Effect size (f2 )

T.-Q. Dang et al.

Predictor construct/dependent construct

BI

PE

0.338

EE

0.368

SI

0.405

FC

0.185

SE

0.254

BI

UB

0.462

4.8 ANN Analysis The present study used PLS-SEM to analyze the linear relationship between the structures. However, to grasp the complexity of technology adoption and use from the perspective of linear assumptions may not be enough because of its simplicity [79, 80]. Therefore, the current study kept employing ANN analysis to deal with this issue, and then to evaluate the non-linear relationship between the structures. As argued by Lim et al. [81] and Wong et al. [55], based on the analysis of non-linear relationships, ANN can make better predictions than classic techniques of regression analysis [55, 82]. Besides, ANN can also predict more accurately even without a hypothesis due to its adaptive learning, fault tolerance, and real-time operation [83]. For this study, the conceptual framework of the study is decomposed into 2 ANN models and depicted in Figs. 2 and 3. Accordingly, the number of hidden neurons created in ANN Models A and B are 2 and 3 respectively. In addition, to prevent overfitting the model, this study used a ten-fold crossvalidation strategy with 10 ANN networks [81]. Specifically, the ratio of data to be partitioned for training and testing is 90:10 [62, 84]. For this study, both models A and B achieve a level of reliability because all Root Mean Squared Error (RMSE) values for both training and testing of the model presented in Table 7 are small, with the mean being within the range of 0.583 to 0.630 [85]. Besides, by using RMSE values to calculate R2 , ANN Models A and B can predict behavior intention (BI) and use behavior (UB) with 99.05% and 98.90% accuracy, respectively. On the other hand, using sensitivity analysis to calculate the normalized relative importance (%), this study measured the importance of each predictor in the neuron network [86–88]. Results from Table 8 shows that for ANN Model A, SI is the most important predictor with the normalized relative importance at 100%, followed by SE (89.2%), PE (89.1%), FC (64.9%), and EE (9.9%). For ANN Model B, since there is only one neuron model in this ANN model, the normalized importance is at 100%. Besides, the findings obtained from the comparison between PLS-SEM and ANN results were shown in Table 9 showing the difference in importance between the factors in model A. Specifically, according to the results of PLS-SEM, SE is the most crucial aspect, then SI, whilst the findings from ANN demonstrate the opposite

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Fig. 2 ANN model A

when SI is the most important factor, then SE. This variance could be due to ANN’s unique ability to capture non-linear correlations through machine learning. Finally, since there is only one predictor, The outcomes of the PLS-SEM match up with ANN Model B.

5 Finding Discussion Students’ openness to learn in metaverse environments was investigated using UTAUT and educational validation. Through the above results, it can be confirmed that the factors PE, EE, SI, FC, SI strongly influence students’ willingness to accept learning by metaverse. This has also been proven in previous astudies through different contexts [14, 28, 89]. Based on the PLS-SEM, It is evident that technical self-efficacy has the greatest effect on the metaverse’s embrace of learning, implying that students’ confidence in their own technological ability is an essential factor in the Metaverse’s adoption [86, 90, 91]. Educators can use this result to develop a

76

T.-Q. Dang et al.

Fig. 3 ANN model B Table 7 RMSE values for BI, UB

Neural network

Model A

Model B

Input: PE, EE, SI, FC, SE

Input: BI

Output: BI

Output: UB

Training

Testing

Training

Testing

RMSE

RMSE

RMSE

RMSE

ANN1

0.569

0.475

0.578

0.684

ANN2

0.602

0.556

0.656

0.548

ANN3

0.610

0.595

0.628

0.500

ANN4

0.557

0.725

0.596

0.711

ANN5

0.596

0.500

0.595

0.719

ANN6

0.565

0.588

0.629

0.714

ANN7

0.582

0.548

0.583

0.591

ANN8

0.605

0.611

0.620

0.643

ANN9

0.601

0.614

0.617

0.636

ANN10

0.570

0.622

0.565

0.556

Mean

0.586

0.583

0.607

0.630

SD

0.019

0.070

0.028

0.078

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Table 8 Sensitivity analysis Neural network

Model A (output: BI) PE

EE

Model B (output (UB) SI

FC

SE

BI

ANN1

0.267

0.049

0.254

0.174

0.225

1.000

ANN2

0.211

0.059

0.245

0.187

0.289

1.000

ANN3

0.246

0.049

0.259

0.197

0.249

1.000

ANN4

0.345

0.047

0.144

0.167

0.298

1.000

ANN5

0.242

0.063

0.267

0.121

0.307

1.000

ANN6

0.218

0.108

0.236

0.108

0.332

1.000

ANN7

0.252

0.070

0.290

0.152

0.236

1.000

ANN8

0.260

0.065

0.240

0.164

0.270

1.000

ANN9

0.262

0.093

0.224

0.186

0.235

1.000

ANN10

0.246

0.123

0.228

0.110

0.293

1.000

Mean relative importance

0.2549

0.0726

0.2387

0.1566

0.2734

1.0000

Normalized importance (%)

89.100

9.900

100.000

64.900

89.200

100.000

Table 9 Comparison between PLS-SEM and ANN results PLS path

Original sample (O)/ path coefficient

ANN results: normalized relative importance (%)

Ranking (PLS-SEM) [based on path coefficient]

Ranking (ANN) [based Remark on normalized relative importance]

Model A (output: BI) PE 0.214 → BI

89.100

3

3

Match

EE 0.047 → BI

9.900

5

5

Match

SI → 0.254 BI

100.000

2

1

Not match

FC 0.174 → BI

64.900

4

4

Match

SE 0.331 → BI

89.200

1

2

Not match

100.000

1

1

Match

Model B (output: UB) BI → 0.832 UB

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long-term strategy for practicing previous technology skills so that pupils can readily accept the metaverse. However, ANN analysis indicated that SI is the most important factor to accept metaverse for learning in study. This is reasonable when Vietnam is one of the countries that belong to the collectivist group [79, 80]. Opinions or behavior of people around students will be highly emphasized. Therefore, educators can take advantage of this to have appropriate strategies.

5.1 Theoretical Implication This study contributes many different aspects in theorical. Despite the fact that the metaverse is the subject of extensive research, there is still a dearth of studies on how it might be used in education. Thus, the study contributes to a practical understanding of the metaverse in education. It serves as a reference for future metaverse research and enhances current metaverse research. Another academic addition of this article is the usage of the UTAUT model. Literature reviews show that UTAUT is the most applicable model to represent this article’s idea because it is commonly used to study the acceptance of new technologies. By identifying the most important elements that impact on respondents’ tendency to make use of metaverse data and their behavior in doing so, the study develops a more thorough understanding of metaverse data by positing causal links between variables. Lastly, the study contributes a perspective on profound learning to the literature. In contrast to previous research, in the current study, a combined analysis method between PLS-SEM and ANN analysis has been used in this paper to provide stronger evidence for the acceptance of the metaverse for learning [92, 93]. Its ability to reduce oversimplification of decision-making makes ANN a unique artificial intelligence technique [17]. Since then, the combination of these two methods has helped us to have a clearer view of the power of variables influencing the use of the metaverse for education, specifically here the technological self-efficacy and social influence.

5.2 Managerial Implication Metaverse technology is a novel form of technological advancement. In light of developing technology, this study’s findings help instructors and learner study more effectively. The effect of environmental changes will probably be lessened if people can successfully execute real-world jobs in a mirrored virtual environment. According to what students believe, a mix of metaverse technologies can improve the studying environment. They are more likely to be enthusiastic to study, to continue using metaverse technology for instruction, and to be able to learn even when not physically restrained if metaverse technology is included into their education.

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By incorporating meta world technology for learning, The learning atmosphere will be more interesting and inspiring for the students. A timely and convenient program for sharing Internet resources encourages students to exhibit positive expectations and awareness. Consequently, these elements will subtly urge students to use metaverse technologies to further their studies. Therefore, it has implications for fostering meta-technical instruction in other fields.

6 Conclusion and Further Research Based on UTAUT theory, this study proposes to build an acceptance model metaverse for learning in higher education. The study can be found for other experimental research ideas to explore more profound hyper-virtual world technologies in various contexts in addition to the field of education. In the future, it will be interesting if researchers can compare metaverse acceptance patterns for learning in different cultures to see which factors to prioritize. In addition, researchers can keep an eye on social variables such as virtual classroom climate beliefs to better grasp the metaverse for learning.

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Investigating the Influence of Technology Anxiety on Healthcare Metaverse Adoption Seckin Damar

and Gulsah Hancerliogullari Koksalmis

Abstract Technological advancements in the healthcare field have become increasingly common, shaping the future of healthcare practices. It’s clear that innovative technological solutions in healthcare deliver results and revolutionize the sector. This study explores the implementation of a healthcare Metaverse using the Technology Acceptance Model (TAM) framework. The primary goal is to enhance medical doctors’ understanding of technology acceptance by considering technology anxiety as a factor within the TAM framework. The data were analyzed through path analysis to examine how perceived usefulness, ease of use, technology anxiety, and behavioral intention to adopt the healthcare Metaverse are interconnected. The findings indicate that perceived usefulness and the perceived ease of use of the system both significantly impress the intention to engage with it, which aligns with research on TAM. Moreover, a substantial correlation exists between perceived usefulness and perceived ease of use. Nevertheless, technology anxiety has a statistically significant influence on perceived ease of use but not on perceived usefulness. Keywords Healthcare metaverse · Technology acceptance model · Technology anxiety

S. Damar · G. H. Koksalmis (B) Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] S. Damar e-mail: [email protected] G. H. Koksalmis College of Engineering and Computer Science, University of Central Florida, Orlando, FL, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_5

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1 Introduction During COVID-19, to foster social isolation, global tactics such as public area closures and stay-at-home directives have been employed. Access to healthcare has been restricted by a lack of resources such as Personal Protective Equipment, intensive care beds, and ventilators. To reduce the spread of the virus, health systems have been overhauled, and face-to-face consultations have been restricted [1]. Medical practitioners may use a variety of tools provided by metaverse technology to improve illness diagnosis and planning, demonstrating the sector’s enormous potential. For instance, during surgical procedures, augmented reality (AR) headsets can display patients’ anatomy images, enabling surgeons to improve presurgical planning and manipulate critical anatomical areas. Virtual avatars can properly forecast the results of aesthetic surgeries thanks to the use of metaverse technology in plastic surgery and other procedures. Furthermore, the Metaverse can enable radiologists to more clearly visualize dynamic images, leading to better diagnoses and more accurate decision-making using sophisticated image visualization tools. Patients may be educated and engaged through the deployment of digital twin solutions in the Metaverse, improving their understanding of their medical issues and empowering them to take an active role in their own treatment [2]. The literature on the Metaverse underscores the importance of exploring its potential applications and impacts across multiple industries. The emergence of the Metaverse, a universe that immerses users, has garnered significant attention in sectors such as education, engineering, and consumer brands [3–8]. Numerous studies have delved into comprehending the factors influencing its adoption and sustainability. Researchers have employed various methods for studying this topic, ranging from covariance-based equation modeling (CB SEM) to deep artificial neural network (ANN) models [7, 8]. The Technology Acceptance Model (TAM) and its extended versions have played pivotal roles in elucidating technology adoption [3–6]. Additionally, some studies have considered personality traits and contextual factors to further grasp the complexities surrounding Metaverse adoption [4, 5, 7]. Notably, psychological needs like autonomy, enjoyment motivation, and social influence have emerged as predictors of its sustainability and acceptance [4, 7, 8]. Nonetheless, challenges persist, including cybersecurity concerns and perceived cyber risk, which can potentially constrain adoption intentions [5, 6]. Furthermore, there have been research papers exploring applications of the Metaverse. For instance, Koohang et al. [9] discuss how the Metaverse can serve as a platform supported by extended reality technologies and its potential impact on fields like education and healthcare. However, Lee [10] delves into the impact of Metaverse services in the healthcare industry and offers a thought-out plan for integrating them into this sector. On the other hand, Bansal et al. [11] have taken an approach in their research. They delve into the ways different technologies can contribute to the growth of the Metaverse and its potential advantages for care. Additionally, they discuss the obstacles that may arise when putting these ideas into practice.

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This study’s primary goal is to examine the implementation of healthcare Metaverse in Turkey, focusing on the TAM. The choice of TAM for this investigation comes because it is among the most prevalent and highly referenced models in the domain of embracing novel technologies. The research collected data from medical doctors enrolled in universities across Turkey to gain insights into their willingness to utilize the healthcare Metaverse. This research contributes to previous studies on healthcare Metaverse adoption in several manners. To begin with, this research enriches current understanding by reinforcing the theoretical applicability and practical relevance of the core TAM in the setting of the healthcare Metaverse. Additionally, there is a limited body of work in the literature on the acceptance of Metaverse practices in healthcare. No similar study has been conducted in Turkey before and this study will determine the acceptability of Metaverse technology by medical doctors in Turkey. Third, the uniqueness of this study lies in its theoretical model that incorporates the technology anxiety variable into the core TAM. This research utilized this theoretical model to estimates medical doctors’ adoption of the Metaverse. Notably, there is no model in the literature that incorporates the technology anxiety variable in this field. In this regard, this research will make a substantial addition to the current body of literature. Fourth, the research’s conclusions can be helpful for developers and implementers who seek to improve the healthcare Metaverse and expand people’s perceptions of it. The remaining sections of the study are organized as follows: The following section introduces the research model and hypotheses. After that, the research methodology is outlined. The final sections encompass the presentation of results, discussion, and limitations and recommendations for future research.

2 Research Model and Hypotheses Development The understanding and prediction of technology acceptance and adoption have been greatly advanced through the development and application of various influential models. Some prominent models in this domain are the TAM, Theory of Reasonable Action (TRA), and Theory of Planned Behavior (TPB). The widely used TAM model, which was formed from TRA and TPB, describes consumers’ beliefs and behaviors in relation to accepting technology [12]. It was first proposed by Davis [13], who contends that examining a person’s behavioral intention to use (BIU) a new technology allows one to gauge that person’s attitude toward using it [14]. Perceived usefulness (PU) and perceived ease of use (PEOU), two factors, are used to assess this attitude [14, 15].

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In order to more efficiently accommodate varied situations, the TAM model has been enhanced by integrating additional components. The TAM was extended by Venkatesh and Davis [16] to include moderators such as experience and volunteerism as well as other components that influence PU such as subjective norm, image, job fit, output quality, outcome demonstrability, and PEOU. This type is known as TAM2. Venkatesh et al. [17] introduced the Unified Theory of Acceptance and Use of Technology (UTAUT). This theory includes performance expectancy, effort expectancy, social influences, and facilitating conditions, as well as moderators like gender, age, experience, and volunteerism. Venkatesh [18] recognized the factors influencing PEOU and constructed a model, subsequently, Venkatesh and Bala [19] fused this model with TAM2, resulting in the formation of TAM3, which includes anchors (computer self-efficacy, anxiety, gaming, and external control) and adjustments (enjoyment, objective usability) as predictors of PEOU. TAM is a generalizable model developed to describe the process of information technology adoption and can be easily adapted to different contexts. It has also been adapted to the information systems framework and has become the most widely wellknown model to date for research on user acceptance behavior [20]. So, this study utilized TAM to investigate the various factors that influence individuals’ behavioral intention to adopt the healthcare Metaverse. BIU expresses “the degree to which a person has formulated conscious plans to perform or not perform some specified future behavior” [21]. In this research, BIU refers to the medical doctors’ desirability to consistently use the healthcare Metaverse system and will consequently influence user behavior. Since the number of people actively using the healthcare Metaverse is not considered to be very high, the study focuses on the BIU system. The identified constructs included BIU, PEOU, PU, and technology anxiety. The visual representation of the research model may be observed in Fig. 1.

Fig. 1 Research model

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2.1 Perceived Usefulness PU refers to “the degree to which an individual believes that using a particular system would enhance his or her job performance” [13]. Earlier research showed a positive connection between PU and BIU [22, 23]. In a study conducted by Akour et al. [24], it is emphasized that students’ PU is significantly influential and has a high influence on their BIU the healthcare Metaverse. Another study conducted by Wu and Yu [25] claimed that when people find the Metaverse useful, they are more likely to continue using it. In line with these findings, it can be said that medical doctors’ intention towards the use of the healthcare Metaverse is directly proportional to the usefulness of the system. Consequently, the hypothesis may be formed as follows: H1: “Perceived usefulness has a positive influence on individuals’ behavioral intention to use healthcare Metaverse.”

2.2 Perceived Ease of Use PEOU is “the degree to which a person believes that using a particular system would be free of effort” [13]. In this study, the concept of PEOU is employed to measure medical doctors’ perceptions of the ease and effortlessness associated with utilizing the healthcare Metaverse. According to the TAM, PEOU directly and positively impacts PU [15]. Previous studies have demonstrated that there is a positive impact of PEOU on BIU [26–28]. As an example, Almarzouqi et al. [29] carried out research in the United Arab Emirates (UAE) to assess how medical students perceive the integration of Metaverse practices in their educational process. They discovered that higher PEOU leads to greater adoption of Metaverse systems. Additionally, prior research has consistently shown a positive association between PEOU and PU [23, 24]. Alawadhi et al. [28] examined how students in the UAE perceive the use of Metaverse technologies in medical education and found a positive association among PEOU and PU. Therefore, the hypotheses can be formulated as follows: H2: “Perceived ease of use has a positive influence on individuals’ behavioral intention to use healthcare Metaverse.” H3: “Perceived ease of use has a positive impact on perceived usefulness of healthcare Metaverse.”

2.3 Technology Anxiety Technology anxiety represents the feelings of fear, worry, and apprehension that some people experience when using computer-based information systems or technology. This may include concerns about making mistakes or losing data. People who have a higher level of anxiety may be more resistant to using information technologies [30].

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Tung and Chang [31] sought to determine the key elements that influence Taiwanese university students’ utilization of online learning. According to the findings, a negative association exists between technology anxiety and PEOU. Also, Almarzouqi et al. [32] intended to discover the components that predict the acceptance of an Electronic Medical Record system in the healthcare system in the UAE. They found that technology anxiety has a negative influence on both PEOU and PU. In light of existing research, we have formulated the subsequent hypotheses: H4: “Technology anxiety has a negative impact on perceived ease of use of healthcare Metaverse.” H5: “Technology anxiety has a negative impact on perceived usefulness of healthcare Metaverse.”

3 Methodology 3.1 Survey Design The survey was broken into 3 phases. The initial phase encompassed the cover letter and informed consent form. The subsequent phase of the survey informed participants about the Metaverse concept and its applications. In the final section, individuals were asked questions regarding their demographic attributes, encompassing gender, age, education qualification, and experience in the healthcare Metaverse. Additionally, this section contained questions related to the constructs being measured.

3.2 Sample An online survey was conducted using a non-probability convenience sampling approach to gather the responses. The questionnaire was provided to medical doctors who were registered at various medical schools across Turkey. The Social Sciences Institute’s Ethics Committee Approval was required in order to collect survey data. After being granted, the approval was sent to Turkish institutions with medical faculties. However, these faculties provided response noting that a formal submission of the survey was required. As a result, the survey was once again forwarded, this time via the Office of the Rector, to the medical faculties. This made sure that the survey was given to the faculties in a formal manner and without additional incident. According to the assumption that there should be 10 instances of each measure, the recommended model needs a minimum sample size of 180 [33]. No missing or outlier values were found in the questionnaire. All responses are usable. Therefore, a total of 225 valid questionnaires were received, with 58.5% of the participants identifying as female. The participants’ average age was determined to be 27.4 years. Table 1 provides a thorough summary of the demographic data.

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Table 1 Sample demographics Age (years) Max: 71

Mean: 27.4

Gender (%) Female: 58.5

Male: 41.5

Speciality (%) Genetics: 14.3

Otorhinolaryngology: 14.3 Cytopathology and embryology: 14.3

Medical biochemistry: 28.6 Internal medicine: 14.3

Basic medical sciences: 14.3

Have you had any experience with using metaverse before? (%) Yes: 22

No: 78

Have you had any experience with the use of metaverse in healthcare before? (%) Yes: 2.4

No: 97.6

3.3 Measures The study utilized a scale comprising four constructs and 18 items. The appendix presents these constructs, their associated items, and references. The viewpoints of the participants regarding each item were rated utilizing a five-point Likert scale, ranging from “strongly disagree = 1” to “strongly agree = 5”. To examine the relevance and comprehensibility of the survey, a pre-test was conducted with five medical doctors who had previous experience with Metaverse applications in healthcare and the findings indicated that the instructions and questions were fully understood. To gather information from the respondents, a cross-sectional online survey approach using Google Forms was used.

3.4 Data Analysis In this work, the examination of the collected data was carried out utilizing the SmartPLS 4 software, employing the partial least squares-structural equation modeling (PLS-SEM) strategy. The assessment of the datasets followed a two-stage evaluation methodology that included the examination of both the structural model and the measurement model [34].

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4 Results 4.1 Measurement Model Before to analyzing the proposed hypotheses, a series of tests were carried out to evaluate the reliability and validity of the measurement items and constructs [35]. The item loadings are evaluated for construct validity. It is advised to consider loadings above 0.7. Among the 18 items examined, 4 items (specifically BI4, PEOU5, PU4, PU5) displayed factor loading values that fell below the desired threshold. To address this issue, we initially eliminated these items from the model and conducted another run. Following this adjustment, only one item, PEOU4, exhibited factor loadings that fell below the acceptable threshold. Consequently, we ran our altered model again without include this item. As a result, all of the factor loadings surpassed 0.7 (Table 2). To ensure constructs’ reliability and internal consistency, it is advisable to establish a threshold of 0.7 as the minimum admissible values for Cronbach’s alpha and composite reliability (CR). All of the constructs meet this requirement as indicated in Table 2. Next, convergent validity was then tested with “average variance extracted—AVE” values. A minimum threshold of 0.5 is considered acceptable for AVE. As depicted in Table 2, the AVE scores for every construct significantly surpassed 0.5, indicating the existence of convergent validity across all of the constructs. After evaluating the evaluation of construct reliability and validity, the subsequent stage involved examining discriminant validity. Discriminant validity was measured by comparison of the correlation among constructs with the square root of the AVE for each construct. To ensure satisfactory discriminant validity, it’s essential for Table 2 Reliability and validity Construct

Item code

Factor loadings

Cronbach’ s alpha

CR

AVE

Behavioral intention to use

BIU1

0.902

0.798

0.876

0.705

0.784

0.871

0.694

0.856

0.908

0.768

0.877

0.911

0.721

Perceived ease of use

Perceived usefulness

Technology anxiety

BIU2

0.896

BIU3

0.706

PEOU1

0.857

PEOU2

0.913

PEOU3

0.718

PU1

0.916

PU2

0.913

PU3

0.793

TA1

0.881

TA2

0.889

TA3

0.840

TA4

0.782

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Table 3 Fornell-Larcker criterion Construct

BIU

PEOU

PU

TA

0.840

BIU PEOU

0.455

0.833

PU

0.634

0.571

0.876

TA

− 0.304

− 0.486

− 0.342

0.849

Table 4 Heterotrait-Monotrait ratio (HTMT) Construct

BIU

PEOU

PU

TA

BIU PEOU

0.518

PU

0.665

0.626

TA

0.303

0.505

0.330

the values along the diagonal (square roots of AVE) to surpass those in the offdiagonal positions (correlations between constructs) [36]. Table 3 demonstrates that this criterion is satisfied. Additionally, the results of the “Heterotrait-Monotrait RatioHTMT” test indicate that all of the HTMT scores are below 0.85 [37], providing further support for the findings based on Fornell and Larcker’s criterion (Table 4).

4.2 Hypotheses The validity of hypotheses was assessed through the implementation of the PLS-SEM approach. Table 5 gives the standardized parameters, corresponding t-statistics, and the hypotheses related to the study. Since the coefficients expressing the relevant relationship in each hypothesis have a positive or negative relationship, one-tailed test is used for hypothesis testing [38]. The findings show that there exists a significant association among PU and BIU (β = 0.556, p < 0.05; H1 supported). PEOU also has positive effect on BIU (β = 0.137, p < 0.05; H2 supported). Furthermore, link between PEOU and PU is supported, with significant coefficients (β = 0.529, p < 0.05; H3 supported). Moreover, technology anxiety significantly impacts PEOU (β = − 0.486, p < 0.05; H4 supported), while its influence on PU is not statistically significant (β = − 0.085, p > 0.05; H5 not supported). Table 5 presents a broad summary of all the hypotheses presented in the study. Also, Fig. 2 displays the RSquare values and path coefficients. The R-Square values demonstrate the fraction of explained variance accounted for by a variable. In particular, the proposed model accounts for 41.5% (R2 = 0.415) of the total variance in BIU.

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Table 5 Findings from hypothesis testing Hypothesis

Path

Path coefficient (β coefficient)

T statistics

Supported (Yes/No)

H1

PU → BIU

0.556*

10,842

Yes

H2

PEOU → BIU

0.137*

2250

Yes

H3

PEOU → PU

0.529*

10,018

Yes

10,987

Yes

1326

No

H4

TA → PEOU

− 0.486*

H5

TA → PU

− 0.085

* p < 0.05

Fig. 2 Outcomes of the research framework (*p < 0.05)

5 Discussion and Conclusion 5.1 Practical Implications This research aims to observe the elements that lead to the willingness of doctors in Turkey to embrace the healthcare Metaverse. It uses a TAM framework to ascertain the determinants that affect the implementation of this approach. The research also proposes a framework that expands on TAM by considering technology anxiety. The path analysis conducted in this research aligns with TAM studies, revealing that PU and PEOU have significant effects on BIU. Furthermore, PEOU has an impact on PU, indicating that upon individuals perceive a technology as easy to use, they also tend to consider it useful. Conversely, higher levels of technology anxiety have a significant influence on PEOU, suggesting that individuals’ PEOU is negatively affected by anxiety towards technology. However, the study did not find evidence for the impact of technology anxiety on PU.

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The findings of this research have consequences for various groups. These findings highlight the significance of governments investing in projects aimed at reducing people’s anxiety towards technology. Governments can help create perceptions of and acceptance for new technical solutions by addressing this anxiety and emphasizing the user-friendly nature of technologies. Based on the findings, it is clear that technology companies should prioritize user-centered design. Developing userfriendly products and services can enhance consumers’ perception of ease of use and, consequently, their overall acceptance of healthcare Metaverse. By leveraging the connection between ease of use and PU and making product enhancements that alleviate technology anxiety, technology companies can positively influence individuals’ perceptions. Medical students and doctors can also benefit from these findings. Understanding that both PU and PEOU significantly impact acceptance of the healthcare Metaverse, they can actively engage with it by focusing on its benefits. For example, they can utilize it in education through anatomy studies using virtual examples of the human body or engaging in cadaver and case practice simulations or surgical simulations designed for medical students and trainee doctors. Moreover, it enables treatment for patients who do not require examination or allows certain surgeries to be performed without doctors and patients having to travel across countries. Participating in programs that aim to reduce anxiety related to technology can also be beneficial. By overcoming these anxieties, individuals can develop an understanding and positive attitudes toward healthcare in the Metaverse. Academic institutions have a role to play in the integration of technology education and training into their curricula, which can contribute to the adoption of healthcare Metaverse. By doing so, they create an environment where students, including professionals, can become familiar with various user-friendly Metaverse technologies. Furthermore, by promoting research that addresses concerns about using the Metaverse among students and doctors, academic institutions can enhance the acceptance of this technology. Investors should also consider factors like user-friendliness, usability, and anxiety reduction when making investment decisions related to the healthcare Metaverse. By understanding how these factors align with the study’s findings, investors can identify opportunities in this evolving market.

5.2 Theoretical Contributions The primary aim of this research is to address the gaps in existing research by examining how users perceive and adopt the healthcare Metaverse, which will contribute to the body of literature. Firstly, it aims to expand the number of studies conducted in this field, since there is limited research on healthcare Metaverse adoption. It’s crucial to emphasize that there has been no prior research conducted on this topic in Turkey. Secondly, it aims to explore whether people’s acceptance of the healthcare Metaverse is impacted by elements such as PU, PEOU, and technology anxiety. While previous studies have examined the effects of PU and PEOU, their impact on acceptance in relation to the healthcare Metaverse has not been thoroughly explored.

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Furthermore, technology anxiety has not been considered as a factor for acceptance of the healthcare Metaverse. Lastly, the results of this study provide substantiating proof for the reliability and validity of the TAM within the realm of the healthcare Metaverse.

5.3 Limitations and Future Research Directions Although current study adds significant value to the existing body of knowledge, it is essential to acknowledge and address certain limitations associated with the research. First, the research focused specifically on the implementation of the healthcare Metaverse by medical doctors in Turkey, which may restrict the adaptability of the outcomes to other geographical contexts. Future studies could investigate the applicability of the proposed model in various cultural settings. Second, this study concentrated on a specific set of variables within the TAM framework, such as PEOU, PU, and technology anxiety. There could be additional factors and constructs that potentially impact the adoption of the healthcare Metaverse. Subsequent research could investigate additional factors, such as social influence or system characteristics, to expand the breadth of understanding regarding the adoption process and facilitate a more comprehensive comprehension of its dynamics. In light of these limitations, further research should try to address these issues and expanding the current study. By conducting cross-cultural studies and adding additional variables to the model, researchers can further enhance our understanding of the adoption of the healthcare Metaverse and provide valuable information for practitioners and policy makers. Acknowledgements This research is a part of doctoral dissertation work by Seckin Damar that is supervised by Gulsah Hancerliogullari Koksalmis.

Appendix: Constructs and Items

Construct

Code

References

Items

Technology anxiety

TA1

[30, 31]

“I have avoided healthcare metaverse because it is unfamiliar to me”

TA2

“Using healthcare metaverse makes me feel uncomfortable”

TA3

“Working with healthcare metaverse makes me anxious”

TA4

“Healthcare metaverse are somewhat intimidating to me” (continued)

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(continued) Construct

Code

References

Items

Perceived ease of use

PEOU1

[14–16, 39]

“I think healthcare metaverse is effortless”

Perceived usefulness

Behavioral intention to use

PEOU2

“I think I can use healthcare metaverse for different educational purposes since it’s easy”

PEOU3

“I think healthcare metaverse will be difficult to use in certain circumstances”

PEOU4

“My interaction with healthcare metaverse would be clear and understandable”

PEOU5

“It would be easy for me to become skillful at using healthcare metaverse applications”

PU1

[14, 39]

“I think healthcare metaverse is useful for live lectures and forums”

PU2

“I think healthcare metaverse adds many advantages to my study”

PU3

“Using healthcare metaverse would make it easier to accomplish my tasks”

PU4

“Using healthcare metaverse would improve my productivity”

PU5

“Using healthcare metaverse would increase my efficiency”

BIU1

[40–42]

“I will definitely use healthcare metaverse in my education”

BIU2

“I intend to increase my use of healthcare metaverse in the future”

BIU3

“I will use healthcare metaverse for limited educational purposes”

BIU4

“For future studies I would use the healthcare metaverse”

References 1. Jnr, B.A., Nweke, L.O., Al-Sharafi, M.A.: Applying software-defined networking to support telemedicine health consultation during and post Covid-19 era. Health Technol. 11, 395–403 (2021) 2. Chengoden, R., Victor, N., Huynh-The, T., Yenduri, G., Jhaveri, R.H., Alazab, M., et al.: Metaverse for healthcare: a survey on potential applications, challenges and future directions. IEEE Access 11, 12765–12795 (2023)

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3. Afkar, E., Hamsal, M., Kartono, R., Furinto, A.: Prediction of perceived consumer experience, perceived brand engagement, and gamification towards the intention to use metaverse: an extended TAM approach. In: 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 239–244. IEEE (2022) 4. Al-Adwan, A.S., Al-Debei, M.M.: The determinants of Gen Z’s metaverse adoption decisions in higher education: integrating UTAUT2 with personal innovativeness in IT. Educ. Inf. Technol. 1–33 (2023) 5. Wiangkham, A., Vongvit, R.: Exploring the drivers for the adoption of metaverse technology in engineering education using PLS-SEM and ANFIS. Educ. Inf. Technol. 1–28 (2023) 6. Al-Adwan, A.S., Li, N., Al-Adwan, A., Abbasi, G.A., Albelbisi, N.A., Habibi, A.: Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaversebased learning platforms. Educ. Inf. Technol. 1–33 (2023) 7. Thabet, Z., Albashtawi, S., Ansari, H., Al-Emran, M., Al-Sharafi, M.A., AlQudah, A.A.: Exploring the factors affecting telemedicine adoption by integrating UTAUT2 and IS success model: a hybrid SEM–ANN approach. IEEE Trans. Eng. Manage. (2023) 8. Al-Sharafi, M.A., Iranmanesh, M., Al-Emran, M., Alzahrani, A.I., Herzallah, F., Jamil, N.: Determinants of cloud computing integration and its impact on sustainable performance in SMEs: an empirical investigation using the SEM-ANN approach. Heliyon 9(5) (2023) 9. Koohang, A., Nord, J.H., Ooi, K.-B., Tan, G.W.-H., Al-Emran, M., Aw, E.C.-X., et al.: Shaping the metaverse into reality: a holistic multidisciplinary understanding of opportunities, challenges, and avenues for future investigation. J. Comput. Inf. Syst. 63(3), 735–765 (2023) 10. Lee, C.W.: Application of metaverse service to healthcare industry: a strategic perspective. Int. J. Environ. Res. Public Health 19(20), 13038 (2022) 11. Bansal, G., Rajgopal, K., Chamola, V., Xiong, Z., Niyato, D.: Healthcare in metaverse: a survey on current metaverse applications in healthcare. IEEE Access 10, 119914–119946 (2022) 12. Maranguni´c, N., Grani´c, A.: Technology acceptance model: a literature review from 1986 to 2013. Univ. Access Inf. Soc. 14(1), 81–95 (2015) 13. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 319–340 (1989) 14. Davis, F.D.: A technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Massachusetts Institute of Technology, Cambridge, Massachusetts, USA (1985) 15. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8), 982–1003 (1989) 16. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46(2), 186–204 (2000) 17. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 425–478 (2003) 18. Venkatesh, V.: Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11(4), 342–365 (2000) 19. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008) 20. Chocarro, R., Cortiñas, M., Marcos-Matás, G.: Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educ. Stud. 49(2), 295–313 (2023) 21. Warshaw, P.R., Davis, F.D.: Disentangling behavioral intention and behavioral expectation. J. Exp. Soc. Psychol. 21(3), 213–228 (1985) 22. ˙Ibili, E., Ölmez, M., Cihan, A., Bilal, F., ˙Ibili, A.B., Okumus, N., et al.: Investigation of learners’ behavioral intentions to use metaverse learning environment in higher education: a virtual computer laboratory. Interactive Learn. Environ. 1–26 (2023) 23. Toraman, Y.: User acceptance of metaverse: insights from technology acceptance model (TAM) and planned behavior theory (PBT). EMAJ Emerg. Markets J. 12(1), 67–75 (2022)

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24. Akour, I.A., Al-Maroof, R.S., Alfaisal, R., Salloum, S.A.: A conceptual framework for determining metaverse adoption in higher institutions of gulf area: an empirical study using hybrid SEM-ANN approach. Comput. Educ. Artif. Intell. 3, 100052 (2022) 25. Wu, R., Yu, Z.: Investigating users’ acceptance of the metaverse with an extended technology acceptance model. Int. J. Human Comput. Interact. 1–17 (2023) 26. Alfaisal, R.M., Zare, A., Alfaisal, A.M., Aljanada, R., Abukhalil, G.W.: The acceptance of metaverse system: a hybrid SEM-ML approach. Int. J. Adv. Appl. Comput. Intell. (IJAACI) 01(01), 34–44 (2022) 27. Mostafa, L.: Measuring technology acceptance model to use metaverse technology in Egypt. J. Finan. Bus. Res. 23(3), 118–142 (2022) 28. Alawadhi, M., Alhumaid, K., Almarzooqi, S., Aljasmi, S., Aburayya, A., Salloum, S.A., et al.: Factors affecting medical students’ acceptance of the metaverse system in medical training in the United Arab Emirates. South Eastern Eur. J. Public Health (2022) 29. Almarzouqi, A., Aburayya, A., Salloum, S.A.: Prediction of user’s intention to use metaverse system in medical education: a hybrid SEM-ML learning approach. IEEE Access 10, 43421– 43434 (2022) 30. AlQudah, A.A., Al-Emran, M., Daim, T.U., Shaalan, K.: Toward an integrated model for examining the factors affecting the acceptance of queue management solutions in healthcare. IEEE Trans. Eng. Manage. (2022) 31. Tung, F.-C., Chang, S.-C.: Nursing students’ behavioral intention to use online courses: a questionnaire survey. Int. J. Nurs. Stud. 45(9), 1299–1309 (2008) 32. Almarzouqi, A., Aburayya, A., Salloum, S.A.: Determinants predicting the electronic medical record adoption in healthcare: a SEM-artificial neural network approach. PLoS One 17(8), e0272735 (2022) 33. Al-Sharafi, M.A., Al-Emran, M., Arpaci, I., Iahad, N.A., AlQudah, A.A., Iranmanesh, M., et al.: Generation Z use of artificial intelligence products and its impact on environmental sustainability: a cross-cultural comparison. Comput. Human Behav. 143, 107708 (2023) 34. Hair, J., Hollingsworth, C.L., Randolph, A.B., Chong, A.Y.L.: An updated and expanded assessment of PLS-SEM in information systems research. Ind. Manage. Data Syst. 117(3), 442–458 (2017) 35. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Second edn. Sage publications (2016) 36. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981) 37. Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43, 115–135 (2015) 38. Kock, N.: One-tailed or two-tailed P values in PLS-SEM? Int. J. eCollaboration 11(2), 1–7 (2015) 39. Doll, W.J., Hendrickson, A., Deng, X.: Using Davis’s perceived usefulness and ease-of-use instruments for decision making: a confirmatory and multigroup invariance analysis. Decis. Sci. 29(4), 839–869 (1998) 40. Al-Aulamie, A., Enhanced Technology Acceptance Model to Explain and Predict Learners’ Behavioural Intentions in Learning Management Systems. University of Bedfordshire, Luton, UK (2013) 41. Barclay, D., Higgins, C., Thompson, R.: The Partial Least Squares (PLS) Approach to Casual Modeling: Personal Computer Adoption and Use as an Illustration (1995) 42. Teo, T., Luan, W.S., Sing, C.C.: A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: an application of the technology acceptance model (TAM). J. Educ. Technol. 11(4), 265–280 (2008)

The Metaverse: A New Frontier for Learning and Teaching from the Perspective of AI Anjali Munde

and Jasmandeep Kaur

Abstract The present communication offers a thorough analysis of the effects of the metaverse on the field of future education. The metaverse notion is briefly discussed and explained in this paper, which also places it in the context of changing educational paradigms. Notably, the study explores relevant ideas including Artificial Intelligence (AI), Deep Learning, and Machine Learning, successfully demonstrating their possible impacts on how education is developed in the future. Due to its potential to change educational environments, the multidimensional virtual environment known as the metaverse has recently attracted a lot of attention. The exploration of novel gamification methods, as well as the incorporation of AI-driven simulations and three-dimensional aspects, are at the centre of this focus. These new methods have the potential to increase student engagement and, consequently, knowledge and information retention. The study also highlights concrete uses of AI and metaverse technology in practical educational settings. It highlights real-world examples of how universities and other educational institutions have proactively adopted these innovations to improve the educational process. Such programs seek to democratize education, promote diversity, and improve pedagogical approaches. This study paper highlights the revolutionary potential of the metaverse and AI in transforming education through its thorough investigation. It offers helpful insights into how these innovations may together lead the way for a more engaging, efficient, and accessible future of education by studying their theoretical foundations and practical manifestations. A fascinating look at how the metaverse, AI, and similar technologies are being integrated into education and how they have the potential to improve learning environments is provided. Keywords Metaverse · AI · Machine learning · Deep learning · Education

A. Munde (B) University of Southampton Malaysia, Iskander Puteri, Malaysia e-mail: [email protected] J. Kaur Ideal Institute of Management and Technology, New Delhi, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_6

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1 Introduction The literal meaning of a metaverse is a “virtual reality space” where the users can interact with some computer-generated environment as well as other users. Artificial Intelligence, on the other hand, has further enabled students to customize the approaches to various educational programs on the basis of different individualistic preferences. It is to be noted that within the educational sector, the metaverse tends to allow the students to attend “virtual lectures or classes” remotely, whilst experiencing the various elements of the real classroom [1]. A large number of educational institutes and technology organizations have been working towards eliminating all kinds of physical barriers whilst making them more immersive, communicative, and engaging. Within the metaverse, it is estimated that the quality of teaching would be superbly elevated, leveraging the power of the “Internet of Things (IoT)”, “Virtual Realities”, “Augmented Realities”, and so on, and via all these novel technologies, the teachers or the faculties would be able to demonstrate lessons on a “real-time” basis within the actual as well as virtual world [2]. In other words, the Metaverse within the education sector tends to enable the learners to attend virtual classes remotely whilst experiencing the elements of the “real classroom” [3]. Within the educational sector, it is to be noted that Metaverse would enable the students to attend virtual classes in a remote manner whilst experiencing the various elements of the real classroom. It is to be noted that Metaverse and Artificial Intelligence have been transforming the field of education by an expected “CAGR of 40.3%” since 2019. Some of the most significant and intriguing examples of metaverse-driven immersive learning experiences include fun learning with gamification, language learning with the help of Machine Learning (ML) and chatbots, task automation opportunities for educators, and so on [4]. Furthermore, Artificial Intelligence tends to make “language learning” more accessible to everyone within the metaverse. It is to be noted that the “educational institutions” and the “technologies companies” have been working towards removing the “physical barriers” whilst making them much more immersive, communicative, and engaging. Artificial Intelligence, on the other hand, has enabled students to personalize their approaches toward education programs based on individual preferences [5]. The onset of the Coronavirus outbreak compelled mankind to rethink new ways of teaching and learning. The metaverse is referred to as a digital or virtual space that is formed from the combination of the real world and the virtual world and has been regarded as a futuristic educational trend with immense potential [6]. As a matter of fact, it is to be noted that as a new and immersive item, the current students barely discuss the concept of metaverse from an educational perspective [6]. However, it goes without mentioning that after the COVID-19 pandemic, one of the most significant changes that took place is that various things within the physical world had transmitted within the “virtual world” [6]. Online/electronic conferences, telecommuting, distance education, e-commerce, and so on have turned into daily practices for human beings. Therefore, humans are now required to broaden their limits of the “practical world” that had been put forward and need to trigger the yearnings for a much more advanced “virtual world” [7]. In

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Flexible leaning with interactive curriculum

Fun learning with gamification

More Interactive Teaching

Easy and quick Discoveries of information

Fig. 1 Advantages of metaverse in education. Source Created by Author

the context of the “breakthrough” brought about by blockchain, virtual realities, augmented realities artificial intelligence, and so on, the “metaverse” which is a three-dimensional space has collapsed the real and virtual boundaries and has been gaining attention rapidly [8]. It is now recognized to be the next generation of the internet that is about to drastically turn the way people interact with each other and the world upside down [7]. The various advantages of metaverse within the field of education are represented in Fig. 1. Within the metaverse, both the teachers as well as the learners would be able to get rid of the limitation of place and time [3]. More importantly, the various aspects and features of the Metaverse are also estimated to unravel a wide range of educational and training activities for the students/learners that further allow them to experience, explore, and make the world in an unparalleled manner [2]. Thus, it can be said that the metaverse, in the future, might unveil new ways and paths for “futuristic education”. Referring to the various affordances and features of the Meta-verse within the field of education is not limited [3]. In this sense it can be said that the metaverse tends to enable both the learners as well as the teachers who are located in different physical places such in the containment zones, homes, or overseas, to avail good scopes to be involved in “educational practices” via “wearable technologies” [3]. With the help of these kinds of education, the participation and the interest of learning of the pupils may also be potentially improved [5]. Moreover, the prevalent problems within the present video conference learnings might have some practical ways to address and with these trends, the metaverse can create as well as evolve various novel standards of “blended education” to arrive in order to foster more effectiveness and better engagement of the learners [5]. It is imperative to note that one of the critical metaverse educational platforms is the “virtual classroom”. The virtual classrooms are considered to be as extra-ordinary assets of the students. However, they are unbelievably generative as they tend to make places for learners with various disabilities, time differences and other types of concerns that tend to obstruct them to physically participate in the classes.

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1.1 Background of the Study In the last three decades, it is to be noted that the concept of metaverse has been more vividly depicted in various science-fiction movies like “Ready Player One”, “Lucy” and “The Matrix”. During that time, the metaverse that used to be visualized in the films, could not be turned into realities. But, in the current decade, the fastpaced advancement of emerging and novel technologies like wearable devices, 3D photography cameras, and so on have enabled people to experience the real-time virtual world [9]. In March, the year 2021, “The sandbox game Roblox” was ranked in New York under “first stock of the metaverse”. Furthermore, another practical example of the metaverse coming into reality is the proclamation by Facebook about its rebranding scheme and its transformation into “meta”. Since that time, large efforts have been put in by a large number of countries all over the world to turn the concept of metaverse into a reality [10]. Moreover, people within the metaverse, for instance, would use Artificial Intelligence language learning chatbots and deep learning technologies (DL) for learning foreign languages as chatbots become more and more intelligent. It is also to be noted that “direct interactions” with chatbots in terms of questions and answers and allow learning via communication and hence can be a very efficient procedure of learning foreign languages [11]. Again, another most significant advantage of Artificial Intelligence and Deep Learning is considered to be task automation, the teachers and educators have to administer and manage a wide variety of responsibilities and tasks, like organization of various teaching materials, learning resources [12]. Filling out student data and preparing the students’ progress reports and so on. These administrative tasks are time consuming, however with the help of AI, which is an important technology of the metaverse, repetitive tasks can be automated and hence human workers can be freed up [13]. In addition to that, it is further to be noted that Artificial Intelligence also enables educational institutions to administer “virtual exams” [12]. Online testing also allows the learners to take exams from any corner of the world. In addition, as the teachers are guided by artificial intelligence, preparing the question papers tends to become much simpler. Due to AI, these types of online examinations are much more secure than the offline exams, as AI enables the educators to use different configurations and settings for making question papers [14]. It is to be noted that there are two most important examples of educational startups entailing metaverse. They are “NextMeet”, and “Axon Park” [12]. NetMeet is an India-based startup which develops an Immersive virtual reality (VR) platform for distance learning, collaborations and remote working. It is further important to note that these platforms tend to feature three dimensional “avatars” that allow the users to interact with one another over the Meta-verse in a virtual manner [12]. This tends to make online education and hybrid working more engaging and interacting as compared to simple video conferences. Another interesting thing about this organization is that “NextMeet” develops “custom three-dimensional worlds” that allow the organization of virtual events like “conferences, trade fairs and launching of

The Metaverse: A New Frontier for Learning and Teaching …

105 The metaverse

The

Learn

real

Smart

world

Educa-

Wearable technologies Avatar

Educational resources

LearnAnal-

Learning authentica-

SUPPORTS

Technological Infrastructure

AnalytiCommunication & Net-

Modelling

Interac-

Computing cal technolo- using wearable technology Figure 2: Entering metaverse tion technol& Rendering technologies

gies

Source: Author

T h l

Fig. 2 Entering metaverse using wearable technology. Source Author

products”. On the other hand, Axon Park is another virtual platform where one can digital form an “educational campus” [12]. The platform further provides online platforms for bringing together the educators and the students, and thus eradicating the obstructions to education. Axon Park develops “virtual campuses” such as from the beginning providing emerging learning opportunities [15]. The platform again allows people to make “quick money” from these virtual campuses via passive incomes from the “course registrations” [12]. It is amongst the leading metaverse of education. The scenario of futuristic education powered by AI is represented in Fig. 2.

1.2 Justification for the Problem It is worth mentioning that within the digital space, for instance, learners or students can dive into a number of learning pods, and also go to the libraries and “breakout rooms”, visit their counselors and coaches, and also can hang out and spend quality time with their peers [16]. Hence, it can be said that digital experience can actually help to democratise learning by bringing people together from a wide range of

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geographic locations and diverse economic backgrounds for learning, facilitating cost-effectiveness, flexibility, and time-efficiency [17]. Moreover, the metaverse tends to provide “embodied”, and “experiential skilling scopes” with real-world conditions as well as “high-pressure situations” where one can commit mistakes without any outcomes. When it is designed well, it tends to put together virtual realities with spatial designs and data science for enhancing learners’ engagement, application, confidence, and so on [18]. Again, it can be said that the metaverse creates boundless opportunities for the educational sector as it can provide intense practices as well as “feedback loops” where the learners tend to practice a large number of variations of concepts to foster and sharpen skills [8]. For example, the “Walmart’s Spark City game” is different every time one plays it. In case of a user is present within 10 ft. of distance, the players can ask them if they can help but not prior to addressing the spills and the other safety issues. Furthermore, the application of “state-dependent learning” can be seen in providence health which triggers cognitive stresses of responding to microaggression within the place [19]. A living person is captured within a threedimensional video and is seen via the camera of mobiles or tablets as a “hologram” within the room just in front [4]. Starting from study materials to providing campus tours to new students, the metaverse has been making education affordable not only for the university but also for students. Therefore, it can be said that if any college or university faces any kind of resources, the inclusion of “metaverse” tends to provide the required study materials [20]. With the help of virtual reality, the “scavenging of the materials” and “courses” are utterly easy for the teachers as well as for the learners as the internet is limitless and hence the availability of learning materials is vast and boundless [21]. Many researchers and scholars have concluded that the learners who had taken classes in virtual reality enabled courses that are learned 4 times faster as opposed to the “classroom environment” [20]. Moreover, one of the most potential advantages of the metaverse, within learning and education is the ability to provide the important subject matters in an enhanced manner and in an incorporated manner [4]. It is further to be noted that the advent of the metaverse had come with the advantage of presenting subjects, thoughts, and ideas in a virtual manner which seems to be real [21]. Moreover, the fact that the metaverse can create boundless educational opportunities for learners holds truth as the metaverse entails inter-personal interactions and experiences [21]. Therefore, it can be said that the metaverse can be regarded as the key to bridging the gaps between virtual and real-life experiences by fostering smooth and natural interactions. In Fig. 3, various important applications of the metaverse are represented.

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Simulat-

Virtual

ing virtual

3-D learning campus

Awareness

Online learning Application of Metaverse in education

Virtual tours

Virtual lab experiments

People & Interdiscipli-

Events

nary

Fig. 3 Application of metaverse in education. Source Author

2 Novel Ways to Reimage and Democratize Education Through Metaverse The education industry had come a long way from the personal computer internet boom that fostered e-learning in1990s. The next wave of mobile computation and social networking has further given rise to “micro-learnings” via “video-based learning” on demands. The industrialists now have been estimating that the 3rd era of revolution is upon us [8]. It is further to be noted that traditional flat static pages on personal computers would be substituted by a Metaverse of “digital threedimensional spaces” where people tend to interact in the form of “life-like avatars” [17]. In other words, it can be said that the metaverse is referred to as “digital three-dimensional spaces” where one is never alone. This is very different than a traditional video call which is time-bound and goes away when one is done. It is to be noted that the metaverse is “always on” and also tends to afford “social interaction with others” [8]. These transitions have some significant implications for “building capabilities” and learning [16]. Before and during the coronavirus pandemic, education and learning had already started to move from “physical classrooms” to more blended and virtual spaces [9]. As a matter of fact, an immersive campus life where the students can enter the virtual campuses with the help of wearable technologies, such as “Virtual Reality headsets” can explore, learn, and even can socialize with one another.

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It is imperative to note that with the power to bend reality, metaverse education platforms are considered to be excellent opportunities to move learners beyond the hypotheticals and into various immersive situations. This is specifically useful in terms of “experiential learning” like social sciences and history classes [2]. Furthermore, metaverse also tends to host online education with the help of virtual tours. For instance, if in an art history class, the educator wants to show the learners “Louvre” but cannot fly to Paris, it is to be noted that the metaverse is the place to go [9]. It goes without mentioning that extracurricular learning is generally considered to be as significant challenge for rural students and students with disabilities as it entails various physical challenges and difficulties involved in connecting the tutors [14]. In this context, it can be said that the metaverse can answer the “quandary” by providing virtual tutoring spaces for the tutors to get connected with the students, exchange thoughts and ideas, and work on various concepts and opinions, all without the need for burdens of the “real world space” [2]. It is further worth noting that although the students are able to learn teamwork within the physical environment, a large number of classroom spaces tend to have hierarchies related to physical accessibilities [12]. Moreover, online accessibilities also tend to open up new spaces for thinking about the way to work in a collaborative manner to attain the objectives [7]. In addition, the virtual meeting spaces offered by Metaverse also is a new avenue for the teachers to get involved with the students in a secure manner. Therefore, it can be said that metaverse-driven education would foster greater flexibility, enhanced participation, and accessibility for the students. Some of the most significant and novel ways of education and learning within the metaverse entail experiential learning, deliberate practices, state-oriented learning, and so on [9]. In terms of experiential learning, it is to be noted that “pharmaceutical leaders can train life-saving kab skills with great fidelity, multiplayer virtual reality simulations. The learners can visit a virtual laboratory so as to interact with the lab instructors and can practice “welding tubes” eliminating bag caps and “labeling bags” with “unlimited do-overs” [4]. Hence, it can be said that the use of metaverse within the field of education can improve “focused learning” as “badges” and other types of recognition tend to keep the learners motivated and can inspire them to remain focused on completion of the allocated works [8]. For instance, with the help of the metaverse, the students would have a chance to explore various geographical areas within the metaverse to learn about the environment and the features of the locations. The learners would also be able to create “avatars” on the basis of the culture of the places.

2.1 Interacting with the Metaverse in Education from the Perspective of AI Artificial intelligence uses computer systems to carry out operations that traditionally and effectively need human cognition, particularly in the areas of learning

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and problem-solving [22]. Algorithms, machine learning, and neural networks are only a few of the many technologies that make up AI [23]. By utilizing big data and algorithmic machine learning methods, artificial intelligence (AI) enables task completion, prediction, and decision-making that are comparable to those of a human [24]. The Horizon Report first mentioned AI in 2017, and its effects have grown significantly [25]. In higher education, AI-related resources are currently being used significantly more often in test generators, plagiarism-detection software, student information systems, learning management systems, and accessibility tools [26]. The use of these intelligent resources enables the identification of at-risk pupils, the analysis of their metrics, and the development of customized learning based on their requirements and academic trajectories [6]. Artificial intelligence (AI) can play a crucial role in promoting interactivity and personalization in the metaverse for education. AI can enable intelligent tutoring systems that can adapt to individual student needs and preferences, creating a more personalized learning experience. This technology can also facilitate real-time feedback and assessment, allowing students to track their progress and receive guidance on how to improve. Moreover, AI-powered virtual assistants can help students navigate the metaverse and find relevant learning resources. These virtual assistants can answer questions, provide explanations, and offer personalized recommendations based on the student’s interests and learning goals. This kind of support can help students stay motivated and engaged, which is critical for successful learning outcomes. Another way AI can promote interactivity is through natural language processing (NLP) and speech recognition technology. These technologies can enable students to engage in conversations with virtual characters and other participants in the metaverse. By providing natural and realistic interactions, these AI-powered characters can enhance the learning experience and create a more immersive and engaging environment. AI can play a vital role in promoting interactivity and personalization in the metaverse for education. With the help of AI-powered intelligent tutoring systems, virtual assistants, and NLP technology, students can have a more personalized and engaging learning experience, ultimately leading to better learning outcomes [10]. The metaverse can provide learners with unique opportunities for interactive learning that go beyond traditional learning environments. In the metaverse, learners can engage with complex systems and simulations, where their actions and decisions can have a significant impact on the outcome. The use of rules-based algorithms can provide a framework for decision-making in simple virtual reality games. However, in the metaverse, learners can engage with more complex systems that require more sophisticated decision-making. For example, learners could interact with simulations of real-world scenarios, such as managing a business or responding to a natural disaster, where the outcomes are not predefined by simple rules. In these scenarios, AI can play a crucial role in enabling interactive learning experiences. AI-powered simulations can respond dynamically to learners’ actions, providing

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feedback and adjusting the simulation in real time. This can help learners develop critical thinking and problem-solving skills, as well as gain a deeper understanding of complex systems. Overall, the metaverse provides an exciting opportunity for interactive learning that goes beyond traditional learning environments. With the help of AI-powered simulations, learners can engage with complex systems and develop valuable skills that will prepare them for success in the real world [4]. Machine learning algorithms that can adapt and evolve based on user data are critical for providing effective and personalized learning experiences in the metaverse. These algorithms can analyze vast amounts of data from learners’ interactions with the metaverse and use that information to improve the learning experience over time [27]. One example of this is the use of recommendation systems in the metaverse. These systems can use machine learning algorithms to analyze learners’ behavior and preferences and provide personalized recommendations for learning resources, activities, and interactions. As learners engage with these resources and activities, the system can continue to learn from their behavior and refine its recommendations, providing an increasingly personalized and effective learning experience. Another example is the use of AI-powered virtual tutors in the metaverse. These virtual tutors can use machine learning algorithms to adapt to individual learner needs and provide customized feedback and guidance. As learners interact with the virtual tutor, the system can learn from their behavior and adjust its recommendations and feedback, leading to a more personalized and effective learning experience. In summary, machine learning algorithms are essential for providing personalized and effective learning experiences in the metaverse. By analyzing learners’ behavior and preferences and adapting to their needs over time, these algorithms can help create a truly interactive and engaging learning environment. The increase in Big Data from social media, targeted marketing, and user search has led to significant improvements in AI that go beyond simple machine learning. With more data available, machine learning algorithms can detect more complex patterns and make more accurate predictions [28]. However, training machine learning algorithms traditionally required human input to label and categorize data. This process was often time-consuming and resourceintensive, making it challenging to scale up AI initiatives. Captcha data is an example of how AI developers have found creative solutions to overcome this challenge. Captcha data is a type of image recognition task that requires users to identify and transcribe distorted text from images. This data is then used to train machine learning algorithms to recognize and read text in images. By using Captcha data, developers can train machine learning algorithms at scale, without requiring human input for labeling and categorization. This has led to significant improvements in AI’s ability to recognize and interpret visual data, including images, videos, and even real-world objects. Overall, Captcha data is an excellent example of how innovative solutions can help overcome the challenges of training machine learning algorithms at scale. As AI continues to evolve, we can expect to see more creative solutions to challenges like these, leading to further advancements in AI technology.

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The integration of deep learning systems like CLIP and GPT-3 into traditional machine learning algorithms is opening up new opportunities for enhanced interactive learning in the metaverse. These systems can analyze vast amounts of data and use that information to generate new insights, create graphics, and provide customized recommendations and feedback to learners. For example, the art generator DALE can create graphics from text commands, allowing learners to create their own visual representations of concepts and ideas. This can help learners engage more deeply with the material and gain a deeper understanding of the concepts they are learning [29]. Similarly, GPT-3’s broad range of capabilities makes it a versatile tool for interactive learning in the metaverse. The system can perform a variety of operations, including natural language processing, machine translation, and even creative writing. This makes it an ideal tool for providing personalized feedback and guidance to learners, as well as for generating new content and resources. Overall, the integration of deep learning systems into traditional machine learning algorithms is opening up new possibilities for interactive learning in the metaverse. As these systems continue to evolve and become more advanced, we can expect to see even more innovative and effective approaches to personalized and interactive learning. The use of deep learning AI in Edu-metaverses promises to revolutionize interactive learning settings and take them to the next level. By using AI to analyze vast amounts of data and generate insights, Edu-metaverses can create more immersive and interactive learning experiences that are tailored to the needs and preferences of individual learners. For example, AI-powered personalization tools can adapt the learning experience to the learner’s skill level, learning style, and interests. This can help learners stay engaged and motivated and make the learning experience more effective. However, there are also obstacles to overcome in the application of deep learning AI in edu-metaverses. One major challenge is ensuring that the AI systems are ethical and unbiased and that they do not reinforce existing inequalities or discriminate against certain groups of learners. Another challenge is ensuring that the AI systems are transparent and explainable so that learners can understand how the system is making decisions and providing feedback. This is particularly important in educational settings, where learners need to understand why they are being asked to complete certain tasks or engage in certain activities. Overall, while there are obstacles to overcome, the potential benefits of using deep learning AI in Edumetaverses are enormous. By creating more personalized, immersive, and interactive learning experiences, Edu-metaverses can help learners achieve their full potential and prepare them for the challenges of the future. The integration of AI and other analytical technologies into the metaverse has the potential to greatly enhance educational services, particularly in the areas of arbitration, simulation, and decision-making. By providing intelligent NPC tutors, tutees, and peers, the metaverse can help learners receive personalized guidance and support based on their unique needs and preferences. Additionally, the metaverse can enable the collection and analysis of large amounts of data related to learners’ actions, emotions, preferences, and performances. This data can be used to track learners’ progress, identify areas where they need additional support, and tailor educational

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resources and services accordingly. Moreover, AI and other analytical tools can help teachers assess learners more thoroughly and accurately. By providing customized resources and services based on learners’ individual needs, the metaverse can help teachers ensure that each student is receiving the support they need to achieve their full potential [1, 30, 31]. Overall, the integration of AI and other analytical technologies into the metaverse has the potential to transform the educational landscape, enabling more personalized, effective, and engaging learning experiences for learners of all ages and backgrounds [14].

2.2 Machine Learning and Metaverse in Education Machine learning and the metaverse have the potential to greatly enhance the field of education. With the integration of AI and deep learning into metaverse environments, intelligent NPCs can be created to provide personalized tutoring and assessment for students. Analytical tools can also be used to track and analyse learning data, such as student actions, emotions, preferences, and performances, allowing teachers to provide customized resources and services to individual learners. In addition, machine learning algorithms can be used in voice recognition and language processing tasks to enable system agents to understand user commands, and in physical activity recognition to perceive user activities and interactions in the virtual world. The use of DLSS and other AI techniques can also improve the visual experience in the metaverse, making it an even more engaging and interactive learning environment. Overall, the combination of machine learning and the metaverse presents a promising future for education. There are several advantages of using machine learning algorithms in the metaverse for education, including: 1. Personalization: Machine learning algorithms can be used to personalize the learning experience for each student by analyzing their past behavior and learning patterns. This can help in adapting the curriculum, providing feedback, and even creating customized learning paths. 2. Automated assessment: Machine learning algorithms can be used to automate the assessment of student performance in real-time, thereby providing immediate feedback to the student and allowing teachers to intervene when necessary. 3. Adaptive learning: The metaverse can be designed to be adaptive, where the system can identify when a student is struggling and adjust the difficulty of the content to match their level of understanding. This helps in creating an optimal learning experience for each student. 4. Improved efficiency: Machine learning algorithms can automate many of administrative tasks, such as grading, data analysis, and reporting, which saves time for educators and allows them to focus on teaching.

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5. Scalability: The metaverse, when combined with machine learning algorithms, can be scaled to accommodate an unlimited number of students, allowing educators to reach a wider audience without compromising the quality of education. 6. Enhanced engagement: Machine learning algorithms can be used to create interactive and immersive learning experiences, making education more engaging and enjoyable for students. Overall, machine learning algorithms have the potential to significantly enhance the educational experience in the metaverse, providing personalized, adaptive, and engaging learning environments that can improve learning outcomes for students. Machine learning algorithms have numerous potential applications in the metaverse for education, including: 1. Personalised learning: Machine learning algorithms can be used to analyze the learning styles and preferences of individual students, and then provide personalized content and recommendations. 2. Adaptive learning: Machine learning algorithms can adapt to the performance and progress of individual students, adjusting the difficulty and pace of instruction accordingly. 3. Intelligent tutoring systems: Machine learning algorithms can be used to create intelligent virtual tutors that can provide immediate feedback, assistance, and support to learners. 4. Natural language processing: Machine learning algorithms can be used to process and analyze natural language data, enabling voice and text-based interaction with educational resources in the metaverse. 5. Assessment and evaluation: Machine learning algorithms can analyze student data to assess their learning outcomes, identify strengths and weaknesses, and provide recommendations for improvement. 6. Content creation: Machine learning algorithms can be used to generate new educational content, such as simulations and interactive exercises, based on student data and learning objectives. 7. Predictive analytics: Machine learning algorithms can be used to predict student performance and learning outcomes, enabling early intervention and targeted support to improve learning outcomes. Machine learning algorithms, including supervised, unsupervised, semisupervised, and reinforcement learning, are widely used in various applications, including voice recognition and language processing tasks. These algorithms help system agents understand and respond to user commands more accurately and efficiently, making interactions between humans and machines more seamless and natural. Deep learning, which is a subset of machine learning, has also been widely used in various applications, including image and speech recognition, natural language

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processing, and game playing. Deep learning structures, such as neural networks, enable machines to learn and improve their performance over time by analyzing large amounts of data and identifying patterns and trends. Overall, the presence of AI, machine learning, and deep learning in various applications and industries is becoming increasingly prevalent, and their significance and potential for innovation and improvement cannot be overstated [32–34]. These DL architectures have shown great success in recognizing and processing various types of input data, including images, text, and time series data. For instance, CNNs are widely used in image processing and object recognition, while LSTMs and RNNs are used in natural language processing and speech recognition. The use of these DL architectures in the analysis of sensor-based signals has enabled the development of new applications in healthcare, sports, and entertainment, among others. In the context of the metaverse, the analysis of sensor-based signals can provide valuable insights into user behavior and preferences, which can be used to personalize the learning experience and improve user engagement [35–37]. DLSS is a technology that utilizes deep learning to improve the performance and image quality of video games, which is accomplished by upscaling lower-resolution images to higher resolutions while retaining the image’s clarity and detail. In addition to gaming, DLSS may be used in the metaverse to enhance visual experiences by increasing frame rates, reducing latency, and improving image quality. This is critical for creating a more immersive and engaging virtual environment for learners, where they can interact with realistic and high-quality simulations. DL’s potential for improving the quality of virtual learning experiences is exciting and holds much promise for the future of education. Metaheuristic algorithms are increasingly being used to improve the performance of AI systems in various domains, including gaming. For instance, the prairie dog optimization algorithm (PDOA), the dwarf mongoose optimization algorithm (DMOA), and the Ebola optimization search algorithm (EOSA) have been used to enhance the efficiency of AI systems in games. These metaheuristic algorithms are inspired by the behavior of animals in the wild and are designed to mimic their adaptive strategies for problem-solving. These algorithms have been shown to be effective in optimizing the performance of AI systems in-game scenarios, as they can quickly and efficiently search through vast solution spaces to identify the best possible solutions [38–40]. The future of machine learning algorithms in the metaverse for education looks promising. As the field of AI and machine learning advances, we can expect even more sophisticated algorithms and models that can better assist learners and educators. One potential application could be the development of intelligent tutoring systems (ITS) that can provide personalized learning experiences for each student based on their individual needs and preferences. These systems could analyze student performance data in real time and adjust the content and delivery of educational materials accordingly. Another potential application could be the development of AIpowered learning assistants that can help students with their homework and answer their questions. These assistants could be integrated into the metaverse and provide 24/7 support for learners. In addition, machine learning algorithms could be used to

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analyze the vast amount of data generated by learners in the metaverse, providing insights into their behaviour, learning preferences, and progress. This data could be used to continuously improve the learning experience and optimize teaching strategies. Overall, the future of machine learning in the metaverse for education holds great promise for improving the learning experience and making education more accessible and personalized for all learners.

3 Managerial Applications of Metaverse in Education The metaverse has several managerial applications in education that can transform the way educational institution’s function. Here are some potential applications: 1. Student Management: The metaverse can be used to manage students’ attendance, performance, and behaviour. It can also be used to identify students who require additional support and provide them with customized learning experiences. 2. Curriculum Management: The metaverse can be used to manage the curriculum, course content, and assessment strategies. Educators can use the metaverse to design courses, create assessments, and monitor student progress. 3. Teacher Management: The metaverse can be used to manage teachers’ performance, including attendance, professional development, and evaluations. It can also be used to facilitate communication between teachers and students. 4. Data Management: The metaverse can be used to manage student and teacher data, including attendance records, grades, and progress reports. This data can be used to identify trends and patterns, which can help educators make data-driven decisions. 5. Resource Management: The metaverse can be used to manage educational resources, such as textbooks, digital media, and other educational materials. It can also be used to identify and allocate resources based on student needs and preferences. Overall, the metaverse has the potential to revolutionize the way educational institutions function by providing educators with powerful tools for managing students, teachers, curriculum, data, and resources.

4 Limitations of Metaverse in Education While the metaverse presents many opportunities for enhancing education through AI and machine learning, there are also several limitations to consider. Here are some of the main limitations: 1. Access: The metaverse requires reliable internet connectivity and highperformance hardware, which may not be readily available to all students or schools, especially those in rural or low-income areas.

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2. Cost: Building and maintaining a metaverse environment can be expensive, requiring significant investments in technology infrastructure and human resources. This may make it difficult for smaller schools or institutions to adopt the technology. 3. Ethics and privacy concerns: The use of AI and machine learning algorithms in education raises ethical and privacy concerns, particularly regarding the collection and use of student data. Careful consideration must be given to ensure that student data is protected and used appropriately. 4. Quality of content: The quality and accuracy of content in the metaverse is dependent on the creators and contributors. There is a risk that inaccurate or misleading information could be presented, and there may not always be a clear way to verify the accuracy of the content. 5. Teacher training: The successful integration of the metaverse into education requires significant teacher training and support. Teachers need to be able to effectively navigate and use the technology to ensure that it is used to its full potential.

5 Theoretical Contribution Through a thorough examination, the current communication adds to the theoretical knowledge of the effects of the metaverse on the future of education. The report provides an insightful analysis of how the metaverse’s diverse effects are changing the educational landscape. This work’s thorough examination of the metaverse’s potential within the context of evolving educational paradigms has theoretical relevance. The investigation of fundamental ideas, including Artificial Intelligence (AI), Deep Learning, and Machine Learning, and their potential impact on education forms the basis of the paper’s theoretical contribution. The study broadens the theoretical discussion surrounding educational change by illuminating how these cutting-edge technologies interact with the metaverse. The study’s theoretical foundation also includes an examination of the attractiveness of the metaverse in the context of education. The article discusses the current increase in interest and attributes it to the creative marriage of gamification techniques with AI-driven three-dimensional features. The study emphasizes how important these strategies are for boosting student engagement and enhancing the process of information acquisition. The paper’s theoretical contribution is also highlighted by its assessment of real-world applications. The study links theoretical understanding with practical application, highlighting the transformative potential of these advances. It does this by highlighting cases when institutions adopt AI and metaverse technology. This message essentially makes a theoretical contribution by exploring the metaverse’s transformative potential in education, taking into account its interaction with cutting-edge technology and practical manifestations. The paper enriches the theoretical discussion on the future of education by its thorough examination, illuminating the potential of AI and the metaverse to transform learning environments.

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6 Conclusion The paper has entailed a brief discussion and narration on how metaverse can foster future education by creating boundless opportunities and by democratizing education. It can be concluded from the paper that 2021 had been known as the “first year” of the metaverse and since then, global metaverse research has been flourishing. Also, the metaverse had been hailed as a “futuristic educational trend” with immense potential. It is further to be noted that the overall presence of the “metaverse” generally tends to couple with a number of new technologies. From the discussion of the paper, it can also be concluded that the presence of metaverse generally gets coupled with several new technologies. Here, the example of Facebook can be taken into consideration. It is to be noted that when Mark Zuckerberg presented the rebranding schemes of “Facebook” in a “live stream virtual manner”, various significant applications of “the metaverse” such as working, gaming, and learning were clearly displayed.

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Understanding the Effects of Social Media Advertising on Purchase Intention Through Metaverse Chee-Hua Chin, Winnie Poh-Ming Wong, Tat-Huei Cham, Ek-Tee Ngian, Siew-Chen Sim, and Jill Pei-Wah Ling

Abstract Due to rapid technological advancements, social media platforms have become one of the most significant applications in influencing consumer decisions in this digital era. This study used the Stimulus-Organism-Response (SOR) model as the theoretical foundation to examine the influence of social media advertising based on perceived uniqueness, perceived vividness, perceived interactivity, and credibility on affective and cognitive involvements in determining purchasing intention among consumers in the metaverse. A closed-ended questionnaire was developed with a list of measurement items adapted from past studies for data collection. A total of 202 usable rows of data was collected from Sarawakian respondents in Malaysia and subsequently analysed using Partial Least Square Structural Equation Modelling (PLS-SEM). The results showed that perceived uniqueness, perceived vividness and perceived interactivity have significant and positive effects on affective involvement, whilst only perceived uniqueness and credibility have positive significant effects on cognitive involvement. Affective and cognitive involvements were found to positively C.-H. Chin · W. P.-M. Wong · E.-T. Ngian School of Business and Management, University of Technology Sarawak, 96000 Sibu, Sarawak, Malaysia C.-H. Chin · W. P.-M. Wong · E.-T. Ngian · J. P.-W. Ling Centre On Technological Readiness and Innovation in Business Technopreneurship, University of Technology Sarawak, 96000 Sibu, Sarawak, Malaysia T.-H. Cham (B) UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia e-mail: [email protected] Tashkent State University of Economics, Tashkent, Uzbekistan Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia S.-C. Sim Faculty of Arts and Social Sciences, University of Nottingham Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia J. P.-W. Ling School of Foundation Studies, University of Technology Sarawak, 96000 Sibu, Sarawak, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_7

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and significantly influence consumers’ purchase intention. The findings were aligned timely with the ongoing social media marketing evolution within the metaverse driven by broader business digitalisation forces in recent times. This study is one of the few studies that examined the less explored area of social media advertising and purchase intention in the metaverse context. Specifically, this study contributed to expanding the current literature on consumer behaviours in a new area of research involving a population of under-represented consumers. Keywords Metaverse · Purchase intention · Social media advertising · S-O-R model · PLS-SEM

1 Introduction Due to the rapid advancements in technology, digitalisation appeared to be one of the main focuses for industries, including education [1, 2], halal food [3], and logistics [4]. In marketing, social media platforms like Facebook, Instagram, Twitter, WhatsApp, and many others have become one of the most significant applications in influencing consumer decisions in this digital era [5–8]. Social media has ingrained itself into consumers’ daily lives worldwide, significantly shaping and influencing their behaviours [9]. Consequently, many businesses rode on the advantages of social media to sell their goods and services, given its massive capability of reaching global consumers without geographical limitations [10]. Businesses modified their business strategies to include social media marketing for commercial, educational, and social reasons [11–14] because the use of social media has continued to permeate every aspect of life [15], which in turn may change consumers’ buying intentions online. Consumers believe that online shopping is an effective and efficient way of making purchases, helping them resolve issues around their purchases conveniently [16, 17]. A recent study by Cham et al. [18] discussed the impact of online fake news on brands; given the speed and convenience of online technology, social media deeply intertwines in consumers’ daily lives for a variety of purposes such as shopping online, finding product information, reading the news, and other forms of entertainment. The advancement of artificial intelligence (AI) has also progressively motivated consumers to morph their habits and lifestyles into virtual environments [19]. The overarching effects of social media have altered how consumers communicate and exchange information, forming a hub as a platform for the dissemination of productrelated information and influencing consumer behaviour [20, 21]. Hence, the study of the impacts of social media has increasingly become an imperative research area in recent years [22]. Specifically, the interfaces and displays on social media platforms play a significant role in influencing consumers’ purchase intention in the virtual reality environment. It is believed that utilising useful information can enhance consumers’ decision-making abilities [23]. It also enriches consumers’ shopping experiences and

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positively impacts their purchase intention with high perceived utility [24]. Valueconscious consumers often rely on social media platforms to discover information about the value and quality of the products they are keen to buy, which eventually affects their purchasing intention via the virtual world. Value consciousness generally refers to the tendency of consumers to focus more intently on the value and quality of the goods they wish to purchase even when the price is higher. However, consumers, who prioritise value, frequently concentrate on product quality and inexpensive pricing [25]. These consumers typically use social media as a platform to gather information and purchase goods with the best value and quality at the lowest prices [26]. The study of Gundecha and Liu [27] discovered that users may encounter the challenges of conflicting desires, competing between the desire to have a large social media following and yet wanting to keep their privacy whenever possible. In the context of the metaverse, it is fairly challenging to seek the right content to use [28]. Moreover, the unidentified user or unverified product information shared on social media networks can be tricky when consumers impulsively buy it without fully understanding how the product works, followed by leaving unfounded positive reviews that may mislead other social media users. Exposed to comments on social media platforms from unknown users or those created by their friends [29], the users are also invariably vulnerable to online scams. In Malaysia, six out of ten consumers have lost money, resulting from online scams they experienced when making purchases on social media platforms [30]. Therefore, researchers are keen to identify the significant factors that influence the purchasing intention in the metaverse-futuristic digital marketplace among the people of Sarawak on social media platforms. In essence, this study contributes to the extant literature in discerning the purchase intention among Sarawakians in the metaverse context. Most importantly, the literature has highlighted that consumer behaviours and perceived value may vary by context, such as those in the metaverse context where research is still in its infancy [4, 31]. Therefore, it is imperative to understand how social media influences consumers’ purchase intention in the metaverse context, particularly among Sarawakians. To date, the data on the Sarawakian consumers’ purchase intention in the metaverse was largely under-researched. Besides, studies that addressed the factors that affect purchase intention within the metaverse are scarce. Against this backdrop, utilising the Stimulus-organism-response (SOR) model, the purpose of this study was to better understand Sarawakian consumers’ purchase intention and the relationships among the exogenous variables such as perceived uniqueness, perceived vividness, perceived interactivity, credibility, cognitive and affective involvements in the metaverse context.

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2 Literature Review and Development of Hypotheses 2.1 The Stimulus-Organism-Response (SOR) Model The Stimulus-Organism-Response (S-O-R) model [32] describes how stimuli in the external environment impact interior emotion, which then influences specific behavioural reactions. This model provides valuable insights, especially the psychology of consumer behaviour, used to describe individuals’ responses to stimuli to understand the dynamics of how virtual stimuli influence individuals’ (human) behavioural responses in the metaverse. When environment factors (perceived uniqueness, perceived vividness, perceived interactivity, and credibility), they have the potential to affect the internal state of individuals (O), subsequently exerting an impact on their behaviour (R). The S-O-R model has historically been used frequently in the study of consumer behaviour, most notably in studies on online buying [33, 34], live-streaming shopping [35], entertainment in Korean popular culture, and the impact of virtual reality tourism-related activities on customer sentiment [36]. An analysis of the relationship between social media marketing activities (stimuli or external variables), brand experience (organism or internal emotional effect), and customer brand equity (response or behavioural response) has recently been conducted using the S-O-R model [37, 38]. Recent research has employed the S-O-R model in tourism settings, such as examining how conceptualised data and interactive experiences obtained through virtual platforms could function as environmental stimuli [39]. Therefore, this study aimed to examine the purchase intention influence by social media advertising (SMA) among Sarawakians based on the S-O-R model (refer to Fig. 1). The authors in the present study utilised the SOR model to systematically investigate how stimuli (perceived uniqueness, perceived vividness, perceived interactivity, and credibility) influenced affective involvement and cognitive involvement (O) and how both affective and cognitive involvements subsequently impacted purchase intention (R).

2.2 Purchase Intention Through the Metaverse The ultimate component in the SOR model is intention to purchase within the metaverse. It is clear from research in marketing, advertising, and selling that a consumer’s buying intention typically materialises during the stage of the decisionmaking process when they have acquired a solid willingness to move forward with a certain product or brand [40]. This purchase intention is a crucial metric for assessing consumer behaviour because it helps estimate a customer’s propensity to make a purchase. In the context of this study, purchase intention through the metaverse refers to consumers’ willingness to engage in purchasing decisions in the virtual environment.

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Fig. 1 Conceptual framework

A recent study by Mirabi et al. [41] found that the most significant factors influencing consumers’ purchase intention are product quality, brand, and advertising. Consumer attitudes [42], perceived value [43], perceived risk, usefulness, and the efficiency of use have been found as significant factors in consumer marketing research studies, albeit some conducted in different contexts [44]. Extensive research [45–48] were conducted to examine variables that potentially influence customer purchase intention in an online environment. More than 80 variables were identified by Chang et al. [49] as antecedents of consumer purchase intention in their study. These variables were divided into three categories, namely perceived website qualities, product characteristics, and consumer attributes. This study is limited to examining the impact of social network marketing and customer engagement on purchase intention in the metaverse context because it is not possible to examine all the factors that may influence consumer buying intention. The prevalence of social media platforms like Facebook, Twitter, and YouTube has given users bountiful opportunities to share and spread knowledge about a company or product. As a result, customers are now more knowledgeable and tend to learn more about product features before making purchases [50]. In the futuristic digital marketplace, the vital function of social media marketing and consumer engagement in teaching other users, influencing their preferences and buying decisions in cyberspace is evident. This, in turn, highlights the importance of metaverse platforms in involving customers, providing comprehensive product information, and generating influential interactions that motivate consumers’ purchase intention eventually in the virtual reality environment.

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2.3 Perceived Uniqueness and Affective as Well as Cognitive Involvements A brand’s perceived uniqueness is what sets it apart from competing products on the market. It refers to having a distinct selling proposition and measures how distinctive a brand is, compared to rivals in terms of being noticed, recognized, and remembered [51]. A brand’s ability to forge and keep distinctive associations tends to decide how much of a reason consumers have to choose it over rival brands [52]. Understandably, a good brand experience piques customers’ interests, engages them through feelings, ideas, and sensations, and ultimately reinforces brand-specific connections and memories [53]. These immersive and trustworthy experiences could lead to positive responses that drove social selling success. Based on choice theory, perceived uniqueness provides customers with analytical information that distinguishes a brand from rival brands and eases their cognitive load, providing a straightforward heuristic for selecting alternatives [54]. Perceived distinctiveness gives the brand an edge over the competition and gives consumers an additional benefit, such as a price premium [51]. According to López et al. [55], consumers tend to have a desire for uniqueness (NFU) to get their intrinsic gratification from feeling different from “the masses”. People can attempt to fulfil their NFU by collecting scarce resources, such as the main brand content shared on social media platforms [56]. As a result, when brand material is distinctive, it is likely to spark consumers’ interest in it. According to Batra and Ray [57], one of the factors influencing how clients feel about an advertisement is perceived uniqueness. Brand content that is fresh and specially tailored to engage users can pique their interest and elicit a favourable emotional reaction [58]. Additionally, the “circumflex model of affect” proposed by Russell [59], to which one’s enjoyment is connected to arousal, can be used to explain the relationships between perceived uniqueness and affective involvement. Similarly, brand content’s uniqueness can arouse viewers, who then better understand how entertaining and pleasurable it is. We argue that a brand’s content’s perceived uniqueness can increase metaverse users’ affective involvement with the material. Uniqueness can draw increased audience attention and result in a more thorough processing of advertisements in traditional media contexts, but there is a boundary condition, claimed Ducoffe and Curlo [60]. According to Spielmann and Richard [61], brand material may not catch viewers’ attention in the case of low uniqueness, which prevents them from being motivated to assess the content and establish a connection to it and their lifestyles. In contrast, cognitive overload in a way that irritates viewers may occur and slow down how they process information if the uniqueness is incredible [62]. The audience only pays full attention to the stimulus when it has a considerable level of perceived uniqueness, which causes them to digest the content information in-depth and thus raise their cognitive involvement [63]. Thus, the following hypotheses were developed: H1: Perceived uniqueness is positively and significantly related to affective involvement.

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H2: Perceived uniqueness is positively and significantly related to cognitive involvement.

2.4 Perceived Vividness and Affective as Well as Cognitive Involvements To materialise long-term engagement, a significant component influencing digital engagement is the users’ sense of perceived content vividness because it communicates branded messaging intended to persuade users [64]. Digital engagement requires one to take into account an SM platform’s perceived content vividness because the medium and how it is perceived affect how unpaid advertising messages are received and disseminated [65]. Perceived content vividness is the extent to which a stimulus has caused a customer to conjure up accurate and convincing images that, for a given period, can help customers resolve ambiguities, alter their beliefs, and become persuaded [66]. Since prospective customers can alter the content of a mediated environment in real-time, a notion known as interactivity, the literature shows that time exposure to stimuli and vividness could have an impact on attitude toward a website simultaneously [67]. When examining brand communities on social media, the interaction between vividness and terms of showing is a sign of the mental demand for digital engagement is crucial [68]. Additionally, the perceived vividness of the material can reduce the effectiveness of communication on social media [69]. The effectiveness of advertising and online product sales is strongly influenced by vividness. For instance, Miller and Marks [70] confirmed that an imagery-evoking advertising strategy with high vividness has a bigger impact on consumers’ effective responses than a strategy with low vividness. Since a high level of vividness in product presentations stimulates more senses, users can perceive more cognitive involvement and experience more joy [71, 72]. As highlighted in the SOR model, past experiences could significantly shape consumer responses. Swani and Milne [73] added that vividness can also be a stimulus factor in social media advertising since it increases stimulation by bringing priority attention to the viewers’ senses and transporting more information. The customers will, therefore, be more likely to pay attention to and be motivated by vivid brand information, which will also enable them to perceive a high level of relevance in terms of affective and cognitive components. Therefore, based on the above discussion, the following hypotheses are constructed: H3: Perceived vividness is positively and significantly related to affective involvement. H4: Perceived vividness is positively and significantly related to cognitive involvement.

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2.5 Perceived Interactivity and Affective as Well as Cognitive Involvements Customers can use interactions to gather relevant information and make purchasing decisions through social and mechanical engagement [74, 75]. According to Yim et al. [72], the current comprehensive view provides a broad definition of interactivity based on technological achievement and human perception. Compared with technical results, user perception focuses more on user quality, encourages a sense of interaction, and emphasises the interest and ease of user interaction with content. The user-to-user personal communication perspective [76] and the user-to-system perspective focusing on interactivity as a media feature are two different perspectives on interactivity as defined by Sreejesh et al. [77]. Huang et al. [78] described interactive communication as communication in which users have active control, information selection, and direct contact, as well as the ability to communicate with each other or between two or more entities. One’s involvement with Internet-based advertising and online purchasing websites is preceded by interactivity [72, 74]. Li and Meshkova [79] argued that interactive rich media can give users more knowledge and excitement. We predict that participatory brand content will improve the viewers’ emotive involvement as a contextual cue in two ways based on the research by Jiang and Benbasat [71]. Users first feel a sense of satisfaction because they can interact with brand content swiftly and freely, which makes them feel independent. Second, consumers’ exploratory interactions with brand material might trigger positive feelings in themselves. These help in boosting consumers’ emotional connection to brand material. Furthermore, involvement can promote positive cognitive processing in viewers. For instance, Coyle and Thorson [67] contended that consumers appreciate a website more and find it interesting if it has high interactivity levels since they may access more information there. Interactivity is a feature of Computer-Mediated Communication (CMC) that emphasises human-message, human-media, and human–human interactions. As a result, it first creates favourable evaluations of the media-consumer and content-consumer interfaces, leading users to believe that the communication product is more beneficial [80]. Interactivity in brand content is typically one of the most crucial elements of an effective brand post that can increase users’ involvement with it in social media advertising [64]. The involvement cue, therefore, can stimulate users’ favourable cognitive evaluation of brand content. Overall, satisfaction with an event is a crucial factor in determining an individual’s intention to revisit. Thus, two hypotheses were proposed as follows: H5: Perceived vividness is positively and significantly related to affective involvement. H6: Perceived vividness is positively and significantly related to cognitive involvement.

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2.6 Credibility and Affective and as Well as Cognitive Involvements The findings on credibility are compatible with those provided by earlier studies [81–83]. According to the results of the exploratory study, the participants’ definition of advertising credibility is based on the requirement that promises made by marketers and advertisers are truthful, dependable, open, and trustworthy. Advertisers, according to participants, should present accurate and comprehensive information about the product and the brand and should not contain any information that is false or does not accurately reflect the genuine meanings of the brand. They went on to say that consumers would only believe the advertising if the brand they purchased had similar traits to those depicted in the advertisement; otherwise, there could be severe consequences for both the brand and the company. Credibility is related to customer belief, faith, and trust in social media advertising, a crucial component of social media communication [84]. Customers will not pay attention to the information or items promoted on social media if they do not trust or have confidence in the media [85]. The Personal Involvement Inventory (PII) idea proposed by Zaichkowsky [86] is a context-free measure for the involvement with products, marketing, or buying scenarios. The involvement comprised affective and cognitive responses. According to Park and Young [87], emotive engagement is the level of personal relevance of the message based on a significant motive, whereas cognitive involvement is the level of personal relevance based on a utilitarian motive. Therefore, the following hypotheses were formulated: H7: Credibility is positively and significantly related to affective involvement. H8: Credibility is positively and significantly related to cognitive involvement.

2.7 Affective and Cognitive Involvements as Well as Purchase Intention In the metaverse, consumers’ psychological and emotional stimuli play a significant role in driving social selling success. The hedonistic and utilitarian elements of a product’s worth are independent. Based on their needs, values, and interests, involvement is an individual’s perception of the relevance of an object [88]. According to Zaichkowsky [86], affective participation is more emotional and hedonistic, whereas cognitive involvement is more rational. As a result, cognitive involvement may be more associated with the utilitarian dimension of attitude, while affective involvement may be more tied to the hedonistic dimension [89]. In the context of social selling, consumers can be socially and intellectually involved while deciding to make a purchase or creating a buy intention based on the two engagement characteristics of cognitive and affective involvements. Marketing experts should, therefore, differentiate between these two dimensions when creating product positioning strategies and designing marketing campaigns [90].

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It is increasingly essential to evaluate customer involvement to understand and forecast consumers’ purchase intentions [91, 92]. According to Jiang et al. [93], involvement with a website is positively correlated with opinions toward it, which in turn influences consumer intention to make purchases from it. Consumer cognitive states have been discovered to influence purchasing decisions [94]. Customers who are highly involved cognitively are more likely to use social media to find product information. When consumers have poor cognitive involvement, they are unable to gather useful product information from websites, which may result in unwillingness to purchase things. As a result, purchase intention is more likely to result from high cognitive involvement. According to feeling states, affective involvement is the term used to characterise intense emotional feelings associated with an online context [93, 95]. Happiness and satisfaction are examples of positive mood states that could increase a customer’s willingness to buy from an online retailer. However, negative emotions like rage and disappointment may make you less inclined to shop there. Purchase intention is indeed affected by emotive and cognitive involvements [96]. In other words, the response could manifest as changes in attitude towards purchase intention via metaverse, with the individual developing a favourable perception influenced by social media advertising. Therefore, the following hypotheses were proposed: H9: Affective involvement is positively and significantly related to purchase intention. H10: Cognitive involvement is positively and significantly related to purchase intention.

3 Methodology The primary mechanism for data gathering will be a closed questionnaire. The survey was later converted to a Google form to gather information on the population of Sarawak. A non-probability, a purposive sampling technique in particular, was used for collecting data from Sarawakians between the ages of 18 and 50. Using G*Power software with a 0.05 significance level, and medium effect size, the suggested minimum sample size for this study was 172. There are two sections in the research questionnaire. The purpose of the first section is to compile background data on the respondents. The second section evaluates the respondents’ assessment of Sarawakians’ willingness to purchase. In this questionnaire, the measurement items were adapted from past studies [10, 15, 20, 22, 24, 97]. Since some of these studies were conducted in other countries, such as South Korea, the United States, Afghanistan, and Saudi Arabia, the items were slightly modified to fit the Malaysian context. The second section of the questionnaire included items measuring factors influencing purchase intention. The items used to measure factors were adapted from past studies [98–102]. The seven-point scale was

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Table 1 Full collinearity of constructs PU

PV

PI

CR

AI

CI

INT

2.107

2.227

1.860

2.552

2.192

2.543

2.782

Note PU perceived uniqueness, PV perceived vividness, PI perceived interactivity, CR credibility, AI affective involvement, CI cognitive involvement, INT purchase intention

used to evaluate the responses to each question. The scale ranged from 1 to 7 (1— strongly disagree; 2—disagree; 3—moderately disagree; 4—neutral; 5—moderately agree; 6—agree; and 7—strongly agree). This research utilised quantitative methodology, namely, survey questionnaires to gather data. Following this, the data collected was processed, which involved the removal of straight-line responses using Statistical Package for Social Science (SPSS) 28.0. A two-stage Partial Least Squares—Structural Equation Modelling (PLS-SEM) analysis was employed in this study. The first stage involved the assessment of construct reliability and validity, while the second stage assessed the relationships between the proposed constructs [103]. The PLS-SEM analyses were evaluated using SmartPLS 4.0 [104], and the subsequent section presents the findings.

4 Results and Discussion 4.1 Common Method Bias A total of 223 voluntary respondents participated in this survey. However, only 202 responses remained and proceeded to further analyses after 21 responses were eliminated due to straight-lining responses, particularly in a similar scale of 2’s. The answers provided for the discarded responses were similar, thus eliminating suspicious patterns of response to ensure data quality [105]. Following that, the full collinearity VIF values of all studied constructs are lower than 5, as demonstrated in Table 1; thus, the absence of common method bias was further confirmed [106–108].

4.2 Respondents’ Profiles The demographic profile of respondents who partook in this survey is shown in Table 2.

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Table 2 Demographic characteristics of participants Characteristics Gender

No. of participants (N = 202) Male

85

42.1

117

57.9

18–20 years old

19

9.4 85.6

Female Age

Race

21–30 years old

173

31–40 years old

8

4.0

41–50 years old

2

1.0

Malay Chinese Iban

Education

No. of purchase on metaverse

12.9 77.7

13

6.4

Melanau

2

1.0

4

2.0

High school and below

6

3.0

Diploma

Income (monthly)

26 157

Kayan

Degree or professional qualification Occupation

Percentage (%)

34

16.8

148

73.3

Postgraduate

14

6.9

Employed

23

11.4

Self-employed

8

4.0

Housewife

1

0.5

Students

170

84.2

Less than RM 1,500

157

77.7

RM 1,501–RM 3,000

23

11.4

RM 3,001–RM 4,500

9

4.5

RM 4,501–RM 6,000

6

3.0

RM 6,001 and above

7

3.5

12

5.9

Never 1–3 times

83

41.1

4–6 times

50

24.8

7–9 times

13

6.4

More than 9 times

44

21.8

4.3 Assessment of the Measurement Model The Partial Least Squares-Structural Equation Modelling (PLS-SEM) method [103] was used in this study to make a more elastic measurement model possible. This method allowed us to find the best-fitting model for our data. Since the valid data of

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202 rows were not normally distributed, the analyses of data were performed using the PLS-SEM approach. A Confirmatory Factor Analysis (CFA) was used to assess the reliability and convergent and discriminant validity of the measures. Loadings that failed to meet the threshold of 0.50 were omitted to ensure internal consistency [109]. Table 3 presents the remaining valid loadings. To be deemed reliable and valid, the values of Composite Reliability (CR) and Average Variance Extracted (AVE) must meet minimum thresholds of 0.70 [110] and 0.50 [111], respectively. The statistical results show that the CR and AVE values in this study meet these requirements. The data were also supported by computing Cronbach’s alpha values for the studied dimensions, which included perceived value, service quality, event image, satisfaction, and revisit intention. These values were deemed decent, while the value of awareness was regarded as acceptable [112]. Table 3 Summary of construct reliability and validity Constructs

No. of items

Items

Loadings

CR

Cronbach’s alpha

AVE

Perceived uniqueness

5

PUniq _01 PUniq _02 PUniq _03 PUniq _04 PUniq _05

0.818 0.833 0.787 0.832 0.791

0.907

0.871

0.660

Perceived vividness

4

PVivid_01 PVivid_02 PVivid_03 PVivid_04

0.799 0.810 0.802 0.803

0.879

0.818

0.646

Perceived interactivity

6

PInter_03 PInter_04 PInter_05 PInter_06

0.740 0.786 0.784 0.663

0.832

0.730

0.554

Credibility

3

Credib_01 Credib_02 Credib_03

0.841 0.816 0.846

0.873

0.782

0.696

Affective involvement

4

Affec_01 Affec_02 Affec_03 Affec_04

0.844 0.820 0.766 0.794

0.881

0.821

0.650

Cognitive involvement

4

Cognit_01 Cognit_02 Cognit_03 Cognit_04

0.741 0.795 0.835 0.768

0.865

0.792

0.617

Purchase intention

4

PurchIn_01 PurchIn_02 PurchIn_03 PurchIn_04

0.826 0.834 0.844 0.808

0.897

0.847

0.686

Note PInter_01 and PInter_02, were discarded due to low loadings

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Table 4 Discriminant validity of constructs PU PU

PV

PI

CR

AI

CI

INT

0.812

PV

0.594

0.804

PI

0.476

0.510

0.745

CR

0.532

0.538

0.484

0.834

AI

0.581

0.613

0.564

0.456

0.806

CI

0.548

0.510

0.483

0.688

0.598

0.785

INT

0.633

0.510

0.521

0.708

0.546

0.683

0.828

Note PU perceived usefulness, PV perceived vividness, PI perceived interactivity, CR credibility, AI affective involvement, CI cognitive involvement, INT purchase intention

Subsequently, according to Fornell and Larcker [111], the values of AVE of each measure were square-rooted and examined against the inter-correlations among the research model constructs. As demonstrated in Table 4, all values exceeded each construct’s correlation [113]. Furthermore, the measurement model was determined to be satisfactory, supported by evidence of reliability, and convergent and discriminant validity.

4.4 Assessment of the Structural Model Following that, Table 5 lays out the outcomes obtained from hypotheses testing. Generally, as a rule of thumb, the value of probability, p-value, must be less than 0.01 or 0.05 significance, and the t-value must be greater than 1.645 or 2.33, particularly for one-tailed hypothesis testing. Subsequently, out of the ten tested direct relationships, the statistical findings of this study supported seven hypotheses. Subsequently, it was revealed that perceived uniqueness had a significant relationship with both affective and cognitive involvements among metaverse users. Moreover, the significant relationships between perceived vividness, interactivity, and affective involvement among users of the metaverse were uncovered. Similarly, the present discoveries indicated that credibility was significantly associated with cognitive involvement, while the users’ affective and cognitive involvements had strong relationships with their intention to purchase online (p < 0.01). Interestingly, H4 and H6, which projected the significant positive relationships between metaverse users’ perceived value, interactivity and cognitive involvement, were not supported by the findings, which indicated that they were insignificant (p = 0.260, p = 0.056). The remaining hypothesis, namely H7, was also found to have no significant relationship between credibility and affective involvement (p = 0.337). Furthermore, Table 6 shows that the R-squared values for both affective and cognitive involvements as well as purchase intention were 0.487, 0.534, and 0.496

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Table 5 Summary of path coefficients and hypotheses testing Hypotheses

Relationship

Standard beta

t-value

p-value

Decision

H1

PU → AI

0.280

2.832

< 0.01**

S

H2

PU → CI

0.195

2.580

< 0.01**

S

H3

PV → AI

0.277

2.845

< 0.01**

S

H4

PV → CI

0.056

0.645

0.260

NS

H5

PI → AI

0.241

3.175

< 0.01**

H6

PI → CI

0.114

1.589

0.056

NS

H7

CR → AI

0.042

0.421

0.337

NS

S

H8

CR → CI

0.499

7.167

< 0.01**

S

H9

AI → INT

0.214

2.460

< 0.01**

S

H10

CI → INT

0.555

6.957

< 0.01**

S

Note t > 2.33 = p < 0.01**; S supported; NS not supported PU perceived usefulness, PV perceived vividness, PI perceived interactivity, CR credibility, AI affective involvement, CI cognitive involvement, INT purchase intention

Table 6 Results of R2 and f 2

Construct

R2

f2

Perceived usefulness

0.096

Perceived vividness

0.048

Perceived interactivity

0.055

Credibility

0.228

Affective involvement

0.487

Cognitive involvement

0.534

Purchase intention

0.496

respectively, thus achieving the minimum threshold of 0.19 as suggested by the rule of thumb [114]. Consequently, the predictive relevance was checked using PLSpredict, a technique that uses a holdout sample to create individual-level predictions for an item or construct level through a tenfold procedure [115]. Compared with the Linear Regression (LM) model, a predictive relevance can be regarded as strong in terms of predictive power when all the item (PLS-LM) differences are lower, followed by moderate power when the majority is lower, and last but not least, low power is implied if minority meets the criteria [115]. In the present PLS model, Table 7 shows that the prediction errors were all lower than the LM model, hence indicating a strong predictive power (for affective and cognitive involvements), while purchase intention demonstrated a moderate predictive power, ensuing majority of their prediction errors which were lower than the LM model.

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Table 7 Outcomes of PLSpredict Item

PLS_RMSE

LM_RMSE

PLS-LM

Q2 _predict

Affective involvement Affec_1

0.611

0.643

− 0.032

0.331

Affec_2

0.625

0.663

− 0.038

0.332

Affec_3

0.679

0.702

− 0.023

0.223

Affec_4

0.589

0.615

− 0.026

0.247

Cognitive involvement Cognit_1

0.780

0.850

− 0.070

0.239

Cognit_1

0.771

0.776

− 0.005

0.379

Cognit_1

0.714

0.759

− 0.045

0.281

Cognit_1

0.653

0.692

− 0.039

0.316

Purchase intention Purc_Inten1

0.780

0.822

− 0.042

0.357

Purc_Inten2

0.643

0.661

− 0.018

0.392

Purc_Inten3

0.773

0.742

0.031

0.422

Purc_Inten4

0.825

0.845

− 0.020

0.283

5 Discussion Based on the statistical results, seven of the ten hypotheses were supported by direct relationships, namely H1, H2, H3, H5, H8, H9, and H10 for online purchase intention through the metaverse, specifically in Sarawak, Malaysia. Consistent with the previous study by López et al. [55], the analysis for H1 showed a significant relationship between perceived uniqueness and affective involvement. Indeed, the extent of a user’s perception of the content offered on the metaverse tends to lead to a greater interest level of use [56], which is believed to provide the respective users with distinctive experiences in their online purchases. Thus, it can be deduced that metaverse users’ perceived uniqueness is one of the determining factors in encouraging their affective involvement. In a similar vein, the significance of perceived uniqueness on cognitive involvement was also uncovered; hence H2 was supported. According to Wang et al. [58], the degree to which users perceive content uniqueness contributes to users’ likelihood of experiencing emotional reactions. This finding highlights the importance of content uniqueness, emphasising its role in fostering cognitive involvement among users. Therefore, businesses that leverage the metaverse platform should strive to deliver unique and distinctive content to enhance cognitive involvement and maximise user engagement on the metaverse. Furthermore, it is worth noting that a significant degree of vivid content plays a crucial role in enhancing affective involvement among metaverse users. This finding supports H3. This aligns with previous studies that have demonstrated the power of

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vividness in intensifying the communication of persuasive messages and ultimately leading to favourable perceptions and increased engagement [64, 66, 73]. Unfortunately, perceived vividness had no significant relationship with cognitive involvement in purchase intention in a metaverse context. Thus, the finding revealed that H4 was not supported. This finding is contrary to the studies of Jiang and Benbasat [71] and Yim et al. [72], who found that perceived vividness influenced users’ cognitive involvement in the purchasing process. Perceived vividness may influence affective involvement in the metaverse due to its ability to evoke strong emotional responses and create immersive experiences, while it is insignificant to cognitive involvement as cognitive processes in the metaverse context might be more influenced by other factors such as information relevance and product evaluation. Moreover, perceived interaction was found to significantly influence affective involvement, leading to users’ purchase intention in the metaverse, suggesting that the interactive nature of the metaverse environment enhances emotional engagement. Hence, H5 was supported. This is consistent with the study conducted by Li and Meshkova [79], which demonstrated that interactive rich media could increase users’ excitement and emotional responses. Regrettably, perceived interaction was found insignificant in predicting cognitive involvement in the purchasing process, indicating that perceived interaction may not directly impact cognitive processes within the metaverse context. Hence, H6 was not supported. The finding contrasts the study conducted by Chun and Lee [64], which concluded that interaction is an essential element that can increase users’ involvement in the online context. Active interaction and affective involvement in the metaverse create a sense of presence, excitement, and emotional connection. However, cognitive involvement in decision-making is influenced by factors like information processing and product evaluation, suggesting that perceived interaction may not directly impact cognitive involvement. The finding did not prove the positive relationship between credibility and affective involvement in the metaverse context. Hence, H7 was not supported. This implies that customers’ beliefs, faith, and trust in the metaverse may not necessarily evoke strong emotional responses or personal relevance based on significant motives. However, it is important to highlight that credibility is positively and significantly related to cognitive involvement, supporting the notion that customers’ trust and confidence in the metaverse play a role in shaping their cognitive engagement with the message. This finding supports H8. When users perceive information in the metaverse as truthful and dependable, they are more likely to engage cognitively with the message and evaluate its relevance and utility. Consequently, users’ trust in the metaverse tends to generate positive attitudes and higher usage intention. The purchase intention on metaverse was revealed to be positively and significantly influenced by both affective and cognitive involvements. The finding supported H9 and H10. The finding aligns with previous studies [91–93, 96], indicating that emotional elements of consumer engagement and cognitive engagement are crucial in influencing purchase decisions. When consumers have a strong emotional connection and positive mood states associated with the metaverse, they are more likely to feel inclined to make a purchase. On the other hand, cognitive involvement highlights the importance of consumers’ rational and cognitive processes in decision-making.

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Highly cognitively involved consumers actively seeking product information are more likely to be influenced in their purchase intention on metaverse.

6 Conclusion This study examined the influence of four main characteristics of metaverse on users’ affective and cognitive involvements when they intend to purchase through metaverse that aligns with the business revolution’s ongoing digitalisation and metaverse development. Specifically, the present study expanded the current knowledge on consumer behaviours on how the characteristics of the metaverse affect user involvement and behaviour. According to the results, perceived uniqueness played a significant role in enhancing affective involvement and cognitive involvement in the metaverse. This suggested that those businesses who leverage the metaverse platform should create unique and distinctive content to generate interest and emotional reactions among users, increasing their engagement and purchase intention. Additionally, the use of vivid content could intensify the communication of persuasive messages and create favourable perceptions, enhancing affective involvement. The findings have implications for businesses that should leverage interactive elements in the metaverse to enhance users’ affective involvement, as the interactive nature of the metaverse environment enhances emotional engagement. However, it is noteworthy that perceived interaction may not directly impact cognitive involvement. Marketers, programmers, and other relevant stakeholders should provide relevant and useful information to facilitate users’ cognitive processes, such as ensuring information accuracy, transparency, and product evaluations. In conclusion, those businesses that leverage the metaverse platform should consider the involvement of users’ emotional and cognitive aspects in the metaverse. By creating unique and vivid experiences, fostering interactive engagement, and building credibility, marketers can effectively influence users’ affective and cognitive involvements, ultimately driving purchase intention on the metaverse platform.

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The Impact of Environmental Factors on Social Selling Intention via Virtual Reality Technology and Social Selling Performance: The Mediating Effect of Self-efficacy Melvin Sin-Yon Tie, Winnie Poh-Ming Wong , Tat-Huei Cham , and Chee-Hua Chin

Abstract With the adverse impact of the COVID-19 pandemic, technology plays a crucial role in the sustainability of businesses. The role of social media and virtual reality technology was discussed, as its capability to act as a business tool to improve business performance. Therefore, this study aims to examine the influence of environmental factors (i.e., pressure of suppliers, social influence, and competitive pressure) that affect social selling intentions via VR technology and subsequently lead to social selling performance in the Malaysian context. Despite the high level of social selling activity in Malaysia, limited studies have been conducted on the intentions when engaging in social selling. Also, no conclusive evidence has shown the relationship between environmental factors, self-efficacy, and social selling intention via virtual reality (VR) technology. This study seeks to fill these gaps by examining how environmental factors affect social selling intentions via VR technology and subsequently lead to social selling performance in the Malaysian context. A total of 497 cases were collected for analysis. The partial least square-structural equation modeling (PLS-SEM) method was utilised by performing both measurement and M. S.-Y. Tie School of Postgraduate Studies, University of Technology Sarawak, Sibu, Sarawak, Malaysia W. P.-M. Wong · C.-H. Chin School of Business and Management, University of Technology Sarawak, Sibu, Sarawak, Malaysia Centre on Technological Readiness and Innovation in Business Technopreneurship, University of Technology Sarawak, Sibu, Sarawak, Malaysia T.-H. Cham (B) UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia e-mail: [email protected] Tashkent State University of Economics, Tashkent, Uzbekistan Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_8

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structural analysis. The findings demonstrated that pressure from suppliers, social influence, and the mediating effect of self-efficacy impact social selling intention via VR technology. Interestingly, it was found that self-efficacy acted as a mediator between social influence and competitive pressure on social selling intention via VR technology. Social selling intention via VR technology was also found to affect social selling performance. Keywords Metaverse · Self-efficacy · Social selling intention · Social selling performance · Virtual reality

1 Introduction Presently, customers are less reliant on traditional selling as information can be accessed online anytime and anywhere [1], some debates about the trustworthiness of online news and its influence on brand (dis)trust [2]. In fact, the COVID-19 pandemic has become one of the main forces for all industries to engage in social commerce [3]. Previous research has focused on the importance of social media as a marketing tool from the perspectives of individual retailers and businesses [3–5]. The utilisation of social media as marketing tools has given rise to both referral traffic and social-driven retail sales. These have resulted in significant pressure for all businesses. With the emergence of virtual reality (VR) technology and metaverse, these technologies brought a lot of opportunities to every stakeholder, including organisations and individual users [6]. For instance, Lifestyle in 360 has created VR-based technologies tailored for the real estate sector. These programmes enable prospective customers to virtually view the house from any location without ever having to make a physical arrangement [7]. Alibaba, one of the global leading e-commerce companies, has tested its VR application which allows customers to shop in a virtual environment. Within the virtual setting, customers can browse any products virtually present, with a variety of features that offer more in-depth product information and the option to add products and services into the shopping cart for purchase. Facebook’s Oculus Rift device portfolio has also been reported to be 28% of the VR market worldwide [8]. Furthermore, the influence of VR has also been studied from a marketing perspective [9]. Businesses have started to notice the impact of VR and its benefits in maximising profits via social media platforms [10]. Virtual reality marketing refers to the marketing campaign utilising virtual reality technology that assists brands in providing customers with unique experiences with mobile devices [11]. For example, Meta (formerly known as Facebook) has started to focus on the execution of VR marketing for all businesses to engage in VR marketing to provide customers with community building and global connectivity [12]. However, decisions toward a behaviour such as social selling via VR technology would be influenced by the self-efficacy of each individual. Self-efficacy explains the confidence level of individuals in their capacity and knowledge of engaging in social

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selling via VR features. Experienced social sellers would have different perceptions of social selling via VR features due to the ability to utilise social media as a business tool [13]. Caliendo et al. [14] also stated that high self-efficacy individuals would have a significant impact on sales performances. Furthermore, no conclusive evidence has shown the relationship between environmental factors, self-efficacy and social selling intention via VR technology [15]. Despite the high level of social selling activity in Malaysia, limited studies have been done on the intentions when engaging in social selling [16]. This study sought to fill these gaps by examining how environmental factors affected social selling intentions via VR features in Malaysia. These findings could provide the Malaysian government, such as the Malaysia Digital Economy Corporation (MDEC) a better insight into Malaysia’s development. Furthermore, the Theory of Planned Behaviour served as the underpinning theory and incorporated self-efficacy as the mediator to evaluate the relationship between the antecedent variables and social selling intention. This research enhanced the body knowledge of the Theory of Planned Behaviour by extending the framework with self-efficacy extracted from the Social Cognitive Theory.

2 Literature Review 2.1 Social Commerce and VR Technology Social commerce refers to businesses that utilise social media and e-commerce to facilitate business transactions. Social commerce enables online communication and ensures that social marketers can reach their targeted market [17], as well as enhance their online shopping. Social media users are both consumers and producers of usergenerated content. Therefore, product information can be obtained and exchanged between social media users with the utilisation of social media [18]. Additionally, social media serves as a tool for information gathering about the strategies of competitors. The content is easily accessible in social media spaces, where the exchange of information, reviews, recommendations, and feedback may result in word-of-mouth [19]. Social commerce is an excellent way to foster customer relationship development. The importance of conducting business with the utilisation of social media has been found in current literature [20, 21]. This phenomenon is attributed to the fact that the proficiency of social technology has been demonstrated by online users [22]. However, Meta is now focused on the transformation of marketing initiatives with the emergence of virtual reality. Virtual reality is emerging as a promising technology for e-commerce applications that can meet the critical need for the integration of both technical and social aspects within the online shopping experience [23]. VR technology is the presence of the senses in a computer-generated threedimensional environment to explore and interact with [24]. The activation of numerous senses, from hearing, and visioning to feeling, results in the illusion. Traditional ways of media presentation are also designed to immerse the consumers.

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However, the traditional ways do not focus on developing an ever-improving illusion of a different reality. The ability to create more immersive experiences than traditional two-dimensional videos is just one of the fundamental attributes that make virtual reality appealing for marketing purposes [25]. This has been supported by Shin [26], who stated that virtual reality can increase empathy through an individualised experience. Virtual reality enables a richer real-world experience when more senses are engaged [27]. The emergence of virtual reality as a marketing tool has opened up new opportunities for customer behaviour analysis [28] by offering more attractive customer experiences [29].

2.2 Underpinning Theory: Theory of Planned Behaviour The Theory of Planned Behaviour (TPB) aims to forecast individuals’ intentions and actions in different contexts [2]. TPB describes the intention of the individuals to engage in certain behaviour will indicate the degree of execution endeavour [30], since individuals tend to plan ahead and have intention on whether or not to execute any behaviour. Additionally, this construct states the three factors that affect intention: attitude, subjective norm, and perceived behavioural control. According to [31], attitude is how an individual assesses a particular behaviour that is engaging, which involves taking into account the consequences while executing that behaviour. Additionally, an individual’s behaviour is influenced by how these individuals react to certain circumstances, such as the transformation of digital platforms, the availability of financial resources, and current business trends. Subjective norm is the influence of social factors in which the normative beliefs are affected by the external social pressures to conform to a particular activity. This construct was developed to address situations in which non-motivational elements, such as a lack of skills, knowledge and financial resources, or interpersonal support, could transform the perceived behavioural control into an intention if an individual is about to start a business [32]. Self-efficacy has frequently been found to be distinctively predicted by intentions [33]. TPB is the fundamental theory that was implemented in this study to address the influence of pressure of suppliers, social influence, and competitive pressure on behavioural intentions of Malaysians to sell on social media.

2.3 Social Selling Performance The prosocial behaviour of customers reflects performance during the sales process [34]. Bowen et al. [35] suggested a chain of mediated performance effects, in which [36] proposed to further describe the performance. These performances comprise individual performance, relational performance, and outcome performance. The availability of social media mobile applications allows businesses to ease

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the barrier of accessibility anytime, anywhere, with wireless communication standards such as 5th Generation (5G) etc. [37]. Barney-McNamara et al. [38] stated that a marketer could create a recognisable “personal brand” with the utilisation of social media. Additionally, social selling via VR drives customer acquisition by effectively reaching out targeted markets and increasing customer experiences [36, 39]. To conclude, the utilisation of social media would undoubtedly enhance the performances of social sellers through increased adaptive selling.

2.4 Social Selling Intention via VR Technology Intention refers to a state of mind, an experience, and a direction of the behaviour of one’s attention toward a specific objective [40]. The intention would form before a behaviour is taken [41]. However, intention varies in different individuals depending on the moment which indicates that everything depends on the individuals on whether these individuals want to conduct a certain behaviour [42]. Selling intention leads to sales-related activities such as sales prospecting [35] and social selling [43]. Intentionality is necessary for a successful business [44]. Renko et al. [45] have further stated that the desire for self-realisation and the time-spend are prerequisites for selling intention. Besides, differences between one’s current situation and the future vision could be a motivation to drive personal development with the intensity of their efforts depending on the wideness of the gap [46].

2.5 Environmental Factors Environmental factors are crucial elements that have an impact on the development and sustainability of businesses [47, 48]. Everyone is affected by the environment which either encourages or discourages these businesses from moving forward [49–51]. Individuals are more likely to move forward in an environment that favours businesses, while the opposite is true if that environment does not encourage any business activities. This study reveals different environmental factors influencing the behavioural intention of the social sellers which comprises pressure of suppliers, social influence, and competitive pressure. The ability to introduce products and services has become a competitive advantage for businesses due to the demand for new services. Suppliers play a crucial role in every business as suppliers have a great influence on operational performance, including the quality of products and services, costs, and supply chain [52]. Operational efficiency and cost reduction are necessary for a radical shift from a supply-driven to a demand-driven market [53]. e-Business tools assist organisations in achieving better integration and linkages for supply chain partners [54]. To obtain cost efficiencies and operational efficiencies, businesses need to fundamentally increase their capabilities to generate sustainable growth [55].

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Social influence refers to the wide range of content areas in which an individual’s decisions would be affected by other individuals [56–58]. According to [59], there are three different forms of influence: compliance, identification, and internationalisation. Compliance occurs when individuals publicly conform while maintaining their original beliefs. Identification arises when individuals embrace collective attitudes or behaviours to develop a sense of belonging. Internalisation occurs when individuals adopt the majority opinions and integrate these opinions into their beliefs. The idea of normative and informational social influence are related to compliance, identification, and internationalisation [60]. With the increased engagement on social networking sites, social sellers might experience the Fear of Missing Out and the influence of Network Externality in relation to their utilisation of these social networks [61]. To conclude, information acceptance is the result of informational influence. Competitive pressure refers to the effects on the business incentive to create innovation processes [62]. Electronic communications are being utilised by more businesses for marketing initiatives across a variety of e-marketing platforms [63]. With the rise of social media, traditional marketing has been supplanted by digital marketing [64]. Industrial players are always impacted by the environmental context [65]. The innovations of businesses are influenced by world economic trends in the business environment that drive businesses to adopt e-business [66]. Businesses will participate in new technology adoption that is utilised by competitors due to competitive pressures [67].

2.6 The Mediator Role of Self-efficacy Self-efficacy is based on an individual’s motivation level, which shows their perseverance and efforts towards the objectives [68]. Self-efficacy illustrates how behaviour is influenced by intrapersonal interactions, different circumstances, and participation in various activities [69]. Self-efficacy affects the expectation of certain behavioural outcomes as well as the ability to perform certain behaviours [70]. According to [71], individuals possessed with elevated levels of self-efficacy are more educated and more competitive than individuals with lower levels of self-efficacy. The availability of online learning on social media applications would provide users the insight into how to conduct business on social media platforms, which in turn motivates these users to learn continuously [72]. In other words, individuals who have a strong sense of self-confidence toward accomplishment are most likely to succeed in their objectives. Therefore, self-efficacy is explained by knowledge in the form of attitude towards a certain behaviour [73].

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2.7 Hypotheses Development Kim and Chai [74] have confirmed that suppliers always have a significant impact on the flow of business due to their innovativeness. Businesses are forced to adopt new technology as a result of the advancement of suppliers’ innovativeness. Fauzi [75] further supported that supplier pressure influences businesses to adopt a new technology. The effects of pressure, control, and direction on adoption intentions were also examined by Unsworth et al. [76], who found that pressure contributes positively to intensity. Information and communication technologies (ICT) penetrate many facets of our lives and become crucial to understanding what influences people’s decisions to accept the use of new technology. Social influence plays an important role in the determination of which technologies succeed, especially social technologies [77–79]. Kabra et al. [80] also stated that the behavioural intention toward new technology is significantly affected by social influence. Moreover, AlSharafi et al. [81] has also found a significant relationship between social influence on the continuous adoption intention of virtual platforms during the COVID-19 pandemic. Leow et al. [82] extended the concept of social influence and discovered that social influence affected behaviour regarding the intention to use technology, by delineating mimetic, coercive, and normative pressures on the study of enhanced teaching. Businesses are likely to adopt innovation in a competitive environment [83]. Businesses would implement e-commerce when the competitors are engaged [84]. The majority of empirical studies have demonstrated an association between competitive pressure and behavioural intention. Unsworth et al. [76] have stated that heightened competitive pressure would result in increased intention to adopt new technology. Dajani [85] emphasised that competitive pressure and external factors are the primary determinants of behavioural intention toward e-commerce acceptance. Based on the literature reviews, the following hypotheses were proposed: H1: Pressure of suppliers is positively and significantly related to social selling intention via VR technology. H2: Social influence is positively and significantly related to social selling intention via VR technology. H3: Competitive pressure is positively and significantly related to social selling intention via VR technology. The adoption of social media would assist an individual or businesses to develop a recognizable and consistent brand for themselves [38]. In the study of customer relationship performance, the utilisation of social media as a marketing tool would also establish thought leadership in B2B settings. Arguably, developing a better reputation and thought leadership as a reliable advisor would assist a business in facilitating other aspects of its performance. According to [86], social selling drives both customer acquisition and retention performance. Jelinek et al. [87] indicated that high-performing salespeople possess an intention to embrace new technology to enhance their selling performance. Itani et al. [88] further supported that the

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adoption of social media into businesses enhanced sales performance by facilitating the acquisition of competitive intelligence gathering and enhancing adaptive selling. With reference to the above, the following hypothesis was developed: H4: Social selling intention via VR technology is positively and significantly related to social selling performance. Ratten [89] indicated that the perspective of social cognitive theory, which involves environmental factors, has a significant impact on the external factors that affect individuals to use cloud computing. Nguyen [90] has also stated that the relationship between environmental support factors is significantly related to self-efficacy as the business processes are shaped by the presence of an environment. The relationship of self-efficacy between the pressure of suppliers and behavioural intentions have been examined in an earlier study [91]. Suppliers and customers today are adopting new technology as a means of communication in which the self-efficacy of businesses could be affected due to the know-how toward the technology adoption [92]. Neneh [93] has also found that social support has positive effects on entrepreneurial intention with the indirect effect of entrepreneurial self-efficacy. The degree of self-efficacy, which mediates the relationship between social influence and technology adoption, has been found to be significant in the study of e-learning adoption [94]. Competitive pressures were found to have a significant relationship with selfefficacy on the adoption of technology in an earlier study of [95]. In the study by Hew et al. [96], coercive pressure, which involves government bodies, associations, and suppliers, has been found to have significant impacts on the adoption of blockchain-based Halal traceability systems. Alamin et al. [97] have also explored the mediation role of self-efficacy between environmental factors and adoption intention among accountants on accounting information systems. Although environmental factors would lead to behavioural intention, the intention of an individual toward certain behaviour would be affected by self-efficacy as it involves an individual’s ability toward certain behaviour. Following that, the following hypotheses have been outlined: H5: Self-efficacy mediates the relationship between environmental factors and social selling intention. H5a: Pressure of suppliers has an indirect relationship on self-efficacy. H5b: Social influence has an indirect relationship on self-efficacy. H5c: Competitive pressure has an indirect relationship on self-efficacy.

3 Research Method Based on the literature, a conceptual framework was proposed (see Fig. 1). Adopting a quantitative research approach, a closed-ended questionnaire was developed as the data collection tool. Section A of the questionnaire includes pressure of suppliers, social influence, competitive pressure, self-efficacy, social selling intention via VR and social selling performance for item measurement, while Section B analyses the

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Fig. 1 Conceptual framework

demographic data of the respondents. In terms of the measurement items, a total of 30 items were adapted from previous studies and modified to the Malaysian context [19, 86, 98–101]. A seven-point Likert scale ranging from strongly disagree to strongly agree was used. In this study, a non-probability method, specifically the purposive sampling method, was used in the selection of respondents who are social media sellers with at least six months of social selling experience and are registered under SSM Malaysia (Suruhanjaya Syarikat Malaysia). Roscoe [102] suggested that the sample size should range from 30 to 500 samples. A total of 504 responses were collected in this study. However, there were only 497 responses selected for data analysis after filtering and conducting an outlier analysis. SPSS 28.0 was used for initial tests to eliminate abnormal responses. Next, SmartPLS 4.0 was utilised to analyse the developed research model as the software is able to predict a small set of dependent variables based on the relationship between a set of predictors [103, 104].

4 Findings 4.1 Assessment of the Measurement Model In the initial phase of the measurement model, confirmatory factor analysis (CFA) was used to evaluate the item loading, discriminant validity, convergent validity, and reliability. The cut-off point of 0.5 [105], minimum criteria of 0.5 on average variance extracted (AVE) [106, 107], and composite reliability (CR) values above

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Table 1 Results of measurement model Model construct

Measurement item

Loading

CR

AVE

Social selling performance (SSP)

SSP2 SSP3 SSP4

0.860 0.900 0.678

0.857

0.670

Social selling intention via VR technology (SSI)

SSI1 SSI3 SSI4 SSI5

0.803 0.694 0.719 0.623

0.804

0.508

Self-efficacy (SE)

SE1 SE2 SE3 SE4 SE5

0.812 0.743 0.728 0.608 0.772

0.854

0.542

Pressure of suppliers (PS)

PS1 PS2 PS4 PS5

0.888 0.860 0.503 0.770

0.817

0.553

Social influence (SI)

SI1 SI2 SI3

0.912 0.851 0.867

0.909

0.769

Competitive pressure (CP)

CP1 CP2 CP3 CP4 CP5

0.822 0.870 0.808 0.868 0.808

0.920

0.698

Note AVE average variance extracted, CR composite reliability. PS3, SI4, SI5, SSI2, SSP1, and SSP5 were deleted due to low loadings

0.7 [108] are just a few of the requirements that must be achieved in the measurement model. Table 1 indicates that all loading levels have exceeded the 0.5 minimum cutoff criterion. Convergent validity was attained as all the CR values [109] were above 0.7, and all the AVE values met the minimum threshold of 0.5 [110]. These findings indicated that internal consistency was obtained for this study. Table 2 presents the heterotrait-monotrait (HTMT) values, which all were reported to be less than 0.90 [111]. In short, the measurement model was deemed to be discriminately valid, convergent, and reliable. The R2 value for social selling intention via VR and social selling performance were reported as 0.600 and 0.223, respectively.

4.2 Assessment of the Structural Model The structural model was utilised to examine the hypotheses with standardised coefficient beta values, t-values, and p-values. Based on the statistical findings (see Table 3),

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Table 2 Discriminant validity of constructs using HTMT Constructs

CP

PS

SE

SI

SSI

SSP

CP PS

0.851

SE

0.536

0.540

SI

0.429

0.794

0.433

SSI

0.675

0.784

0.859

0.670

SSP

0.339

0.437

0.839

0.295

0.632

Note Discriminant validity is established as HTMT < 0.90 [111]

six out of eight hypotheses were supported. The findings demonstrated that pressure of suppliers, social influence, and the mediation effect of self-efficacy have an impact on social selling intention via VR technology. Besides that, it was found that self-efficacy acted as a mediator between social influence and competitive pressure on social selling intention via VR technology. Social selling intention via VR technology was also found to affect social selling performance. Thus, the findings demonstrate that H3 and H5a were not supported but H1, H2, H4, H5, H5b, and H5c were supported. No multicollinearity issues were found across the investigated constructs as all the variance inflation factors (VIF) stated in Table 3 were less than 10 [112].

4.3 Discussion The study explored the influence of environmental factors (i.e., pressure of suppliers, social influence, and competitive pressure) on social selling intention and performance. Besides, this study aimed to determine the mediating effect of self-efficacy between the pressure of suppliers, social influence, and competitive pressure on the intention to sell on social media with the result of social selling performances. Six of the eight hypotheses were ultimately supported. In Hypothesis 3, the relationship between competitive pressure and social selling intention via VR was examined. Surprisingly, the findings showed that competitive pressure did not affect social selling intention via VR (p = 0.711; t = 0.371). This result is in line with the study by Ludwig and France [113]. This finding might be explained by the fact that businesses tend to be mostly focused on the quality of services and repeated customers and that oftentimes businesses possess their competitive advantage. Hypothesis 5a, which examined the relationship of self-efficacy between the pressure of suppliers and social selling intention via VR, was also found to have no discernible effect (p = 0.075; t = 1.778). Different results could be explained by the fact that most people are technologically literate. In fact, businesses are not afraid to engage in new technology with the pressure of suppliers such as VR due to the ease-of-use of social media [114]. Therefore, it was assumed that some obstacles to

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Table 3 Path coefficients and hypotheses findings Hypothesis

Standard beta

p-Value

Decision

VIF

H1: Pressure of suppliers → social selling intention via VR technology

0.284

t-Value 5.887

0.000

Supported

2.749

H2: Social influence → social selling intention via VR technology

0.169

4.053

0.000

Supported

1.818

H3: Competitive pressure → social selling intention via VR technology

0.017

0.371

0.711

Not supported

2.031

H4: Social selling 0.472 intention via VR technology → social selling performance

13.252

0.000

Supported

1.000

H5: Self-efficacy → social selling intention via VR technology

0.475

12.404

0.000

Supported

1.436

H5a: Pressure of suppliers → self-efficacy → social selling intention via VR technology

0.054

1.778

0.075

Not supported

2.749

H5b: Social influence → self-efficacy → social selling intention via VR technology

0.065

2.610

0.009

Supported

1.818

H5c: Competitive pressure → self-efficacy → social selling intention via VR technology

0.184

7.688

0.000

Supported

2.031

social selling existed with the unsupported hypothesis. Besides, Hypothesis 1 and Hypothesis 2 examined the relationship between the pressure of suppliers and social influence on social selling intention via VR. Since people are continually working remotely throughout the Covid-19 Pandemic, the majority of social sellers have seen friends and family sharing information, providing support and raising awareness within their closed-social network [115]. As expected, the findings demonstrated

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that pressure of suppliers (p = 0.000; t = 5.887) and social influence (p = 0.000; t = 4.053) showed a positive effect on social selling intention via VR. The findings indicated that environmental factors could influence social sellers to opt for superior performances. On the other hand, Hypothesis 5b and Hypothesis 5c were supported. The statistical results indicated that self-efficacy mediates the relationship between social influence (p = 0.009; t = 2.610) and competitive pressure (p = 0.000; t = 7.688) toward social selling intention via VR. Besides that, self-efficacy showed a substantial influence on social selling intention via VR (p = 0.000; t = 12.404). Social selling intention via VR was also found to have a significant effect on social selling performance (p = 0.000; t = 13.252). This result was in line with the study of [35], which has linked the relationship between social media usage and performances. The utilisation of social media would assist social sellers in terms of their relational performance and outcome performances [36].

5 Conclusion 5.1 Theoretical and Practical Implications The Theory of Planned Behaviour was utilised as the underlying theory that examined the relationship between pressure of suppliers, social influence, and competitive pressure on social selling intention via VR technology, which in turn led to social selling performance. Notably, the main objective was to determine how selfefficacy mediates pressure of suppliers, social influence, and competitive pressure on social selling intention via VR technology. Although VR has been around for years, researchers and businesses have not yet fully understood its potential as an engaging marketing tool. The findings of this study would provide future researchers with fundamental knowledge on the phenomenon of social selling intention via VR technology. The study also identified social influence and competitive pressure as the determinants for social sellers to engage in social selling activities. Businesses must continually monitor market trends to adapt VR-based strategies effectively. Early adoption of VR for businesses provides a competitive edge by positioning themselves as customer-centric and innovative brands. Businesses should focus on value innovation to achieve sustainability and competitive advantage by developing customer loyalty and customer satisfaction [116]. For example, automotive brands could provide immersive experiences for test-drive while real estate companies can offer virtual property tours. Understanding the mediating effect of self-efficacy and recognizing the significance of self-efficacy as a mediating role, businesses would have more understanding on the importance of psychological empowerment by focusing on training and skill development to ensure the knowledge development of the social sellers.

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The findings also highlighted the importance of environmental factors, particularly pressure from suppliers and social influence, in shaping individuals’ new technology adoption. Therefore, a key takeaway for virtual reality is needed to improve the social aspect of these tools. For example, as one of the moving forward strategies, it was recommended that the government should concentrate on educating businesses and implementing a new strategy on the overall movement toward a new technology adoption to engage in social selling activities. This offers the government the ability to stimulate the economy and increase Malaysian quality of living. This study has contributed to the literature on social selling via VR and business performances [117]. The study showed that social influence is one of the key constructs to analyse self-efficacy and behavioural intention with the eWOM. This research also expanded the concept of Theory of Planned Behaviour by empirically confirming the positive relationship between self-efficacy and behavioural intention.

5.2 Limitations and Direction for Future Research Several possible avenues for future research were derived from the findings of this study. As this study focused solely on social sellers’ perspective on environmental factors, the limitation of this study concerns the alternative factors, such as technology acceptance dimensions [118], that could contribute to the impact on social selling initiation via VR. A wider range of possibilities would have more to find out. Therefore, future studies on alternative factors can be conducted to broaden our understanding of social selling intentions via VR. Furthermore, the non-significant result for Hypothesis 3, which demonstrated that competitive pressure did not have a significant influence on the intention to sell on social media was one of the interesting findings. The result indicated that competition was not the driving force for social sellers toward the adoption of social media, but the customer did. Future studies should investigate the circumstances in which the usage of social media might or might not be influenced by competitive pressure. Acknowledgements The author(s) would like to thank the University of Technology Sarawak (UTS) for supporting this research.

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Reshaping Sport with Extended Reality in an Era of Metaverse: Insights from XR the Moroccan Association Experts El Mostafa Bourhim

and Oumayma Labti

Abstract Extended reality (XR) is becoming a growing technology used by athletes, trainers, and other sports professionals. Despite the rapid growth of XR, its application in sports remains largely unexplored. This study is designed to identify and prioritize factors affecting the implementation of XR in Moroccan sports science institutes. To achieve this, the study employs the A’WOT methodology, a hybrid multi-criteria decision method combining the Strengths, Weaknesses, Opportunities, and Threats (SWOT) technique with the Analytic Hierarchy Process (AHP). Through expert group discussions, the study identifies and categorizes the factors affecting XR implementation into SWOT groups. Subsequently, the AHP methodology is employed to determine the relative importance of each factor by conducting interviews with a panel of sports and XR experts. The study’s findings, obtained through the A’WOT methodology, establish a ranking of the fundamental factors for successful XR implementation in Moroccan sports science institutes. The findings suggested that a strategic approach for implementing XR technology in Morocco needs to be driven principally by a combined approach based on the SWOT opportunities and strengths groups. Keywords Extended reality · Metaverse · XR the Moroccan Association · Sport · A’WOT · Moroccan Sports Science Institutes

E. M. Bourhim (B) · O. Labti Moroccan Association of Innovation and Scientific Research in Artificial Intelligence and Extended Reality, BP.154 Settat, Morocco e-mail: [email protected] O. Labti e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_9

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1 Introduction Improving the sports performance of trained and experienced athletes is a major challenge for sports professionals. The different actors use specialist knowledge from a range of leading scientific disciplines, such as motor control, biomechanics, perception, physiology, and even nutrition and psychology, to enhance performance in training and competition [1]. Training is a practice that is carried out repeatedly with an increase in both intensity and training load, although achievement is the result of combining physical attributes, skills, strategies, and psychological maturity, the training and preparation for competition used to rely on instinctive methods, with athletes following established practices without a thorough understanding of the underlying reasons [2]. Thus, traditional training methods may be limited in their application to sport practice environments as a result of small numbers of attendees, geographical location, high cost of facilities and general access to resources. To effectively overcome these challenges, all aspects of sport and all its stakeholders have started from the premise that any meaningful improvement for the future will be reliant on technology to a certain degree [3]. Technology enable more effective training, stimulation, athlete management, and monitoring, accurate results, better visibility for spectators, performance development, and injury prevention, among many other functions [2]. With the specific use of technology, athletes will benefit from a level of enhancement that we have never encountered before. Among the different technologies available, XR technology is making its entry into the sports domain [1]. It can be used to recreate difficult environments for training simulation, reducing the risk of damaging expensive equipment and ensuring a more secure training environment. The use of XR technology in sports is surely still in its initial stages and many unknowns remain. Therefore, this study plays a vital role in determining the strengths, weaknesses, opportunities, and threats of XR in sports using the SWOT framework and evaluating the relative importance of these factors by employing the AHP method. Through this analysis, valuable insights will be gained to inform sports organizations, athletes, and stakeholders on the potential benefits, challenges, and future directions of XR integration in the field of sports in Morocco.

2 Related Work 2.1 Extended Reality (XR) XR, an umbrella term encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), represents three distinct types of computer-generated simulations. It is an emerging technology that has found applications in various highperformance fields like psychology, medicine, and the military [4]. Its purpose is to enhance perceptual-cognitive and motor skills. However, the use of XR in sports,

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especially at the elite level, has only recently gained attention. The concept of “XR” originates from the “Virtuality Continuum,” which defines a continuous spectrum ranging from completely real to completely virtual environments [5]. The Virtuality Continuum was initially proposed by Milgram and Kishino as a taxonomy for MR displays [6]. Real environments comprise solely of real objects and involve direct viewing of the physical scene without electronic display systems. However, some studies have challenged this framework, considering MR represents a particular form of reality, or a further form of AR [7, 8]. AR refers to a system combining real and virtual components in real time. Unlike VR, AR allows users to see the real world while superimposing or compositing virtual objects onto it. As immersive technologies have advanced, the conventional AR-VR-MR landscape has expanded to include various technologies merging the real and virtual worlds. This broader context has introduced the term “XR” to encompass the range of taxonomies within the reality-virtuality continuum. XR aims to integrate virtual objects into the real world, enabling users to engage in activities that are not feasible in purely digital or real environments [9]. As we delve into the transformative capabilities of XR technology, it becomes evident that XR is a pivotal building block in the evolution towards the Metaverse. A concept that has emerged as a transformative vision for the future of digital interaction and immersion. Interestingly, XR technology, which includes VR, AR, and MR, plays a central role in shaping the Metaverse. XR technologies provide the fundamental building blocks for creating immersive, interactive, and interconnected virtual spaces where users can engage in a wide range of experiences, from social interactions to educational endeavors [10]. Although, the Metaverse represents an interconnected, fully immersive virtual space detached from physical reality. It is characterized by a persistent digital space where users interact and coexist exclusively, transcending the mixed reality of XR [11].

2.2 XR in Sport Two key aspects characterize XR technology’s implementation in sports: athlete involvement in the virtual environment, as well as the utilization of XR technology to generate sports content [12]. XR technology has a wide range of benefits: firstly, it serves as an aid to sports training, providing virtual training scenarios according to specific requirements. It can allow some sports to carry out training tasks with no environmental constraints such as waves or wind [12]. It also enables personalized tactical training for certain elements and the possibility of enhancing athletic skills in a three-dimensional way [3]. In the field of sports rehabilitation [13], virtual rehabilitation systems go beyond the limits of traditional training methods and offer precise measurement, control, assistance, and training systems to guarantee the efficacy of sports rehabilitation training [14]. Despite the increasing relevance of XR technology, the literature lacks in-depth investigations that systematically assess the multifaceted factors that must be considered by policymakers, institutions, and stakeholders for its

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effective implementation. By addressing this gap, our study endeavors to furnish valuable scientific insights, enabling evidence-based decisions that propel the strategic implementation of XR in Moroccan sports science institutes, and potentially offering a blueprint for broader.

3 SWOT-AHP for XR in Sport 3.1 SWOT SWOT is a widely utilized tool for analyzing internal and external environments to facilitate a systematic approach and provide support for strategic decision-making [15, 16]. Strategic factors, which are the most critical internal and external factors for an organization’s future, are identified through SWOT analysis. These factors, also known as SWOT factors, are categorized into four groups: strengths, weaknesses, opportunities, and threats. The primary objective of applying SWOT in the strategic planning process is to develop and adopt a strategy that aligns effectively with both internal and external factors. Additionally, SWOT can be employed when a new strategy alternative emerges, necessitating an analysis of the relevant decision context. When utilized correctly, SWOT analysis can serve as a solid foundation for strategy formulation. However, there is room for improving the efficiency of SWOT analysis beyond its typical applications [15, 17]. One limitation of SWOT analysis is that it often fails to comprehensively assess the strategic decision-making situation, as it tends to focus solely on identifying factors. Moreover, the expression of individual factors in SWOT analysis is often too general and concise [17, 18]. Additionally, SWOT analysis lacks analytical methods for determining the importance of factors and evaluating decision alternatives based on those factors. Consequently, the further application of SWOT analysis largely relies on qualitative analysis conducted during the planning process and the knowledge and expertise of the individuals involved. As a result, the outcomes of SWOT analysis often consist of mere listings or incomplete qualitative examinations of internal and external factors.

3.2 AHP AHP is a decision-making approach that addresses complex problems by breaking them down into a hierarchical framework consisting of objectives, criteria, and alternatives [19]. This method employs pairwise comparisons to determine the relative importance of variables at each level of the hierarchy and to select the best option among alternatives at the lowest level. AHP is particularly effective when dealing

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with subjective decision-making scenarios, especially when criteria can be structured hierarchically into sub-criteria [18, 20]. In systems with multiple hierarchical levels, AHP assesses relative priorities on absolute scales through discrete and continuous paired comparisons [20, 21]. The prioritization process involves assigning numbers from a comparison scale developed by Saaty [22] to indicate the relative significance of criteria (see Table 1). The significance calculation involves pairwise comparison matrices of these elements. The AHP technique is based on three fundamental principles: the structure of the model, the comparative evaluation of criteria and/or alternatives, and the synthesis of priorities. AHP accomplishes this by breaking down complex multicriteria decisionmaking problems into a hierarchy of interconnected decision elements, such as criteria and decision alternatives. A typical hierarchy consists of at least three levels: the top level represents the overall goal of the problem, the middle level comprises several criteria that define the alternatives, and the lowest level consists of the options [23]. In this study, the AHP is employed to prioritize SWOT aspects. The prioritization process begins to evaluate the relative importance of the criteria. Pairwise comparisons of the criteria are conducted at each level, taking into account the criteria provided at the higher level and their respective levels of impact. The AHP method utilizes a standardized nine-level comparison scale for conducting multiple pairwise comparisons [18]. Let C = {Cj|j = 1, 2, …, n} denote the set of criteria. The results of the pairwise comparisons among the n criteria can be represented by an evaluation matrix A with dimensions (n × n), where each element a_ij(i, j = 1, 2, …, n) represents the ratio of weights assigned to the respective criteria. To facilitate this pairwise comparison, a square and reciprocal matrix can be utilized, as depicted in Eq. (1). ⎡

a11 ⎢ a21 ⎢ ⎢ A = (ai j )n.m = ⎢ a31 ⎢ . ⎣ ..

a12 a22 a32 .. .

a13 a23 a33 .. .

··· ··· ··· .. .

⎤ a1n a2n ⎥ ⎥ a3n ⎥ ⎥ .. ⎥ . ⎦

an1 · · · an3 · · · ann

Table 1 Pairwise comparison scale Importance

Explanation

1

Two criterions contribute equally to the objective

3

Experience and judgement slightly favor one over another

5

Experience and judgment strongly favor one over another

7

Criterion is strongly favored and its dominance is demonstrated in practice

9

Importance of one over another affirmed on the highest possible order

2, 4, 6, 8

Used to represent a compromise between the priorities listed above

(1)

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Table 2 Random index n

1

2

3

4

5

6

7

8

9

10

RI

0

0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

After normalizing each matrix, relative weights can then be obtained. Relative weights are derived using the right eigenvector (w) corresponding to the largest eigenvalue (λmax). The relative weights can be expressed as follows: Aw = λmax.w

(2)

If all pairwise comparisons are valid, the matrix A will have a rank of 1 (X = n). In such cases, weights can be derived by normalizing any of the rows or columns of A [18]. It is important to emphasize that the consistency of the pairwise comparison judgments is closely linked to the quality of the AHP results. The consistency is determined by the relationship between the elements in A: a_ij × a_jk = a_ik. To evaluate the consistency, the Consistency Index (CI) can be calculated using the following formula: CI =

λmax − n n−1

(3)

The level of consistency in the assessments can be determined by calculating the final Consistency Ratio (CR). The CR is obtained by dividing the Consistency Index (CI) by the Random Index (RI) as described in Table 2 using the equation below: CR =

CI RI

(4)

The commonly accepted upper threshold for the CR is 0.1. If the calculated CR exceeds this value, it indicates a lack of consistency in the evaluation process. In such cases, the evaluation process should be repeated to enhance consistency.

3.3 SWOT-AHP SWOT enables the development of simple but effective strategies. By combining SWOT with AHP, the relevance of each decision-making factor can be assessed quantitatively [18]. SWOT-AHP can be performed following a four-step process [19]. During the first step, external and internal factors are selected as input to the SWOT analysis, with a recommended maximum of ten factors per SWOT group. In the second step, all SWOT groups are prioritized by means of pairwise comparisons of their components. In the third phase, relative comparisons are performed and relative weights are determined. To ensure that respondents’ answers are not arbitrary,

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Fig. 1 SWOT-AHP analysis framework

a consistency index is determined. The framework adopted in the present study is illustrated below in Fig. 1.

4 Results 4.1 SWOT Analysis In the present study, SWOT analysis was employed to evaluate XR’s ability to revolutionize training and learning practices in sports. SWOT matrix validity was first examined by an external expert on XR within sports, who reviewed, verified, and validated the main findings. The findings consist of 6 strengths, 5 weaknesses, 5 opportunities, and 4 threats.

4.1.1

Strengths

S1: Enhanced Learning/Training Experience: XR technology breaks down geographical boundaries, permitting distance collaboration and more personalized learning/training experiences. The realistic environments delivered by XR technology strengthen the sense of immersion, which is the key to optimizing training and learning [24]. Efficient learner-centric XR environments can provide engaging, provocative, reflective experiences that challenge conceptions, evolve skills and transform the way people comprehend themselves and their environment. Appropriately designed, XR settings can reinforce involvement and learning by implementing the “flow concept”. Flow is when people generally experience deep pleasure,

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as well as creativity and full engagement. XR technologies open up transformative opportunities for sports learning and training. XR offers immersive and involving learning experiences, allowing users to actively contribute and to more effectively retain knowledge. S2: Improving Training/Learning Sports and Skills Development: by enhancing movement diversity and challenges, allowing the development of customized content, delivering innovative training methods for athletes, and enabling them to exercise in realistic environments, XR can enhance physical performance. These technologies facilitate skill development, tactical analysis, and mental preparation, leading to improved performance. Moreover, XR devices have the potential to power players’ sensorial modes with both virtual and augmented content, helping them to gain a better insight into their real-world environment. XR processes integrate perceptual learning methods to create more specific and solid training/learning. S3: Adaptive, Customizable Training Programs: XR platforms provide adaptive and customizable training programs, tailored to the needs and skill levels of individual athletes. Athletes can progress through training modules at their own pace, with the option of adjusting difficulty levels or focusing on specific areas of improvement. This personalized approach of training optimizes skills development and accelerates progress. Furthermore, XR offers the possibility of repetitive and deliberate practice, enabling athletes to refine their techniques and strategies. S4: Providing Additional Information and Improving Decision-Making Capabilities: The key feature of implementing XR to boost sports skills relies on the ability to generate additional information that can assist users in making decisions and managing their behavior. In this instance, artificial information is superimposed on real-world situations to improve the user’s level of understanding and knowledge. Particularly with novice athletes, providing additional information has proven to enhance their experiences, enjoyment, and sense of immersion [25]. Within XR, students and athletes are exposed to a range of situations, which enables them to acquire knowledge across different domains, and thus make decisions more quickly. S5: Injury Rehabilitation and Prevention: Physical injuries can be considered an inevitable part of the career of most athletes. AR, VR, and MR can be used for injury rehabilitation through the creation of controlled virtual environments for therapy and recovery. Preventive training simulations and biomechanical scanning are helping to avoid injury and maximize the physical well-being of athletes. In alignment with [14, 26], XR might make it easier to identify some aspects of sports-related injuries. Secondly, recuperation might be enhanced by delivering mental exercises to maintain vigilance and readiness by simulating real-life scenarios and/or by assisting athletes in sustaining proper movement during rehabilitation. Furthermore, when returning to the sport, XR can be deployed to work side by side with the injured athlete in order to mitigate the fear of re-injury, which represents a significant source of stress. Additionally, XR may be utilized at the onset of an injury to enhance the athlete’s comprehension of the injury/recovery processes. XR can also be utilized as a medium

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to diminish feelings of isolation. It is plausible that retaining contact within the team environment may decrease the negative affective. S6: Real-Time Feedback: In the sports field, feedback constitutes a key instrument for the optimization of movement techniques. It enables athletes to assimilate and internalize the correct execution of movements for improved performance. One of the most commonly used methods of feedback in sports is by responding to expert advice. With the help of XR, such as VR, AR, and MR, it is possible to obtain visual feedback via screens during the execution of a movement in order to make real-time adjustments to motor learning [27]. XR-based real-time feedback can lead to a better perception of self-movement and faster learning, with a positive effect on the development of athletes’ performance. Performing the right movements in sports is important for achieving certain goals and avoiding injury. Thus, XR can provide additional feedback on performance and behavior. The feedback provided by XR can be used to display the experience to reveal a “before and after” condition. This allows faster integration of players/athletes and a reduction in the time required for the whole team to refine positioning and learn new tactics.

4.1.2

Weaknesses

W1: Cost and Infrastructure Requirements: The implementation of AR, VR, and MR in sports demands significant financial investment, especially in hardware, software, and infrastructure. An accurate network infrastructure, high-speed connectivity, and dedicated maintenance support are crucial for smooth operation. The initial investment and ongoing costs associated with XR technologies can be prohibitive for some sports organizations and users. Furthermore, not all individuals may have the necessary access to the necessary devices or technologies. W2: Technical Challenges: Such technologies necessitate robust hardware, software, and infrastructure, which can create technical challenges regarding compatibility, performance as well as reliability. Headsets also present technical limitations [28], such as data analysis and processing power to create an accurate virtual recreation of the speed and excitement of sports, especially live experiences. XR experiments have to be implemented with a very clear comprehension of what is technically possible and acceptable to the user. W3: Lack of Social Experiences: Sports learning and training often involve team dynamics, collaboration, and social interaction with teammates and opponents. XR experiences, especially isolated environments, can limit and shut out the social aspects of sports [28]. The lack of real human interactions may affect the enhancement of teamwork, communication, and other interpersonal skills that are crucial in team sports. Therefore, among the main risks to the widespread adoption of XR is the potentially fatal loss that can result from the reduction of social and rich experiences to a totally isolated and individual virtual interface.

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W4: Health and Safety Issues: XR systems can pose a weakness to certain aspects of health. Long-term use of AR, VR, and MR may lead to physical discomfort, motion sickness, or eye strain, which may limit their widespread adoption in sports. Wearing a VR headset, for example, can be heavy and uncomfortable, causing discomfort and potentially straining the neck and shoulders. Prolonged use of VR devices can lead to eyestrain, headaches, and dizziness, affecting athletes’ general physical well-being. XR experiences often involve athletes being immobile or having limited physical movement. This lack of physical activity can be problematic, especially for sports that require continuous movement and physical effort. Inadequate physical activity can lead to deconditioning, reduced cardiovascular capacity, and a general decline in physical performance. In addition, XR experiences can create a sense of immersion, sometimes blurring the boundary between virtual and reality. This immersion can lead to psychological stress and anxiety, particularly during high-pressure sports scenarios reproduced in XR environments. Mental tension can affect an athlete’s concentration, decision-making abilities, and overall performance, which in turn can have physical repercussions. W5: Adaptability to Real-Life Conditions: Athletics are often performed in dynamic and unpredictable environments, such as changing weather conditions, varied terrain, or interactions with opponents. XR can struggle to accurately simulate and adapt to these real-world conditions, limiting its ability to provide comprehensive training experiences. This lack of adaptability can hinder athletes’ ability to transfer their skills from the XR environment to real-life sporting scenarios. Certain XR sensations are currently non-realistic, which could result in unnatural motion patterns, along with underuse, overuse, and/or injury. Furthermore, XR experiments may not fully replicate the psychological pressures and stress that athletes face in real competitions. Such factors as crowd noise, time pressure, and the emotional intensity of real sporting occasions can be difficult to recapture in XR training. The lack of these psychological drivers can restrict athletes’ capacity to achieve mental resilience and perform effectively under pressure.

4.1.3

Opportunities

O1: Sports Analysis and Insight: AR, VR, and MR can enhance the collection and analysis of complex sports data, enabling a better insight into player performance, game trends, and strategic decision-making. XR can enable real-time data visualization, allowing coaches and players to analyze performance parameters more effectively. Sport-specific information can be added to XR systems to enhance skills. O2: Sponsorship and Branding Opportunities: In XR training environments, brands can integrate their products, logos, or advertisements, enhancing the realism and immersion of the experience. XR opens up new sponsorship and branding opportunities for sports organizations and athletes. Athletes can train with branded equipment or apparel, reinforcing the association between the brand and the sport. Interactive advertising campaigns within XR training experiences offer educational value,

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engaging athletes in interactive challenges or quizzes related to branded products. In addition, brands can participate in sponsoring XR training programs, tutorials, or virtual coaching classes developed by sports organizations or athletes, contributing to the creation of high-quality training content while increasing brand exposure and reach. O3: New Revenue Streams: XR introduces new revenue streams for sports organizations and athletes. Virtual competitions and events conducted through XR offer athletes the opportunity to generate revenue through entry fees while fostering a competitive training environment. Through the creation of XR content, Athletes and organizations can create and monetize XR training programs and tutorials, providing athletes with in-depth skill development and strategy breakdowns. Athletes can also offer personalized XR experiences, such as one-on-one virtual coaching or customized training plans, charging a premium for tailored training services. Furthermore, XR enables the sale of virtual merchandise within XR training experiences, and licensing agreements can be established with XR platform providers. O4: Sports Science Research and Innovation: for researchers, XR presents an exceptional opportunity for testing athletes with a high degree of reliability. XR technology allows organizations and countries to commit to cutting-edge sports science research and innovation. By taking advantage of XR technology, researchers can investigate various aspects of sports performance, biomechanics, and cognition in virtual environments. These studies can help to develop new training methods, injury prevention strategies, and a better understanding of sports performance. O5: Cross-Sport Training and Skill Transfer: XR technology offers sports institutions and countries the opportunity to facilitate cross-sport training and skill transfer. By leveraging XR simulations and virtual environments, students/athletes can explore and benefit from training techniques and strategies from different sports. For example, a basketball player can utilize XR to simulate volleyball games, enhancing their hand–eye coordination, agility, and ability to anticipate movements. By enabling cross-sport training, athletes can diversify their skill set, increase their versatility, and gain a broader understanding of athletic movement patterns. Similarly, a track and field athlete can use XR to engage in simulations of swimming, allowing them to improve their breathing techniques, overall fitness, and understanding of water dynamics. This interdisciplinary approach to training can lead to improved performance, innovative strategies, and a fresh perspective on their own sport. Moreover, sports institutions and countries can leverage XR to create collaborative training programs where athletes from different sports come together in virtual environments to exchange knowledge, share training methods, and foster a culture of cross-disciplinary learning.

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Threats

T1: Market Competition: Rapid advancements in technology may lead to the emergence of alternative entertainment options or new technologies that compete for the attention and engagement of sports fans and athletes. This increased competition can pose challenges and potential threats in several ways. First, quality and Differentiation: With multiple companies offering XR solutions, there may be a wide range in the quality and capabilities of the products and services. Lower-quality XR solutions or those lacking innovative features may negatively impact user experiences and limit the potential benefits of XR in sports. It becomes crucial for organizations to carefully evaluate the available options and select the most suitable and reliable XR providers to ensure optimal performance and user satisfaction. Second, pricing and Affordability: The competitive market can lead to price wars or pricing strategies that make XR technology less affordable for some sports organizations or individuals. Higher prices may limit access to XR training and learning opportunities, particularly for smaller clubs, teams, or athletes with limited financial resources. Balancing the cost of implementation with the expected benefits becomes a key consideration for those considering adopting XR technology. Third Support and Service: Competition in the XR market can affect the level of support and service provided by different vendors. The last point is about Market Saturation and Innovation: As the market becomes saturated with multiple XR providers, there is a risk of diminishing innovation and differentiation. T2: Ethical and Privacy Concerns: Using XR technology implies the collection and storage of sensitive data, such as biometric information and athletes’ personal data, raising concerns about data confidentiality and security. Clear policies and practices are required by organizations to ensure the protection and appropriate use of such data, including obtaining informed consent from athletes. Further ethical considerations include algorithmic bias and fairness, as XR systems rely on algorithms that may unintentionally discriminate against individuals based on factors such as race or gender. Furthermore, the immersive nature of XR experiences necessitates a focus on physical and psychological safety, with measures in place to prevent harm and mitigate risk. It is crucial to ensure the integrity of XR content to avoid manipulation or misuse and to take into account the social and cultural implications of implementing XR in sports. T3: Regulatory and Legal Challenges: The adoption of XR technology in sports introduces a range of new considerations that may require compliance with existing regulations or the development of new ones. The use of XR in sports may raise questions regarding fair competition, anti-doping regulations, and the authenticity of results. Additionally, issues related to intellectual property rights, and liability must be carefully navigated. Organizations must proactively address these regulatory and legal challenges to mitigate risks, protect the interests of athletes and stakeholders, and ensure the responsible and lawful use of XR technology in the

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sports industry. Ensuring compliance with relevant laws and regulations, collaborating with regulatory bodies, and staying updated on emerging legal frameworks become critical. T4: User Acceptance and Adoption: XR’s successful integration depends on the willingness of users, including students, athletes, coaches, and sports organizations, to embrace and utilize it effectively. Resistance or hesitation towards XR may stem from various factors such as unfamiliarity, concerns about disruptions to traditional training methods, or perceived barriers to entry. Organizations should focus on building trust, delivering seamless user experiences, and actively involving users in the decision-making process to promote a positive perception of XR and maximize its potential for enhancing sports training and learning. Overcoming these challenges requires proactive efforts to educate users about the advantages of XR, address their concerns, and demonstrate its value through tangible results.

4.2 AHP Results The relative importance of each SWOT group and each driver within the SWOT clusters are listed in Table 3 and Fig. 2. AHP was used to generate priority scores for each of the 20 factors, based on factor priority and overall priority. These scores unveil variations and commonalities in the perceived importance of the factors and SWOT categories. Pairwise comparisons demonstrate the consistency of all matrices. Among the SWOT groups, Opportunities carry the highest weight, followed by Strengths, Threats, and Weaknesses. The criterion “Opportunities” received the highest weight, with factors ranked in order of importance as follows: O4 > O5 > O1 > O3 > O2, with scores ranging from 0.028 to 0.1263 (see Tables 3 and 4). These findings emphasize the significant potential of XR technology to revolutionize sports training and learning. By investing in sport science research and innovation, organizations can harness XR to create stateof-the-art training methods and technologies that optimize athlete performance and prevent injuries. Cross-sport training and skill transfer are enhanced through XR, as athletes can immerse themselves in virtual environments and simulate various sports, facilitating the transfer of skills and strategies across disciplines. Furthermore, XR provides sponsorship and branding opportunities by creating immersive platforms for sponsorships, advertising, and brand collaborations, while also offering the potential for new revenue streams through the development and commercialization of XR training programs and applications. Overall, these opportunities highlight the transformative impact of XR in sports training and learning, ranging from improved performance and skill transfer to advanced analytics and revenue generation. The criterion “Strengths” holds the second position in terms of importance, with factors ranked as follows: S5 > S3 > S4 > S2 > S1 > S6, obtaining scores ranging from 0.034 to 0.126 (see Tables 3 and 5). This order highlights the crucial strength of XR in injury rehabilitation and prevention, as it enables athletes to engage in

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Table 3 Priorities and consistency ratios of comparisons of the Swot groups and sub factors SWOT group

Priority of the group

Strengths (S)

0.359

Weaknesses (W)

Opportunities (O)

Threats (T)

Global CR

0.056

0.529

0.057

SWOT factors

Local priority

Global priority

Inconsistency ratio 0.01

S1

0.106

0.038

S2

0.163

0.059

S3

0.216

0.078

S4

0.178

0.064

S5

0.247

0.089

S6

0.089

0.032

W1

0.217

0.012

W2

0.102

0.006

W3

0.107

0.006

W4

0.269

0.015

W5

0.305

0.017

O1

0.172

0.091

O2

0.053

0.028

O3

0.065

0.034

O4

0.498

0.263

O5

0.213

0.113

T1

0.136

0.008

T2

0.436

0.025

T3

0.183

0.010

T4

0.245

0.014

0.03

0.04

0.02

0.02

virtual simulations for safe and effective recovery while minimizing the risk of reinjury [14, 26]. The adaptive and customizable training programs offered by XR enable tailored training plans that cater to individual athletes’ needs, ensuring optimal skill development and performance enhancement [29]. XR’s ability to provide additional information, such as real-time feedback and performance metrics, empowers athletes and coaches with valuable insights for informed decision-making. Moreover, XR improves training and learning by providing interactive and engaging experiences that facilitate skill acquisition and strategy development. With an enhanced learning/training experience, athletes can be immersed in realistic virtual environments, enhancing their understanding and retention of sports concepts. Consequently, these strengths demonstrate the transformative potential of XR in sports training and learning, spanning from injury rehabilitation and prevention to adaptive training programs and enhanced learning experiences. Based on the SWOT-AHP analysis for XR in sports training and learning, the identified threats are ranked as follows: market competition (T1), user acceptance

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Fig. 2 Graphical interpretation of the results of pair-wise comparisons of SWOT groups and factors Table 4 Pair-wise comparison matrix of the opportunities criteria O1

O2

O3

O4

O5

O1

1

4.583

3.464

0.258

0.577

O2

0.218

1

1.732

0.149

0.333

O3

0.289

1.732

1

0.167

0.236

O4

3.873

6.708

6

1

3.162

O5

1.732

3

4.243

0.316

1

CR = 0.04

CI = 0.045

λmax = 5.179

Table 5 Pair-wise comparison matrix of the strengths criteria S1

S1

S2

S3

S4

S5

S6

1

0.707

0.408

0.632

0.408

1.291

S2

1.414

1

1

0.894

0.577

1.732

S3

2.449

1

1

1.291

1

2.121

S4

1.581

1.118

0.775

1

0.816

2

S5

2.449

1.732

1

1.225

1

3

S6

0.775

0.577

0.471

0.5

0.333

1

CR = 0.01

CI = 0.012

λmax = 6.062

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and adoption (T4), ethical and privacy concerns (T2), and regulatory and legal challenges (T3) (see Tables 3 and 6). These threats highlight the potential obstacles that XR faces in its implementation and widespread adoption in the field of sports training and learning. Market competition poses a significant threat as the XR market becomes increasingly saturated with competing technologies and solutions. This intensifying competition could pose challenges for XR providers in terms of differentiation and market share acquisition. User acceptance and adoption represent another concern, as the success of XR heavily relies on the willingness of athletes, coaches, and organizations to embrace and integrate XR into their training routines [29]. Overcoming skepticism, resistance to change, and the provision of adequate training and support are critical factors in ensuring the broad acceptance of XR. Ethical and privacy concerns arise due to the collection and usage of personal data during XR training and learning experiences. Organizations must address these concerns by implementing robust privacy measures and ensuring transparent data practices to maintain user trust. Regulatory and legal challenges also pose a threat, as XR technologies may face evolving regulations and standards that need to be navigated carefully. Adhering to these regulations while continuing to innovate and develop XR solutions is crucial. Addressing these threats requires proactive strategies such as market differentiation, user education and support, privacy safeguards, and staying informed about regulatory landscapes. The criterion “Weaknesses” is seen as the least important within SWOT groups. The factors describing this criterion obtain scores between 0.006 and 0.017 with the following order of importance: W5 > W4 > W1 > W3 > W2 (see Tables 3 and 7). These weaknesses shed light on the potential barriers and limitations that must be addressed for the successful implementation of XR in sports training and learning. One notable challenge is the adaptability of XR to real-life conditions, as virtual environments may not fully replicate the complexities and nuances of real sports scenarios. Bridging this gap is crucial to ensure the effectiveness and transferability of skills from virtual to real-life settings [29]. Health and safety concerns arise due to the physical and physiological demands of sports activities, particularly in highly immersive XR experiences. Safeguarding the well-being of athletes and implementing appropriate safety measures becomes essential to mitigate potential risks. Cost and infrastructure requirements also pose a weakness, as the initial investment for XR technology, equipment, and infrastructure can be substantial [29]. Overcoming this challenge involves finding cost-effective solutions and addressing Table 6 Pair-wise comparison matrix of the threats criteria T1

T2

T3

T4

T1

1

0.258

1

0.5

T2

3.873

1

1.732

2

T3

1

0.577

1

0.707

T4

2

0.5

1.414

1

CR = 0.02

CI = 0.018

λmax = 4.054

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Table 7 Pair-wise comparison matrix of the weaknesses criteria W1

W2

W3

W4

W5

W1

1

3

2.236

0.577

0.632

W2

0.333

1

1.414

0.354

0.333

W3

0.447

0.707

1

0.471

2.121

W4

1.732

2.828

2.121

1

1.414

W5

1.581

3

0.471

0.707

1

CR = 0.03

CI = 0.034

λmax = 5.134

accessibility issues for widespread adoption. Lack of social experiences is a concern as XR training can be isolating, limiting social interaction and team dynamics that are integral to sports. Strategies to foster social experiences and collaborative training environments need to be developed. Technical challenges encompass issues such as system reliability, latency, and data integration, which need to be resolved for a seamless and efficient XR experience. Addressing these weaknesses requires a multi-faceted approach, including improving adaptability to real-life conditions, prioritizing health and safety measures, exploring cost-effective solutions, enhancing social interactions, and addressing technical limitations. CRs indicate the degree of agreement between participants regarding the factors considered, with lower CRs indicating greater agreement. In this study, the CRs (see Table 3) obtained from XR The Moroccan Association experts were well within the accepted range (CR = 0.002 < 10%). Concerning the factors associated with the Strengths category, participants showed almost perfect agreement (CR = 0.01). In addition, participants showed similar levels of agreement (CR = 0.03 for weaknesses, CR = 0.04 for opportunities, and CR = 0.02 for threats) in their perceptions of the underlying factors in the other three categories. The high level of agreement between participants underlines the reliability and consistency of their assessments.

5 Discussion To the best of our knowledge, this study represents the first SWOT-AHP analysis conducted on the use of XR technologies in sports learning and training. The AHP method was used to establish the hierarchy of importance of various factors that either support or hinder the implementation of XR technologies in sport-related contexts within Moroccan institutes. The results show a consistent pattern of perceptions regarding the importance of each factor, providing a basis for further exploration. The SWOT-AHP analysis conducted demonstrates a comprehensive understanding of its potential within the field. Strengths such as injury rehabilitation and prevention, and the provision of additional information highlight the transformative impact of XR. Furthermore, in alignment with [29] our findings underscore that one of the

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principal strengths of immersive technologies lies in their capacity to provide personalized programs and training experiences, tailoring learning and skill development. In addition, opportunities such as multi-sport training and skill transfer, and sport analytics highlight the wide-ranging opportunities for XR to enhance performance, generate revenue and develop cross-disciplinary skills. In contrast to Miah et al. [29]. This study reveals that immersive technologies introduce novel research avenues, expanding the horizons of inquiry and exploration within the sports science field. By strategically addressing these challenges, XR in sports training and learning can realize its full potential and revolutionize the way athletes train, learn and perform, while ensuring ethical practices, user acceptance and overall safety.

5.1 Theoretical Implications The integration of SWOT analysis with the AHP in this research offers a relevant and novel contribution to the existing literature on XR technology adoption in sports science institutes. Existing literature often relies solely on SWOT analysis for assessing the factors affecting technology implementation, providing valuable qualitative insights. However, the addition of AHP brings a quantitative dimension, allowing for a more precise evaluation of these factors’ relative importance. By combining these methodologies, the study transcends traditional qualitative SWOT analysis, offering a quantitative approach that aligns with the resource-based view theory and dynamic capabilities theory. This enhancement is especially relevant in the context of XR, a rapidly evolving field where resource allocation and strategic decision-making are paramount.

5.2 Practical Implications The main results can have practical implications, as they provide empirical guidance on what the policy makers could rely on the implementation of XR in Moroccan sports science institutes. Our main findings highlight that the decision to implement XR is mostly influenced by both external factors, such as Opportunities, and internal factors like strengths, rather than weaknesses and threats. Considering the significant role of recent technologies in science and education [30, 31], sports science institutes should seriously consider developing and hosting an XR-enhanced platform for immersive sports training and education. The results reveal that Moroccan sports science institutes should adopt a holistic XR integration strategy. This strategy should emphasize leveraging strengths and opportunities, addressing weaknesses, and mitigating threats in a balanced manner. It involves creating immersive XR training programs that improve athlete performance, optimizing infrastructure, fostering innovation through data analysis, and prioritizing ethical considerations and user acceptance.

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6 Conclusion The present study investigates the benefits, challenges and opportunities of XR technology in Moroccan sports science institutes based on the SWOT-AHP framework. The strengths and opportunities ratings based on XR The Moroccan Association perspectives are positively inter-preferred for XR technology. Thus, based on this research, the framework provided can be interpreted as a roadmap for supporting the development of the strategic implementation of XR technology in Moroccan sport science institutes, while providing more credible information for decision-makers in the overall process. An in-depth analysis of the findings enables us to conclude that the strategic implementation of XR technology in Moroccan sports science institutes has to be driven principally by the opportunities factors that could assist in overcoming the identified main weaknesses and threats, along with maximizing the strengths. Following these guidelines, decision-makers are expected to initiate a range of activities in order to establish the right external environment in which opportunities can be fully exploited to tackle the principal weaknesses and threats revealed by the analysis. This research provides strong evidence for XR deployment in the sense that it reflects the views of XR The Moroccan Association practitioners and researchers on XR technology. Regardless of the above results, some limitations must be noted. First of all, the AHP survey included only seven experts. While this number is adequate for the AHP analysis, representativeness may be considered low. Secondly, SWOT analysis and focus group interviews are qualitative methods by nature. The issue of intersubjectivity remains unresolved in our results. Further studies on the acceptance of XR technology should be conducted to substantiate the results of our study. The combination of SWOT and AHP can be altered for further research, to compare the results of this work with those of SWOT-TOPSIS, SWOT-Scoring, or SWOT-ELECTRE. Acknowledgements The Authors gratefully acknowledge the financial support and technical assistance provided by the Moroccan Association of Innovation and Scientific Research in Artificial Intelligence and Extended Reality (XR The Moroccan Association), BP.154, Settat, Morocco. Without its generous support, this publication would not have been possible. Funding The Authors gratefully acknowledge the financial support and technical assistance provided by the Moroccan Association of Innovation and Scientific Research in Artificial Intelligence and Extended Reality (XR The Moroccan Association), BP.154, Settat, Morocco.

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Unveiling the Influence of Social Technologies on Online Social Shopping in Malaysia Kin Leong Tang , Hon Tat Huam , Tat-Huei Cham , and Boon Liat Cheng

Abstract The Internet technology adoption, the evolution of social networking technology and its application have emerged as innovative tools to assist online social shopping, particularly among generation Z, who cannot live without social networking sites. This paper investigates the factors influencing Malaysian generation Z consumers’ online social shopping purchasing intentions for Cosmetic and Personal Care products. Specifically, it examines the indirect effects of social desire, commercial desire, and trust. Purposive sampling was used in this study, and data were assessed using the Partial Least Squares Structural Equation Modelling technique. The findings show that online social shopping purchase intentions are significantly influenced by social desire, commercial desire, and trust. Trust is the most important factor affecting the purchase intention of online social shipping, followed by commercial desire and social desire. They also play as mediators. This study filled the literature gaps by extending MGB. These results are expected to generate significant insights for practitioners and marketers in developing effective marketing campaigns and promoting Cosmetic and Personal Care products through online social shopping purchases. Keywords Online social shopping · Purchase intention · Attitude · Desire · Metaverse · Trust · eWoM

K. L. Tang · T.-H. Cham (B) Universiti Tunku Abdul Rahman, Sungai Long, Kajang, Selangor, Malaysia e-mail: [email protected] K. L. Tang · H. T. Huam Putra Business School, Seri Kembangan, Selangor, Malaysia T.-H. Cham UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia Tashkent State University of Economics, Tashkent, Uzbekistan B. L. Cheng Sunway University Business School, Subang Jaya, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_10

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1 Introduction Cosmetic and personal care products (CPC) are an essential part of the daily necessities for beauty and grooming. In 2023, the CPC industry recorded US$571.1 billion in revenue and is expected to grow at a compounded annual growth rate (CAGR) of 3.8% between 2023 and 2027 [1]. In Malaysia, it contributed US2.88 billion in revenue and is estimated to grow by 3.1% annually [2]. This growth is driven by factors such as high per-capita incomes, the influence of social network influencers, and the strong desire among Malaysian consumers to maintain physical attractiveness, appearance, self-satisfaction, and lifestyle [3–5]. In addition, the adoption of technology in business operations adds to the convenience and accessibility of online shopping. Coupled with personalised marketing and providing consumers with greater product information and services, shopping experience, and choice, this has catalysed the industry’s growth. Social technology is a product of innovation in communication and the Internet. Social networks and user-generated content, such as comments and review sections or question and answer (Q&A) boards, vlogs, metaverse, and live streaming, are all examples of social technology that can be used to facilitate social interaction for product discovery, review, rating, recommendation, and shopping transactions. It enhances consumers’ shopping experiences and sharing by connecting them in a virtual environment with their family and friends, online acquaintances, and fans in the brand group, which is not feasible in the physical market. Social technologies and innovations have clearly been recognised as opportunities in virtual environments, boosting the adoption of social shopping and driving the development of social commerce. Thus, social media adoption is seen as an indispensable competitive advantage tool for engaging and building customer relationships, as well as influencing less experienced consumers through social technology recommendations shared by experienced customers. However, it has not been well investigated how social technology in a virtual environment influences social shopping behaviour. The adoption of technology and Internet and social technologies is not new to the younger generation. In fact, these technologies play a vital role in all aspects of life, including seeking information, communicating between peers, entertainment, and supporting day-to-day activities. For instance, Facebook, Instagram, YouTube, and Twitter have emerged as the most popular social networking sites, with a majority of online users engaging with at least one or two of these platforms in different aspects on a daily basis [6]. Malaysians, in particular, spend an average of 3.01 h per day and 97.1% of them participate in social networks [7]. Moreover, 61% of Malaysians use the Internet to shop, and 73% of consumers are influenced by a brand’s social network presence when making a purchase decision [8]. This infers the impact of social networks on Malaysian consumers’ lifestyles and their decision-making processes [9]. As social networks have a substantial impact on people’s time and online activities, they have reshaped consumer purchasing patterns and the overall shopping journey. Increasingly, consumers rely on social networking sites to conduct product

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research, easily finding individuals with similar interests to share shopping experiences and gather insights [10]. In short, social networks have become a gateway for consumers to discover brands, explore products, and make informed purchase decisions. Moreover, the COVID-19 pandemic and lockdown have accelerated the growth of e-commerce and social commerce, due to the accessibility of products and shopping convenience [11]. Considering this trend, businesses are urged to recognise how to utilise social networks in formulating business strategies and marketing initiatives. Online social shopping specifically refers to consumers’ e-commerce activities being influenced by their friends, as they seek credible suggestions, discover products and services, and make purchases [12]. In this process, customers not only contribute to content generation, such as writing product reviews, but their friends, family, online acquaintances, and social communities also play a role in their purchase decisions [13]. According to a statistic by Statista online shopping is heavily influenced by social networks, with 11% of social network users purchasing products immediately after discovering them, and 44% of users purchasing later online [14]. In another study by Hirschmann, friends and family have the most significant influence on CPC product purchases in Malaysia, accounting for 68%, Instagram and photo sharing, and online videos account for 56% and 49%, respectively [15]. However, a similar study is lacking. Hoffman and Novak argue that computer-mediated shopping engages goaloriented behaviour and experiential behaviour [16]. Online social shopping purchase intention is a goal-directed behaviour where the consumer completes an online search before purchasing. Perugini and Bagozzi argue that attitude alone does not directly influence future behaviour, but desire serves as the driving force that transforms motivation into action [17–19]. Unfortunately, the role of desire has often been overlooked by researchers when studying behavioural intention [20]. As social networks seamlessly integrate commercial features, it becomes important to investigate the mediator role of social desire and commercial desire in online social shopping purchase intention. To address these gaps, this study adopts the Model of Goal-directed Behaviour (MGB) to examine the influence of social desire and commercial desire in the context of online social shopping purchase intention. eWoM and trust are critical success factors in social shopping [21]. Consumers increasingly rely on social networks for reviews and recommendations to evaluate and support their purchasing decisions. Past research has focused on the characteristics of eWoM or its impact on e-commerce purchase intention or adoption [22–24], but the direct impact on intention is debated [25]. Through the presence of a mediator, eWoM may change the direction of the intention. This study proposes that eWoM indirectly influences online social shopping through the mediating variable, trust. However, the relationship between eWoM, trust, and online social shopping requires further investigation. By adopting the MGB to close literature gaps, this study examines the indirect impact of social desire, commercial desire, and trust on the online social shopping intentions of Malaysian generation Z consumers purchasing CPC products. Despite the high offline distribution of CPC products in Malaysia, the industry is

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projected to grow in the coming years [26]. This presents an opportunity for retailers to leverage social shopping as a new retail format and increase customer engagement, and create economic value [27]. The study’s findings are valuable for policymakers and marketers in shaping their strategies. It enables businesses to better understand consumers’ online social shopping behaviour, effortlessly incorporating insights into the creation of buying and selling avenues through the adoption of social commerce.

2 Literature Review 2.1 Online Social Shopping Purchase Intention Online social shopping is a method of e-commerce that adds e-commerce tools to social networks or includes social network functions and activities in e-commerce sites [28]. This allows shoppers to participate, share, recommend, and comment on their purchases and recommendations through online social networks [29]. Thus, online social shopping purchase intention refers to the behaviour of online consumers participating in an online shopping environment, interacting with friends and family, networking communities to obtain product or service reviews, seeking advice and suggestions, or contacting sales personnel to obtain more product information and discounts before making a purchase decision [30, 31]. Similarly, Hajli believes that social shopping, by integrating recommendations, comments, and suggestions made by peers on social networks or online social shopping platforms, will significantly influence the online social shopping decisions and behaviours of shoppers [29].

2.2 Model of Goal-Directed Behaviour (MGB) Much of the literature has shown that researchers use TRA and TPB to explain consumer purchase attitudes and intentions [27]. Meanwhile, the MGB is an extension of the TPB proposed by Perugini and Bagozzi [17]. The researchers argued that the TPB neglected the role of desire in the formation of behavioural intention and primary reasons for behaviour [17, 32]. In the MGB, desire denotes a person’s wants or wants to achieve, whereas intention reflects the person’s willingness to act [33]. Desire plays the essential mediator role, mediating the antecedent of behavioural intention. The MGB is claimed to be more predictive and parsimonious than the TPB. It surpasses TPB by better explaining the impact of intention and antecedents through desire, shedding light on how reasons for action translate into goal consequences [17, 33].

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3 Hypotheses Development 3.1 Social Desire as Mediator Online social shopping encompasses the integration of commercial functions within social networks, allowing for both social interaction and commercial activities [34]. Attitude, as defined by the Expectancy-value model, refers to the favourable or unfavourable evaluations of behaviour and the perceived interest in performing that behaviour [35]. In the context of online social shopping, social attitude represents the positive evaluation of social networks for social interaction. Desire, on the other hand, serves as the driving force behind action. Under the MGB, desire is conceptualised as personal motivation influenced by favourable or unfavourable attitudes. Social desire is thus referred to as motivation in social engagement. According to Li, Liu, and Tukkinen, consumers use social networks for a variety of reasons, such as making friends, searching for information, keeping in touch with friends, or entertainment [36]. Consumers with strong social attitudes are more likely to use social networks to socially interact with their online community and acquire or exchange information with people who share their interests. Therefore, social network users who actively engage in social activities, browser content, stories consumption, or participate in user-generation content on the social network are indicators of high social desire [37]. Users who are favourable to social networking will actively engage with their friends and family, online acquaintances, or fans in brand groups. Horng and Wu argued that communicative social users display a high level of social attitudes and social desire, as they participate in activities such as commenting, asking questions, writing reviews, and sharing personal experiences on social networking sites [38]. In addition, Herrando, Jiménez-Martínez, and Martín-De Hoyos argued that enjoying the interaction and socialising with other social network users lead to the development of positive social attitudes and increased engagement in social network activities [39]. As a result, it is posited that consumers’ strong social attitudes which boost the frequency of use of social networks for engagement with online communities, searching, and information sharing impact their strong social desires. Social desire is one of the predictors of consumers’ online social shopping intention. Customers who have strong social desires will actively participate, contribute, and generate content such as posts, comments, and recommendations, as well as share their product purchase experiences [40]. Kudeshia et al. argued that product or brand reviews, recommendations, and endorsements can be easily obtained by consumers in their social engagement and would influence their purchase intention [41]. Makmor and Alam also support this argument, highlighting that consumers continuously seek product information, reviews, and suggestions to aid their purchasing decisions [42]. When consumers seek reassurance for their purchase decisions, their social desire intensifies, driving them to gather more information about the brand or product to alleviate uncertainties. Consequently, a stronger social desire corresponds to a higher online social shopping purchase intention. Additionally, Jung, Santella, Hermann,

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and Lawrence found the development of an emotional desire leads to the formation of intention [43]. This has been observed in various contexts, such as the relationship between attitude, desire, and the decision to consume fruits and vegetables among university students [43], as well as the decision to switch to nicotine replacement therapy in the UK [44]. Consumers actively seek product information, reviews, and suggestions for their purchasing decisions. Stronger social desire emerges when they need reassurance, driving higher online social shopping purchase intention [41, 42]. Hence, the above discussion suggests the following hypotheses: H1: Social attitude has a positive effect on social desire. H2: Social desire has a positive effect on online social shopping purchase intention. H3: Social desire mediates the relationship between social attitude and online social shopping purchase intention.

3.2 Commercial Desire as Mediator Consumers increasingly rely on social networks to seek recommendations and opinions before making a purchase decision, while marketers use social networks for advertising and sales promotion. According to Chiang et al., consumers are interested in advertising information, actively search for more brand or product-related information, and share it, thus forming a positive commercial attitude [45]. In other words, commercial attitude encompasses the interest in advertising information, searching, reading reviews and recommendations of a brand or product, and sharing brand or product information. Consumers’ engagement and browsing of social networking sites for brand or product information, seeking suggestions, exchanging purchase experiences, or spreading the electronic word of mouth (eWoM) can be viewed as a commercial desire [38, 46]. Wang and Sun argue that commercial attitude affects consumers’ motivation to seek additional brand and product information [45]. Yaakop et al. further claim that commercial desire is the motivational driving force for engaging in online commercial activities, such as advertising and promotion activities [47]. Similarly, Liu, Tan, and Sutanto claim that commercial desire motivates consumers to seek commercial information, to obtain others’ shopping experiences, and to seek favourable deals or discounts [48]. Imran posited that an individual’s desire to use social networks and their response to online advertising are related to their attitudes [49]. Similarly, Alsamydai and Hamdi Al Khasawne argue that when online advertising information is informative, entertaining, and credible, consumer forms a positive commercial attitude [50]. This positive commercial attitude leads consumers to be more receptive to commercial advertisements and actively seek information about the advertised products, thereby fostering commercial desire. Engaging in commercial activities and browsing social networks exposes consumers to relevant advertising content, arousing their interest and prompting them to seek additional information about the brand or product, ultimately driving purchase

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intention [40]. According to Kijek et al., consumers with a strong desire to seek and share product information and opinions are more likely to form purchase intentions [51]. Additionally, Tsai and Men noted that brands and online social retailers frequently distribute and disseminate discounted product information, coupons, or promotion on social networking sites [52]. Thus, consumers with a positive commercial attitude are motivated by economic rewards and incentives, further fuelling their commercial desire to achieve purchasing goals. In conclusion, a positive commercial attitude influences social network users’ commercial desire to seek additional brand or product information, interact with social network sellers, and ultimately increase their purchase intention. Therefore, the following hypotheses are proposed in this study: H4: Commercial attitude has a positive effect on commercial desire. H5: Commercial desire has a positive effect on online social shopping purchase intention. H6: Commercial desire mediates the relationship between commercial attitude and online social shopping purchase intention.

3.3 Trust as Mediator The emergence of social networks brought a new aspect of word of mouth (WoM) by enabling engagement with friends, family, as well as online acquaintances. Electronic word of mouth (eWoM) is the customers’ positive or negative statements about a brand or product on social networking sites [53]. The interactive nature of social networks facilitates the dissemination of eWoM, allowing individuals to share their shopping experiences and product or service information with others [21]. Ismagilova et al. assert that eWoM is the information exchanged among potential, actual, and former consumers through the internet, which can influence human behaviour [54]. This exchange information includes comments on brands or products, ratings and purchase experiences [55]. Therefore, eWoM is considered a reliable source of product or brand information communicate over the Internet and social networking sites [51]. Trust is fundamental to the success of e-commerce, playing a crucial role in eliminating concerns and fears associated with online transactions [12, 56]. Trust encompasses a sense of security and willingness [57]. Al-Tit et al. argue that trust in the context of online social shopping refers to the perception of benevolence, integrity, credibility, and competence of online social retailers or platforms on social networks [58]. Besides, trust acts as a belief system that reduces complexity and vulnerability, fostering a positive behavioural attitude [59–61]. Therefore, Consumers’ trust in social shopping is based on the extent to which a specific online retailer or platform meets their purchasing expectations and demonstrates credibility [62]. eWoM on social networking sites refers to authentic user-generated comments, opinions, and evaluations regarding a brand or product [63]. It emerges through the interactive communication facilitated by social networks, allowing individuals to

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engage with friends, family, acquaintances, or brand communities to share information, exchange viewpoints, and express opinions. Consumers perceive eWoM on social networks as highly reliable. When making online purchases, consumers often rely on eWoM to validate their trust in a brand or seller. Consequently, a substantial presence of positive eWoM for a product or brand enhances trust levels [53, 63]. In addition, positive eWoM contributes to building brand value, fostering confidence in following recommendations and suggestions, and establishing trust in online social retailers and brands. However, Tang and Tan’s research findings contradict this assertion [56]. Trust plays a crucial role in consumers’ decision-making process when engaging in social shopping. Previous research has consistently demonstrated the positive and significant impact of trust on purchase intention [64, 65]. According to Cheng et al., higher levels of trust in the product or brand increase the likelihood of purchase intention [64]. Similarly, consumers are more likely to purchase when they perceive online social retailers or online social shopping platforms are honest, integrity, and capable of meeting their purchase expectations [65]. Furthermore, when consumers sense the willingness of online social retailers or online shopping platform operators to safeguard their customers, it cultivates benevolence-based trust. According to Matute et al., eWoM posted on a seller’s website or online shopping platforms is considered credible [53]. Farivar et al. asserted that consumers tend to infer eWoM [66]. eWoM has a great persuasive effect in building trust and subsequently influencing purchase intention. In addition, according to See-To and Ho [23], a large amount of positive eWoM available on social networking sites or social shopping platforms creates a positive expectation, leading to trust in online social retailers and influencing online social shopping intentions. Furthermore, trust can be easily formed through eWoM obtained from friends and family, online social acquaintances, or brand social communities. Consumers consider such eWoM highly credible, triggering the need for identification and increasing confidence in their purchase evaluation process. Similar studies by Li et al. and Cheng et al. also support this claim that eWoM is highly persuasive, effectively reducing consumers’ uncertainty, thus, increasing trust and directly influencing purchase decisions [64, 67]. However, Tang and Tan found that trust did not mediate between eWoM and purchase intention, possibly due to consumers’ prejudice regarding eWoM being paid to write [56]. It is thus hypothesised that: H7: eWoM has a positive effect on trust. H8: Trust has a positive effect on online social shopping purchase intention. H9: Trust mediates the relationship between eWoM and online social shopping purchase intention.

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4 Methodology 4.1 Instruments All the measures in this study were derived from earlier research. A pre-test and a pilot test were conducted to assess their face validity and reliability. Face validity was assessed by five academics and two industry experts, and their feedback was used to revise the survey questions. Subsequently, a pilot test was conducted with 28 participants. The results show all constructs achieved a Cronbach’s Alpha value greater than 0.7, indicating satisfactory reliability. Lastly, a self-administered online survey was created to obtain the participant’s perceptions.

4.2 Data Collection Method and Procedure This study takes individual Malaysian generation Z online social network users, as the unit of analysis. Malaysia has an estimated 28 million social network users in 2021 [68], with a significant portion residing in Kuala Lumpur and Selangor, which have a combined population of 8.34 million and a strong GDP [69]. Generation Z, known for their proficiency in social media usage, heavily relies on social networks for product information and recommendations when making online purchase decisions. Notably, Kuala Lumpur and Selangor alone account for 21.5% of the Generation Z population in Malaysia, making them suitable and representative regions for sampling. Thus, the sample for this study consists of individual Generation Z consumers aged between 18 and 26, residing in Kuala Lumpur and Selangor, which are considered representative due to their population size, commercial function, and ethnic group composition. As the exact sampling frame is not available due to the large population, a purposive sampling technique was employed to ensure better representation and generate more precise insights within the Generation Z consumer segment. Kline’s guidelines for sample size in structural equation modelling (SEM) suggest that a sample of 100–200 is considered medium, while over 200 is considered large. Considering practical limitations, particularly during Covid-19, this study considered using power analysis to determine the minimum sample size. Using G*Power, at 0.15 effect size (medium effect), alpha at 0.05, and power at 0.80, a minimum sample of 98 was determined [70].

4.3 Common Method Bias Due to the use of a single data collection method, i.e., a survey was used, this might introduce common method bias. This study performed a full collinearity assessment using the variance inflation factor (VIF) to confirm the common method bias issue

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[71]. Kock proposed that a VIF threshold value of 3.3 or less is free of common method bias [71]. The results show there is no common method bias.

4.4 Data Analysis Given the model’s complexity, PLS-SEM is better suited for statistical analysis [72]. PLS-SEM is also superior in assessing mediators and if the model is complicated [73]. We followed the two steps approach in the assessment, first evaluating the measurement model before proceeding to structural model assessment. When assessing mediation analysis, a transmittal approach is adopted, which focuses on how mediators transfer the effect onto the dependent variable [74].

5 Results and Discussion 5.1 Profile of Respondents After eliminating response bias and outliers in the survey, 288 datasets remained for data analysis. Females accounted for 73.61% of the participants, while males accounted for 26.39%. Most respondents were Chinese (49.31%), followed by Malay (38.54%). 90.97% of respondents are currently pursuing or have completed their undergraduate studies, while 5.9% are currently employed. This study revealed that Malaysian generation Z spends a lot of time on social networks. 45.14% of them spend more than 4 h per day on social networks, while 21.88% spend 3–4 h per day. Instagram (30.3%) is the most popular social networking site, followed by YouTube (24.8%) and Facebook (17.6%) as presented in Table 1.

5.2 Measurement Model Assessment As shown in Table 2, the statistical results indicate that the composite reliability (CR) values are above the threshold value of 0.7, and the average variance extracted (AVE) values are above the threshold value of 0.5. Notably, item P2 (loading = 0.662) factor loading is below 0.7. Chin suggested that the threshold of factor loading should be at least 0.6 if the CR and AVE were above the threshold value [75]. This indicates that all items were in the acceptable factor loading range. It implies the absence of validity and the presence of convergent issues. The square root of AVE in each construct is higher than its correlation with another construct approach, which has been widely used to confirm the discriminant

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Table 1 Demographic profile (N = 288) Attribute

Value

Gender

Male

Ethnicity

Education level

76

26.39

Female

212

73.61

Malay

111

38.54

Chinese

142

49.31

Indian

18

6.25

Others

17

5.90

2

0.69

A-level/Foundation Diploma Undergraduate Postgraduate

Employment status

Frequency Percentage

Students Working adults

The type of social media and networks typically Facebook use Instagram

16

5.56

262

90.97

8

2.78

271

94.1

17

5.9

154

17.6

265

30.3

Twitter

108

12.4

YouTube

217

24.8

Others

130

14.9

Time spends on using social media and networks Less than an hour

3

1.04

1–2 h

37

12.85

2–3 h

55

19.10

3–4 h

63

21.88

130

45.14

More than 4 h

validity. It has, however, been widely criticised for being overly biased and overestimating indicator reliability [76]. Therefore, this study suggests adopting Heterotraitmonotrait (HTMT) criterion, which is a better approach with high sensitivity and specificity in detecting discriminant validity [76, 77]. Table 3 shows the discriminant validity test via Fornell and Larcker criteria, while Table 4 shows the result via HTMT. Both results ascertain no discriminant validity issues in this study. The results of collinearity show that all three predictor constructs, namely, social desire (VIF = 1.410), commercial desire (VIF = 1.513) and trust (VIF = 1.357) were below the threshold value.

5.3 Structural Model Assessment Table 5 shows the structural path analysis results and reveals that hypotheses H1, H2, H4, H5, H7 and H8 are significant. Figure 1 presents the results of the hypotheses

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Table 2 Measurement model, item loading, construct reliability and convergent validity Construct

OSSPI

SA

SD

CA

CD

eWoM

Trust

Item

Loading

Cronbach alpha

RhoA

Composite reliability (CR)

Average variance extracted (AVE)

0.751

0.764

0.843

0.574

0.836

0.840

0.902

0.754

0.875

0.876

0.915

0.729

0.920

0.922

0.938

0.715

0.892

0.893

0.925

0.756

0.903

0.905

0.932

0.775

0.898

0.899

0.925

0.711

P1

0.723

P2

0.662

P3

0.829

P4

0.806

SA1

0.862

SA2

0.904

SA3

0.837

SD1

0.756

SD2

0.889

SD3

0.891

SD4

0.873

CA1

0.835

CA2

0.827

CA3

0.850

CA4

0.844

CA5

0.855

CA6

0.860

CD1

0.835

CD2

0.880

CD3

0.896

CD4

0.865

E1

0.878

E2

0.874

E3

0.902

E4

0.868

T1

0.826

T2

0.864

T3

0.836

T4

0.874

T5

0.816

OSSPI Online Social Shopping Purchase Intention; SA Social Attitude; SD Social Desire; CA Commercial Attitude; CD Commercial Desire

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Table 3 Discriminant validity via Fornell and Larcker criteria and correlation Construct

SA

SA

0.868

CA

eWoM

CA

0.498

eWoM

0.353

0.458

0.881

SD

0.426

0.417

0.262

SD

CD

Trust

OSSPI

0.845 0.854

CD

0.444

0.655

0.464

0.503

Trust

0.364

0.474

0.363

0.409

0.869 0.474

0.843

OSSPI

0.420

0.503

0.340

0.328

0.376

0.393

0.758

Note Bold values indicate the square root of AVE of each construct

Table 4 Discriminant validity via HTMT

Construct

SA

CA

eWoM

SD

CD

Trust

OSSPI

SA CA

0.568

eWoM

0.406

0.501

SD

0.489

0.459

0.293

CD

0.515

0.719

0.517

0.568

Trust

0.421

0.520

0.402

0.463

0.530

OSSPI

0.533

0.601

0.410

0.402

0.460

0.474

Note HTMT.85

testing. The finding shows social attitude (β = 0.426, p < 0.001) has a positive and significant relationship with social desire, commercial attitude (β = 0.655, p < 0.001) has a positive relationship with commercial desire, and eWoM (β = 0.363, p < 0.001) has a positive relationship with trust. It also suggests social desire (β = 0.129, p < 0.05), commercial desire (β = 0.194, p < 0.001), and trust (β = 0.248, p < 0.001) have a significant direct effect on online social shopping purchase intention. Trust was found to have a greater impact on online social shopping purchase intention than commercial desire and social desire. In this study, we adopted a transmitted approach to assess mediation using a bootstrapping method to determine the mediating role played by social desire, commercial desire, and trust. Table 6 shows the mediation effect. The results indicate that social attitude has a specific indirect effect on online social shopping purchase intention in the presence of social desire (β = 0.055, LCL = 0.003, UCL = 0.129). Hence, hypothesis H3 is supported. Hypothesis H6 was also found to be significant, suggesting commercial attitude (β = 0.127, LCL = 0.039, UCL = 0.219) has a specific indirect effect on online social shopping purchase intention through commercial desire. Similarly, the results show eWoM (β = 0.090, LCL = 0.045, UCL = 0.148) has a specific indirect effect on online social shopping purchase intention through trust.

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Table 5 Structural path analysis: direct effect Path H1: SA → SD

Beta

SE

T-value

P-value

95% CI

Decision

Lower

Upper

0.426

0.052

8.117

0.000

0.327

0.530

Supported

H2: SD → OSSPI 0.129

0.066

1.945

0.026

0.007

0.266

Supported

H4: CA → CD

0.655

0.038

17.405

0.000

0.583

0.728

Supported

H5: CD → OSSPI

0.194

0.066

2.935

0.002

0.062

0.319

Supported

H7: eWoM → Trust

0.363

0.050

7.257

0.000

0.264

0.461

Supported

H8: Trust → OSSPI

0.248

0.056

4.404

0.000

0.141

0.361

Supported

Fig. 1 Structural model: hypotheses testing

Thus, hypothesis H9 is supported. We concluded that all three mediators play a mediator role. Finally, the predictive power of the model, coefficient of determinant (R2 ), effect size ( f 2 ) and root mean squared error (RMSE) were reported. In terms of predictive power, the results indicated that the r-square of social desire (R2 = 0.181), trust (R2 = 0.132) and online social shopping purchase intention (R2 = 0.213) are considered Table 6 Structural path analysis: indirect effect Path

H3: SA → SD → OSSPI

Indirect effect

Mediation

Beta

95% CI Lower

Upper

0.055

0.003

0.129

Yes

H6: CA → CD → OSSPI

0.127

0.039

0.219

Yes

H9: eWoM → Trust → OSSPI

0.090

0.045

0.148

Yes

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weak (below the threshold value of 0.25), whereas commercial desire (R2 = 0.429) is considered moderate. The effect size as suggested by Cohen (1988), indicates that eWoM ( f 2 = 0.152) has a small effect size on trust, social attitude ( f 2 = 0.221) has a moderate effect size on social desire, and commercial attitude ( f 2 = 0.750) has a high effect size on commercial desire. Surprisingly, social desire ( f 2 = 0.014) has no effect, whereas commercial desire ( f 2 = 0.031) and trust ( f 2 = 0.058) were having a small effect on online social shopping purchase intention.

6 Discussions This study found that trust emerged as the strongest predictor of online social shopping purchase intention in the CPC products context, followed by social desire and commercial desire. This suggests that trust plays a crucial role in influencing online transactions and purchasing decisions, reducing uncertainty for generation Z consumers. Commercial desire was found to be a stronger motivator than social desire, indicating that generation Z consumers are driven to engage in commercial activities such as searching for discounts, promotions, or coupons when they are interested in a product or brand. Additionally, this study highlights the significant impact of social desire, as consumers actively seek information, exchange reviews, and receive recommendations from friends, family, online acquaintances, or brand communities to support their purchasing decisions within the social networking context. Consistent with earlier literature, the study found that social desire plays a mediation role. This aligns with the idea that social desire represents the state of desire for engaging in social networks and seeking information and social interaction [37]. When consumers maintain a positive attitude toward networking sites (i.e., social attitude), they are more inclined to actively engage in social networks (i.e., social desire) [78]. Likewise, when individuals perceive the benefits outweigh the costs, they will have a greater desire to use and engage in social interaction activities. As a result, a positive social attitude increases active engagement, brand awareness, and product or brand knowledge increase, resulting in social desire and leading to purchase intention. Similarly, active participation in brand groups and the sharing of product discussions, evaluations, and purchase experiences by others will motivate consumers’ social desire to share information and experiences, which leads to purchasing intention. Our study suggests that in the presence of commercial desire, the commercial attitude has a specific indirect effect on online social shopping purchase intention. This finding is supported by Ko [34]. When Malaysian generation Z consumers are interested in a product or brand, they develop a stronger commercial attitude, which leads to a stronger commercial desire to seek more information and engage in discussion about the product or brand within their social community. Consumers with a strong commercial desire are more likely to search for coupons, promotions, discounted products, or contact online social retailers for further information through

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social networks. This high level of commercial engagement enhances consumers’ knowledge about the product or brand, leading to increased arousal and conviction, ultimately influencing their intention to make online social shopping purchases. This study’s finding further confirmed that eWoM has a direct impact on trust, which in turn influences online social shopping purchase intention. This finding is consistent with the studies by Sharma et al. and Al-Debei et al. [79, 80]. The mediation test also confirms that trust plays a role as a mediator in this relationship. eWoM is perceived as highly credible and trustworthy, making it highly persuasive when shared on social networking sites. Besides, consumers rely on eWoM for many reasons, including reducing uncertainty, obtaining prepurchase information, making better decisions, or staying updated about a product or brand [22]. Because eWoM is user-generated content originating from an online community, consumers place trust in the opinions of third parties. Thus, the present of a substantial amount of eWoM will foster trust in online social retailers or online social shopping platform operators, which leading to increase in purchase intention.

7 Theoretical and Practical Implications The results of this study add to the body of knowledge about social shopping and confirm the benefits of technology adoption in business processes. It also confirms the mediating effect of social desire, commercial desire, and trust on consumers’ online social shopping purchase intentions. Under the MGB, desire is a strong predictor of behavioural intention. Given that social networks can be used for both social interaction and commercial purposes, this study posits that social attitude and commercial attitude influence the consumer’s social desire and commercial desire, respectively. This hypothesis is solid and supported by previous studies [40, 81]. eWoM on social networks strengthens consumers’ trust, which in turn develops purchase intention. The study also fills gaps in the existing literature that were previously overlooked [56]. From a practical standpoint, these findings can help online social retailers, online social shopping platform operators, and marketers in formulating effective online social marketing strategies to attract new social network consumers. Online social shopping is a fusion of social networking and shopping. Consumers’ social attitudes and commercial attitudes help explain differences in users’ intention to engage in the social search for product reviews and suggestions, as well as their intentions to seek discounted products, spread brand/product information, and exchange purchase experiences. The indirect effects of social desire, commercial desire, and trust have provided a better understanding of the mechanisms, thus, can be translated into strategies that encourage social network users to develop social shopping attitudes and desires, which then lead to a purchase intention. Trust emerges as the most crucial factor influencing online social shopping purchase intention, while commercial desire exerts a stronger impact than social desire. Marketers are advised to create compelling marketing content and disseminate it strategically on relevant social networking

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platforms to capture the attention and commercial attitude of generation Z consumers, fostering both commercial and social desire for increased likelihood of purchase.

8 Conclusion There is no doubt that social networks become prevalent, and individuals spend more time engaging in social networking activities, attitudes towards online social shopping and the way we shop are expected to change. Online social shopping purchase intention, however, there is still little study on online social shopping purchase intentions. Social networks provide a comfortable social environment for social interaction while also serving commercial purposes for convenience. It is found that social desire, commercial desire, and trust are the motivators for online social shopping purchase intention. Social desire and commercial desire are the motivators to engage in social interaction to acquire knowledge about a product or brand, whereas commercial desire is the motivator to pay attention to the product or brand and further engage in commercial activities to confirm their purchase. Additionally, trust emerges as one of the most influential factors impacting the online social shopping purchase intentions of Malaysian Generation Z consumers, particularly for CPC products.

9 Research Limitations and Future Research The current study focused on generation Z consumers in Kuala Lumpur and Selangor, Malaysia. To enhance generalizability, future research should expand the geographical coverage. Additionally, the study had an uneven gender distribution, with mostly female respondents. As men become more conscious of their appearance, future studies need to ensure an equal gender distribution and examine gender differences in CPC product purchase intention in online social shopping. Furthermore, investigating the impact of marketing mix elements like rewards and incentives is recommended, as these factors significantly influence consumers’ purchasing intentions for CPC products.

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Mobile Payment Adoption in Vietnam: A Two-Staged SEM-ANN Approach Luan-Thanh Nguyen , Tien-Thao Cong Phan, Duc-Viet Thi Dang , and Thuy-Thanh Thi Tran

Abstract By extending the newly proposed mobile application adoption paradigm to include prospect theory and flow theory and to make the behavioral purpose clearer, this study examines the adoption of mobile payments in Vietnam. Data from mobile payment consumers who have utilized mobile payment services was collected using a self-administered questionnaire. The behavioral intention to accept mobile payments is positively and meaningfully correlated with mobile social influence, mobile structural assurance, mobile facilitating condition, mobile performance expectancy, mobile effort expectancy, mobile perceived trust, and mobile perceived hedonic motivation, according to analyses using partial least squares structural equation modeling and artificial neural networks. With the implementation of mobile payment in Vietnam, the results also showed interactions between behavioral intention to use and mobile enabling circumstances. Practical and theoretical implications are subsequently discussed in light of the results. Keywords The modified UTAUT · Flow theory · Prospect theory · Mobile payment · Vietnam · ANN

1 Introduction Fintech, a digital revolution of the financial services industry in the Fourth Industrial Revolution, can lead to a cashless society and widespread financial inclusion [1]. Mobile financial services, a Fintech application, have grown rapidly since the L.-T. Nguyen (B) · T.-T. C. Phan Ho Chi Minh City University of Foreign Languages-Information Technology, HUFLIT, Ho Chi Minh City, Vietnam e-mail: [email protected] D.-V. T. Dang Posts and Telecommunications Institute of Technology, PTIT, Tran Phu, Ha Dong, Hanoi, Vietnam T.-T. T. Tran Vinh University, Vinh City, Nghe An Province, Vietnam © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_11

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early 2000s. Mobile banking services are growing due to the COVID-19 pandemic, e-commerce, and ICT. Mobile financial services encompass many money-related activities that users can access on their phones. Mobile financial services are “a variety of apps that enable users to exploit their bank account through their mobile phones,” allowing users to make purchases, move payments, and acquire credit and insurance [2]. Each category has three mobile financial services with similarities and distinctions. Mobile payment systems can process purchases at remote stores and near merchants. Mobile payments are mostly used for peer-to-peer (P2P) payments and remittances in developing nations, but are also used for electricity bills and purchases [3]. Cashless and smartphone payment technologies benefit consumers and businesses. Thus Vietnam promotes them. A national financial strategy may incorporate mobile e-payment in Vietnam. Over 60% of rural Vietnamese lack banking services. Mobile financial services enable cashless transactions and give microfinance and loans to the underserved, inspiring them. A cashless world would benefit companies and customers. The study, “ASEAN cash digitization-what it means for future corporate treasurers and customers,” found that cash payments dominate regional transactions despite electronic payment options [4]. Vietnam’s online cash-on-delivery rate is 90.17%, higher than Indonesia’s 65.30%. Vietnam has fewer electronic payments than its neighbors. The World Bank reports that 4.9% of non-cash transactions in Vietnam [2]. As it targets a cashless world and financial inclusion, the government should study mobile payment drivers. Reviewing the existing literature on mobile payment has identified several research gaps. First, previous research has extensively investigated the behavioral intention of individuals who utilize mobile payment services, employing various theoretical frameworks such as the Technology Acceptance Model (TAM) [5], Theory of Planned Behavior (TPB) [6], Unified Theory of Acceptance and Use of Technology [7], Innovation Diffusion Theory (IDT) [3], and Mobile Technology Acceptance Model (MTAM) [8]. However, limited attention has been given to the actual usage and adoption of mobile payment systems, which subsequently impact customer loyalty and are considered a comparative advantage for sustainable development. The discrepancy between user intentions and actual usage of mobile payment services becomes apparent when consumers discontinue their utilization due to encountering challenges or dissatisfaction [4]. There is a need to augment the current literature review on the aforementioned hypotheses by analyzing the factors that contribute to the practical adoption of mobile payment. Second, while a considerable amount of scholarly research on the adoption of mobile payment has predominantly concentrated on the utilization of Information System theories, which predominantly emphasize personal and technological factors, several studies have incorporated additional environmental factors into the traditional framework to enhance the understanding and forecasting of adoption [1, 3, 4, 9], which could not well reveal its complexity. Utilizing mobile technology is a multifaceted and complex phenomenon encompassing personal, technological innovation, and environmental influences [1]. Consequently, it is imperative to conduct a comprehensive examination of the adoption of mobile payment, considering the three distinct categories of

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factors concurrently. To effectively harness the potential of mobile technology, it is crucial to embrace a comprehensive strategy that considers the multifaceted aspects of its interaction and adapts to the ever-changing technological landscape. Third, a significant portion of academic research pertaining to mobile payment is frequently carried out in sub-Saharan African countries, including Ghana and Kenya, as well as in regions encompassing America (Brazil, U.S.), Europe (Spain, Italy), and South and East Asia (China, Indonesia, Malaysia) [1]. There is a necessity to conduct research pertaining to the practical implementation of mobile payment systems within various regions of developing nations, encompassing Vietnam. In order to fill the aforementioned gaps, this research will investigate the factors that influence the utilization of mobile payment in Vietnam, taking into account many viewpoints, including technological, human, and environmental factors. In order to accomplish this objective, it is necessary to integrate the Unified Theory of Acceptance and Use of Mobile Technology (UTAUMT), Flow theory, and Prospect theory. The study question focuses on the elements that have an impact on Vietnam’s adoption of mobile payments. The investigation will make three important contributions by responding to this question. By analyzing mobile payment from a variety of angles, the current study seeks to add to the body of literature already on the topic. This study provides insightful information and findings that can help those involved in the mobile payment service business create campaigns to encourage the adoption of mobile payments. Additionally, it can help government organizations create incentives and rules that will ease the transition to a cashless society. The remainder of the paper is divided into the following sections. This paper’s second section offers a thorough evaluation of the UTAUT model, prospect theory, and flow theory. The research hypotheses that will be examined in the analysis that follows are also included in this part. The research techniques used for this study are described in Sect. 3 of the document. The data analysis and discussion of the findings are presented in Sect. 4. The analysis of the research contributions, limitations, and suggestions for additional research are included in Sect. 5 of the paper.

2 Literature Review 2.1 The Unified Theory of Acceptance and Use of Mobile Technology, UTAUMT The coherent theory of technology adoption and use, UTAUT [10–12], incorporates eight models to represent technological acceptance activities and evaluates the most critical criteria for estimating the behavioral purpose to utilize a technology. Thus, the authors propose a unified theoretical model that combines fundamental elements of previous models. Age, gender, experience, and voluntariness of use affected two direct drivers of use activities (Behavioral Intention and Facilitating Conditions) and three direct determinants (Performance Expectation, Effort Expectation, and

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Social Influence). During a comprehensive analysis of prior research that followed the UTAUT model to explain its logic, merits, and drawbacks, Yan et al. [13] found that many studies did not follow all model constructs and instead used external variables to evaluate a single theory rather than the whole model. UTAUT was criticized for failing to adapt to services and organizations other than its original ones [14]. According to Yuan et al. (2021), invention diffusion theory has two fundamental concepts: the macro mechanism of innovation spread and the micro implementation process. The model’s focus on corporate settings and staff technology adoption has also been questioned [15]. Different job kinds and interaction complexity affect technology adoption [16–18]. UTAUT faces additional hurdles when explaining the uptake of mobile studies like mobile payment since potential adopters are niche mobile customers rather than generic technology consumers whose decisions are primarily motivated by their personal past. In light of the above and face validity, the UTAUT will be updated and renamed “the unified theory of acceptance and use of mobile technology” (UTAUMT) based on the most relevant literature review on mobile technology and four constructs similar to UTAUT to recognize its importance in studying IT and IT system adoption or use. Mobile performance and effort, social impact, and enabling conditions will comprise the four constructs.

2.2 Prospect Theory Kahneman and Tversky [19] developed the prospect principle to explain how individuals analyze and select options that include uncertainties and unpredictable effects. As a theory of cognitive science, prospect theory infers that the of individuals’ decision-making is more inclined to benefits and defeats rather than the end consequence. As such, deterrents are often more relevant than drivers in people’s decisionmaking, such as threats [20]. While Prospect theory was initially formulated for psychology and behavioral fields, it has been used to explain threats associated with technology usage in other settings, such as information management analysis. As there are inherent threats to mobile payment systems that may discourage citizens from utilizing the technology, it is important to consider the consequences of these risks. Thus, this study uses the prospect theory in this analysis to decide whether mobile perceived threats of mobile payment affect the usage of innovation by citizens to gain predictive power.

2.3 Flow Theory Flow was identified by Nakamura and Csikszentmihalyi [21] as a fun experience of paying maximum concentration while performing a task. A person becomes highly focused while witnessing flow and filters out unrelated emotions, impressions, and ideas. The perimeter of their consciousness is increasingly dwindling, and

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they respond only to precise objectives and immediate feedback [22]. It is essential to understand the role of flow in implementing mobile payment. This is because mobile payment takes less time to execute a transaction, which could be a cause for enjoyment. The flow principle has been implemented in various studies in recent years to explore the relationship between customers and technology [5]. In general, when conducting an action, the flow has been viewed as a desirable intrinsic reward [23] and thus serves as an essential construct in the mobile payment usage. Although the flow principle is an inherent incentive or hedonic motivation felt by people when utilizing technology [24], it is inserted into the parsimonious UTAUMT to supplement its technical values that only affect the characteristics of mobile technology. The flow principle is operationalized as mobile perceived hedonic motivation in this analysis, analogous to Nguyen et al. [2], to explore the degree of intrinsic enjoyment or hedonic incentive of using mobile payment in financial transactions.

2.4 Hypotheses Development and Proposed Conceptual Framework Tew et al. [25] coined the term “mobile performance expectancy” (MPE) to describe how much people expect mobile technology to improve their performance. The more useful mobile payment is; the more likely people are to use it. PE significantly improved behavioral intention to use mobile payment services, according to Nguyen and Nguyen [11]. Kang [26] also found that PE best predicts behavioral intention for users and non-users. Mobile payment is contactless and improves payment convenience, check-out speed, and financial management compared to cash and visa/master cards. Mobile payment apps allow users to add up to three credit cards and choose which ones to use on their phones. The results reduce and prevent credit card loss and disease transmission. This hypothesis assumes that customers will accept mobile payment if they see its benefits. The hypothesis is: H1: Mobile performance expectancy increases mobile payment intention in Vietnam. Venkatesh et al. found that mobile consumers expect mobile payments to be easy. In the early stages of mobile payment acceptance, ease of use affects consumer adoption. As shown by previous research, MEE and intent to act are firmly linked. Leong et al. [27] found that MEE affects public transportation NFC use. Al-Seadi et al. found that PE improves mobile payment intention [28]. These studies suggest that mobile payment system ease increases behavioral intention to use them. The hypothesis is: H2: Mobile effort expectancy increases mobile payment intention in Vietnam. Ooi et al. [28] coined “mobile social influence” (MSI) to describe how social networks affected mobile payment adoption. Mobile payment system users’ social links may

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influence their decisions. Based on their feelings about their influences, they may change their behavior. Leong et al. [27] found that social influence affected non-users more than users in mobile payment BI. More Vietnamese are losing money using Momo and other third-party mobile payment apps, which may affect their willingness to recommend or use them. The hypothesis is: H3: Mobile social influence decreases mobile payment intention in Vietnam. Venkatesh et al. found that MFC quality affects consumers’ expectations of mobile payment service convenience, security, and privacy. Facilitating conditions strongly predict mobile payment adoption [29]. Mobile payment services require a smartphone, internet access, and phone control. Mobile users in poor countries like Vietnam, where financial and technology literacy is low, need support to use cash less. The hypotheses are: H4a: Mobile facilitating condition increases mobile payment intention in Vietnam. H4b: Vietnamese mobile payment adoption is positively correlated with mobile facilitating conditions. In technological services, “mobile perceived risk” (MPR) is the fear that something bad will happen if the user uses the service [30]. Ooi et al. [30] say most technological advances are dangerous. Customers’ reluctance to use mobile payment services in this study is understandable given the risks of adopting and using cutting-edge technology. Many studies [27, 31] have found a negative relationship between risk perception and mobile payment service use. Increasing risk perception may decrease people’s desire to use. The hypothesis is: H5: Mobile perceived risk decreases mobile payment intention in Vietnam. Mobile structural assurance is trust in “guarantees, regulations, promises, legal recourse, or other procedures to promote success” (MSA) [32]. Legal and technical safeguards protect mobile payment users’ personal and financial data. Thus, if structural assurance is high, people feel safer using their phones to buy things and use mobile payments. This allows us to form a hypothesis: H6: Mobile structural assurance increases mobile payment intention in Vietnam. The intuitive belief that others will keep their promises and not act dishonestly when they want a mobile purchasing service is called “mobile perceived trust” (MPT). Kind, open, and knowledgeable retailers increase mobile payment usage [14, 25, 32, 33]. The lack of physical contact during online interactions causes this reaction. Thus, mobile perceived trust is crucial for online and smartphone purchases. Qualitative interviews showed that m-payment systems with trusted suppliers reduced risk [25]. Tew et al. [25] found that mobile payment confidence increases online purchase intent in China. Teo et al. [34] found a strong correlation between mobile users’ perceived trust and their mobile payment propensity. The hypothesis is:

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H7: A positive relationship exists between mobile perceived trust and mobile payment service use. Mobile perceived hedonic motivation includes intrinsic, hedonic, and satisfaction from mobile device use. Research suggests that hedonistic incentives may influence technology adoption and market acceptance [11]. Mobile payment makes financial more manageable and modern, which consumers like. The hypothesis is: H8: Hedonic motivation and behavioral intention to use mobile payment services are positively correlated. Behavioral Intention (BI) is a user’s likely behavior [2]. Making a technology’s behavioral goal more ambitious may increase adoption. Technology adoption studies have found a positive correlation between behavioral purpose and use [35]. In most mobile payment research, behavioral intention is the key variable [17, 36]. This means we do not know how much consumers’ preferences affect their mobile payment system use. “There is a substantial difference between a technology’s behavioral purpose and its actual use,” said Venkatesh et al. This suggests the hypothesis: H9: Mobile payment adoption is positively correlated with behavioral intention. From the hypotheses, the conceptual model of this study is provided in Fig. 1.

Fig. 1 Conceptual model

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3 Research Methodology From October 2022 to April 2023, three major Hochiminh City retail malls in Vietnam conducted a Google form survey to confirm the study methodology. Hochiminh City was chosen because it has 10% of the Vietnamese population. This study used nonprobability sampling because mobile payment users were not sampled. This profile includes respondents with a mobile payment account and made at least one purchase in the past year. Purposeful sampling provides the minimum sample size for PLS-SEM. G*Power version 3.1 with 0.8 statistical power, 0.05 margin error, 0.15 impact size, and eight predictors was also used to determine the minimum sample size. An outcome sample size of 109 is recommended. The minimum sample size is the minimal sample size needed to confirm or reject a minimum influence on SEM significance and power [37]. In PLS-SEM studies, Ng et al. [38] researchers are expected to use a larger sample size than the minimum because “larger samples more accurately represent the characteristics of the populations from which they are derived”. Over 391 questionnaires were available, meeting the sample size needed to confirm or deny a minimum impact. The survey instrument was based on our review of current and historical literature relevant to the study model. Some wordings were altered to fit the mobile payment environment. The analysis used the seven-point Likert-type scale from “1 (strongly disagree)” to “7 (strongly agree)”. Two sub-sections were used in the survey. The first focused on respondent demographics. The second of 31 questions focused on model core constructs. Due to sustainability in science, MPE, MFC, MSI, and MEE were revised using a scale from Venkatesh et al. [10] and Venkatesh et al.’s UTAUT study. Dang et al. [7] developed and verified MPHM measures. Nguyen et al. [12] provided MPSA, MPT, and MPR measurements. Then, the six-person expert panel was contacted to review the material. Content validity analysis was performed on the instrument to ensure that the selected items accurately represented the construct of interest [39]. Lynn [40] recommends three to ten professionals on the panel. Item-level content validity was first assessed using the content validity index (I-CVI), which is 1.00 for groups of 3–5 experts and 0.83 for groups of 6 experts [40]. The scale’s validity was assessed using Universal Agreement (UA) and Ave (Average) and content validity index for scales (S-CVI). For this reason, S-CVI/Ave > 0.90 is preferred [40]. S-CVI/UA may be excessively strict with many specialists.

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Table 1 Demographic profiles Demographic design

Frequency

Percentage (%)

159

40.66

Gender

Male Female

232

59.34

Educational level

Bachelor degree

234

59.85

Master degree

107

27.37

PhD degree Age

Income level

Experience in using MP platforms

50

12.79

From 19 to 25 years old

195

49.87

From 26 to 34 years old

116

29.67

From 35 to 45 years old

61

15.60

Above 45 years old

19

4.86

Less than $400

138

35.29

From $401 to $800

198

50.64

From $801 to $1200

24

6.14

Above $1200

31

7.93

Less than 1 year

89

22.76

1–3 years

195

49.87

More than 5 years

107

27.37

4 Results and Discussion 4.1 Profile of Respondents Female make up a larger share of mobile payment users than males. This indicates that women are more likely to use mobile payment methods purchasing online. This is intuitive, given the proliferation of online sales and discounts at the time of the data collection. The data also showed that, on average, respondents were between 19 and 25. In addition, most people who use mobile payments have at least a bachelor’s degree; thus, they are highly educated and capable of understanding all facets of the mobile payment system. Table 1 displays the respondents’ demographic information.

4.2 Assessing Outer Model First, evaluate the measurement model (outer model) to determine its utility and applicability before testing hypotheses using the structural model. The outer model evaluation statistical analyses tested internal consistency reliability, indicator reliability, convergent validity, and discriminant validity. Cronbach’s Alpha and Composite dependability should exceed 0.7 for internal consistency reliability assessment [41– 43]. The table showed that Cronbach’s Alpha and Composite dependability were

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above 0.7 [44–47]. Therefore, internal consistency reliability is established. Factor loading and average variance will also assess convergent validity—the degree to which construct measurements are conceptually comparable. For factor loading, only indicators with scores above 0.7 will be preserved and those below will be deleted from the final model (MEE1, MFC2) to test fit. Thus, Table 2 factor loading results imply reliability. The table shows all factor loadings exceeding 0.7. The averages above revealed that AVE values are within the acceptable 0.5 range. Table 2 Reliability and convergent validity Constructs

Items

Loadings

rho_A

Composite reliability

Average variance extracted (AVE)

BI

BI1

0.837

0.758

0.855

0.663

BI2

0.789

BI3

0.817

MEE2

0.919

0.836

0.922

0.856

MEE3

0.931

MFC1

0.826

0.812

0.886

0.722

MFC3

0.851

MFC4

0.871

MPA1

0.877

0.798

0.879

0.709

MPA2

0.802

MPA3

0.845

MPE1

0.832

0.808

0.880

0.711

MPE2

0.896

MPE3

0.798

MPHM1

0.916

0.894

0.929

0.813

MPHM2

0.866

MPHM3

0.923

MPR1

0.811

0.868

0.910

0.771

MPR2

0.918

MPR3

0.902

MPT1

0.880

0.838

0.894

0.737

MPT2

0.851

MPT3

0.844

MSA1

0.847

0.812

0.887

0.723

MSA2

0.840

MSA3

0.865

MSI1

0.781

0.828

0.890

0.730

MSI2

0.900

MSI3

0.877

MEE MFC

MPA

MPE

MPHM

MPR

MPT

MSA

MSI

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Table 3 HTMT criterion BI

MEE

MFC

MPA

MPE

MPHM

MPR

MPT

MSA

BI MEE

0.771

MFC

0.641

0.632

MPA

0.762

0.705

0.608

MPE

0.662

0.679

0.559

0.558

MPHM

0.834

0.672

0.622

0.583

0.537

MPR

0.830

0.679

0.542

0.599

0.588

0.688

MPT

0.772

0.681

0.539

0.807

0.584

0.696

0.609

MSA

0.759

0.756

0.718

0.642

0.683

0.799

0.733

0.757

MSI

0.605

0.621

0.686

0.530

0.478

0.648

0.571

0.585

0.685

In PLS-SEM, assessing discriminant validity is reconsidered. The discriminant validity-heterotrait-monotrait (HTMT) correlation technique ratio is a new way to measure discriminant reliability [46, 48–50]. When the HTMT result is near 1, the test lacks discriminant validity. The HTMT can be compared to predefined criteria. A higher HTMT value than this cutoff may indicate discriminant validity issues. Several researchers recommend a 0.85 score. Table 3 displays HTMT criteria scores above the indicated range and meeting the minimum meaning.

4.3 Examining Inner Structural Model The structure of the model has been revised after validating the measures model. This requires calculations of the coefficient of determination, path coefficients, and bootstrapping 5000 samples. As shown in Table 4 and Fig. 2, hypothesis testing has been assessed. The findings indicated that mobile social influence, mobile facilitating condition, and mobile structural assurance have insignificant relationships with behavioral intention. Thus H3 (β = − 0.033, p > 0.05), H4a (β = 0.062, p > 0.05), and H6 (β = − 0.071, p > 0.05) are unsupported. Moreover, mobile perceived risk has a significant negative effect on mobile payment user’s behavioral intention to use mobile payment. Hence, H5 (β = 0.293, p = 0.000) is supported. Furthermore, the result revealed that mobile performance expectancy, mobile effort expectancy, mobile perceived trust, and mobile perceived hedonic motivation have a significant positive relationship with behavioral intention to use mobile payment. Therefore, H1 (β = 0.080), H2 (β = 0.131), H7 (β = 0.183), and H8 (β = 0.346) are supported. Moreover, the relationship between behavioral intention and mobile payment adoption and between mobile facilitating conditions with mobile payment adoption are significantly positive. Thus, H4b (β = 0.253) and H9 (β = 0.468) are supported.

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Table 4 Hypothesis testing results Hypotheses

Path

Path coefficients

T Statistics (|O/ STDEV|)

P Values

Remarks

H1

MPE → BI

0.080

1.991

0.047

Supported

H2

MEE → BI

0.131

2.676

0.007

Supported

H3

MSI → BI

− 0.033

0.774

0.439

Unsupported

H4a

MFC → BI

0.062

1.104

0.270

Unsupported

H4b

MFC → MPA

0.253

5.286

0.000

Supported

H5

MPR → BI

0.293

5.960

0.000

Supported

H6

MSA → BI

− 0.071

1.295

0.195

Unsupported

H7

MPT → BI

0.183

3.145

0.002

Supported

H8

MPHM → BI

0.346

6.419

0.000

Supported

H9

BI → MPA

0.468

9.068

0.000

Supported

p>0.05: unsupported

Fig. 2 Structural model testing

4.4 ANN Analysis This study uses a multi-layer perceptron ANN because feed forward backpropagation reduces analytical errors in ANNs [44, 51]. A ten-fold cross-validation approach used 90% of the data for training and 10% for testing to prevent overfitting [52]. The number of hidden neurons was automatically generated by SPSS v25 using a sigmoid function as the non-linear activation function for the hidden and

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output layers [53]. To assess prediction accuracy and relevance, researchers should calculate root-mean-square error values and count hidden neuron synaptic weights [54]. A small root-mean-square error indicates a good data fit and prediction [33, 48]. After determining predictive accuracy and predictive relevance, a sensitivity analysis was performed to determine the relative significance of exogenous factors to the endogenous variable. As shown in Fig. 3, each ANN model’s non-zero synaptic weights are linked to at least one hidden neuron, indicating predictive significance. Table 5 also shows that these models predict accurately with root-mean-square errors near zero. The sensitivity analysis in Table 6 compares the importance of input neurons (exogenous variables) in each ANN model. This matters because ANN models are accurate and relevant. MPT is the most important factor in ANN Model A’s behavioral intention. ANN Model B’s analysis of mobile payment adoption ranks BI as the most important factor. However, MPR, MEE, MPE, and MPHM have the greatest impact on mobile payment intention.

4.5 Discussion The study found no positive impact on Vietnamese users’ Mobile Service Innovation (MSI), Mobile Financial Capability (MFC), and Mobile Service Adoption (MSA) for mobile payment. The current study found no statistically significant association between the above criteria and mobile payment service use. Our findings on MSI match those of Senyo and Osabutey [55]. Influencers or prominent individuals may not be the main driver of mobile payment adoption in Vietnam, as the decision to use mobile payment is based on individual preferences and needs. Similar to the mobile enabling condition, the behavioral intention was not statistically significant. This finding supports Senyo and Osabutey [55] but contradicts Loh et al. [9]. The prevalence of mobile payment services and the ease of using mobile devices and payment apps explain their adoption. Mobile structural assurance and behavioral intention in Vietnam are also insignificant. This finding supports Tew et al. [25] but contradicts Nguyen et al. [12]. In Vietnam, people trust the Internet and social media information. Their main concern is understanding mobile payment app legal remedies and rules in case of issues. Mobile communications service providers limit mobile payment users to VND 10 million monthly. However, Fintech applicants must deposit funds from their bank accounts before making payments. Thus, financial loss is rare, and structural certainty in the mobile context is not a significant concern. This study supports Al-Saedi et al. [56] findings that performance expectations improve. These studies show that individuals are more likely to adopt mobile payment systems if convenient. However, unlike previous studies, this study found no significant influence of mobile payment drivers on behavioral purposes. In Vietnam, most people use mobile payment methods to transfer small amounts of money. The financial risks of investing in mobile apps limit funding. Mobile payment platforms allow up to $1200 in monthly transfers and payments. Thus, the mobile payment system

222

Fig. 3 The ANN models developed

L.-T. Nguyen et al.

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Table 5 Root-mean-square error values incurred during training and testing stages Neural network

Model A (output: BI)

Model B (output: MPA)

Training

Testing

Training

Testing

ANN1

0.376

0.350

0.363

0.250

ANN2

0.357

0.316

0.375

0.442

ANN3

0.356

0.375

0.378

0.319

ANN4

0.363

0.383

0.352

0.543

ANN5

0.337

0.480

0.371

0.293

ANN6

0.334

0.421

0.427

0.388

ANN7

0.346

0.318

0.402

0.364

ANN8

0.357

0.316

0.377

0.306

ANN9

0.343

0.362

0.389

0.244

ANN10

0.369

0.410

0.362

0.375

Means

0.354

0.373

0.380

0.352

SD

0.013

0.050

0.021

0.087

Table 6 Sensitivity analysis Neural network

Model A (output: BI) MPE

MEE

MPR

Model B (output: MPA) MPT

MPHM

MFC

BI

ANN1

0.118

0.151

0.196

0.315

0.219

0.264

0.736

ANN2

0.134

0.167

0.228

0.273

0.199

0.259

0.741

ANN3

0.241

0.049

0.255

0.322

0.134

0.252

0.748

ANN4

0.171

0.081

0.168

0.351

0.229

0.194

0.806

ANN5

0.129

0.113

0.229

0.290

0.240

0.288

0.712

ANN6

0.149

0.121

0.344

0.258

0.128

0.243

0.757

ANN7

0.172

0.063

0.253

0.331

0.199

0.291

0.709

ANN8

0.200

0.169

0.232

0.255

0.143

0.246

0.754

ANN9

0.106

0.160

0.262

0.238

0.233

0.352

0.648

ANN10

0.137

0.101

0.318

0.297

0.146

0.302

0.698

Average relative importance

0.156

0.118

0.249

0.293

0.187

0.269

0.731

Normalized relative importance (%)

46.70

64.40

91.40

100.0

45.00

25.40

100.0

may not be as effective as thought. The effort required to use mobile payment services also affects people’s willingness to adopt them. This supports the hypothesis that mobile payment convenience increases adoption. To increase mobile payment adoption in Vietnam, developers should focus on creating user-friendly and manageable apps. The perception of confidence in mobile platforms also affects Vietnamese

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people’s intention to use mobile payment services. This conclusion agrees with Tan et al. [31]. According to research hedonic motivation is the strongest predictor of mobile payment adoption. Mobile payment apps are a major driver of customer selfmotivation, according to Dang et al. [4]. Nguyen et al. [2] suggest that people may be interested in innovative methods that address their hedonic motivation. Therefore, people prefer to acknowledge and embrace specific inventions because they associate them with hedonic pleasure. Additionally, people associate pleasure and escape with their electronics. The software’s appealing designs and colors reinforce this association [27]. Nguyen et al. [2] found that smartphone users often feel pleasure and amusement when using mobile payment services. This is because these apps are easy to use, improving customer satisfaction. The traditional banking channel’s time-consuming nature frustrates prospective customers [35]. Mobile devices are associated with fun, reducing the stress of traditional banking and improving the customer experience. The present study defines perceived risk as a major determinant of mobile payment adoption, following Hammood et al. [35]. According to Tan et al. [57], users will adopt mobile payment if service providers provide secure, user-friendly, and satisfactory services. Vietnamese users may be less likely to use apps due to their perception of their risks. The study found that behavioral intention positively affects mobile payment adoption and use. This finding suggests that people who intend to use mobile payment services will do so. This means that consumer tools and support programs make mobile payment services more appealing. MFC and customers’ technology adoption have also been studied [12].

5 Conclusion and Practical Implications The primary objective of the proposed research is to generate multiple significant theoretical advancements. This study aims to offer a comprehensive understanding of the determinants of mobile payment adoption in Vietnam by incorporating the Unified Theory of Acceptance and Use of Mobile Technology (UTAUMT), Flow theory, and Prospect theory. Integrating of various factors enables a comprehensive comprehension of adoption, encompassing technological acceptance, psychological experiences, and decision-making processes. Classifying factors into technological, environmental, and personal dimensions provides a comprehensive and detailed examination of the diverse influences that impact the process of adoption. By conducting a thorough analysis of empirical usage patterns, this research serves to establish a connection between theoretical frameworks and practical implementation, thereby improving the relevance of theoretical constructs in real-life situations. The study also aims to identify the factors that promote and hinder the adoption of mobile payment, offering a comprehensive understanding of the dynamics involved in the adoption process. The present study possesses the capacity to provide valuable cross-cultural perspectives, enhance existing theoretical frameworks, and make

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meaningful contributions to the ongoing development of technology adoption theories. The primary objective of this study is to enhance comprehension regarding the adoption of mobile payment methods and to provide valuable insights for industry stakeholders and governmental organizations in developing effective strategies. The use of mobile technology in Vietnam has increased. Smartphones have GPS and games that users can quickly access, improving their quality of life and convenience. Wireless connectivity and digital media are replacing physical mediums for financial transactions and remittances. The growing information technology sector in Vietnam offers improved purchasing capabilities and accessibility, supporting the goals of a cashless society and financial inclusion. Mobile phone payment is popular. Thus, these tools are highly beneficial and user-friendly for locals. Examining mobile payment technologies’ adoption and use factors. Mobile payment companies, telecommunications providers, merchants, banks, and app developers depend on value research. This study shows mobile payment system designers practical variables to consider. MEE, MPR, MPHM, MPE, MPT, and MFC can affect consumer approval. Therefore, service providers must stay abreast of efforts to reduce costs and improve benefits to increase behavioral intention. For instance, if mobile payment services cost more than alternatives or fail to pay bills or transfer funds, users uninstall the app and complain to others. Mobile payment system developers must also prioritize user-friendliness and consumer benefits. Trust also promotes longterm user engagement with mobile payment services, promoting financial inclusion. Users trust and feel safe using mobile payment systems if their data is secure and confidential. It is essential to conduct additional research on customer loyalty in the future, considering the constraints of the current study. The present study is impeded by its utilization of a cross-sectional design, which limits the ability to obtain a comprehensive understanding of the evolution of consumer behavior. The tendency of users to discontinue their usage of mobile payment applications as a result of concerns related to security or satisfaction, especially in the presence of technical malfunctions, highlights the significance of implementing enduring relationship strategies.

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Millennials Fintech Services Adoption: What Matters Most? Mosharrof Hosen , Ai-Fen Lim, Taslima Jannat, Naveed R. Khan, and Chuen-Khee Pek

Abstract Despite the rise of fintech in today’s digital world, there still appears to be a lack of comprehensive understanding and research on fintech adoption by millennials. This is especially alarming since millennials are considered a critical group for fintech adoption, showing a higher tendency to adopt financial technology than other generations. Various studies have underscored the significant connection between financial behavior and fintech adoption, and in-depth investigation is necessary to unravel the factors contributing to the decision of millennials to adopt or not to adopt fintech. Therefore, this study bridges the gap by investigating whether financial knowledge (FK), level of trust (LT), relative advantage (LA), level of convenience (LC), and service quality (SQ) have an impact on millennial fintech adoption (MFA). A total of 236 usable data were collected through the questionnaire survey, which was analyzed using a two-hidden-layer deep learning ANN for statistical analysis. Results show that the strongest predictor of MFA is FK*LT (100%), followed by RA (98.7%), FK*RA (83.51%), FK*SQ (74.33%), SQ (72.35%), LT (59.95%), and FK*LC (55.67%). Nevertheless, LC (43.27%) and FK (32.24%) are not significantly related to MFA. Financial institutions and policymakers may devise methods to promote financial inclusion and expand the reach of fintech solutions by understanding the factors that influence millennials’ adoption of fintech. Keywords Financial Technology (FinTech) · ANN · Millennials · Financial services · Digitalization

M. Hosen (B) · A.-F. Lim · T. Jannat · N. R. Khan Faculty of Business and Management, UCSI University, Kuala Lumpur, Malaysia e-mail: [email protected] C.-K. Pek Taylor’s Business School, Faculty of Business and Law, Taylor’s University, Subang Jaya, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_12

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1 Introduction The financial technology (fintech) revolution has transformed the way financial services are assessed and consumed around the world. Fintech narrows the gap between financial institutions and consumers by reducing the cost of accessing financial services, thereby promoting financial inclusion at large [1]. It utilizes technological advancement to enhance various financial activities incorporating both incremental measures like APIs and disruptive technologies such as AI and blockchain [2]. Through these innovations, fintech facilitates the provision of a wide range of financial services, transcending traditional business models within the financial industry. It encompasses almost all services offered by conventional financial institutions [3]. Due to the convenience, accessibility, and flexibility of fintech services, it has the potential to attract and engage users having technological orientation. Studies suggested that millennials are the significant demographic group that has been considered the largest consumer of fintech [4–6]. Millennials grow up in a digital age and exhibit unique technological and social characteristics [7], as a result, it becomes more interesting when it comes to the usage of technological-based financial services. Thus, researching millennials’ fintech services adoption is crucial for understanding these underlying concepts to draw conclusions for further research and provide guidelines for the fintech service providers. More specifically, research on this theme harnesses insights into the behavior of the digitally savvy generation and contributes to government’s financial inclusion efforts. It enables service providers to strategically plan more robust services for their customers, thus expanding their business. This research aims to examine the drivers of adaptation patterns of fintech services among millennials in Malaysia, a country that witnessed rapid growth in both its fintech industry and millennial population [8, 9]. Our research contributes to the advancement of fintech as a transformative force in the financial industry and its impact on the lives of individuals and societies. According to the Department of Statistics Malaysia, the number of millennials in the country has grown steadily, and account for 28% of the total population in 2022. This figure shows a significant cohort of digitally savvy individuals who can be the prime target for fintech adaptation. Simultaneously, Malaysia’s fintech market has also witnessed remarkable growth [10] due to the support of government initiatives [11], regulatory framework [12], and a vibrant startup ecosystem [13]. Thus, the country has positioned itself as a fintech hub in the Southeast Asia region. As reported by Securities Commission Malaysia, the fintech market’s transaction value reached MYR 8.8 billion in 2022, which reflect a compound annual growth rate of 22% from 2018 to 2022. Despite the potential and growth opportunities, the adoption of fintech services among millennials in Malaysia is not without its challenges. Factors such as trust [14], convenience [15], relative advantage [16], services quality [17] and financial knowledge [18] play a crucial role in shaping millennials decision-making processes when it comes to engaging with fintech platforms. PwC Malaysia reported that in 2022 while 60% of Malaysian millennials were aware of fintech services, only 28% had adopted

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them. Thus, this discrepancy highlights the need for a deeper understanding of the factors driving or impeding fintech adaptation among millennials in Malaysia. Therefore, this research investigates how trust, convenience, relative advantage, services quality, and financial knowledge shape millennials fintech adoption. These dimensions are discussed briefly in this section followed by the comprehensive discussion in literature review section. Trust has been found to be an important factor in engaging users to adopt fintech and received considerable attention in online commerce since it significantly affects user adoption of various services [16]. In the context of online transaction, trust is the level of confidence that enables consumers to willingly place themselves in a vulnerable position with online vendors in anticipation of receiving desired service after assessing the vendor attributes [19]. Therefore, it is vital for service providers to build the trust of millennials since it encourages them to adopt fintech. Convenience also plays a pivotal role in driving fintech adoption services. In the context of online transaction, convenience refers to quick transaction, high acceptance rate, easy information access and greater utility [16]. In order to facilitate individuals to conduct their businesses without any difficulty, fintech service providers must consider increasing the convenience in their services. Relative advantage refers to the extent to which an innovation is perceived as superior to the idea it replaces. The greater the recognition of an innovation’s relative advantage, the greater the likelihood of its adoption [20]. Through fintech users can perform financial transactions easily and quickly, thus saving time and effort as compared with the traditional methods [21]. Therefore, it may develop a favorable inclination towards adoption of fintech among individuals. Service quality in technology-based services encompasses factors such as system response speed, reliability, reducing uncertainty, user friendliness and fulfilling expectations [22]. Thus, quality services influence user adoption intention by demonstrating the provider’s ability to meet service requirements [16]. Furthermore, service quality enhances effort expectancy by facilitating technology usage. Therefore, service quality significantly contributes to the user’s intention of the adoption of fintech services among individuals. Financial knowledge equips individuals with key financial concepts through which individuals can make informed decisions when selecting and utilizing fintech services [23]. Financial knowledge empowers individuals to evaluate and compare the various financial products and series offered by fintech platforms [24], thus fintech providers should consider factors such as fees, interest rates, terms and conditions and regulatory compliance. This enables the individuals to choose suitable services and thus promotes financial inclusion through fintech adoption. This research provides a comprehensive understanding of the factors that influence millennials’ fintech adoption and contributes to the realm of knowledge on technology adoption. This study not only fills a gap in the literature but also provides insights that can drive the growth of fintech services tailoring to the unique needs of Malaysian millennials, thus fostering financial inclusion and economic development.

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Furthermore, the findings offer practical implications for fintech providers, policymakers, and marketers on how to enhance service quality and trust and highlight the importance of convenience, relative advantage, and financial knowledge in attracting and retaining millennial customers.

2 Literature Review Financial technology (fintech) has recently gained substantial attention, due to its potential to transform the financial transaction landscape and provide consumers with a simpler way to access different financial products through digital platforms [2]. Researchers have identified several predictors that can significantly influence consumers’ fintech adoption behavior. However, they argued that the context and complex interaction of different antecedents including trust [14], convenience [15], relative advantage [16], service quality [17], and financial knowledge [18] may play a crucial role in fintech adoption behavior. Researchers also found that age can significantly influence an individual’s fintech adoption behavior [25]. Therefore, future studies should emphasize diverse groups [26], and cultural contexts to provide deep insight on the fintech adoption behavior [25, 27]. Trust can be considered as a fundamental factor driving fintech adoption among consumers [16]. It is conceptualized as a psychological expectation that is developed over time through experience with vendors, focusing on attributes such as competency, predictability, and goodwill [28]. The benefits and risks including service quality, information quality, and system quality, as well as perceived uncertainty associated with fintech transactions significantly influence an individual’s level of trust, and, consequently, on commercial transactions [16, 29, 30]. Prior research found evidence that trust plays a significant role in consumers’ willingness to adopt mobilebased commercial transactions [16] and other fintech services [28, 31]. However, there have been conflicting results, whereby several studies suggested that trust is not significantly related to millennials’ fintech adoption behavior [32]. Researchers argued that issues related to system competency and transparency of current fintech systems could be a reason behind the inconsistent results [32], thus more research is required in this area [27]. Relative advantage is another important predictor that refers to an individual’s perception of an innovation’s superiority over existing practice [33]. Researchers argued that individuals are more likely to adopt fintech services if they perceive the new technology is better than the traditional methods. Evidence from early studies demonstrates the significance of relative advantage in shaping consumers’ adoption of Islamic banking services [34], mobile banking [35, 36], and fintech products and services [33, 37]. Nonetheless, several researchers found no significant relationship between relative advantage and SMEs’ adoption of fintech in the Indonesian context [38]. The mixed findings indicate the importance of taking context into account when analyzing fintech relative advantage and adoption behavior.

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Convenience plays a crucial role in fintech adoption behavior. Convenience encompasses aspects such as time flexibility, easy access to information, and location facility during the online purchase [25]. Previous research found that convenience, a dimension of perceived benefit can significantly influence users’ fintech service adoption intention [39]. Researchers also found that the flexibility of time and location offered by fintech service [40] has a positive impact on customer satisfaction and fintech adoption behavior [41]. Thus, this study posits that the seamless experience provided by the fintech services may contribute to attract and retain millennial consumers. Some studies emphasized service quality as it is an important determinant of technology-based services adoption behavior [22]. Service quality refers to an individual’s comparison of the actual service they received with what they anticipated. Extant studies found that service quality can significantly influence customer satisfaction and consequently, AI-enabled fintech service adoption behavior [41]. In South Korea, researchers examined the relationship between service quality and consumers’ fintech adoption intention [27], and found that service quality has an indirect effect on consumers’ fintech continuance intention where trust and perceived risk mediate the relationship between these variables. The authors argued that the relationship between these variables may change based on different context thus an alternative model should be developed, and different context should be considered to determine the relationship between trust, perceived risk, IT quality, and fintech adoption behavior [27]. Researchers also argued that financial knowledge is a key factor that equips individuals with the necessary understanding to make informed decisions about fintech services [23]. Financial knowledge encompasses the basic knowledge of financial concepts and numeracy skills applicable in a financial context [42], and can significantly influence an individual’s attitude and level of confidence in financial products [42] and, consequently, fintech adoption behavior [18, 43]. Extant studies have found that financial knowledge can moderate the relationship between attitude, perceived behavioral control and investment intentions [44] as well as the relationship between individuals’ locus of control and their financial behavior [45]. It also moderates users’ perception of ease of using fintech and reduces the perceived risk of fintech use [46]. Researchers also found that it is not actual financial knowledge, but perceived financial knowledge is related to fintech adoption behavior [25]. This study thus argues that since knowledge empowers individuals to evaluate and compare different financial products, it might strengthen the relationship between millennials’ fintech adoption behavior and its other predictors. This study thus emphasizes providing a comprehensive understanding of the factors that influence millennials’ fintech adoption behavior and contributes to the realm of knowledge on technology adoptions (Fig. 1).

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Fig. 1 Research framework

3 Research Method The data were collected from millennials who are in the range of 25–40 years old and currently either working or studying in Malaysia. The population sample could not be easily contacted; hence the snowball sampling method was utilized to choose potential respondents. Since most of the institutions do not disclose staff information on the website due to privacy issues, some assistance from well-known staff and students was needed to contact the respondents in the first round. In later stages, participants were invited to name potential participants, who were then sent an official invitation email. Furthermore, [47–49] advised against using measurement items from only one source because some of them might not work in various research situations, therefore leading to bias. In order to retain what they were designed to measure, construct items were collected from various published articles, and questionnaire statements were modified accordingly. Before the questionnaires were sent to the respondents, the pre-test and pilot study were performed to enhance the face and contend validity of the questionnaire [50]. After refinement, the questionnaire was distributed to final respondents using a drop-off and pick-up strategy. A total of 236 valid questionnaires were received and used for further statistical analysis.

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Table 1 Demographic analysis Profile

Total

Percent

Profile

Total

Percent

Occupation

Sex Male

122

51.69

Service holders

22

9.32

Female

114

48.31

Professional

46

19.49

Office worker

78

33.05

Age 35–40

92

38.98

Civil servant

41

17.37

31–34

89

37.71

Homemakers

24

10.17

25–30

55

23.31

Students

17

7.20

Others

8

3.39

PhD

7

2.97

Mobile internet experience

Masters

72

30.51

6

77

32.63

Education background Business

83

35.17

Science

62

26.27

Arts and humanities

63

26.69

Others

28

11.86

4 Analysis 4.1 Demographic Profile As shown in Table 1, most respondents have a bachelor’s degree or higher and have used mobile internet more than once.

4.2 Data Analysis and Results This study used SPSS software, which included the artificial neural network (ANN) predictive-analytical technique. ANN is an intelligent machine meant to mimic the way the human brain functions. ANN is effective for analyzing non-compensatory decision and non-parametric datasets while providing the highest prediction quality [45, 51]. The input, hidden, and output neurons of an ANN can capture both linear and non-linear relationships between variables in a large network [46, 52]. Given that all variance inflation factor (VIF) values were less than the threshold value of 3 [47, 53],

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Table 2 Result of reliability and multicollinearity Constructs Items Loadings α FK1 Financial knowledge FK2 FK3 Level of trust

Relative advantage

0.863

VIF

Constructs

Items

Loadings α

0.864

0.965 1.071 Level of LC1 convenience LC2

0.917

0.899

LC3

0.971

LC4

0.798

LT1

0.813

LT2

0.892

LT3

0.839

LT4

0.657

LT5

0.646

RA1

0.746

RA2

0.869

RA3

0.893

RA4

0.902

0.856 1.042 Service quality

Millennials 0.886 1.031 fintech adoption

0.869

SQ1

0.631

SQ2

0.884

SQ3

0.879

SQ4

0.776

MFA1 0.974

VIF

0.940 1.130

0.820 1.052

0.979

MFA2 0.988 MFA3 0.976

as shown in Table 2, the multicollinearity problem was eliminated. Also, the Cronbach’s alpha (α) values were above the limit of 0.70, indicating that all constructs’ items were reliable. Next, we employed two hidden layers of neurons with sigmoid activation functions to automate feed-forward back-propagation. Furthermore, by using the suggested procedures of ten-fold cross-validation with 90% of training data and 10% for data testing with a minimum of four hidden neurons, we could improve prediction accuracy and good data fit [48]. The ANN model (Fig. 2) demonstrated higher prediction accuracy because the root mean square of error (RMSE) values for both training (0.181) and testing (0.171) were minimal (Table 3). Furthermore, the amount of non-zero synaptic weights connected with two-layer hidden neurons (Table 4) indicated the validity of the predictor constructs. We performed sensitivity analysis to rank the significance of independent variables based on normalized relative relevance (NRR) values. From Table 5, the most important predictor of MFA is FK*LT (100%), followed by RA (98.7%), FK*RA (83.51%), FK*SQ (74.33%), SQ (72.35%), LT (59.95%), and FK*LC (55.67%). However, both LC (43.27%) and FK (32.24%) were shown to have no significant connection with MFA. Indeed, based on the goodness-of-fit test, the two-hidden-layer ANN model demonstrated a strong prediction accuracy of 77.2% on the variance in MFA.

5 Discussion and Conclusions Based on the NRR above 50%, we found that MFA has a significant and positive correlation with FK*LT, RA, FK*RA, FK*SQ, SQ, LT, and FK*LC, but LC and FK were revealed to have no significant correlation with MFA.

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Fig. 2 ANN model

Uddin et al. [18] in their conceptual study discussed how the FK and essential trust could influence the adoption of fintech, which has been partially confirmed in our empirically validated study. We found that consumers who have a higher LT in fintech platforms were more likely to use fintech. Furthermore, Lian and Li [14] acknowledged that compatibility by confirming that mobile payment RA could affect consumer adoption intention. Our research found that customer perceptions of RA increased the perceived value of using fintech and stimulated its adoption. Gao and Waechter [16] asserted that compatible services improved mobile government services, and our study found that consumers who are satisfied with the quality of fintech services are more likely to use them. Contrary to Uddin et al. [18], a sufficient level of financial knowledge alone would not result in a readiness to use fintech. Instead, it is the influence of FT, RA, and SQ on the relationship between FK and MFA that will ultimately result in a willingness to adopt fintech. Given that Malaysian millennials are more inclined to check online ratings on electronic products [49], their considerable FK will drive them to choose fintech after gaining an understanding of mutual trust, RA, and SQ through

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Table 3 Measurement of RMSE Input neurons: FK, LT, RA, LC, SQ, FK*LT, FK*LC, FK*RA, FK*SQ Output neuron: MFA Neural network

Training

Testing

N

SSE

RMSE

N

SSE

RMSE

Total

1

212

6.884

0.180

24

0.697

0.170

236

2

209

6.547

0.177

27

1.048

0.197

236

3

209

6.785

0.180

27

0.896

0.182

236

4

213

6.549

0.175

23

0.966

0.205

236

5

209

7.261

0.186

27

0.532

0.140

236

6

215

6.728

0.177

21

0.824

0.198

236

7

210

6.847

0.181

26

0.663

0.160

236

8

211

6.903

0.181

25

0.735

0.171

236

9

211

7.258

0.185

25

0.340

0.117

236

10

208

6.944

0.183

28

0.822

0.171

236

Mean

6.871

0.181

Mean

0.752

0.004

SD

SD

0.171 0.027

online reviews. Furthermore, our study disagreed with [16] that perceived convenience provided benefit to users in facilitating their adoption of mobile payments. We discovered that in general, Malaysian millennials do not believe that the degree of convenience can directly cause them to adopt fintech, but that it may influence them if they have adequate FK. Millennials, encompassing those individuals born between 1980 and 1996, exhibit a strong proclivity for embracing novel technologies in comparison to other age groups. In order to garner millennials’ trust, it is imperative that fintech enterprises ensure their platforms’ robustness and reliability. Notably, fintech has created opportunities for new entrants into the financial industry, placing traditional entities under the yoke of competition. In light of this phenomena, financial institutions must continue to adapt and innovate to keep up with evolving consumer needs while retaining their market share. Furthermore, a consequence of millennial fintech adoption is the possibility of enabling greater financial inclusion through offering access to financial services and products that were formerly too expensive or unattainable for many people. Therefore, financial institutions must contemplate how to harness fintech to satisfy all their clients’ requirements while simultaneously remain competitive. Additionally, apprehending the factors that influence millennials’ adoption of fintech and meeting their demands constitute a critical task for the banking industry. By attending to these concerns, traditional financial institutions can tap into the burgeoning community of fintech-savvy millennials while keeping abreast of new patterns in the industry. It is vital to note that while millennials have

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Table 4 Validity of input neurons Input neurons

Networks 1

2

3

4

5

6

7

8

9

10

FK

7

7

7

7

7

7

7

7

7

7

LT

7

7

7

7

7

7

7

7

7

7

RA

7

7

7

7

7

7

7

7

7

7

LC

7

7

7

7

7

7

7

7

7

7

SQ

7

7

7

7

7

7

7

7

7

7

FK*LT

7

7

7

7

7

7

7

7

7

7

FK*RA

7

7

7

7

7

7

7

7

7

7

FK*LC

7

7

7

7

7

7

7

7

7

7

FK*SQ

7

7

7

7

7

7

7

7

7

7

TMC

5

5

5

5

5

5

5

5

5

5

EM

5

5

5

5

5

5

5

5

5

5

TR

5

5

5

5

5

5

5

5

5

5

TE

5

5

5

5

5

5

5

5

5

5

IN

5

5

5

5

5

5

5

5

5

5

FK*LT

5

5

5

5

5

5

5

5

5

5

FK*RA

5

5

5

5

5

5

5

5

5

5

FK*LC

5

5

5

5

5

5

5

5

5

5

FK*SQ

5

5

5

5

5

5

5

5

5

5

Hidden layer 1

Hidden layer 2

emerged as the leading users of fintech services, other age groups may likewise display an interest in embracing these technologies, particularly when fintech products improve and advance. In short, some observers of fintech speculate that older generations have forestalled admittance to fintech due to its failure to address their unique concerns [54]. Although this study has substantial contributions it is not without limitations. This study collected data from Kuala Lumpur and Selangor area, therefore the results derived from this study may not represent the whole country population, and hence we suggest that future studies could collect data from different states of Malaysia. Moreover, we only used ANN analysis which can only tell us which constructs have more influencing power. Further studies can be carried out through SEM which can address constructs’ significance or insignificance.

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Table 5 Sensitivity analysis Neural network

FK

1

0.063 0.107 0.190 0.115 0.114 0.081

LT

RA

LC

SQ

FK*LT FK*RA FK*LC FK*SQ 0.149

0.016

0.166

2

0.019 0.105 0.144 0.060 0.178 0.135

0.196

0.018

0.144

3

0.023 0.147 0.185 0.016 0.163 0.094

0.219

0.070

0.084

4

0.016 0.073 0.223 0.054 0.130 0.173

0.128

0.092

0.111

5

0.035 0.170 0.133 0.070 0.172 0.056

0.133

0.195

0.036

6

0.080 0.093 0.177 0.072 0.115 0.157

0.124

0.032

0.151

7

0.093 0.018 0.178 0.096 0.113 0.245

0.084

0.055

0.118

8

0.057 0.032 0.155 0.067 0.085 0.141

0.094

0.094

0.275

9

0.045 0.202 0.183 0.085 0.088 0.156

0.048

0.155

0.037

10

0.089 0.020 0.024 0.063 0.009 0.375

0.172

0.171

0.077

Mean importance

0.052 0.097 0.159 0.070 0.117 0.161

0.135

0.090

0.120

83.51

55.67

74.33

3

7

4

Normalized 32.24 59.95 98.70 43.27 72.35 100.00 importance (%) Ranking

9

6

2

8

5

1

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Perceived Risk of Users’ Intention to Use Cryptocurrency in Malaysia: A Multi-analytic Approach Redhwan Al-amri, Shuhd Al-shami, and Gamal Alkawsi

Abstract Cryptocurrency, as the primary successful implementation of blockchain technology, has revolutionized the financial industry. These decentralized digital currencies operate without a central authority, enabling users to conduct independent and efficient transactions for goods and services. By leveraging blockchain platforms, cryptocurrencies confirm the security and legitimacy of these transactions while keeping fees low. Despite its potential, the global acceptance of cryptocurrencies, including Malaysia, has not reached desired levels. This can be attributed to consumer concerns regarding perceived ease of use, perceived usefulness, social influence, and perceived risk. Considering these factors, this research aims to investigate the users’ perceptions of accepting cryptocurrency applications from a risk perspective. The research utilises the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Perceived Risk Theory to establish and evaluate various hypotheses. The study involved a sample size of 496 individuals. The proposed model is validated via Structural Equation Modelling (SEM), and the data is utilised as inputs for an Artificial Neural Network (ANN) model to predict perceived risk factors. The outcomes show that Performance Expectancy, Effort Expectancy, Social Influence, Financial Risk, and Psychological Risk significantly influence the users’ behavioral intention to use cryptocurrency in Malaysia from a risk perspective. R. Al-amri (B) Centre for Professional Learning and Leadership, Institute of Education and Humanities, University of Wales Trinity Saint David, Swansea, UK e-mail: [email protected] S. Al-shami School of Business, Institute of Management and Health, University of Wales Trinity Saint David, Swansea, UK e-mail: [email protected] G. Alkawsi Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Malaysia Faculty of Computer Science and Information Systems, Thamar University, Thamar, Yemen G. Alkawsi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_13

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Keywords ANN · Adoption · Cryptocurrency · Intention · Perceived risk · Malaysia · UTAUT

1 Introduction Centralised infrastructure is the underlying foundation of all financial institutions, which operates under the concept of intermediaries to support every formal financial transaction, from paying a bill to purchasing and transferring money and other services. Governments and central banks play a major role in such infrastructure by regulating and controlling every transaction. The conceptual pattern of centralised payment methods has held steady despite the application of modern technologies, which makes the transaction process costly and time-consuming [1]. In Malaysia, cryptocurrencies currently exist in a legal grey area, where their status is neither explicitly acknowledged as legal nor illegal. The central bank informed the people to be vigilant about the risks associated with using cryptocurrency [2]. Similarly, Malaysia provides a particular opportunity for cryptocurrency activities to be explored. Bank Negara Malaysia has regulations and guidelines for preventing money laundering and anti-terrorist funding, specifically addressing the use of cryptocurrencies to generally legalise it in the country, which took into consideration feedback received on the reporting proposal announced on Dec. 14 2017 through the public consultation era. The policy aims to ensure the implementation of robust measures to mitigate the dangers of money laundering and terrorism financing linked to utilizing cryptocurrency. Additionally, it seeks to improve the transparency of cryptocurrency usage within the Malaysian context [2]. Cryptocurrency, being the pioneering implementation of blockchain, has revolutionized the financial sector. It empowers consumers to engage in secure and efficient transactions, including virtual payments for goods and services and low-cost fund transfers [3]. By leveraging blockchain technology, cryptocurrency ensures the authenticity and validity of these transactions [4]. Several governments, including the Malaysian government, are discussing regulating cryptocurrency and proposing their digital currency to support the nation’s digital economy [5]. Not only that but also planning on the taxation methods imposed on cryptocurrency usage [6]. However, the global cryptocurrency adoption status, including in Malaysia, has not reached the anticipated level due to the limited inclination of consumers to adopt this technology. This low level of adoption is primarily attributed to concerns associated with perceived ease of use and perceived usefulness [7–9], awareness [10], perceived risk [11, 12], perceived trust [13], social influence [14, 15] and finally government regulation [16, 17]. Despite numerous studies conducted by researchers in different countries worldwide about cryptocurrency and blockchain technology from various perspectives, few have emphasised the factors influencing the users’ intention to accept cryptocurrency from risk perspectives [18]. The scarcity of academic research highlights the necessity for comprehensive and fundamental research to shed light on the low usage

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of cryptocurrency among consumers in Malaysia. By exploring those factors, valuable insights can be obtained. This research aims to design an acceptance model that determines the factors influencing the users’ intention to use cryptocurrency in Malaysia from risk perspectives.

2 Literature Review 2.1 Overview of Cryptocurrency Cryptocurrency, defined as a decentralised digital currency runs without a central authority or issuer [1]. Users can transfer or use cryptocurrency amongst themselves to buy products and services if they can find merchants prepared to accept them [19]. Buying and selling cryptocurrencies for national currencies is also possible via many unofficial Internet-based exchanges [1]. Cryptocurrency operates on a peer-to-peer network and utilises cryptography to create and distribute currency units [4]. The decentralised validation of transactions, eliminating the need for a central authority, plays an essential part in this process. Validation of transactions checks the sums of transactions or even if the payer controls the money they attempt to spend whilst guaranteeing that currency units are not spent twice. This method of validation is known as mining [20]. Cryptocurrencies utilise, a range of mining techniques based on their specifications. Some cryptocurrencies, for example, emphasise limiting the number of authenticated transactions per unit of time, whereas others aim to achieve quick, lightweight services [21].

2.2 Related Research Numerous studies examining the intention to adopt new technologies highlight the significance of individuals’ perception of perceived risk in influencing their decision to adopt such technology [4, 19, 22–28]. When consumers first embrace cryptocurrency, they may perceive significant risk and uncertainty. Therefore, it becomes crucial to establish trust to mitigate perceived risk [29]. Recently, research was undertaken to study the determinants of customer behavioral intention to use cryptocurrency as a transaction method in Malaysia [23]. The research focuses on examining the integration of UTAUT theory, along with perceived risk theory, to predict behavioral intention. In this model, age and gender are considered as moderators. According to the study, three of the five factors considered, namely performance expectancy, effort expectancy, and facilitating condition, significantly impact the behavioral intention of individuals to adopt cryptocurrency as a transaction medium. Surprisingly, perceived risk did not emerge as a significant predictor, which contradicts previous research findings. Moreover, the relationship

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between BI and social influence becomes significant when age is introduced as a moderator. These findings highlight the importance of factors such as performance expectations, effort expectations, and facilitating conditions in influencing individuals’ intention to adopt cryptocurrency while also emphasising the role of age in moderating the influence of social factors on behavioral intention. Similarly, in 2022, another research was performed to examine the use of cryptocurrency as an investment [29]. The main objective of this research is to understand the perspectives of Malaysian investors by investigating how perceived risk and perceived value impact their decision to adopt cryptocurrencies. The study also considers demographic characteristics as control variables. Data for analysis were collected through goal-directed sampling, and the analysis of this study included 211 respondents from diverse cities across Malaysia. The outcomes supported that perceived value significantly impacts the adoption of cryptocurrencies among Malaysian investors. Interestingly, the perceived risk does not demonstrate a major influence on the acceptance of cryptocurrencies between investors in Malaysia. Within the community of IT and cryptocurrency enthusiasts, a study on cryptocurrency adoption was conducted [25]. This research aimed to assess the intention of individuals in Malaysia towards cryptocurrencies and identify the key factors that predict their behavior in adopting cryptocurrencies. A survey was conducted among individuals in Malaysia who possessed knowledge about cryptocurrencies, and the model was analysed via the PLS-SEM modelling approach. The findings revealed a strong intention among Malaysian individuals to adopt cryptocurrencies, and factors such as performance expectancy, social influence, facilitating condition, and price value were found to influence their adoption behavior significantly. This study can be extended to other countries, particularly developing nations with similar emerging economies as Malaysia, by applying or adapting the proposed research model. Additionally, considering the varying regulations surrounding cryptocurrencies across nations, further investigation into the impact of government support, which does not predict individuals’ intention to use cryptocurrency, can offer a more profound understanding of the study’s results. Likewise, a study was performed to determine cryptocurrency adoption behavior in Malaysia [26]. The research examines the correlation between output quality, result demonstrability, and perceived security on the user’s behavioral intention to adopt cryptocurrency as an alternative method for payment. The study involved 553 participants from multiple age groups. The findings obtained through the deployment of (PLS-SEM), indicate significant positive correlations between output quality, result demonstrability, perceived security, and users’ adoption behavior towards cryptocurrency. These findings provide valuable insights into understanding the factors influencing the adoption behavior of cryptocurrency. They can contribute to the development of effective financial management mechanisms and interventions, which are crucial in enhancing confidence and understanding among existing and potential cryptocurrency users.

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Another study examined factors impacting cryptocurrency adoption in digital business transactions [4]. The study specifically examines the impact of social influence, transparency, price value, traceability, and attitude as dependent variables. Additionally, the study explores the role of customer satisfaction as a mediator variable in cryptocurrency adoption. A random sampling technique was employed to ensure a comprehensive analysis of the research objectives, resulting in 295 individuals who participated in a specially designed survey for cryptocurrency users in Malaysia. The findings indicate that social influence, price value, traceability, and attitude significantly impact the acceptance of cryptocurrencies within Malaysia’s digital market, with customer satisfaction mediating these relationships. Yet, it was found that TR has a negative impact on Malaysia’s digital market. Comparably, a study was conducted on the factors affecting cryptocurrency acceptance, focusing on cryptocurrency adoption [19]. The research asserts with confidence that the prevalence of cryptocurrencies will continue to increase. This empirical research focuses on investigating the impact of trust, transaction transparency, volatility, facilitating conditions, performance expectancy, and intention to use on adopting cryptocurrencies. The findings from this research helps future researchers to replicate the research in different locations and industries, using equivalent constructs to further expand our current understanding and knowledge on this subject matter. From a Shariah perspective, a study aims to investigate users’ perceptions regarding accepting cryptocurrency system applications [27]. In this study, UTAUT was employed, along with the incorporation of Shariah compliance, to develop and assess several hypotheses that aimed to examine the proposed factors. The research sample comprised 496 participants, and the findings from hypothesis testing showed that Performance Expectancy, Effort Expectancy, Social Influence, and Shariah compliance significantly influenced the users’ intention behavior in adopting cryptocurrency from a Shariah perspective within the Malaysian context. These findings highlight the significance of these factors in shaping individuals’ attitudes and intentions toward cryptocurrency adoption within the context of Shariah compliance.

2.3 Related Research Summary Various research studies investigating technology adoption among Malaysian consumers have been proposed. Yet, a lack of research emphasises the pivotal role of individuals’ perception of perceived risk in shaping consumers’ decision to adopt new technologies. This is particularly relevant in the context of initial cryptocurrency adoption in Malaysia, where consumers often encounter substantial levels of risk and uncertainty. Thus, establishing trust becomes a critical factor in mitigating these perceived risks and encouraging the adoption of cryptocurrency technology.

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Fig. 1 Proposed conceptual model

3 Conceptual Model Development This study utilises the UTAUT framework as the foundational theory, incorporating the Perceived Risk Theory. However, certain variables have been excluded from the basic model of this research. The dependent variable, Use Behavior, has been omitted as it falls out of the scope of this research. The research’s main objective is to examine the factors influencing users’ behavioral intention to use cryptocurrency (Fig. 1; Table 1).

4 Research Hypothesis Development To attain the research’s main objective, a set of nine primary research hypotheses are developed and proposed. These hypotheses serve as guiding principles for the investigation and are as follows.

4.1 Performance Expectancy (PE) Performance expectancy is known as the degree to which an individual believes that utilising a specific system or service will lead to improved job performance [32]. This construct draws upon various established theories. Within existing theories, performance expectancy has consistently emerged as a significant predictor of users’ intention, regardless of whether the usage is voluntary or mandatory, and across multiple measurement points [27].

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Table 1 Criteria of the proposed conceptual model Author

Model

Dimension

[30]

UTAUT

Performance expectancy

Effort expectancy

Social influence Facilitating condition

Behavioral intention Use behavior

[31]

Perceived risk theory (PR)

Performance risk Financial risk Time risk Psychological risk Social risk Privacy risk Overall risk

Inclusion √

√

√ X

√

X √ √ √ √ √ √ X

Reasons Performance expectancy consistently emerges as a robust predictor of intention, maintaining its significance across all measurement points in both voluntary and mandatory contexts Effort expectancy plays a crucial role in influencing intention, regardless of whether the usage is voluntary or mandatory Behavioural intention is directly influenced by social influence Facilitating condition is viewed as a direct indicator of the perception of individuals regarding the availability of organisational and technical infrastructure necessary to support system usage Behavioral intention is an indication of one’s readiness to perform a certain behavior Use behavior measures the actual performance which is out of the scope of this study Measures the users’ performance risk Measures the users’ financial risk Measures the users’ time risk Measures the users’ psychological risk Measures the users’ social risk Measures the users’ privacy risk Overall risk is excluded as it is repetitive to the above variables

Performance expectancy encompasses the perception of perceived usefulness, capturing the belief that the system/service will yield improvements in job-related outcomes [28]. It also considers extrinsic motivation, emphasising the external factors that drive individuals to engage with the system/service. Additionally, work fit, relative gain and result expectation are incorporated, focusing on the alignment with work requirements, comparative advantages, and anticipated outcomes, respectively [33]. By incorporating these dimensions from various theories, performance expectancy provides a comprehensive framework to assess users’ perceptions of the benefits and advantages related to using or accepting a specific system or service. The consistent

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and robust predictive power of performance expectancy across different contexts underscores its significance in understanding individuals’ intentions and behaviors related to technology adoption. Hypothesis 1 (H1): Performance Expectancy (PE) will have a positive effect on Behavioral Intention (BI).

4.2 Effort Expectancy (EE) Effort expectancy expresses the level of ease or difficulty perceived by an individual when using a particular system. This construct captures the notion of perceived ease of use in (TAM) and the concepts of difficulty in the (DOI and MPCU) [28, 34]. Across these various theories, effort expectancy consistently emerges as a significant factor influencing users’ behavior and intention, both in optional and compulsory usage contexts [27]. The construct of effort expectancy holds particular significance during the post-training assessment phase, aligning with prior research findings and predictions. It indicates that users’ perceptions of the ease of system usage become more salient and influential after receiving training or becoming more familiar with the system. This suggests that as individuals gain experience and proficiency, their perception of the effort required to use the system becomes more accurate and influential in shaping their intentions and behaviors [35]. By incorporating the constructs related to perceived ease of use and difficulty from different theories, effort expectancy provides a comprehensive understanding of users’ perceptions of the ease of using a system. This insight is valuable in designing user-friendly interfaces, providing effective training programs, and reducing barriers to system adoption. Hypothesis 2 (H2): Effort Expectancy (EE) will have a positive effect on Behavioral Intention (BI).

4.3 Social Influence (SI) Social influence refers to the degree to which an individual perceives that a significant individual in their life expresses approval for their adoption of new technology [33]. Similar constructs have been utilised in existing theories. Looking at existing theories, it is evident that the construct of social influence behaves consistently. Its impact is negligible in voluntary contexts, but it becomes relevant when system usage is compulsory. Previous research has demonstrated that this effect is primarily driven by enforcement in mandatory situations [27, 36]. It appears to be more influential during the early stages of personal experiences, especially when rewards or punishments are applicable. On the other hand, in voluntary contexts, social influence operates by shaping individuals’ perceptions of the technology and influencing

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their decision-making processes. Understanding the role of social influence is crucial for designing strategies to promote system adoption and usage [37]. In mandatory contexts, emphasising the importance of conformity and highlighting the enforcement mechanisms can increase compliance. Focusing on creating positive perceptions, demonstrating social proof, and leveraging social networks can effectively influence users’ attitudes and intentions in voluntary contexts. By considering the construct of social influence across different theories, researchers and practitioners can understand how social factors shape individuals’ acceptance and usage behaviors. Hypothesis 3 (H3): Social Influence (SI) will have a positive effect on Behavioral Intention (BI).

4.4 Perceived Risk (PR) Recently, Perceived risk has been redefined, influenced by changes in customer behavior and their inclination towards online transactions. Initially, perceived risk was primarily associated with fraud or product quality concerns. However, the current definition of perceived risk encompasses a broader scope, referring to “the potential for loss in the pursuit of a desired outcome of using an e-service” [31]. Within this expanded definition, researchers have identified six types of risk: performance risk, psychological risk, financial risk, privacy risk, time risk, and social risk. Recognising the significance of perceived risk, Featherman and Pavlou emphasised the need to incorporate it into technology adoption theories [31]. Clients attach value to the notion of risk while evaluating goods or services for potential purchase or adoption, and this evaluation process can evoke feelings of anxiety and discomfort. A more comprehensive understanding of customer behavior can be achieved by integrating perceived risk into acceptance models. Clients’ perception of risk plays a pivotal role in shaping their attitudes and intentions toward adopting or using technology-based products and services. Recognising and addressing these perceived risks can help organisations build trust, alleviate customer concerns, and facilitate smoother adoption processes [31]. Hypothesis 4 (H4): Performance Risk (PER_R) has a negative effect on Behavioral Intention (BI). Hypothesis 5 (H5): Financial Risk (FIN_R) has a negative effect on Behavioral Intention (BI). Hypothesis 6 (H6): Time Risk (TIM_R) has a negative effect on Behavioral Intention (BI). Hypothesis 7 (H7): Psychological Risk (PSY_R) has a negative effect on Behavioral Intention (BI). Hypothesis 8 (H8): Social Risk (SOC_R) has a negative effect on Behavioral Intention (BI).

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Hypothesis 9 (H9): Privacy Risk (PRI_R) has a negative effect on Behavioral Intention (BI). These hypotheses form the foundation for the study, and through rigorous analysis and examination, they will provide valuable perceptions about the factors influencing individuals’ intention to use cryptocurrency.

5 Methodology This research utilised a quantitative research methodology to examine the factors influencing the users’ intention to use cryptocurrency in Malaysia from risk perspectives. Data was gathered via a self-administered questionnaire, which is known for its predictive value in assessing individual efficiency and capturing perceptions, behaviors, beliefs, and opinions [38]. The questionnaire used in this research incorporated measurement items that were adapted from prior research to align with the specific target behavior of cryptocurrency adoption. The questionnaire employed a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5), to capture respondents’ agreement with the provided statements. Additionally, nominal or ordinal scales were used to gather background data. The survey sample consisted of 496 individuals who were clients of banks and had previous experience with Internet banking services but had not yet ventured into cryptocurrencies. Bank Negara Malaysia, the central bank of Malaysia, reports that there are currently 30 local and international licensed banks in the country providing Internet banking services [39]. The total number of bank account holders in Malaysia is estimated to be 2,044,245, representing approximately 8.17% of the Malaysian population of 25,000,000, as reported by Bank Negara Malaysia (2014). To guarantee an unbiased representation of the population, a randomly selected subset of the population was chosen for this positivist study [40]. Convenience sampling, a non-probability sampling technique, was employed to recruit potential participants due to the researcher’s limited knowledge about the population and its data collection flexibility. While convenience sampling may introduce some limitations, it was deemed suitable for this study considering the research objectives and available resources. VB-SEM via SmartPLS was used in evaluating the proposed model of this research since there was an existent theoretical foundation for this research, also since this research aimed at predicting the behavior of relationships among constructs of the perceived risk of using cryptocurrency and at exploring the underlying theoretical concept used in conducting this research. The ANN analysis method was used to find complicated linear and non-linear correlations among independent and dependent variables [41, 42]. The ANN technique, in particular, is inadequate for hypothesis testing and investigating causal relationships; consequently, a two-step (SEM-neural network) strategy is adopted

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in this research [43]. SEM first examines the hypothesis and identifies the important constructs [44]. Second, the SEM significant predictors are sent into the neural network for verification. In empirical investigations, this hybrid technique is gaining popularity in the literature [45, 46]. Overall, the chosen research methodology and sampling approach aimed to gather relevant data from potential cryptocurrency users in Malaysia, allowing for insights into their perceptions and intentions regarding cryptocurrency adoption from a risk perspective.

6 Finding and Analysis This section will analyse and illustrate the findings concerning the factors that influence the users’ intention to use cryptocurrency in Malaysia from risk perspectives. The study’s objective was to understand the primary factors that shape individuals’ attitudes and intentions toward adopting cryptocurrency while considering the associated risks. Through a meticulous examination of the collected data, several significant factors have emerged, highlighting their influence on the acceptance of cryptocurrency in the Malaysian context.

6.1 Descriptive Analysis The descriptive analysis and demographics of the respondents are presented below.

6.1.1

Demographic Profile of the Respondents

This sub-section discusses the descriptive analysis of the participants’ profiles. The participants’ data is summarised via simple descriptive statistics like frequencies and percentages, to be able to provide meaningful insight. Table 2 presents the distribution of demographic profiles among the participants of the study population, displaying the frequencies and percentages. The table reveals that out of the total participants, 211 individuals (42.5%) are male, while the remaining 285 individuals (57.5%) are female. These figures indicate a higher representation of male participants compared to female participants. Regarding the age distribution of the sample, 57.5% of participants fall within the 18–29 years old age group. Additionally, 30.4% of the total participants are between 30 and 39 years old, while 10.3% belong to the 40–49 years old age group. Lastly, 1.8% of the total participants are 50 years old and above. In terms of education level, participants holding a Ph.D. qualification are 26 (5.2%), while 60 (12.1%) of the participants have with master’s degree. At the same

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Table 2 Demographic profile Frequency Gender

Age

Valid percent

Cumulative percent

Male

211

42.5

42.5

42.5

Female

285

57.5

57.5

100.0

Total

496

100.0

100.0

18–29

285

57.5

57.5

57.5

30–39

151

30.4

30.4

87.9

40–49

51

10.3

10.3

98.2 100.0

50 years and above Education level

Percent

9

1.8

1.8

Total

496

100.0

100.0

Ph.D

26

5.2

5.2

Master’s degree

5.2

60

12.1

12.1

17.3

Bachelor’s degree

289

58.3

58.3

100.0

High-school

121

24.4

24.4

41.7

Total

496

100.0

100.0

time, most of the participants fall under the category of bachelor’s degree Holders which makes them 289 (58.3%) and finally rest are high-school holders which marks 121 (24.4%).

6.2 Measurement Model Using Confirmatory Factor Analysis Confirmatory Factor Analysis (CFA) plays a crucial role in examining the compatibility between the measurement items of a construct and their intended interpretation within a study. By applying CFA, researchers aim to assess whether the observed data aligns with the expected patterns specified by the hypothesised calculation of the proposed conceptual model. This analysis serves as a valuable tool for evaluating the consistency between the conceptual framework and the empirical data, thereby providing insights into the validity and reliability of the measurement instruments used in the study. Through CFA, researchers can gain confidence in the adequacy of their chosen measurement model and ensure that it appropriately captures the essence of the constructs under investigation.

6.2.1

Reliability Testing

To assess the internal reliability of the measurement items utilised with this research to examine the proposed factors. Cronbach’s alpha was used. Cronbach’s alpha is a widely used statistical tool that examines the reliability of a set of items within

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257

Reliability statistics Independent variable

Cronbach’s alpha

N of items

PE

0.900

4

EE

0.875

4

SI

0.865

4

PER_R

0.874

4

FIN_R

0.873

4

TIM_R

0.857

4

PSY_R

0.884

4

SOC_R

0.864

4

PRI_R

0.902

4

BI

0.936

4

a scale. This research calculated the Cronbach’s alpha values for each factor. The Cronbach’s alpha coefficient for Performance Expectancy was found to be 0.900, indicating high internal reliability. Similarly, Effort Expectancy demonstrated a Cronbach’s alpha coefficient of 0.875, indicating good internal reliability. Social influence exhibited a Cronbach’s alpha coefficient of 0.865, also indicating satisfactory internal reliability. Additionally, Performance Risk demonstrated a Cronbach’s alpha coefficient of 0.874, Financial Risk had a coefficient of 0.873, Time Risk had a coefficient of 0.857, Psychological Risk had a coefficient of 0.884, Social Risk had a coefficient of 0.864, Privacy Risk had a coefficient of 0.902, and Behavioral Intention had a coefficient of 0.936. These results suggest that the measurement items for the independent variables possess good internal reliability. Table 3 provides a comprehensive overview of the independent variables along with their corresponding Cronbach’s alpha coefficients.

6.2.2

Indicator Reliability: Loadings

Factor loading is a statistical technique utilised to evaluate the reliability and validity of indicators within the constructs. It examines the strength and significance of the relationship between each indicator and its corresponding construct. Higher factor loadings denote a stronger association between the indicators and the construct [47]. In this research, factor loadings were employed to evaluate the indicators’ reliability. The recommended threshold for factor loadings is typically set at 0.50 or higher, indicating a substantial relationship between the indicators and the construct [48]. Looking at Table 4, it can be observed that all the items in this research surpassed the suggested threshold of 0.50 for factor loadings. This implies that the indicators reliably captured the underlying construct, demonstrating a strong association with their respective constructs. Consequently, the factor loadings of every item in this study met the required criteria, affirming the reliability of the measurement model.

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Convergent Validity: Average Variance Extracted

Convergent validity refers to the degree to which a measure aligns with other measures of the same construct. It is commonly assessed using the average variance extracted (AVE), which indicates convergent validity [47]. The AVE represents the average square loading value of the indicators related to a specific construct. The AVE is a measure that indicates the extent to which a construct explains the variance in its indicators. If the AVE value is 0.50 or higher, it implies that, on average, the construct accounts for more than half of the variance in its indicators. Equally, if the AVE value is lower than 0.50, it suggests that there is more measurement error in the items than the variance explained by the construct [47]. In short, the AVE is a crucial measure for evaluating convergent validity. It provides insight into the extent to which a construct captures the shared variance among its indicators. An AVE value Table 4 Factor loadings EE EE1

0.862

EE2

0.870

EE3

0.859

EE4

0.822

FIN_R

FIN_R1

0.882

FIN_R2

0.851

FIN_R3

0.868

FIN_R4

0.791

BI

INT1

0.898

INT2

0.911

INT3

0.936

INT4

0.919

PE

PE1

0.850

PE2

0.862

PE3

0.902

PE4

0.898

PRI_R

PRI_R1

0.895

PRI_R2

0.837

PRI_R3

0.909

PRI_R4

0.853

RER_R

RER_R1

0.913

RER_R2

0.691

RER_R3

0.856

RER_R4

0.866

PSY_R

PSY_R1

0.768

PSY_R2

0.896

SI

SOC_R

TIM_R

(continued)

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Table 4 (continued) EE

FIN_R

BI

PE

PRI_R

RER_R

PSY_R

PSY_R3

0.870

PSY_R4

0.900

SI

SI1

0.858

SI2

0.883

SI3

0.876

SI4

0.758

SOC_R

SOC_R1

0.758

SOC_R2

0.994

TIM_R

TIM_R1

0.742

TIM_R2

0.827

TIM_R3

0.857

TIM_R4

0.860

of 0.50 or higher signifies strong convergent validity, indicating that the construct effectively explains a significant portion of the variance in its indicators. AVE is calculated using the given formula: AVE =



K2 /n

K = factor loading of each item, and n = number of items in a model. The findings of the convergent validity analysis using the average variance extracted (AVE) are presented in Table 5. The outcomes denote that all AVE values surpass the threshold of 0.50. This demonstrates that the construct of the conceptual model exhibits satisfactory convergent validity. In this research, the AVE values exceeding 0.50 indicate that the construct successfully explains nearly 50% of the variance in its corresponding indicators. This suggests that the construct is effectively capturing the commonality among its indicators, reinforcing the convergent validity of the conceptual model. Overall, the findings of the convergent validity analysis provide support for the reliability and consistency of the measurement items and their alignment with the underlying construct.

6.2.4

Discriminant Validity

The discriminant validity was supported by evidence from criterion validity, crossloadings, and the Fornell-Larcker criterion [47]. The findings of the discriminant validity test, presented in Table 6, showed that the square roots of the average variances extracted (AVEs) on the diagonals, which are bolded, were greater than the correlations between the constructs. This means that the constructs are more closely

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Table 5 Average variance extracted Average variance extracted (AVE) EE

0.728

FIN_R

0.720

BI

0.839

PE

0.772

PRI_R

0.764

PR

0.698

PSY_R

0.740

SI

0.714

SOC_R

0.781

TIM_R

0.677

Table 6 Square root of the aves EE EE

FIN_R

BI

PE

PIR_R

PR

PSY_R

− 0.045

0.849

BI

0.605

− 0.149

PE

0.742

− 0.071

0.608

0.878

PIR_R

0.016

0.636

− 0.081

− 0.007

PR

0.019

0.731

− 0.082

− 0.029

0.607

PSYR_R

0.106

0.538

− 0.178

− 0.121

0.561

0.587

0.860

SI

0.693

− 0.054

0.671

0.656

− 0.038

0.010

− 0.058

FIN_R

SI

SOC_R

TIM_R

0.853

0.916

0.874 0.836

0.845

SOC_R

− 0.046

0.368

− 0.065

− 0.040

0.374

0.404

0.637

0.016

0.884

TIM_R

− 0.063

0.581

− 0.111

− 0.056

0.587

0.589

0.685

− 0.042

0.574

0.823

related to their own indicators than they are to each other, suggesting high discriminant validity [47]. The relationship between exogenous constructs is weak, with a correlation coefficient of less than 0.855 [49]. Hence, all constructs have achieved discriminant validity.

6.3 Structural Model Assessment The structural equation model (SEM) is another important component of the analysis. Once the measurements have been validated, the correlations between the constructs can be defined, and the structural model can be depicted. The structural model provides detailed descriptions of the relationships between variables. To evaluate the structural model, several indicators are examined, including the beta (β) coefficients, R2 values, and their corresponding t-values. To provide a comprehensive analysis,

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Fig. 2 Structural model assessment

effect sizes (f2) and predictive relevance (Q2) should also be reported. While p-values determine the occurrence of an effect, they do not reveal the magnitude of the effect. The results of the PLS bootstrapping analysis, including T statistics, conducted using SmartPLS, are presented in Fig. 2. This analysis allows for a robust assessment of the significance and strength of the correlations between the variables in the structural model.

6.3.1

Effect Size F2

In this research, effect sizes (f2) were also calculated to measure the impact of exogenous latent constructs on endogenous latent constructs [50]. Effect size f2 helps determine whether the influence of an exogenous latent construct is substantial, moderate, or weak. Examining the variation in f2 values is recommended to gain a comprehensive insight into the effects. To interpret the magnitude of f2, a commonly used guideline suggests that values of 0.35 indicate large effects, 0.15 indicate medium effects, and 0.02 indicate small effects. Table 7 presents the results of the effect sizes (f2) obtained in this study, providing insights into the magnitude of influence of the exogenous latent constructs on the endogenous latent constructs.

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Table 7 Effect size f2 Exogenous construct

Endogenous construct INT

EE

0.015

FIN_R

0.007

PE

0.035

PRI_R

0.001

PSY_R

0.000

SI

0.011

SOC_R

0.181

TIM_R

0.001

6.3.2

Hypotheses Testing

The evaluation of the structural model, as depicted in Fig. 2 and Table 8, provides insights into the hypothesis tests. The findings show that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Financial Risk (FIN_R), and Psychological Risk (PSY_R) significantly predict Behavioral Intention (BI). Hence, H1, H2, H3, H5 and H7 are accepted. However, H4, H6, H8 and H9 are rejected. It is worth noting that the consistent path coefficients reveal the strength of the relationships among exogenous and endogenous constructs. In this regard, the direct effects of Social Influence (SI) on Intention (BI) are significantly stronger compared to other constructs, as evident from the higher path coefficient values. Additionally, the direct effect of Effort Expectancy (EE) on Intention (BI) is the second strongest. The results presented in Table 9 show that Performance Expectancy, Effort Expectancy, Social Influence, Financial Risk, and Psychological Risk are supported by the findings of this study. These constructs demonstrate a significant impact on the outcome variable. However, the constructs of Performance Risk, Time Risk, Table 8 Structural path analysis result Hypo

Relationship

Coefficients (β)

t-value

p-value

Decision

H1

PE → BI

0.203

3.412

0.000

S

H2

EE → BI

0.142

2.492

0.006

S

H3

SI → BI

0.430

8.433

0.000

S

H4

PER_R → BI

0.011

0.193

0.423

NS

H5

FIN_R → BI

− 0.090

1.732

0.042

S

H6

TIM_R → BI

0.027

0.421

0.337

NS

H7

PSY_R → BI

− 0.119

1.942

0.026

S

H8

SOC_R → BI

0.022

0.309

0.379

NS

H9

PRI_R → BI

0.028

0.523

0.300

NS

*S: Supported, NS: Not Supported

Perceived Risk of Users’ Intention to Use Cryptocurrency in Malaysia … Table 9 Predictor and predicate variables extracted from SEM

Constructs

Target

PE

BI

263

EE SI FIN_R PSY_R

Social Risk, and Privacy Risk did not demonstrate a significant relationship with the outcome variable, as indicated by the lack of support in the study.

6.4 Neural Network Analysis SEM predictors were fed into Artificial Neural Network (ANN) models to assess the relative importance of important predictors. Neural networks were modeled in this study utilising the Python programming language. Only the SEM predictors that were statistically significant were used as inputs into the ANN models [51]. Table 9 contains 5 predictors that serve as inputs to four ANN models. The model consists of five main input variables and one output variable. In the neural network model, ten hidden neurons were utilised, as illustrated in Fig. 3. To prevent overfitting and ensure robustness, a tenfold cross-validation technique was used. This technique divided the available data into ten subsets or “folds.” The model was then trained using 80% of the data, while the remaining 20% was utilised for testing and evaluating the model’s performance. This approach effectively assessed the model’s ability to generalise and perform well on unseen data [52]. The accuracy of the neural network models was evaluated via the Root Mean Square of Error (RMSE), a commonly used metric for measuring prediction accuracy. Table 10 displays the RMSE values, providing insights into the ANN model’s predictive performance. The mean RMSE values of the ANN models range from 0.6322506 to 0.9197495. These values suggest that the models have demonstrated high accuracy in capturing the relations between the predictors and the output variable. Therefore, it can be inferred that the ANN models are reliable and effective in their ability to make accurate predictions based on the given data. Table 11 summarises the relative importance of input variables in neural network analysis. Performance Expectancy holds the highest importance at 21.66%, followed by Effort Expectancy at 17.06%, indicating that perceived benefits and ease of task execution are critical factors. Social influence (15.37%) and concerns about Financial Risk (11.78%) are also significant, along with attention to Psychological Risk (11.85%). These findings reveal that in the context of this study, people prioritise expected performance outcomes and the effort required, with social and financial factors also playing substantial roles in their decision-making or evaluation process.

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Fig. 3 Neural network model Table 10 RMSE values of ANN

Network

RMSE

1

0.8168637

2

0.6322506

3

0.8471244

4

0.7503445

5

0.8785199

6

0.8553000

7

0.9398670

8

0.8376440

9

0.9197495

10

0.6941160

Mean

0.817178

Standard deviation

0.092690

Perceived Risk of Users’ Intention to Use Cryptocurrency in Malaysia … Table 11 Average normalised importance percentage of input variables

Variable

265

Percentage (%)

Performance expectancy

21.66

Effort expectancy

17.06

Social influence

15.37

Financial risk

11.78

Psychological risk

11.85

6.5 Hypothesis Discussion The analysis findings indicate that certain factors significantly influence the users’ intention to use cryptocurrency in Malaysia. Specifically, performance expectancy, effort expectancy, social influence, financial risk, and psychological risk emerged as significant predictors. These factors are crucial in shaping individuals’ intentions and perceptions to use cryptocurrency in Malaysia. Whereas, performance risk, time risk, social risk, and privacy risk are not supported in this study. These results highlight the significance of studying factors that contribute to individuals’ intention to use cryptocurrency in Malaysia. By understanding the Influence of performance expectancy, effort expectancy, social Influence, financial risk, and psychological risk, stakeholders can develop targeted strategies and interventions to promote cryptocurrency adoption among individuals in Malaysia. However, it is important to acknowledge that the non-significant findings for performance risk, time risk, social risk, and privacy risk should not be disregarded entirely. Further investigation is necessary to investigate more into the complexities of such factors and explore their potential role in the cryptocurrency adoption process. The discussion below highlights these findings.

6.5.1

Performance Expectancy

The regression analysis findings revealed a significant association between performance expectancy and the users’ behavioral intention to use cryptocurrency in Malaysia. This finding indicates a significant effect of performance expectancy on behavioral intention, which is aligned with existing research such as [53–58]. Thus, it can be inferred that performance expectancy plays a vital part in influencing users’ intention to use cryptocurrency, supporting the notion that higher performance expectancy leads to a greater likelihood of acceptance of cryptocurrency. These consistent findings across multiple studies provide further support for the significant influence of performance expectancy on users’ behavioral intention in the context of cryptocurrency usage.

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Effort Expectancy

The regression analysis findings show an important impact on effort expectancy and users’ behavioral intention to use cryptocurrency in Malaysia. The outcomes implied a substantial influence of effort expectancy on the user’s behavioral intention within the proposed conceptual model. These results align with existing research [53–58], that also indicated similar findings. Hence, it is concluded that effort expectancy is vital in influencing users’ behavioral intention to use cryptocurrency. Higher levels of effort expectancy will likely enhance the likelihood of individuals intending to adopt cryptocurrency.

6.5.3

Social Influence

The regression analysis results provide robust evidence for a positive correlation between social influence and users’ behavioral intention to use cryptocurrency in Malaysia. The analysis outcomes strongly indicate that social influence plays a major role in shaping users’ behavioral intention toward cryptocurrency usage. These outcomes are aligned with previous research [53–58], that have reported similar findings. Therefore, based on the consistency of these findings, it can be suggested that social influence significantly influences users’ behavioral intention to use cryptocurrency. Individuals are more likely to accept cryptocurrency when they perceive a strong social influence encouraging its usage.

6.5.4

Performance Risk

According to the analysis findings, performance risk has no direct and vial influence on users’ behavioral intention to utilise cryptocurrency. This result contradicts prior studies [59–61] that have reported an important correlation between performance risk and behavioral intention. However, it is worth noting that the rejection of this hypothesis in this particular study only reflects the specific findings observed, and the role of performance risk may vary when influenced by other variables or specific triggering factors. Further research is recommended to investigate the potential moderating effects and contextual influences that may impact the correlation between performance risk and behavioral intention in the context of cryptocurrency adoption.

6.5.5

Financial Risk

Compelling evidence has been obtained to substantiate the positive relationship between financial risk and users’ behavioral intention to use cryptocurrency. This implies that financial risk exerts a substantial influence on shaping users’ behavioral intention toward cryptocurrency adoption. These findings align with previous

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research, including studies conducted by Refs. [59–61]. The consistent support from prior research strengthens the validity of the correlation between financial risk and behavioral intention, emphasising the importance of considering the impact of financial risk on users’ decision-making processes related to cryptocurrency usage.

6.5.6

Time Risk

The findings indicate that time risk availability has no direct and significant influence on users’ behavioral intention to use cryptocurrency. Such results contradict prior studies that established a positive effect of time risk on behavioral intention [59–61]. However, rejecting this hypothesis is only applied in this study and may lead to different findings in other research. It is possible that the role of time risk may vary when influenced by other factors or contextual circumstances. Further exploration and examination of the interplay between time risk and behavioral intention in different contexts are warranted to comprehensively understand their relationship.

6.5.7

Psychological Risk

The regression analysis findings demonstrate a significant correlation between the independent variable, psychological risk and the dependent variable behavioral intention. These results align with previous research conducted by [59–61]. This suggests that psychological risk is crucial in influencing the users’ behavioral intention to use cryptocurrency in Malaysia. The outcomes underscore the significance of incorporating psychological factors in examining individuals’ intentions to accept and use cryptocurrency.

6.5.8

Social Risk

The outcomes of the regression analysis reveal no positive impact of social risk on users’ behavioral intention to use cryptocurrency in Malaysia. These outcomes contradict existing research that has reported a significant impact of social risk on users’ behavioral intention to use cryptocurrency [59–61]. Noting that such rejection of the hypothesis in this study reflects the specific findings obtained, and the role of social risk may differ when influenced by other variables or specific contextual factors. A deeper analysis is required to explore the influence of social risk on behavioral intention in the context of cryptocurrency usage.

6.5.9

Privacy Risk

The analysis findings denote that privacy risk does not influence the user’s behavioral intention to use cryptocurrency in Malaysia. These results contradict previous studies

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that have reported a vital correlation between privacy risk and behavioral intention to use cryptocurrency [59–61]. It is worth mentioning rejection that such rejection of the hypothesis in this study only reflects the specific findings obtained. Other factors or contextual influences may influence the correlation between privacy risk and behavioral intention. Further work is needed to investigate the complex dynamics and potential moderating factors involved in the correlation between privacy risk and users’ behavioral intention to use cryptocurrency.

7 Conclusion This research utilised a questionnaire administered to 496 users in Malaysia to investigate the factors influencing the users’ behavioral intention to use cryptocurrency in Malaysia. The findings highlight the significance of performance expectancy, effort expectancy, and social influence on the user’s behavioral intention to use cryptocurrency in Malaysia. The analysis results reveal that performance and psychological risks are significant factors influencing the users’ behavioral intention to use cryptocurrency in Malaysia. However, the study did not find significant effects of financial risk, time risk, social risk, and privacy risk on the behavioral intention to use cryptocurrency in Malaysia. These findings provide insights into the specific factors that shape users’ intentions to use cryptocurrency in the Malaysian context. Notwithstanding the fact that cryptocurrency in Malaysia has made a revolution in the way that Malaysian clients handle their banking transactions. In short, exploring users’ perceived risk in relation to their intention to use cryptocurrency in Malaysia carries significant implications for both academic research and practical application. The article results contribute to the existing body of knowledge by shedding light on the intricate interplay between perceived risk and users’ behavioral intentions in adopting cryptocurrencies. Identifying perceived risk as a crucial determinant underscores the need for a nuanced understanding of the factors that influence users’ decision-making processes. These insights can guide future research endeavors by encouraging a deeper investigation into the underlying mechanisms driving perceived risk within the context of cryptocurrency adoption. Moreover, from a practical standpoint, these implications are valuable for policymakers, financial institutions, and cryptocurrency platforms. Understanding users’ perceptions of risk can aid in the development of targeted strategies to alleviate apprehensions and enhance confidence in cryptocurrency usage. By addressing these concerns, stakeholders can work towards creating a more conducive environment that promotes widespread adoption and usage of cryptocurrencies, thereby fostering the growth and integration of digital financial technologies within Malaysia’s economic landscape. Future research can explore other areas from different perspectives, which are market orientation and a more comprehensive assessment of relationship marketing, that can promote more effective promotion of cryptocurrency itself. Different cryptocurrency platforms are available, i.e. (Bitcoin, Ethereum, etc.), and future research may need to focus on a specific platform of cryptocurrency services. Additionally,

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future research is advised to take an additional step forward by investigating the actual behavior (Usage) in the proposed model and not only behavioral intention. Even though behavioral intention has proven to be a good predictor of actual behavior in many fields of behavioral sciences, research examining the power of predicting users’ intention on actual behavior is still limited.

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Prediction of Consumer Repurchase Intention with Food Delivery Apps: The Mediating Role of Prior Online Experience Using PLS-SEM-ANN Approach Fadi Herzallah , Amer J. Abosamaha , and Mohammed A. Al-Sharafi

Abstract The snowballing approval of food delivery apps (FDAs) in developing countries, including Palestine, calls for a deep insight into this trait. However, limited research has been conducted in Palestine regarding the acceptance and usage of FDAs. Addressing this research gap, this study examines the mediating role of prior online experience (POE) in the relationship between UTAUT2 constructs “performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivations” and consumer repurchase intention (RI) from FDAs. With collecting a sample of 392 customers, a two-step hybrid approach is employed, utilizing partial least squares structural equation modelling (PLS-SEM) for testing hypotheses and artificial neural networks (ANN) for assessing the significance of the construct. The results confirm all hypotheses, indicating that UTAUT2 constructs positively influence POE. Furthermore, POE significantly affects RI, acting as a mediator between UTAUT2 constructs and RI. The analysis of the ANN demonstrates that effort expectancy and facilitating conditions are the most influential factors on POE, while social influence shows relatively lower importance. This research contributes to raise awareness of FDA adoption and usage patterns in developing countries, providing valuable insights for policymakers, businesses, and consumers. The findings emphasize the crucial role of POE in shaping consumers’ RI from FDAs. Keywords UTAUT2 · Food delivery apps · Prior online experience · PLS-SEM-ANN approach · Palestine

F. Herzallah (B) · A. J. Abosamaha Palestine Technical University—Kadoorie, Tulkarm, Palestine e-mail: [email protected] M. A. Al-Sharafi Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, 43000 Putrajaya Campus, Kajang, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_14

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1 Introduction The advancement of information and communication technology (ICT), particularly in the smartphone industry, has recently created substantial transformations in consumers’ lifestyles [1]. Though this phenomenon has been anticipated, the outbreak of the COVID-19 crisis has expedited such changes. Therefore, the landscape of business operations has undergone a notable shift, with a primary reliance on online transactions [2]. Simultaneously, there has been a surge in internet usage and the acceptance of mobile applications [3]. The online food delivery (OFD) sector has not been immune to these developments [4]. Recent statistics reveal a consistent increase in the number of smartphone applications, with over 194 billion applications downloaded by users. By the end of 2023, this figure is projected to reach 255 billion, representing a growth rate of 31.44% over a four-year period [5]. Such growth signifies the expansion of new online business opportunities, including OFD [3, 6]. Online Food Delivery is a platform that enables customers to conveniently purchase pre-prepared meals from a variety of restaurants using their smartphones, with just a few clicks [7]. These platforms essentially function as intermediaries, connecting a logistics network comprising restaurants, drivers, and consumers. Consequently, on OFD platforms, the real process of food preparation is not engaged [8]. Within the context of OFD, the applications employed in this domain are commonly referred to as Food Delivery Applications (FDAs). These applications assume responsibility for displaying food items, establishing pricing, facilitating the payment process, and managing transactions between restaurants and consumers [9]. From the perspective of restaurants, these applications have proven beneficial by enabling them to expand their customer base and reduce marketing costs. For consumers, these applications provide access to a wide range of restaurant options, along with valuable information such as ratings [10]. Consequently, FDAs effectively save time and effort for both parties involved [6]. Prior to the COVID-19 crisis, the (OFD) market has exhibited “a gradual growth rate, achieving a Compound Annual Growth Rate (CAGR) of 24% between (2017) and (2019)” [11]. However, the pandemic resulted in a significant acceleration of this growth, reaching 57% in 2020, with the total market value reaching 0.52 trillion [11]. This surge in demand for OFD services can be attributed to various factors, including lockdown measures, social distancing requirements, increased individual income, and heightened awareness of the convenience benefits associated with these services [12]. Moreover, OFD services have proven effective in reducing waiting times and alleviating traffic congestion, distinguishing them from traditional approaches such as dining in restaurants or placing direct orders [3]. Looking ahead, the market is expected to sustain its growth trajectory, with a projected value of 1.45 trillion by 2027, indicating a CAGR of 28% from 2017 to 2027 [11]. Previous research has indicated that food delivery applications (FDAs) have garnered significant attention in developing countries, but they have not received

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sufficient scholarly investigation [13, 14]. Notably, the emergence of FDAs in Palestine, including Wheels Now, Yummy, Food on Time, and recently HAAT Delivery, has been observed. Numerous studies have examined the acceptance and usage of online FDAs, investigating various factors such as performance, self-standards, attitudes, behaviour control, social isolation, and the COVID-19 impact on their acceptance [10, 14, 15]. Yet, research work addressing this area in Palestine remains limited. This research gap underscores the necessity of conducting studies exploring the factors affecting the acceptance and adoption of FDAs in Palestine. Addressing this gap adds deep understanding about the utilization of FDAs in Palestine and guides the process of developing strategies to enhance their acceptance, utilization, and adoption. Various theories of the technology acceptance are employed in prior research studies, i.e., the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT and UTAUT2), and other related theories. Among these, UTAUT2 stands out as the most comprehensive theory, as it amalgamates eight different theories into a unified framework [16]. Notably, UTAUT2 is considered to possess superior explanatory power compared to its predecessor, UTAUT1 [17]. Despite utilizing UTAUT2 in the domain of the Online Food Delivery (OFD), researchers emphasize the necessity for further investigation in this specific context [18–21]. Despite the merits of UTAUT2, prior studies have recommended integrating it with other theories or modifying its constructs by either adding or removing certain constructs [22, 23]. Accordingly, this research will adopt the UTAUT2 theory, while making adjustments to the research model by removing specific constructs and introducing a mediating effect. Online shopping activity, despite its novelty, continues to carry inherent risks [24]. To help consumers perceive and mitigate these risks, their previous experience in online shopping plays a crucial role [25]. Prior Online Experiences (POEs) encompass an individual’s prior involvement with the internet and online activities [26]. This also encompasses past customer experiences with web applications [27]. Similarly, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) is broadly studied in relation to intentions, attitudes, and habits [28–30], particularly within the area of the Online Food Delivery industry [14, 19, 31]. Nevertheless, the influence of UTAUT2 factors on POE receives little to no consideration in existing research. As a pioneering investigation in this domain, this research aims at illuminating this unexplored aspect. The role of POE in digital consumer behaviour is an area that requires further exploration. Prior research has examined this aspect using different approaches, including treating POE as an exogenous variable, an endogenous variable, or a moderating variable. Some studies, however, have recognized the potential mediating role of POE, there remains a scarcity of research exploring it as a mediating construct, particularly in the scope of FDAs. By exploring the mediating role of POE in the relationship between Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) constructs and the intention to repurchase from FDAs, this paper offers a wide-ranging insight of the complex dynamics among these variables.

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The researchers acknowledge the existing studies considering POE as a mediator but argue that further exploration is necessary to bridge the existing knowledge gap and shed more light on the influential role of POE in the process of the decision-making of the consumer. However, this paper precisely addresses the following research questions: 1. Do UTUAT2 constructs “Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, and Hedonic Motivation” affect the Prior Online Experiences (POE)? 2. To what extent does the POE mediate the relationship between UTAUT2 constructs “Performance Expectancy, Effort Expectancy, Social Influence Facilitating Conditions, and Hedonic Motivation” and the repurchasing intention from online FDAs?

2 Related Literature and Hypotheses Development 2.1 Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Constructs The Technology Acceptance Model (TAM), initially proposed by Davis [32], is a theoretical framework utilized to elucidate the process used by individuals for accepting and adopting information technology. This model posits that the IT acceptance is predicated upon two fundamental factors: perceived ease of use and perceived usefulness [32]. TAM is extensively utilized in pieces of research examining technology acceptance and adoption, together with those focused on the acceptance of Food Delivery Apps (FDAs) [33–35]. However, despite its widespread use, the TAM model has faced criticism due to its limitations in analyzing the relationships among variables in IT environments and its inability to fully account for the impact of external variables on TAM variables [36]. Furthermore, the TAM model has been criticized for its lack of comprehensive interpretability relating to the adoption of various information systems [22]. Proposing the “Unified Theory of Acceptance and Use of Technology (UTAUT1)” has overcome the said limitations [37]. This theory amalgamates various existing theories and models employed in the technology acceptance domain, notably the Technology Acceptance Model. UTAUT1 stands out by offering a more comprehensive and robust model that elucidates individuals’ behaviors towards technology usage. In UTAUT1, supplementary factors such as “performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC)” are incorporated. Researchers have applied this theory to consider the acceptance and adoption of diverse technologies such as artificial intelligence [38] and FDAs [39]. Nevertheless, similar to other theories, the UTAUT1 model has faced criticism for its failure to account for psychological and cognitive factors that influence individuals’ intention to adopt technology [19]. In reference to this criticism, [17] has

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brought together an updated version of the theory, UTAUT2, incorporating novel psychological and cognitive factors [17]. The UTAUT2 theory integrates eight theories within the information technology adoption and acceptance field [16]. UTAUT2 encompasses four factors from its predecessor, UTAUT1, namely: “Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)”, while also incorporating cognitive and psychological factors such as “Hedonic Motivation (HM), Price Value (PV), and Habit (HT)”. Additionally, UTAUT2 considers moderating factors including age, gender, and experience. Several studies have also demonstrated that UTAUT2 possesses greater explanatory power compared to UTAUT1 [40, 41]. Moreover, it is substantial to acknowledge that the technological landscape has significantly evolved between the emergence of UTAUT1 and the advent of UTAUT2, rendering the latter more applicable to modern technologies. These factors collectively establish UTAUT2 as the optimal and most reliable model for studying technology adoption [16]. The UTAUT2 theory has been widely utilized in various studies pertaining to information systems [42–46] including online food delivery (OFD) [14, 19, 31]. Nevertheless, numerous researchers have identified the necessity for additional investigations in this specific domain [18–21]. While previous studies have predominantly focused on examining consumers’ attitudes and behaviors, the present study aims to incorporate the UTAUT2 theory, complemented by the mediating role of Prior Online Experience. The present study incorporates the entire independent factors of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) previously examined, with the exception of two factors: “Habit (HT) and Price value (PV)”. PV refers to the consumer’s evaluation of the cost of utilizing a specific technology in relation to the provided benefits [14]. Alternatively, Habit (HT) denotes a recurring behaviour of employing a particular technology, often manifested unconsciously [47]. HT has been identified as a cognitive construct that evolves based on consumers’ prior experiences [48]. To enhance the explanatory capacity of the research model, the Price value (PV) construct is omitted due to the fact that downloading the FDA from the app store is free of charge, and users are not obligated to make any additional payments solely for browsing the application [30]. Moreover, the habit (HT) construct has also been excluded as the POE construct will serve as a mediating variable. It is reasonable to posit that the effects of habit will be captured by the POE construct. The model employed in this research is illustrated in Fig. 1.

2.1.1

Performance Expectancy (PE) and Prior Online Experiences (POEs)

PE, which refers to the idea that the use of technology improves the performance of the individuals [17], aligns with the concept of “Perceived usefulness” in the TAM theory [19]. Perceived usefulness denotes the degree that a specific technology assists users to perform tasks, with greater benefits leading to higher acceptance of

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Fig. 1 The research framework

the technology. Existing research has presented mixed findings regarding the effects of PE. For instance, [3] finds that PE does not significantly impact the customers’ attitudes or intentions towards FDAs. Similarly, [49] observe no significant association between PE and adopting mobile banking services. Conversely, several studies have indicated that, in the context of FDAs, consumers tend to respond positively to applications that save time and effort, and offer a variety of purchasing options [14, 19]. These studies collectively demonstrate that PE significantly influences the intention to reuse and continue using FDAs. Regarding POE, [50] indicate that a key role is played by the expected performance in overall customer experience evaluation. Bilgihan et al. [51] highlighted PE as a fundamental factor for creating positive experiences in e-commerce. To ensure a positive experience, companies must meet perceived requirements and benefits that are crucial for user acceptance. This sentiment is echoed by Refs. [52, 53], emphasizing that the perceived value or benefit of using an application can enhance the customer’s online experience. Thus, this study proposes that when customers perceive that using FDAs will improve their performance, such as by saving time, their experience is more likely to be positive. Accordingly, hypothesis (1) is read: H1: There is a significantly positive impact of PE on POE.

2.1.2

Effort Expectancy (EE) and Prior Online Experiences (POEs)

EE, which stands for effort expectancy, is a measure of the level of ease related to using technology [17]. It aligns with the concept of “perceived ease of use” in the theory of the Technology Acceptance Model, which refers to the amount of effort and ease necessary to use a specific technology. Consistent with this theory, higher ease of use is related to higher acceptance levels of the technology [19, 48]. Conversely, technologies requiring extra efforts to use are generally met with lower levels of

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acceptance. For instance, if a specific application is difficult to navigate, users are likely to reject it or refrain from making further purchases through it [54]. Previous pieces of research have shown varied results concerning the EE effects. Refs. [3, 39] find that EE has not significantly impacted the customers’ intention to use FDAs. In contrast, other studies have underscored the importance of EE in the scope of technology acceptance [1, 48, 55]. For example, [56] emphasizes the significant and positive impact of EE on customers’ intention to continue using FDAs. Additionally, [53] indicates that EE influences the POE of users. Specifically, when the effort required to interact with technology is lower and task performance is enhanced, a positive experience is more likely to be created. In other words, ease of use reduces the physical and cognitive effort needed to interact with technology, leading to a positive experience. Furthermore, [51] assert that customers value web applications that are easy to navigate and use. When an application is user-friendly and intuitive, it results in a more positive online experience for customers. Thus, this study posits that when customers find it easy to use FDAs efficiently and with minimal effort, their experience tends to be positive. Within this rationale, hypothesis (2) is read: H2: There is a significantly positive effect of EE on POE.

2.1.3

Social Influence (SI) and Prior Online Experiences (POEs)

According to Venkatesh et al. [17], social influence (SI) refers to the degree to which a person is affected by others in terms of their adoption or rejection of technology. It also encompasses the influence of others’ opinions and attitudes, such as those of relatives and friends, on consumers’ attitudes towards the use of a specific technology [48]. The level of SI experienced by a consumer directly affects their likelihood of accepting or rejecting that technology. For instance, societal opinions and comments regarding a particular application on app stores can influence consumers’ decisionmaking [57]. Previous research has highlighted the significance of SI in the technology acceptance domain [28, 58]. Specifically, within the realm of FDAs, both [3, 39] have underscored the substantial and positive impact of SI on customers’ intentions. Furthermore, [19] have affirmed that SI greatly influences the continued use of FDAs. The POE concept, as proposed by Wang [53], highlights the significance of social interactions in delivering appreciated and captivating customer experiences. On one hand, establishing an interactive community for communication within the application enhances customer satisfaction and loyalty, thereby creating appealing experiences [59, 60]. On the other hand, such interactions contribute to improving the application’s reputation [61]. User-generated content, namely: comments and reviews, on trusted social networking platforms with individual interaction communities, significantly influences how other customers perceive a product or service. Therefore, encouraging such interactions fosters a sense of belonging and enhances a positive online customer experience. Thus, this study proposes that when customers have easy access to ratings and reviews while using FDAs, their overall experience tends to be positive. Based on the aforementioned points, hypothesis (3) is put forth:

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H3: There is a significantly positive effect of SI on POE.

2.1.4

Facilitating Conditions (FC) and Prior Online Experiences (POEs)

As stated by Venkatesh et al. [17], facilitation conditions (FC) is the level of support and resources accessible for facilitating the utilization of technology. Put simply, it reflects the consumer’s thought in the presence of essential infrastructure and support for utilizing a particular technology [28]. The availability of more favourable conditions when employing a particular application leads to an increased acceptance level [19, 58]. For instance, if an application offers an enjoyable user experience, such as a real-time tracking feature for food orders, it improves the intention of using the application again [62]. Previous research has exhibited diverse effects of FC. Okumus et al. [58] find that the FC does not impact customer acceptance of FDAs. Conversely, several shreds of research have emphasized the significance of FC in the realm of technology acceptance. For example, [39] validated the significant and positive effect of FC on customers’ intention to use FDAs. Relating to the POE of the user, the presence of FC provided by the application can engender a sense of comfort for the customer [63], consequently enhancing the customer’s overall experience [60]. Despite the little literature directly addressing the direct effect of FC on the POE, related factors such as service quality and service integration have been mentioned. Wang [53] suggests that effective support and prompt response to customer inquiries positively contribute to generating positive experiences. Thus, it can be inferred that the availability of features such as ease of the ordering process, real-time tracking, and customer support services can facilitate a positive experience for the customer. Accordingly, this study posits that when customers encounter FC while using FDAs, their experience is inclined to be positive. With this premise, hypothesis (4) is read: H4: There is a significantly positive effect of FC on POE.

2.1.5

Hedonic Motivations (HM) and Prior Online Experiences (POEs)

According to Venkatesh et al. [17], the concept of Hedonic Motivations (HM) pertains to users’ desire for enjoyment while using technology. In simpler terms, HM reflects the level of pleasure experienced by consumers during the use of a specific technology [22]. Greater enjoyment during technology use correlates with a higher level of technology acceptance. HM is a newly added factor in the UTAUT2 theory and is considered a primary driver of consumer behaviour [17]. As confirmed by Alalwan et al. [55], individuals prefer enjoyable and entertaining technologies, particularly in the domain of FDAs [14]. Multiple scholars have underscored the significance of HM in technology acceptance [30, 48, 55]. For instance, [14] asserts the substantial and positive effect of HM on the intention of the customers to continuously utilize FDAs.

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Similarly, [64] verify the indirect impact of HM on customers’ behavioural intentions to use FDAs. Regarding the POE, HM plays a crucial role in shaping users’ online experiences by integrating features that go beyond the functional aspects of web applications. Li et al. [50] stated that customer experience evaluations are influenced by the functional and emotional characteristics of the application. HM represents an emotional aspect, and its presence can enhance the experience of the consumer in an online environment. The addition of these features, evident in the mobile application design, its content, and user interaction, helps in enhancing user enjoyment, engagement, and their inclination to return to the application [51]. Consequently, this study proposes that when customers derive enjoyment from using FDAs, their experience tends to be positive. Based on this rationale, hypothesis (5) is put forward: H5: There is a significantly positive effect of HM on POE.

2.1.6

Prior Online Experiences (POE) on Repurchase Intention (RI)

RI, or repurchase intention, is a concept used to assess the likelihood of consumers purchasing additional services or products from a specific brand at some point [62]. Essentially, it pertains to the extent of a customer’s ongoing engagement with a particular brand [65]. RI, in the current work, refers to the customers’ willingness to reorder meals through FDAs instead of using traditional methods. Notably, RI has been extensively employed in technology acceptance research by various scholars [66, 67]. Furthermore, the intention to repurchase has significantly and directly impacted a company’s revenue generation [68]. Although several studies have investigated customers’ intentions and the adoption of such applications [14], there has been limited attention paid to examining the factor of RI, particularly within the context of FDAs [6, 62, 67, 68]. Consequently, it is crucial to comprehend the factors influencing consumers’ inclination to continue purchasing from these applications to ensure their success and sustainability [9]. Accordingly, this study primarily aims at exploring consumers’ repurchase intentions specifically within the realm of FDAs. Online marketing, in comparison to traditional shopping, is a relatively recent phenomenon and is perceived as a risky endeavour by consumers [24]. Consumer behaviour during purchases is greatly influenced by their past experiences, which holds significant importance [69]. The term “Prior Online Experience” (POE) encompasses a customer’s previous encounters with the Internet and engagement in online activities [26], including their familiarity with web applications [27]. POE can substantially shape the future behaviour of an individual, such as the likelihood of utilizing a specific website or service [70]. Furthermore, it serves as a crucial factor in alleviating concerns associated with online shopping [25, 71]. Therefore, consumers’ purchasing behaviours in the future will be influenced [72]. Khalid et al. [73] have highlighted the significance of POE in enhancing consumers’ RI [73]. For instance, individuals having a positive online shopping experience in the past can engage in future purchase [74]. Conversely, those who have had a negative experience are less inclined to make repeat purchases [26]. Research indicates that

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POE can have a direct impact on various aspects of online behaviour [62, 75–77], such as online customer satisfaction [78], future hotel usage behavior [70], and perseverance in online training courses [79]. Therefore, positive POE can exert a favorable influence on customers’ opinions and behaviours in the online environment, including their repeat intention towards FDAs. Based on these premises, hypothesis is read: H6: There is a significantly positive effect POE on RI.

2.1.7

Prior Online Experiences (POE) as a Mediator

Despite the significant role of POE in shaping digital consumer behavior, limited attention has been given to studying POE in the scope of FDAs [64]. Previous research has primarily approached POE in three ways: as an independent variable [20, 62, 64, 70], as a dependent variable [50], or as a moderating variable [71, 72, 80]. However, the examination of POE as a mediating variable has received limited attention. Only few studies have explored this aspect, focusing on POE as a mediator in the domain of hotel hosting [76], value-added services (VAS) [53], and retail trade [60]. Nevertheless, there is a dearth of studies investigating POE as a mediator in the context of repurchasing from FDAs. This research gap is noteworthy, considering that the main function of a mediator is to exert or speed up influence [81], a role that POE is well-suited to fulfill. Despite the acknowledged benefits of the UTAUT2, scholars have suggested the need for its integration with other theories or the inclusion/exclusion of certain constructs [22, 23]. By incorporating multiple theories, the explanatory power of research models can be enhanced [82]. Accordingly, the present study incorporates a mediating effect into the research model, which is theoretically justified. In this study, it is proposed that the POE can exert a positive influence on the relationships between the UTAUT2 factors and the RI. It is postulated that consumers with positive prior experiences are more inclined to engage in repurchasing from FDAs. Consequently, our research aims to examine the POE role as a mediator between the UTAUT2 factors and the intention to repurchase. By doing so, this study seeks to offer a more precise understanding of how previous online experiences shape the intention to repurchase, thus yielding potential implications for FDAs. Building upon the aforementioned rationale, hypotheses (7, 8, 9, 10, and 11) are formulated: H7: POE mediates the relationship between PE and RI. H8: POE mediates the relationship between EE and RI. H9: POE mediates the relationship between SI and RI. H10: POE mediates the relationship between FC and RI. H11: POE mediates the relationship between HM and RI.

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3 Research Method This quantitative analysis approach is employed, along with utilizing purposive sampling to collect data from a specific target group comprising individuals with prior experience using Food Delivery Applications (FDAs). The analysis unit was determined through screening questions. Assessing the relationships within the developed model necessitates creating valid and reliable measurement scales for each construct drawn from established sources. Three items adapted from previous studies are used to evaluate the performance expectancy [3, 21, 47]. Similarly, effort expectancy is also assessed using three items adapted from previous research [3, 47, 83]. Social influence is evaluated using three items adapted from studies by Refs. [3, 21, 47], while facilitating conditions are considered using three items from [17, 19, 58]. Hedonic motivations, however, are measured using three items adapted from the works of [17, 19, 47]. Additionally, the mediating role of Prior Online Experience is quantified using four items from studies done by Refs. [70, 72], while Repurchase Intention is measured using four items from [6, 84–86]. All research variables are assessed using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The items’ reliability and validity have been established in previous research. The research data is obtained through the administration of an online questionnaire utilizing a purposive sampling technique. Specifically, individuals possessing prior experience using FDAs are selected to participate. To ascertain eligibility, participants are first presented with a filter question inquiring about their previous use of any FDAs. Respondents who indicated “yes” have proceeded to answer the remaining questions, while those who have responded “no” are directed to terminate their participation. A Google link is used to distribute the questionnaire, targeting the users of the mobile phones. Subsequently, the returned responses are carefully scrutinized to identify any instances of incompleteness or inconsistency. Ultimately, a total of 392 valid questionnaires are acquired for completing this research.

4 Data Analysis For an extended period, the statistical analysis is utilized as a reliable methodology to augment their ability to investigate, advance, and authenticate experimental findings [87, 88]. The utilization of these statistical methods has experienced a notable surge in recent years, primarily due to advancements in computer technology. In this section, a comprehensive explanation of the analytical methods used in this study shall be provided, alongside elucidating the rationale behind their implementation. This study employed methodologies similar to those utilized in prior research studies [89–93] to ensure comparability and consistency. Validating the research models and examining the proposed research hypotheses necessitates employing a two-step analytical approach, combining Artificial Neural Network (ANN) with

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Partial Least Squares Structural Equation Modeling (PLS-SEM). Firstly, the PLSSEM is utilized to identify the determinants of the UTAUT2 framework and assess their impact on the POE, as well as its mediating effect on the intention to repurchase. Given the intricate nature of the model and the wide array of indicators involved, the adoption of variance-based PLS-SEM is deemed essential. This choice is further justified by the investigative nature of this work, which differs from confirmatory investigations, as supported by previous research [87, 94]. While PLS-SEM exhibits robustness against non-normal distribution, it lacks the capability to analyze nonlinear relationships among constructs. To overcome this limitation, researchers have proposed integrating the ANN approach with PLS-SEM to ascertain the relative status of significant variables following normalization [91, 92]. The second research stage involves the application of the ANN method to evaluate the significance of the predictors, as maintained by the previous studies [90, 95–97]. By utilizing ANN in this study, complex linear and non-linear associations between the variables in the developed model and the determination of consumer repurchase intention can be uncovered. Furthermore, the inclusion of ANN demonstrates has enhanced predictive capabilities compared to conventional regression techniques [91, 96, 97].

5 Results 5.1 Non-response and Common Method Bias Similar to past pieces of research conducted by Refs. [98, 99], the assessment of non-response bias in this study has involved comparing the responses obtained from early and late participants. Determining the availability of any significant differences requires conducting t-tests among the initial 100 participants and the final 100 participants, following the approach recommended by Armstrong and Overton [100]. The t-test-based findings indicate no significant difference between the early and late respondents, proposing that non-response bias does not have an impact on the research outcomes. Additionally, since the data collated in this work obtained from a single source, it has effectively addressed the issue of bias resulting from common methods. The study concludes that common method bias does not have a significant impact. To assess the potential common method bias further, Harman’s one-factor test is conducted on the seven essential variables, as recommended by Podsakoff et al. [101]. The findings indicate that a single factor has marked a maximum of 38.39% of the variance, which is below the recommended threshold of 50% outlined by Podsakoff et al. [101]. Therefore, it is found that common method bias is an insignificant issue in this research. Moreover, following the recommendation by Kock [102], the Variance Inflation Factor (VIF) is utilized to assess the common method bias (CMB). By employing the PLS algorithm, it is observed that the VIF values for all constructs have remained below the commonly utilized threshold of 3.3,

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as established by Refs. [101, 102]. These findings indicate that the adopted research instrument is free from common method bias.

5.2 Measurement Model Assessment Prior to examining the hypothesis relationships in the structural model, the initial research phase involved an assessment of the measurement model. Specifically, the focus was on measuring discriminant validity, convergent validity, and internal consistency reliability [103]. To determine convergent validity, several indicators were considered, including factor loadings, Cronbach’s Alpha (CA), Composite Reliability (CR), and Average Variance Extracted (AVE). As presented in Table 1, the obtained results demonstrated the achievement of convergent validity, with factor loadings surpassing the threshold of 0.70, CA and CR values exceeding 0.70, and AVE values exceeding 0.50 [103]. Discriminant validity was assessed using the Heterotrait-Monotrait Ratio (HTMT), which is considered more rigorous than the Fornell-Larcker criterion [104]. A threshold of 0.85 was employed, and the relationship values among the constructs were found to be below this critical threshold as displayed in Table 2. Besides, the variance inflation factor (VIF) values for the constructs have ranged from 1.683 to 2.978, remaining below the recommended threshold of 3.3. These findings indicate the absence of multicollinearity issues among the predictor constructs, supporting the existence of satisfactory discriminant validity. In summary, the findings of this study provide evidence of successful attainment of discriminant validity, convergent validity, and internal consistency reliability in the measurement model analysis.

5.3 Structural Model Assessment Having validated the measurement models, the following procedure was to evaluate the structural model, specifically focusing on its predictive capabilities and the interrelationships among various constructs [87, 103]. The bootstrap resampling method with 5000 re-samples was employed to assess the structural model. This approach allowed calculating the R2 path coefficients and their corresponding t-values. Upon examination of Table 3, it becomes evident that all the proposed hypotheses in this study received substantial support, reinforcing the relationships under investigation. As shown in Table 3, there were six direct relationships and five mediated relationships. PE (H1: β = 0.122, t = 3.135), EE (H2: β = 0.343, t = 8.220), SI (H3: β = 0.085, t = 2.134), FC (H4: β = 0.370, t = 10.389), and HM (H5: β = 0.114, t = 2.776) showed a positive relationship with POE. This adds support to H1, H2, H3, H4, and H5. Also, POE (H6: β = 0.477, t = 10.599) also a positive association with RI, supporting H6. The R2 for POE was 0.651 while it was 0.228 for RI, indicating

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Table 1 Results of the reliability and convergent validity Construct

Indicators

Loading

CA

CR

AVE

Performance expectancy (PE)

PE1

0.854

0.831

0.898

0.745

PE2

0.892

PE3

0.843

EE1

0.862

0.841

0.902

0.753

EE2

0.844

EE3

0.897

SI1

0.907

0.875

0.921

0.796

SI2

0.910

SI3

0.860

FC1

0.866

0.861

0.911

0.772

FC2

0.904

FC3

0.866

HM1

0.857

0.866

0.915

0.783

HM2

0.910

HM3

0.886

POE1

0.811

0.881

0.893

0.679

POE2

0.799

POE3

0.732

POE4

0.940

RI1

0.888

0.913

0.938

0.790

RI2

0.895

RI3

0.893

RI4

0.881

Effort expectancy (EE)

Social influence (SI)

Facilitating conditions (FC)

Hedonic motivation (HM)

Prior online experiences (POE)

Repurchase intention (RI)

Table 2 Heterotrait-Monotrait ratio (HTMT) results EE

FC

HM

PE

POE

RI

EE FC

0.566

HM

0.634

0.678

PE

0.416

0.381

0.297

POE

0.762

0.755

0.630

0.491

RI

0.479

0.314

0.352

0.538

0.538

SI

0.652

0.585

0.650

0.393

0.609

0.346

PE Performance Expectancy; EE Effort expectancy; SI Social Influence; FC Facilitating Conditions; HM Hedonic Motivation; POE Prior Online Experiences; RI Repurchase Intention

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Table 3 Results of the hypotheses testing Structural paths

Path coefficient

t-value

p-value

LLCI

ULCI

Empirical evidence

H1

PE → POE

0.122

3.135

0.002

0.045

0.199

Supported

H2

EE → POE

0.343

8.220

0.000

0.261

0.423

Supported

H3

SI → POE

0.085

2.134

0.033

0.009

0.165

Supported

H4

FC → POE

0.370

10.389

0.000

0.300

0.439

Supported

H5

HM → POE

0.114

2.776

0.006

0.033

0.194

Supported

H6

POE → RI

0.477

10.599

0.000

0.389

0.567

Supported

H10

PE → POE → RI

0.058

2.844

0.004

0.020

0.101

Supported

H7

EE → POE → RI

0.164

6.704

0.000

0.119

0.214

Supported

H11

SI → POE → RI

0.041

2.068

0.039

0.004

0.081

Supported

H8

FC → POE → RI

0.177

7.609

0.000

0.135

0.225

Supported

H9

HM → POE → RI

0.054

2.707

0.007

0.016

0.096

Supported

H hypotheses; PE Performance Expectancy; EE Effort expectancy; SI Social Influence; FC Facilitating Conditions; HM Hedonic Motivation; POE Prior Online Experiences; RI Repurchase Intention

that 65.1% of the variance in POE is elucidated by the 5 values, while 22.8% of the variance in RI is elucidated by the POE. An examination was conducted on the effect size (f2 ) of the endogenous constructs to assess the extent to which each exogenous construct accounted for the variability in the endogenous constructs. Based on [105], effect sizes of 0.02, 0.15, and 0.35 are categorized as small, medium, and large, respectively. Given the reading of the f2 , SI (f2 = 0.012), HM (f2 = 0.020), and PE (f2 = 0.035) explained the response POE with a small effect. Likewise, the results show a medium effect size of EE on POE (f2 = 0.196), FC on POE (f2 = 0.230) and POE on RI (f2 = 0.295). Hence, a predictive relevance was attained in the structural model as a cross-validated redundancy findings of the two endogenous variables (based on the blindfolding procedure with a 7-omission distance) were > 0 (POE Q2 = 0.399, and RI Q2 = 0.176) [106]. In assessing the mediation effects, PE → POE → RI (β = 0.058, t = 2.844), EE → POE → RI (β = 0.164, t = 6.704), SI → POE → RI (β = 0.041, t = 2.068), FC → POE → RI (β = 0.177, t = 7.609), HM → POE → RI (β = 00.054, t = 2.707), were all significant, with no 0 straddling the lower and upper confidence intervals. Therefore, H7, H8, H9, H10, and H11 were supported.

5.4 Artificial Neural Network Analysis PLS-SEM, being limited to linear models, sometimes simplifies the complexities to determine POE. Overcoming this limitation involves the adoption of an ANN approach, allowing for the identification of both linear and non-linear relationships.

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Not like conventional regression methods, ANN techniques require no distribution assumptions and often yield better predictions [42, 92, 93, 107]. However, thanks to the “black-box” operational mode of ANN, it is unsuitable for testing causal hypotheses. To address this, the causal relationships were first examined using PLSSEM, and the attained significant factors were then used as inputs for the ANN model to avoid overfitting. The research model in this study consists of two endogenous constructs decomposed into two separate ANN models. These models were analyzed using the ANN multilayer perceptron network. Each ANN model included a single hidden layer, with the number of neurons given by the structure [91, 108]. The first model had three hidden neurons Fig. 2, while the second model had one hidden neuron Fig. 3. To address the issue of overfitting, a ten-fold cross-validation approach was adopted, partitioning the data into 70:30 ratios for training and testing, correspondingly. Likewise, this approach was utilized to determine the relative significance of the predictors [90, 109]. The ANN models’ accuracy was assessed using the root-mean-square error (RMSE), a standard accuracy metric used in prior research [93, 107]. The RMSE

Fig. 2 First model (three hidden neurons)

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Fig. 3 Second model (one hidden neuron)

show the error during both the training and testing phases. Table 4 presents the RMSE values for model 1, ranging from 1.747 to 2.155 for training and from 0.562 to 1.129 for testing. For model 2, the training RMSE values ranged from 0.073 to 0.161, but the testing RMSE values ranged from 0.113 to 0.159. Considering the minor differences in RMSE values and the calculated standard deviation for both the training dataset (0.137 and 0.025 for the two models, respectively) and the testing dataset (0.147 and 0.015 for the two models, respectively), the models attained higher precision through the ANN implementation. Consequently, it is believed that the ANN model developed in this research yielded accurate and dependable results. The relative importance of predictors in the ANN modeling process was determined by calculating the normalized importance, which represents each predictor’s average value compared to the highest mean value, articulated as a percentage [91, 109–111]. Table 5 provides the normalized and mean significance of the whole utilized predictors. From the sensitivity analysis results in Table 5, it is evident that EE has the highest relative importance (95.91%) in relation to POE, followed by FC (93.45%). Conversely, SI exhibits the lowest effect on POE, with a relative significance of 44.23% Table 5.

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Table 4 RMSE values Network

Model 1

Model 2

Sum of square error (training)

Sum of square error (testing)

Sum of square error (training)

Sum of square error (testing)

1

2.155

1.129

0.161

0.113

2

1.747

0.751

0.073

0.124

3

1.815

0.893

0.145

0.159

4

1.781

0.944

0.150

0.142

5

1.989

0.824

0.144

0.158

6

1.906

0.918

0.154

0.135

7

2.025

0.792

0.152

0.138

8

1.904

0.857

0.145

0.156

9

1.820

0.928

0.151

0.144

10

2.086

0.562

0.152

0.141

Mean

1.923

0.860

0.143

0.141

Standard deviation

0.137

0.147

0.025

0.015

Table 5 Sensitivity analysis Network

EE

FC

HM

PE

SI

1

0.218

0.293

0.161

0.228

0.100

2

0.274

0.272

0.171

0.150

0.133

3

0.315

0.295

0.142

0.127

0.120

4

0.313

0.240

0.110

0.160

0.176

5

0.286

0.299

0.094

0.176

0.145

6

0.350

0.308

0.106

0.109

0.126

7

0.305

0.278

0.138

0.161

0.118

8

0.258

0.270

0.136

0.180

0.157

9

0.284

0.243

0.154

0.147

0.172

10

0.294

0.315

0.187

0.131

0.073

Mean significance

0.290

0.281

0.140

0.157

0.132

Normalized significance %

95.91

93.45

46.86

52.66

44.23

Ranking

1

2

4

3

5

PE Performance Expectancy; EE Effort expectancy; SI Social Influence; FC Facilitating Conditions; HM Hedonic Motivation

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6 Discussions and Conclusion In brief, an examination of repurchase intention (RI) specifically pertaining to food delivery applications has been conducted. To investigate this phenomenon, a set of five independent variables derived from the UTATUT2 framework is also employed. These variables encompass performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and hedonic motivations (HM). Additionally, the study incorporated the use of perceived overall experience (POE) as a mediator in the proposed model. The attained results furnish significant evidence to support all of the recommended hypotheses, underscoring the importance of various factors in shaping users’ experiences and their subsequent intentions to repurchase from Food Delivery Apps (FDAs). The first set of hypotheses has examined the factors derived from the UTAUT2, directly influencing users’ prior online experiences (POE). The results confirmed H1, demonstrating a statistically significant and positive effect of performance expectancy on POE. This finding is in line with the previous study conducted by Refs. [50–53], which supports the notion that users with high expectations regarding the performance and functionality of FDAs can gain positive prior online experiences with the same app or similar online activities. Likewise, our findings also supported H2, revealing a significantly positive effect of effort expectancy (EE) on POE. This result is in accordance with the conclusions drawn in prior pieces of research conducted by Refs. [51, 112]. Therefore, it can be inferred that users perceiving FDAs as easy to use and navigate can obtain positive prior online experiences. Regarding the impact of social influence (SI) on perceived ease of use (POE), H3 was corroborated, demonstrating a statistically significant positive effect of SI on POE. This finding aligns with prior research conducted by Refs. [53, 60], indicating that users who perceive SI, such as receiving recommendations or opinions from others, are more inclined to experience positive POE by Refs. [53, 60]. Furthermore, H4 revealed a significantly positive association between facilitation conditions (FC) and POE, consistent with the results obtained by Refs. [53, 60]. This finding suggests that users who have access to the necessary resources, such as clear instructions and technical support, to effectively utilize the FDAs are more likely to experience positive POE. Additionally, H5 provided support for the significantly positive impact of hedonic motivations on POE, in agreement with the results reported by Refs. [50, 51]. This implies that users who derive pleasure, enjoyment, or emotional satisfaction from using FDAs are more likely to experience positive POE. H6 postulated that the POE has a positive influence on RI regarding Food Delivery Applications (FDAs), which aligns with prior research findings [73, 75, 76]. The significant support for this hypothesis indicates that users who perceive FDAs favourably are more inclined to develop the intention to repurchase. Put differently, when users have positive experiences with regard to various aspects such as app functionality, user interface, delivery speed, order accuracy, customer service and

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support, and overall satisfaction, they are more likely to consider using the app again in the future. The second set of five hypotheses explored the mediating role of POE in the relationship between the UTAUT2 factors and repurchase intention (RI). Although no past studies have specifically investigated these relationships, these hypotheses were formulated based on theoretical foundations and logical reasoning. Specifically, H7 found support, suggesting that users who hold high expectations regarding the performance and functionality of FDAs, and have these expectations fulfilled through positive POE, are more inclined to develop the intention to repurchase. Similarly, H8 received support, indicating that users who perceive FDAs as easy to use and navigate, and these perceptions are congruent with their positive POE, can develop the intention to repurchase. Furthermore, the findings of this study provide support for H9, which posits that users’ perception of social influence (SI) and receiving positive recommendations or opinions that align with their prior positive experiences with FDA’s increase their likelihood of developing an intention to repurchase. Additionally, H10 is supported, suggesting that users’ access to necessary support, such as frequently asked questions (FAQs) or customer service, which facilitate smooth usage of FDAs, and the congruence of these support mechanisms with their prior positive experiences, enhance their intention to repurchase. Lastly, H11 is also supported, demonstrating that users who derive pleasure and satisfaction from their past experiences with FDAs can develop an intention to repurchase. Furthermore, a sensitivity analysis was conducted to consider the relative importance of each construct. The results of this analysis revealed that the construct of EE exhibited the highest level of relative importance regarding the formation of POE, followed by the construct of FC. Conversely, the construct of SI demonstrated the least influence on POE, as indicated by its relatively lower importance in the analysis. Although this study focused specifically on FDAs, it is important to acknowledge that the findings may have context-specific implications. Enhancing the generalizability of these results involves conducting future research to explore other online platforms or industries, extending the understanding of user behavior in different contexts. Furthermore, to achieve a more comprehensive understanding of the subject matter under investigation, it would be valuable to explore alternative mediators that can shed light on the relationship between relevant variables. Rather than solely relying on the POE, considering attitude or trust as potential mediating factors could provide deeper insights. Investigating how these factors influence the use of technology and acceptance and can contribute to a more holistic understanding of user behavior. Additionally, integrating additional models such as the status quo bias (SQB) into the research framework can offer a more nuanced perspective by considering both facilitators’ and inhibitors’ viewpoints. By incorporating SQB alongside the UTAUT2, future studies can conduct a more comprehensive analysis, enabling a thorough exploration of the subject matter from multiple angles. This integrated approach will facilitate a robust understanding of the factors influencing technology adoption and usage, and uncover the underlying mechanisms driving user behavior in various contexts.

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Effects of Platform Values on Consumers’ Willingness to Continue and Subscribe to Metaverse Live-Streaming: With a Moderating Effect of Digital Literacy Zhiying Hou, Yet-Mee Lim, and Yu Zhang

Abstract Metaverse live-streaming refers to broadcasting and streaming live content within the Metaverse, a virtual universe where users can interact with each other and the digital environment. Digital literacy is an individual’s ability to find, evaluate, and communicate information using digital technologies and platforms. This research investigates the moderate effect of customer digital literacy on the relationship between platform values and customer immersion on the Metaverse livestreaming platform. The Continuance Intention Theory was adopted in this study to research customers’ willingness to continue and subscribe to the metaverse livestreaming program. A survey questionnaire was designed for this research, and 353 qualified respondents were collected and analyzed by Smart PLS software. The result of the data analysis shows that the Contextual Value and Symbolic Value of the metaverse live-streaming platform have a positive impact on customer Immersion, and customer immersion has a significant positive impact on customer’s Willingness to Continue and Subscribe to the metaverse live-streaming program. Keywords Platform value · Metaverse live-streaming · Digital literacy · Immersion · Willingness to continue and subscribe

1 Introduction The metaverse has emerged as an increasingly important live-stream platform, offering users a new immersive and interactive experience [1]. Metaverse livestreaming commerce involves e-commerce activities conducted within the metaverse through live-streaming platforms. It integrates the principles of e-commerce and live streaming within the immersive, virtual universe of the metaverse, which combines physical, augmented, and virtual reality to create an environment where users can Z. Hou (B) · Y.-M. Lim · Y. Zhang UCSI Heights, No. 1, Jalan Puncak Menara Gading, Taman Connaught, 56000 Cheras, Federal Territory of Kuala Lumpur, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_15

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interact, make purchases, play games, and participate in various experiences as they would in the real world [2]. Live-streaming commerce in the metaverse revolutionizes retail experiences, providing opportunities for brands to build communities, bridge the gap between physical and digital retail, and offer unique, immersive shopping experiences [3, 4]. In 2022, Tiktok launched online disco forms for summer parties for young groups as a type of metaverse live streaming, and the total exposure of activities in the live streaming broadcast was nearly 1.5 billion [5]. Past study has limited research related to customer continued behavior or post-usage behavior to metaverse live streaming. As a new type of live streaming technology, analysis of the customer’s willingness to continue and subscribe to Metaverse live streaming is becoming a central issue in the Metaverse industry. Besides customers’ willingness to continue and subscribe to Metaverse live streaming, past studies have limited research on whether customers’ own characters will impact their behavior. Understanding customer digital literacy is critical to interpreting their behavior in the metaverse. Consumers with higher levels of digital literacy might use the metaverse differently than those with lower levels, so researching this aspect can provide valuable insights into consumer behavior [6]. Thus, this research aims to analyze the impact of customer digital literacy on their behavior in metaverse live-streaming programs. With the live-streaming industry growing explosively and consumer time spent on stream-watching increasing [7], understanding why consumers continue to watch live streams becomes essential. Metaverse live-streaming represents a convergence of real and virtual worlds where audiences can have more interactive and immersive experiences [1]. As such, the factors influencing continuance intention in this new and dynamic context, which differ from those in traditional live streaming, necessitate more targeted and specific research. Therefore, it is necessary to analyze customer behavior in metaverse live streaming. This research aims to investigate the impact of platform values on consumer immersion and then affect consumer’s willingness to continue and subscribe to the Metaverse live-streaming platform.

2 Literature Review 2.1 Metaverse Live Streaming Past studies put large attention in the social media area [8–12]; as a new type of social media, the concept of the metaverse has gained significant attention in recent years [13–15], and one of its applications is in the realm of live streaming. Live streaming refers to the real-time broadcasting of video content over the internet [16]. In the context of the metaverse, live streaming can take on a new dimension, allowing users to virtually attend events, interact with others, and explore virtual spaces in real-time [14].

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Although there is limited research specifically addressing the combination of the metaverse and live streaming, it is reasonable to speculate on the potential benefits and challenges of this integration. Live streaming within the metaverse could provide users with immersive and interactive experiences, allowing them to virtually attend events, concerts, conferences, and other social gatherings. This could have implications for various industries, including entertainment [17], gaming, and social media [18–20]. Regarding the metaverse live-streaming launched in Chinese marketing [5], it is necessary to conduct research in this area.

2.2 Continuance Intention Theory The Continuance Intention Theory is a model that explains post-adoption behavior in Information Systems [21]. The theory assumes that the success of Information Systems depends on continued use rather than first-time use. It is argued that after initial use, cognitive beliefs such as individual perceptions of system usefulness and satisfaction with it may change, leading to repeated behaviors or discontinued usage [21]. This study aims to identify the impact of the platform value on customer immersion with a moderate effect on customers’ digital literacy and, furthermore, improve the customer willingness to continue and subscribe to the live-streaming program in the metaverse. Thus, this research applying the Continuance Intention Theory to metaverse live-streaming provides insights into user behavior by evaluating factors that influence the continued use of metaverse live-streaming.

3 Hypothesis Development 3.1 Platform Values of the Metaverse Live-Streaming Platform value refers to the economic and social benefits a digitalized interactive platform can provide its users [22]. This includes not only the direct benefits of using the platform (such as access to new products or services) but also the indirect benefits that arise from network effects and other forms of positive feedback. Metaverse live-streaming offers an immersive and engaging experience by blending virtual reality, streaming video, mobile games, social media, and artificial intelligence. The virtual world of the metaverse creates a more dynamic and interactive experience than traditional video streaming. This technology allows users to become part of the environment and interact with the digital world in real-time as they would in the physical world [23]. In the context of live-streaming, Metaverse offers unique platform value. Live-stream shopping, for example, has gained popularity with the advancement of digital platforms [24]. This research classifies the platform values of

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metaverse live streaming into four types: functional value, contextual value, aesthetic value and symbolic value. Functional value refers to the utility that users perceive as the quality and performance of the metaverse platform [22]. The metaverse allows for a more interactive, personalized, and adventurous consumer experience. It allows consumers to discover and explore products in new ways, fuse physical and virtual product experiences, and establish connections between people and brands through AI-powered bots [6]. Functional value in metaverse live-streaming can contribute to customer engagement and activeness. Immersive features and personalized experiences can enhance customers’ involvement and participation, increasing engagement and activeness in the metaverse environment [25]. A study suggests that consumers’ perception of utilitarian value can have a positive influence on their purchase intentions [26]. Contextual value refers to metaverse live-streaming provided to its users by offering personalized and localized services [22]. Contextual value is created by providing customized experiences in accordance with customers’ unique preferences to create contextual value for them [22]. These values come from customized products or services and contextualized services, such as using big data analysis technology to interpret a user’s location and specific needs and personalizing the user’s experience accordingly. Immersion refers to people’s feeling of being absorbed in the real or ordinary world [27]. Aesthetic value refers to metaverse live-streaming’s perceived appearance and beauty [28]. Technologies like smartphones are not only bought for their usefulness or functional value but increasingly also for their aesthetic value, which can convey different meanings to other people [29]. Thus, Customers have a specific demand for aesthetic value. Aesthetic value enhances the immersive nature of the metaverse. By designing visually engaging and interactive spaces, creators can captivate their audience and enhance their overall experience. For instance, Mark Zuckerberg’s metaverse presentation depicted an avant-garde glass apartment filled with various objects and a breathtaking view of the cosmos, thus making the environment visually appealing and intriguing [1]. The symbolic value of metaverse live streaming to the perceived message a product communicates regarding a consumer’s self-image to both them and to others based on visual elements [28]. Symbolic value can have a significant impact on consumer behavior [26]. Live events in the metaverse, like a concert performed by a renowned artist, can have symbolic value by providing unique, immersive experiences that are not bound by physical constraints [30]. From a commerce perspective, the symbolic value of live-streaming lies in the potential to reduce product uncertainty and build trust through interactive and immersive experiences [31]. Therefore, this research hypothesizes: H1: Functional value positively impacts immersion in the metaverse live-streaming. H2: Contextual value positively impacts immersion in the metaverse live-streaming. H3: Aesthetic value positively impacts immersion in the metaverse live-streaming. H4: Symbolic value positively impacts immersion in the metaverse live-streaming.

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3.2 Moderate Effect of Digital Literacy Digital literacy refers to the ability to understand, analyze, and use information from various sources accessible via computer devices [32]. In other words, digital literacy refers to an individual’s ability to find, evaluate, and communicate information using digital media platforms. It involves a combination of both technical and cognitive skills in employing information and communication technologies to create, evaluate, and share information [33]. It is an essential skill in today’s digital age and plays a crucial role in many areas, including marketing, business, education, and even personal development [34]. A consumer with high digital literacy can better understand, evaluate, and interact with digital content, which in turn affects how an audience views and responds to marketing content. If consumers lack digital literacy, there can be gaps in how they perceive and comprehend marketing messages [35]. Digital literacy involves more than just knowing how to use computers. It is about the array of practices involved in interacting with digital technologies. Thus, customers with high digital literacy and low digital literacy have different reactions to metaverse live streaming platform value, and it is necessary to analyze whether the increase in customer digital literacy will enhance the impact of the platform value on customer immersion in metaverse live streaming. Therefore, this study hypothesizes that: H5: Customer’s digital literacy enhances functional value’s impact on the immersion in the metaverse live streaming. H6: Customer’s digital literacy enhances contextual value’s impact on the immersion in the metaverse live streaming. H7: Customer’s digital literacy enhances aesthetic value’s impact on the immersion in the metaverse live streaming. H8: Customer’s digital literacy enhances symbolic value’s impact on the immersion in the metaverse live streaming.

3.3 Customer’s Immersion in the Metaverse Live Streaming Customer immersion in metaverse live-streaming is a topic of growing interest in academic research. The metaverse, which combines the physical and virtual worlds, has the potential to revolutionize the way customers and organizations co-create experiences and values [36]. Studies have explored various factors that contribute to customer immersion in metaverse live streaming. For example, the quality of the virtual environment and its level of realism can enhance immersion [37]. Interactivity and social presence, such as the ability to interact with other participants, also play a role in creating a sense of immersion [37]. A study explored the impact of information quality and interaction quality on swift guanxi (a form of social exchange) and

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Fig. 1 Research framework

customers’ purchase intention in live-streaming platforms [38]. They found that information quality and interaction quality positively influence swift guanxi, which in turn affects customers’ purchase intention. Another study found that sex appeal does not significantly influence continuous watching intention in educational live streaming [39]. The willingness to continue and subscribe refers to the likelihood or inclination of consumers to maintain their subscription to a video streaming service over time [40]. Thus, this research hypothesis that: H9: Immersion positively impacts customer’s willingness to continue and subscribe to the metaverse live-streaming (Fig. 1).

4 Research Methodology 4.1 Data Collection The target population of this research is the people who have watched the metaverse live-streaming program in China. The judgmental sampling method was used in this research. Regarding the questionnaire design, a screen question was set on the first page: “Do you have watching experience on Metaverse live-streaming?” Only if the respondents select yes can they fill out the following questions. This function was operated by Wenjuanxing (www.wjx.cn), a popular questionnaire service platform in China.

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Table 1 Measurement item Variable Functional value

Number of measurement items 3

Reference [42]

Contextual value

3

[43]

Aesthetic value

3

[28]

Symbolic value

3

[28]

Digital literacy

10

[44]

Immersion

3

[45]

Willingness to continue and subscribe

3

[40]

The minimum sample size of this research is 98, which calculated G*Power software (f2 = 0.15, α = 0.05, (1 − β) = 0.8 and the number of predictors = 6) [41]. The number of effective respondents in this research was 353, meeting the requirement of the minimum sample size.

4.2 Measurement Development This research designed a self-complete questionnaire and divided it into two main sections. Section A was used to collect respondents’ demographic profiles, while Section B measures the opinions of respondents. A 7-point Likert scale was used to measure the customer’s opinion toward the item. Table 1 shows the sources of each measurement item.

4.3 Profile of Respondents The survey collected responses from 75.4% of female and 24.6% of male participants. There are 73.1% of respondents aged between 18 and 25. It is reported that generations like Gen Z and millennials are more likely to play virtual platforms [46]. Thus, in terms of age distribution, the respondents are representative. There is 58.1% of respondents’ income level below 2000 CNY (official currency of the People’s Republic of China) per month.

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5 Data Analysis 5.1 Statistical Analysis This research conducts quantitative research. Thus, this research selects the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach, operated by SmartPLS software version 4.0.9.5. to evaluate the measurement model and the structural model.

5.2 Common Method Bias Analysis Due to this study conducting a self-administered survey, the common method bias needs to be tested [47]. Table 2 shows the common method bias analysis result. As shown in Table 2, all the Ra estimated significance at the P < 0.001 level; also, all the items in Ra2 are higher than Rb. Therefore, this research doesn’t have a problem with common method bias [48, 49].

5.3 Assessing the Outer Measurement Model Regarding Table 3, all Cronbach’s alpha Composite reliability and pA are higher than 0.7, which reflects the measurement items have good internal consistency [50– 52]. All factor loading values are higher than 0.7, and all AVE values exceeded 0.5, which reflects the convergent validity that was established in this measurement model [53–55]. Table 4 shows the result of the Fornell-Lercker test. All the AVE of latent variables (which was bolditalic in Table 4) are higher than the squared correlations between the latent variable and all other variables. Thus, the discriminant validity was established [56–60].

5.4 Structural Model Regarding structural model assessment, to assess the goodness of model fit, the result of Standardized Root Mean Square Residual (SRMR) showed that the saturated and estimated model is 0.041 and 0.044, respectively, which is less than 0.08 to show the model fit was good [61–65]. According to Table 5, the path coefficients range greater than 0.1 is acceptable [66–69]. Thus, H2, H4 and H9 were supported. The hypothesis of all moderated effects (H5, H6, H7, H8) in this study is unsupported.

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Table 2 Common method bias analysis Latent construct

Indicators

Substantive factor loading (Ra)

Ra2

Method factor loading (Rb)

Rb2

Aesthetic value Aesthetic value → AV1

0.918***

0.843

0.063*

0.004

Aesthetic value → AV2

1.008***

1.016

− 0.029NS

0.001

Aesthetic value → AV3

1.013***

1.026

− 0.034NS

0.001

Contextual value → CV1

1.008***

1.016

− 0.040NS

0.002

Contextual value → CV2

1.070***

1.145

− 0.099NS

0.010

Contextual value → CV3

0.836***

0.699

0.139*

0.019

Digital literacy → DL1

0.963***

0.927

− 0.078NS

0.006

Digital literacy → DL2

0.982***

0.964

− 0.080NS

0.006

Digital literacy → DL3

0.887***

0.787

0.066NS

0.004

Contextual value

Digital literacy

Functional value

Immersion

Digital literacy → DL4

1.078***

1.162

− 0.152*

0.023

Digital literacy → DL5

1.043***

1.088

− 0.117NS

0.014

Digital literacy → DL6

1.115***

1.243

− 0.210*

0.044

Digital literacy → DL7

1.023***

1.047

−

Digital literacy → DL8

1.044***

1.090

− 0.149NS

Digital literacy → DL9

0.465***

0.216

Digital literacy → DL10 0.528***

0.279

Functional value → FV1 0.988***

0.976

− 0.021NS

0.000

Functional value → FV2 1.003***

1.006

− 0.03NS

0.001

Functional value → FV3 0.891***

0.794

0.054NS

Immersion → I1

0.844***

0.712

0.126NS

Immersion → I2

1.123***

1.261

Immersion → I3

0.891***

0.794

0.063**

0.004

0.931***

0.867

0.027NS

0.001

Symbolic value Symbolic value → SV1

Willingness to continue and subscribe

0.096NS

0.009 0.022

0.455***

0.207

0.383***

0.147

− 0.194*

0.003 0.016 0.038

Symbolic value → SV2

0.951***

0.904

−

Symbolic value → SV3

0.975***

0.951

− 0.019NS

0.000

Willingness to continue and subscribe → WS1

0.804***

0.646

0.155NS

0.024

Willingness to continue and subscribe → WS2

1.277***

1.631

Willingness to continue and subscribe → WS3

0.725***

0.526

Average

0.942

0.915

Notes ***p < 0.001; **p < 0.01; *p < 0.05, NS insignificant

0.008NS

0.000

− 0.441* 0.239*** − 0.001

0.194 0.057 0.031

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Table 3 Internal consistency and convergent validity Constructs

Loadings

Cronbach’s alpha

rho (pA)

Composite reliability

Average variance extracted (AVE)

Aesthetic value AV1

0.977

0.979

0.979

0.986

0.960

AV2

0.981

AV3

0.982

CV1

0.970

0.970

0.970

0.981

0.944

CV2

0.975

CV3

0.970

DL1

0.889

0.978

0.979

0.981

0.837

DL2

0.906

DL3

0.948

DL4

0.934

DL5

0.933

DL6

0.918

DL7

0.933

DL8

0.906

DL9

0.891 0.959

0.960

0.973

0.924

0.947

0.948

0.966

0.904

0.948

0.948

0.967

0.906

0.911

0.923

0.944

0.849

Contextual value

Digital literacy

Items

DL10

0.887

Functional value

FV1

0.969

FV2

0.975

FV3

0.941

Immersion

I1

0.962

I2

0.940

I3

0.951

Symbolic value SV1

0.956

SV2

0.943

Willingness to continue and subscribe

SV3

0.957

WS1

0.951

WS2

0.860

WS3

0.951

5.5 Predictive Relevance and Effect Size R2 higher than 0.75 indicated that the endogenous latent variable could be respectively substantial [70–73]. Thus, Immersion and Willingness to Continue and Subscribe have a substantial level of predictive accuracy. Regarding the f2 values, the strength of the relationship is considered moderate if it is between 0.150 and 0.349 and large if it is 0.350 or higher. However, if the f2 value is less than 0.020, there is no effect [74–79]. Thus, the Contextual Value has a moderate effect on the Immersion,

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Table 4 Fornell-larcker test Constructs

Aesthetic Contextual Digital Functional Immersion Symbolic Willingness value value literacy value value to continue and subscribe

Aesthetic value

0.980

Contextual value

0.913

0.972

Digital literacy

0.779

0.829

0.915

Functional value

0.858

0.935

0.806

0.961

Immersion

0.866

0.901

0.799

0.860

0.951

Symbolic value

0.888

0.913

0.796

0.869

0.931

0.952

Willingness 0.843 to continue and subscribe

0.885

0.802

0.863

0.933

0.917

0.921

Symbolic Value has a large effect on the Immersion, and Immersion has a large effect on Willingness to Continue and Subscribe. The Q2 values are all greater than zero, indicating that the path has a high level of predictive relevance [80–85] (Table 6).

6 Research Implications 6.1 Discussions The result of the data analysis shows that the Contextual Value and Symbolic Value of the metaverse live-streaming platform have a positive impact on customer Immersion, and customer immersion has a significant positive impact on customer’s Willingness to Continue and Subscribe to the metaverse live-streaming program. On the other hand, Functional Value and Aesthesis Value didn’t show an impact on customer Immersion. Customer’s Digital Literacy also didn’t significantly impact the relationship between Platform Values and Immersion. The research implication was separated into theoretical implications and practical implications.

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Table 5 Hypothesis testing for direct and moderating effects Hypothesis

Paths

Path coefficients

H1

Functional value → immersion

0.038

H2

Contextual value → immersion*

H3

T statistics

P values

Remarks

0.476

0.317

Unsupported

0.204

1.784

0.037

Supported

Aesthetic value → immersion

0.041

0.449

0.327

Unsupported

H4

Symbolic value → immersion***

0.605

7.345

0.000

Supported

H5

Digital literacy * functional value → immersion

0.063

0.718

0.236

Unsupported

H6

Digital literacy * contextual value → immersion

0.048

0.275

0.392

Unsupported

H7

Digital literacy * aesthetic value → immersion

− 0.112

0.721

0.235

Unsupported

H8

Digital literacy * symbolic value → immersion

0.029

0.298

0.383

Unsupported

H9

Immersion → willingness to continue and subscribe***

0.933

89.489

0.000

Supported

Note *Significant at T = 1.96, P < 0.05 level, **Significant at T = 2.58, P < 0.01 level, ***Significant at T = 3.3, P < 0.001 level; *means small significance, **means middle significance, ***means large significance Table 6 Explanatory power Predictor(s)

Outcome(s)

R2

f2

Q2

Aesthetic value

Immersion

0.888

0.002

0.868

Contextual value

0.023

Symbolic value

0.452

Functional value

0.001

Digital literacy

0.017

Digital literacy * functional value

0.004

Digital literacy * contextual value

0.001

Digital literacy * aesthetic value

0.009

Digital literacy * symbolic value Immersion

0.001 Willingness to continue and Ssbscribe

0.870

6.704

0.848

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6.2 Theoretical Implications This research identifies the concept of platform values of the metaverse live-streaming platform. The result shows that contextual value and symbolic value play an important role in consumer immersion in the metaverse live streaming. Functional and aesthetic values didn’t impact customer immersion in metaverse live streaming. These results provide a perspective for future research to investigate the effect of platform values of metaverse live streaming. This research applies the Continuance Intention Theory [21] to investigate consumer behavior in metaverse live streaming. The result shows that platform values (e.g., contextual value and symbolic value) and customer immersion play a significant role in customer continuous behavior. Thus, the platform value and customer immersion could be important factors in explaining customer post-usage behavior in Continuance Intention Theory.

6.3 Practical Implications This research finds that contextual value and symbolic value play an important role in consumer immersion in the metaverse live streaming. Thus, the metaverse industry could put more effort into improving the contextual value and symbolic value; in other words, focusing on the information providing and distinctive image of customers themselves in the virtual world will enhance the customer immersion in the metaverse live streaming. Furthermore, it will improve the customer’s willingness to continue and subscribe to the Metaverse live-streaming program. Another marketing implication is the gender of the respondents shows 75.4% are female. According to Cecilia [86], women are key users in the metaverse platform. Thus, the result of this research is helpful to the marketing manager of the metaverse platform to attract female users through improving platform values.

7 Limitations and Recommendations for Future Research A limitation of this research is the geographical coverage of respondents. All respondents are Chinese customers who have watched the metaverse live streaming. The past study shows that Chinese consumer behavior was impacted by Chinese culture [87]. Thus, the result may not be representative from the international perspective. Futura research is suggested to conduct a cross-cultural comparison to identify the cultural impact on customer behavior in metaverse live streaming. However, future research is suggested to make a deep classification of platform values of metaverse live streaming. Metaverse live-streaming is different from traditional live-streaming. Instead of simply watching a video call on a 2D screen, users

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can virtually meet friends at a virtual concert, attend virtual classes or workshops, explore historical events or distant places in an immersive manner, and even shop in virtual stores that blend physical and digital products [1]. Thus, it is necessary to identify the platform value of this kind of new type of live-streaming platform.

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Organizational Adoption of Blockchain Based Medical Supply Chain Management Murat Tahir Çalda˘g

and Ebru Gökalp

Abstract The disruptive impact of blockchain technology on transactions, contracts, networks, and supply chains is widely recognized across business sectors for its benefits of security, privacy, and transparency. However, some critical industries like healthcare and defence can generate more value from blockchain technology. To identify, categorize, and rank the determinants of blockchain-based medical Supply Chain Management (SCM) adoption in the context of the organizations, a Systematic Literature Review (SLR) and Analytic Hierarchy Process (AHP) analysis were conducted. The SLR revealed fourteen sub-factors categorized according to the Technology-Organization-Environment (TOE) framework. The AHP analysis identified the five most essential sub-factors of blockchain-based medical SCM system adoption as top management support, government support, competitive pressure, inter-organizational trust, and organizational culture. The five least significant factors were identified as complexity, standardization, IT infrastructure, perceived benefit, and financial resources. This study provides insight to all the stakeholders of the supply chain in the medical context to improve the adoption of blockchain technologies taking into account key factors that influence its success. Keywords Blockchain · Supply chain management · Technology adoption · Healthcare systems · Organizational adoption

M. T. Çalda˘g (B) Department of Technology and Knowledge Management, Ba¸skent University, Ankara, Turkey e-mail: [email protected] E. Gökalp Department of Computer Engineering, Hacettepe University, Ankara, Turkey © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_16

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1 Introduction In today’s highly competitive global environment, organisations must embrace new technologies to survive and maintain their place. As data has become the “new oil” researchers have explored different ways of generating value. Emerging technologies such as big data, artificial intelligence, cloud computing, the internet of things, blockchain, 5G, nanotechnology deliver disruptive opportunities, including creating new jobs and the extinction of others. The market value growth of these frontier technologies is expected to be 9.5 trillion $ in 2030 from 1.5 trillion $ [1]. Assessment of new technologies adoption as cloud computing and services [2–5], artificial intelligence [6], building information modelling [7], chatbot integrations [8], internet of things [9], open data [10, 11] etc., can provide the barriers and drivers for organizations, managers, developers and citizens. Of these frontier technologies, blockchain is estimated to generate $3.1 trillion for business by 2030 [1]. Another research on blockchain claims that out of 603 firms throughout the United Kingdom, China and the United States 87% of them plan to invest in blockchain-based solutions [12]. Blockchain technology is defined as a digital ledger that is shared through a peer-to-peer (P2P) network with decentralized, transparent, traceable, immutable and cryptographic security features [13–15]. Blockchain can has the potential to revolutionise healthcare by providing personalized healthcare, easier tracking of patients’ health with increased security and privacy, improvement of the medical supply chain and secure health information exchange [16]. The decentralization aspect that blockchain provides is also valued for clinical trials [17]. Another advantage is the speed factor on data transactions, especially on healthcare data, which is highly sensitive [16]. Supply Chain Management (SCM) refers to the flow of information, finances, products and services from producers, distributors, organizations, wholesalers, etc., to customers [18]. The effective management of different parties requires careful planning and effective communication channels. The advancement of technology and the digital revolution caused SCM systems to adopt new practices [19]. Integration of blockchain technology in SCM systems can be seen in small-medium enterprises [20], the airport industry [21], the automotive industry [22], the pharmaceutical industry [23] and the agriculture-food industry [24]. SCM in healthcare systems is different from other sectors because of the high risk associated with human life. Since medical SCM systems have several stakeholders such as manufacturers, retailers, wholesalers, distributors, etc., ensuring the traceability, transparency and security of the pharmaceuticals and medical equipment becomes challenging [25]. Blockchain technology provides a potential solution to improve healthcare SCM processes by creating new ways to track and manage the supply chain. This research aims to identify, categorize and rank the determinants of blockchainbased medical SCM adoption in the context of the organizations. Initial scanning of the literature presented a research gap in identifying the significant drivers and barriers affecting blockchain-based medical SCM adoption. To fill this gap, a Systematic Literature Review (SLR) was conducted, and findings were discussed with

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three experts until a consensus was established. The results of SLR presented fourteen determinants that are categorized according to the Technology-OrganizationEnvironment (TOE) framework [26]. In order to validate the research model and prioritize the factors, an Analytic Hierarchy Process (AHP) analysis was conducted with ten experts in both blockchain-based and SCM systems. The data analysis of these factors’ weights and rankings presented the significant factors that practitioners need to consider. The remainder of the chapter is established as follows: Section 2 contains the methodology and research design of the study. Section 3 includes the SLR analysis processes and findings. In Sect. 4, the proposed research model and the determinants are expressed in detail. Section 5 contains data collection and data analysis of the AHP analysis. In Sect. 6, the findings of the study are discussed in detail. The final section concludes with a summary of results, contributions and limitations.

2 Methodology 2.1 Research Design The research methodology is compromised of three main stages that can be seen from Fig. 1. The first part of the research consists of an SLR for identifying the determinants of blockchain-based medical SCM adoption in the context of organizations. The second stage of the study includes gathering expert opinions and finalizing the research model according to the TOE framework. Suggestions from three experts in blockchain technology adoption and SCM are collected to finalize the proposed research model. The last stage covers conducting AHP surveys and analysis. AHP surveys were conducted with ten experts in blockchain technology and SCM in the healthcare sector, who have at least five years of experience in the field. After the data collection with the help of the AHP-OS tool [27], the local weights, local and global ranks of the determinants were calculated for further interpretation.

2.2 TOE Framework Advancements in technology present new ways of improvement, individually and collectively. Although emerging technologies can potentially provide competitive advantage in the context of organisations, they can be disruptive to the organizations by altering the infrastructure, changing the processes and requiring new resources. Therefore, it is essential to analyze technology adoption barriers and determinants before investing time and resources. In the literature, there are several commonly used models for the adoption and acceptance of technologies individually and collectively, as the Technology

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Fig. 1 Research design

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Acceptance Model (TAM), Technology Organization Environment (TOE), Theory of Planned Behavior (TBP), Diffusion of Innovation (DOI) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks [26, 28–31]. Since the TOE framework is well accepted for the organizational level of technological adoption. As this study’s aim is to provide a comprehensive model on adoption determinants of blockchain-based medical SCM in the organizational context, the TOE framework is selected. The TOE framework consists of three dimensions: technological, organizational and environmental. The technological dimension represents the technical aspects of the technology and the organisation’s technical resources. The organizational dimension refers to intra-organizational resources such as finance, human resources, IT infrastructure and cultural factors such as top management support and collaboration. The environmental dimension includes the inter-organizational factors that affect the organization.

2.3 AHP Analysis The AHP is a method used for multi-criteria decision-making problem solving established by [32]. It is a quantitative method that ranks multi-level factors by expert judgements through pairwise comparison matrices. The practicality of AHP analysis is that complex problems can be divided into more minor, more manageable issues which can be analysed independently. The application of AHP involves two main steps. It starts with the establishment of the hierarchical structure of the model. The context, main factors and sub-factors are determined and hierarchically linked. The second step consists of pairwise comparisons of the factors. Comparisons are conducted with a nine-point scale from equal to extremely more important [32]. After comparing local weights, local and global rankings are calculated for detailed analysis. ⎡

⎤ X1 X2 X3 X1 ⎢ 1 a b⎥ ⎥ X2 ⎢ ⎣ 1/a 1 c ⎦ X3 1/b 1/c 1

(1)

The calculation of local weights consists of creating a comparison matrix (n × n), which n stands for the number of factors. An example of a comparison matrix is given (1) where X 1 , X 2 and X 3 are represented as different factors. The weight (W) of each factor is calculated by the geometric mean of the factors as given in Formula (2). Each calculated factor weight is then normalized by division to the sum of all factors weights as given in Formula (3). After the normalized weights (NW) for every factor are computed to construct and eigenvector matrix Aw (4) with λmax is the maximum eigenvalue. Lastly, the consistency index (CI) is calculated with the

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Formula (5). W1 =

√ N 1×a×b

(2)

N W1 = W1 /(W1 + W2 + W3 )

(3)

Aw = λmax × W

(4)

C I = (λmax − n)/n − 1

(5)

Due to its practical and reliable measurements, AHP analysis is used in different context and frameworks. In the context of organizational adoption of technologies, AHP is applied to finding the determinants or barriers of different context as cloud computing adoption of organizations [33], biogas dissemination barriers [34], open data portal quality comparison [35] and blockchain adoption [14].

3 Systematic Literature Review An SLR was carried out according to the guidelines proposed by [36] to explore the determinants of blockchain-based medical SCM systems. The SLR method was selected due to its rigorous methodology, providing a reliable and repeatable summary of the literature on the research domain [37].

3.1 Research Questions The focus of this research is to identify and prioritize the determinants of blockchainbased medical SCM systems adoption in the context of organizations. To achieve this, we have formulated two research questions for our SLR as follows: • Research Question 1: What are the existing studies in the literature researching the adoption of blockchain-based medical SCM systems in the context of organizations? • Research Question 2: What are the adoption determinants of blockchain based medical SCM systems in the context of organizations?

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3.2 Search Strategy The search was conducted in Scopus and Web of Science databases according to title, abstract and keywords within the scope of the research as “blockchain” AND “supply chain management” AND (“adoption” OR “acceptance”) AND (“medical” OR “medicine” OR “healthcare”).

3.3 Inclusion and Exclusion Criteria Initial search results from the Scopus and Web of Science databases provided 50 papers. The inclusion and exclusion criteria consisted of language and publication types. Journal articles written in English were included without any date limitations. Only journal articles were selected in order to avoid duplicate resources. The search language was selected as English to provide a holistic framework. After the language and publication type exclusion criteria, the pool was scaled down to 35 papers. Removing the duplicates from databases shortened the search results to 24 studies. The SLR results are given in Fig. 2.

Fig. 2 The SLR results

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3.4 Primary Pool The remaining 24 studies were analyzed according to the paper’s research questions by title, abstract and keywords. After full-text analysis, five papers were identified as primary studies in blockchain based medical SCM organizational adoption. The primary pool consisting of five studies are given in Table 1 in more detail. As a result of the SLR, it was figured out that there are a limited number of studies in the literature, and more empirical research is required.

4 Proposed Research Model The proposed research model given in Fig. 3 was developed based on the TOE framework which categorised the fourteen determinants in technological, organizational and environmental dimensions.

4.1 Technological Dimension The technological dimension is comprised of the determinants related to the technology aspect of blockchain-based medical SCM systems consisting of compatibility, complexity, perceived benefit, security and privacy and standardization. Compatibility is referred as the degree of an innovation’s consistency with the organization’s systems, existing values, practices and requirements [30, 42]. In the context of SCM, compatibility is an important factor for adoption that indicates the synchronization of organization’s process, practices, IT infrastructure and stakeholder integration [14, 25]. Integration of blockchain in a medical SCM minimizes the compatibility issues as verifying the correctness of information, integration problems of different entities or stakeholders in SCM [43]. Therefore, compatibility is identified as a determinant in the proposed research model. Complexity is defined as “the degree to which an innovation is perceived as relatively difficult to understand and use” [30]. In the context of technological adoption complexity is an important technological determinant [44]. As SCM complexity increases, several issues arise as the cost of products and transactions increase, the accessibility of products and customer satisfaction [45]. In this study complexity is referred as an important determinant in blockchain based medical SCM systems. Perceived Benefit is referred as the degree of belief that using a system will have positive effect on performance [29]. In the context of technology adoption, perceived benefit is a driver for both individuals and organizations. Blockchain-based SCM systems provide several benefits as efficiency, transparency and traceability that

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Table 1 SLR primary pool No

Aim of the study

Model

[25] To explore the role of TOE blockchain technology in the sustainability and effectiveness of the pharmaceutical supply chain

Factors Perceived benefit, complexity, compatibility, organizational readiness, top management support, blockchain knowledge, regulatory environment, government support

[38] To emphasize the necessity of introducing a blockchain-based joint logistics system to strengthen the competency of medical SCM

UTAUT Performance expectancy, facilitating conditions, social influence

[39] To identify the adoption barriers of blockchain adoption in SCM

TOE

Technological: Security challenge, access to technology, the negative perception toward technology, immutability challenge of blockchain technology, immaturity of technology Organizational: Financial constraints, lack of management commitment and support, lack of new organizational policies for using blockchain technology, lack of knowledge and expertise, difficulty in changing organizational culture, hesitation to convert to new systems, lack of tools for blockchain technology implementation in sustainable supply chains Supply Chain: Lack of customers’ awareness and tendency about sustainability and blockchain technology, Problems in collaboration, communication and coordination in the supply chain, challenge of information disclosure policy between partners in the supply chain., challenges in integrating sustainable practices and blockchain technology through SCM, cultural differences of supply chain partners Environmental: Lack of governmental policies, market competition and uncertainty, lack of external stakeholders’ involvement, lack of industry involvement in blockchain adoption and ethical and safe practices, lack of rewards and incentives (continued)

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Table 1 (continued) No

Aim of the study

Model

Factors

[40] To develop a scoring – model to evaluate healthcare organization’s readiness to adopt blockchain in the context of electronic health records system management

Financial: Budget availability, financial risk and uncertainty, cost savings Social: Talent and knowledge acquisition, stakeholder’s awareness and acceptance, blockchain ecosystem, disintermediation and business process Technical: Infrastructure and Platform integration, standardization, security and privacy, blockchain maturity and use cases Organizational: Management support, training and skills, healthcare IT strategy Regulation and Legal: Regulation compliance, regulatory uncertainty and governance, incentives availability

[41] To investigate how blockchain technology benefits the performance of healthcare supply chain management

Blockchain Technology: Transparency, immutability, proper monitoring Stakeholder Involvement Healthcare sustainable supply chain practices

–

Fig. 3 The proposed research model

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directly effects the adoption decisions [14, 25, 38, 41]. Therefore, perceived benefit is selected as a significant determinant of the research model. Security and Privacy is defined in the context of data as “the safeguard policies and procedures to protect an organization’s data and information assets according to confidentiality regulations, contractual agreements and business requirements” [46]. Healthcare organizations and SCM systems require security measures, policies and procedures to protect sensitive health information [39–41]. One of the advantages of blockchain integration is the enhancement of security and privacy as default by the immutability and decentralization properties [38]. Thus, it is included as a determining factor of blockchain-based medical SCM. Standardization refers to using, managing and recognizing standard settings for data and processes [40]. Standardization is significant in SCM systems because of the inclusion of external entities participation and collaboration [14]. Blockchain integration also requires data quality standardisation to function properly [40]. Therefore, standardization is included in the research model.

4.2 Organizational Dimension Organizational dimension is comprised of the factors related to the firm level determinants of blockchain-based medical SCM systems consisting of IT infrastructure, financial resources, human resources and competencies, organizational culture, stakeholder participation and collaboration and top management support. IT Infrastructure is referred to as sufficient and compatible software and hardware infrastructure for all the parties in the SCM to support adoption of blockchain based systems [40]. Even though IT infrastructure is an important factor for technology adoption in the context of SCM, it is significance is much more with the increased size number of external and internal parties [25, 38–40]. Thus, IT infrastructure is identified as a determinant of blockchain based medical SCM adoption. Financial Resources are referred as necessary funds for the organization to pursue adoption of technological innovations. Financial resources include budgetary resources, financial risk and cost savings [40]. Requirement of financial resources to an organization is crucial for the sustainability and survivability aspects. Therefore, the decision to invest in blockchain integration on SCM systems is dependent on financial resources [25]. Thus, it is a determinant in the research model. Human Resources and Competencies are stated as the current talent and knowledge pool of the organization. Higher quality of human resources in an organization promotes adaption of new technologies [47]. The talent and skill pool of the organization affects the integration of new technologies on supply chain processes [48]. Also training and learning capacity provide organizations with a more effective transition to blockchain-based SCM systems [25, 38–40]. Therefore, human resources

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and competencies are included in blockchain-based medical SCM adoption as a determinant. Organizational Culture is defined as shared assumptions and behavior by a group against internal and external changes or problems [49]. Innovation adoptions encounter resistance in organizations because of the changes required as new practices and processes. Especially adaptation of new systems requires organizational environment changes to maximize the effectiveness of innovations. Because blockchain technology is disruptive, integration with existing systems and processes can encounter resistance in organizations [39, 40]. Also, innovative culture affects organizations’ awareness of new technologies and general positive tendency to use and sustain innovations. Thus, it is included as a significant determinant in the research model. Stakeholder Participation and Collaboration is referred as the degree of contribution of all internal and external parties on the business processes. In the pharmaceutical sector SCM systems have multiple external parties contributing to the production or business processes [50]. To have an efficient system all parties must participate and collaborate on the required processes by effective communication and tracking channels. Blockchain integration benefits the system with transparency and traceability which creates a collaborative environment for all stakeholders [14]. Lack of stakeholder involvement, communication and coordination problems, cultural differences of stakeholders present significant barriers on SCM system [39, 40]. Therefore, stakeholder participation and collaboration is included as a determining factor in blockchain-based medical SCM adoption. Top Management Support is defined as the top management’s degree of support in adopting a new technology [51]. Management support is one of the most significant factors effecting adoption of blockchain technology in healthcare organizations [40]. The support of the management present unique opportunities especially if the technology is generally unknown. Thus, it is included in the research model as a determinant.

4.3 Environmental Dimension Environmental dimension includes the blockchain based medical SCM determinants related to the external environment of the organization as competitive pressure, government support and inter organizational trust. Competitive Pressure is defined as the pressure enforced upon from the organization’s competition [44]. Pressure from the competitors encourage new technology adoption in SCM systems [48]. In healthcare organizations competitive pressure is considered as a significant driver of blockchain adoption [38–40]. Therefore, competitive pressure is included as a significant determinant in the research model.

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Government Support is referred as political commitment of governments and politicians on technological adoption of innovations [52]. The government support enhances the awareness and intention to adopt technologies by individuals, organizations and sectors. The adoption of blockchain in medical SCM can benefit from government support by providing incentives, promoting healthcare strategy, establishing rules, policies, intellectual property laws and tax policies [25, 39, 40]. Another significant effect of laws and regulations is reducing uncertainty and preventing criminal activity. Thus, government support is selected as a crucial determinant in the research model. Inter Organizational Trust is defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” [53]. Transparency, immutability and traceability benefits of blockchain technology, promote trust between the parties of SCM [39, 41]. Thus, it is included in the research model.

5 Analytic Hierarchy Process 5.1 Data Collection The results of the SLR can be supported with various methods for increasing validity, reliability and usefulness. Therefore, expert’s opinions of blockchain based medical SCM were collected by utilization of the AHP analysis. The hierarchical structure of the proposed research model is given in Fig. 4. The main objective consists of determination of blockchain based medical SCM adoption factors. The hierarchy’s first layer comprises technology, organization and environment factors that are compared according to a 3 × 3 matrix. The second layer analyses include the comparison of sub factors that are associated with the same main factor. Three groups of factors require three different AHP matrices to analyze. For the technology context, a pairwise comparison matrix of 5 × 5 was established to include factors such as compatibility, complexity, perceived benefit, security and privacy, standardization is established. According to organization context, a 6 × 6 pairwise comparison matrix compromised of IT infrastructure, financial resources, human resources and competencies, organizational culture, stakeholder participation and collaboration and top management support was established. The third context for environment factors was analyzed using a 3 × 3 matrix, including competitive pressure, government support and inter organizational trust factors. The AHP analysis for individual assessments was conducted using nine point scale, as suggested by [32]. Ten experts in blockchain based technology adoption and medical SCM systems participated in this study. Prior research on AHP suggests that sample size of the analysis doesn’t require large number of participants in order to answer the research questions [14, 54].

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Fig. 4 AHP hierarchy of blockchain-based medical SCM adoption factors

5.2 Data Analysis To ensure accuracy, consistency ratios (CR) are used to check the expert judgements on the proposed research model. The CR of the judgements must be less than or equal to ten percent to be analysed [55]. The individual judgements are inputted into the AHP software online tool created by [27] to aggregate the rankings. The first-level results of the AHP context analysis are given in Table 2. The results indicate that “Organization” is the most significant context with an AHP weight of 0.464. The “Environment” context is the second significant context with an AHP weight of 0.295 and “Technology” context is the least significant context with an AHP weight of 0.224. Table 2 AHP context analysis results Context

Technology

Organization

Environment

Weight

Rank

Technology

1

0.48

0.87

0.241

3

Organization

2.07

1

1.46

0.464

1

Environment

1.14

0.68

1

0.295

2

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Table 3 Pairwise assessment matrix for the context of technology Context

Compatibility Complexity Perceived Security Standardization Weight Rank and benefit privacy

Compatibility

1

2.96

1.43

0.85

1.25

0.257

2

Complexity

0.34

1

0.47

0.47

0.75

0.107

5

Perceived benefit

0.70

2.12

1

0.63

1.02

0.188

3

Security and privacy

1.17

2.14

1.60

1

1.51

0.272

1

Standardization 0.80

1.34

0.98

0.66

1

0.177

4

Cr: 0.9%

According to the analysis results, organizational factors are crucial in the adoption of blockchain-based medical SCM systems. The second-level analysis factors are ranked according to their respective contexts. According to the local rankings of the technology context the most influential subfactor is security and privacy (0.272). The results present that safeguarding policies and procedures blockchain technology provides is one of the most significant determinants of the adoption of blockchain technology in medical SCM. The pairwise assessment of the technology context is given in Table 3. In the organizational context, top management support (0.370) is scored as the highest sub-determinant. Analysis results present that the degree of support from the management is considered a top priority in organizational context to establish and sustain blockchain-based medical SCM systems. The pairwise assessment of organization context is given in Table 4. Analysis of the environmental context presented the most significant sub-factor is government support (0.382). The legislation, laws, tax policies and political support are considered important innovators in blockchain-based medical SCM systems. The pairwise assessment of the environment context is given in Table 5. The local weight of each factor is calculated with the geometric means of all pairwise comparison the participants scored. The global weight of each sub-factor is analyzed according to their respective local weights and main factors. The calculation consists of multiplication of local weights with their respective main factor weights. Lastly the local and global rankings are determined according to the calculations and comparisons which is summarized in the Table 6.

6 Discussion The findings of the AHP analysis emphasize the significant adoption determinants of blockchain-based medical SCM systems and provide insight to organizations and stakeholders. Organizations must highlight technological, organizational and

1

1.20

1.18

1.52

1.65

4.08

IT Infrastructure

Financial resources

Human resources and competencies

Organizational culture

Stakeholder participation and collaboration

Top management support

Cr: 0.8%

IT infrastructure

Context

3.33

1.68

2.14

1.23

1

0.84

Financial resources

2.73

0.90

0.98

1

0.81

0.85

Human resources and competencies

Table 4 Pairwise assessment matrix for the context of organization

2.14

0.98

1

1.02

0.47

0.66

Organizational culture

3.15

1

1.02

1.12

0.59

0.60

Stakeholder participation and collaboration

1

0.32

0.47

0.37

0.30

0.25

Top management support

0.370

0.142

0.159

0.136

0.098

0.095

Weight

1

3

2

4

5

6

Rank

336 M. T. Çalda˘g and E. Gökalp

Organizational Adoption of Blockchain Based Medical Supply Chain …

337

Table 5 Pairwise assessment matrix for the context of environment Context

Competitive pressure

Government support

Inter organizational trust

Weight

Rank

Competitive pressure

1

0.94

1.30

0.352

2

Government support

1.06

1

1.47

0.382

1

Inter organizational trust

0.77

0.68

1

0.266

3

Cr: 0.1% Table 6 Global ranking of adoption determinants of blockchain-based medical SCM Context

Relative weight

Technology

0.241

Organization

Environment

0.464

0.295

Determinant

Local weight

Local rank

Global weight

Global rank

Compatibility

0.257

2

0.062

9

Complexity

0.107

5

0.026

14

Perceived benefit

0.188

3

0.045

11

Security and privacy

0.272

1

0.065

7

Standardization

0.177

4

0.042

13

IT Infrastructure

0.095

6

0.044

12

Financial resources

0.098

5

0.045

10

Human resources and competencies

0.136

4

0.063

8

Organizational culture

0.159

2

0.074

5

Stakeholder participation and collaboration

0.142

3

0.066

6

Top management support

0.370

1

0.172

1

Competitive pressure

0.352

2

0.104

3

Government support

0.382

1

0.113

2

Inter organizational trust

0.266

3

0.079

4

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M. T. Çalda˘g and E. Gökalp

environmental factors to successfully adopt the systems. The risk averseness of the healthcare industry presents an opportunity for blockchain adoption to nearly every practice. The SCM systems integration of this technology on different domains presented improvements in transparency, efficiency, and security as well as created a competitive advantage [19]. The SLR analysis provided fourteen sub-factors categorized according to the TOE framework and prioritized by AHP analysis. The results of the AHP presented that organization (0.464) context as the most significant determinant of the adoption of blockchain-based medical SCM systems. Organizational resources, culture and top management support factors are considered crucial in every aspect of technological adoption and the sustainability of the changes. Especially top management support and organizational culture sub-factors provides the foundation for innovative culture in the organization. The local ranking results of AHP analysis present a more detailed perspective on the research model. According to the technology context, security and privacy (0.272) and compatibility (0.257) sub-factors are considered the most significant, followed by perceived benefit (0.188), standardization (0.177) and complexity (0.107). The immutability, decentralized management and transparent structure of blockchain is highly desired in healthcare systems to provide improved security and privacy of sensitive private data [56]. The findings presented compatibility another significant factor since organizations in the medical SCM systems require integration solutions with the blockchain integration. The complexity of the technological innovation is considered lowest which presents the least significant for organizations to invest time and resources. In the organization context, top management support (0.370) is considered the most essential sub factor followed by organizational culture (0.159), stakeholder participation and collaboration (0.142), human resources and competencies (0.136), financial resources (0.098) and IT infrastructure (0.095). The importance of top management support can be addressed as without the commitment of the organization’s ruling cadre technological adoption can not progress. In the healthcare context top management support is also considered as one of the most important factors that is related to the awareness of blockchain technology, acceptance and realization of the benefits [40]. Organizational culture plays an important role in technological adoption in an organization, easing the transition and providing less resistance to changes [39]. Stakeholders’ participation and collaboration present important benefits on SCM systems as creating effective communication channels between partners and improving the whole system’s efficiency [14]. Organization’s resources are considered less important than other factors, which can be addressed as the organization’s commitment and way of working is more crucial to finding or establishing human, financial and IT resources in technology adoption. Lastly, in the environmental context government support (0.382) is considered the most significant followed by competitive pressure (0.352) and inter-organizational trust (0.266). Government support is crucial in healthcare with legislations, regulations and laws on medicine and medical equipment use, transport and distribution.

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Also, the policies can enhance or interrupt the decision to adopt blockchain technology to medical SCM systems. Therefore, government support is crucial to successfully implementing blockchain technology in medical SCM systems. Competitive pressure is the other significant driver of blockchain adoption. Organizations require innovations to gain competitive advantage; in this case, blockchain adoption provides benefits such as reduced costs and improved transparency and traceability [38]. Although inter-organizational trust is scored the lowest, the importance of trust between stakeholders and trading partners is required to prevent security breaches and counterfeiting [14, 40]. Analysis of the global rankings provides the five most essential sub-factors on blockchain-based medical SCM system adoption: top management support (17%), government support (11%), competitive pressure (10%), inter-organizational trust (8%) and organizational culture(7%). These findings are aligned with the literature, as top management support in the adoption of blockchain-based SCM systems in Indian and Chinese organizations is found to be an important factor [57, 58]. Also, government support, inter-organizational trust and competitive pressure are essential factors in blockchain-based SCM adoption in the United Kingdom, China and Turkey [14, 57]. The least five significant factors are identified as complexity (3%), standardization (4%), IT infrastructure (4%), perceived benefit (5%) and financial resources (5%). The lack of awareness of blockchain technology is aligned with factors like perceived benefit, standardization, and complexity ranked lower. Since blockchain is an emerging technology, it’s uses and benefits are not widely known. The IT infrastructure and financial resources scores align with the literature as they do not significantly impact adopting of blockchain-based SCM systems [57].

7 Conclusion With increased transparency, traceability and immutable structure, blockchain technology can be seen as an essential tool for improving medical SCM systems. However, organizations are not fully aware of the benefits, and adoption is slow as the technology is still in its early stages. This study aims to provide the most significant drivers and barriers to investing for the easier adoption of blockchain throughout the supply chain of healthcare organizations and partners. The contributions of the study are as follows: • The SLR results presented a limited number of studies investigating blockchain adoption and supply chains in the context of healthcare. Further analysis of these studies presented the lack of empirical research, and requirement of a holistic model. This study contributes to a comprehensive model of blockchain-based medical supply chain management adoption in the context of organizations to fill the research gap.

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• The prioritization of the organizational adoption determinants provides supply chain parties in healthcare with factors to invest in and improve. The top management support, organizational culture, government support, inter-organizational trust and competitive pressure factors were ranked highest, which are considered most significant. Organizations need to increase awareness of blockchain integration and benefits for increasing top management support. Also, governmental actors are required to further increase adoption. • The determination of the least significant factors provides where not to invest heavily. IT infrastructure and financial resources do not improve adoption significantly. Also, the lack of awareness of blockchain technology presents a challenge since perceived benefit and standardization factors are considered unimportant. Therefore, an organization’s mindset requires more investment than its resources. • Lastly, for further research, this study can provide a reliable review of the literature as well as a structured model to use. Theoretical and practical implications of this study are to provide a comprehensive model for researchers and organizations to gain insight into factors that influence blockchain-based medical SCM systems. Another significant implication is to indicate the research gap on the subject for further studies. One of the practical implication is to indicate the factors to focus on that will improve the SCM system by providing the rankings of the factors. Lastly this study’s findings can provide easier adoption of blockchain-based SCM systems in the healthcare sector by providing improved transparency, security and privacy for citizens and healthcare workers. This will eventually improve the individual and collective health of the citizens. A limitation of this study is the number of participants in the AHP analysis. More data can be collected and analysed to strengthen the generalizability of the results. Also, comparing different supply chains using blockchain technology in the healthcare sector to find alternatives is planned as a future study.

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Understanding the Use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) Features for the Classification of Depression Speech Mohana Shanmugam, Nur Nesa Nashuha Ismail, Pritheega Magalingam, Nik Nur Wahidah Nik Hashim, and Dalbir Singh

Abstract Depression has been affecting people all around the world, including Malaysians. Early detection mechanisms are vital for assisting clinical professionals in identifying depressed patients at an early stage. Although this can be accomplished through interviews and questionnaires, the time-consuming method has several additional disadvantages. Acoustic Measurement and MFCC have notably been adapted to detect speaker emotion. Numerous researchers have employed various languages for the purpose of prediction. Its efficiency varies across research, although it contributes significantly to diagnosing depression. As it appears that culture diversity influences how emotion is perceived, depression detection mechanism can vary between different languages. This paper provides a comprehensive analysis based on relevant studies published from 2000 to 2023 to show the effectiveness of acoustic M. Shanmugam (B) Department of Informatics, College of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia e-mail: [email protected] N. N. N. Ismail Cyber Emerging Technology Department, Deloitte SEA Service Sdn. Bhd., Kuala Lumpur, Malaysia e-mail: [email protected] P. Magalingam Advanced Informatics Department, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia (UTM), Kuala Lumpur, Malaysia e-mail: [email protected] N. N. W. N. Hashim Advanced Material and Subsea Technology, Petronas Research Sdn. Bhd., Bangi, Malaysia e-mail: [email protected] D. Singh Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_17

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measurement and MFCC in depression detection. It was discovered that Support Vector Machine (SVM) is extensively utilised and can successfully contribute to the detection of depressed patients using biometric characteristics. The outcome of this study encourages experimental investigation on the effectiveness of acoustic measuring and MFCC for depression identification among Malaysian speakers. Keywords Depression · Acoustic measurement · Mel frequency cepstral coefficient (MFCC) · Support vector machine (SVM) · Malay language

1 Introduction Affecting people of all ages and genders, Major Depressive Disorder (MDD) is a predominant mental disease [1]. Approximately 264 million individuals worldwide suffer from depression at present [2]. In recent years, the incidence has risen steadily. According to the Malaysian Mental Healthcare Performance Report [3], the National Institute of Health (NIH) observed 29.2% of incidences of depression are among persons aged sixteen and over in 2016. In 2020, 2.3% of Malaysia’s adult population, or approximately 500,000 individuals, were affected by depression. Due to the standard of living, stressful working culture, family problems, and social demand, those with incomes below RM4000 (B40) are disproportionately affected by depression. Depression is typically viewed as an emotional condition; however, its behavioural manifestations, such as social isolation and psychological abnormalities, are also likely to impair a patient’s mood, conduct, and sleep patterns [4]. Utilizing interviews to collect medical and background information via questionnaire is one of the strategies employed in this research. The evaluation using the responses to the questionnaires helps to identify the patient’s pain severity and pain threshold. These examinations are challenging and time-consuming, and they rely heavily on the patient’s transparency and willingness to disclose their emotional and mental state. Thus, it is worthwhile to research a field involving speech analysis, particularly the automatic categorization of depressed and non-depressed individuals. A depressed person’s speech is void of dynamic expressiveness, energy, and vigour, as well as an assertive accent. Based on these characteristics, a quantitative model can be developed to aid psychiatrists in making clinical judgements. There are a number of studies that perform classification using the mentioned characteristics as features for identifying MDD through speech signals [1, 5–9]. There are commonalities between these studies in that they utilise prosodic, spectral, and MFCC, but separately in each study. In addition, these examples of successful studies included multiple languages from different countries such as German, Estonian, Hindi and Arabic, and it is unknown whether a single model can accommodate multiple languages. Emotion identification aids in the early detection of depression, hence this study focuses on emotion identification as well. This encourages the development of an early detection system that employs biometric parameters

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to detect depression among Malaysians; Malay-speaking individuals. The motivation is to understand the use of Acoustic Measurement and Mel Frequency Cepstral Coefficient (MFCC) as features for classifying depression through speech. With the global rapid growth of mental health disorders, an early warning mechanism based on biometric characteristics in humans is needed. This system could accurately detect a person’s emotional or psychological state. In the past 5 years, many successful research studies have been undertaken in classifying emotion disorders using biometric characteristics, especially speech in various languages for identifying depression state. Yet none of these research used a corpus in Bahasa Malaysia native in doing the classification. Language relatedness can influence the accuracy of crosslanguage emotion attribution and it is necessary to assess speech precisely according to the native language. As indicated in [2], the precision of cross-language emotion attributes can be influenced by language relatedness. As a result, the observed issue is that limited classification systems use a native Bahasa Malaysia (Malay) corpus. Further, due to the lack of a good foundation for combining features for speech and emotion identification [7], this study will dive into the use of acoustic measurement. This study compiles a list of previous studies that are related to depression detection, mainly focusing on acoustic signal classification. The studies were extracted from various online database sources that were published between 2000 and 2023. Among the studies compiled, spectral features are identified to be most commonly used with MFCC mentioned the most. That finding becomes the point of this study to perform a comprehensive review on the role of acoustic measurement and MFCC in depression detection. As such, this study aims to identify suitable features that can be used in the classification of healthy and depressed speakers, to understand the combination of feature extraction involving acoustic measurement of prosodic, spectral, and MFCCs for the classification model. Finally, this study can demonstrate and provide an understanding of the effectiveness of utilizing acoustic measurement combined with MFCC for depression detection.

2 Related Works 2.1 Speech as Objective Indicator Speech is one of the critical needs of every human for communication. Humans use speech to represent intangible knowledge which includes feelings, sentiments, and opinions. Communication encompasses not only spoken language, but other elements such as intonation, accent, loudness, and tempo. It is a complex mechanism dependent on the speaker’s psychological, physiological, and pathological factors. From a psychological standpoint, vocal characteristics can be correlated with the emotional state to diagnose depression or honesty. Physiologically, voice characteristics vary with age, gender, body size, habits, and hormone status. Pathological conditions, comprising a variety of diseases such as chronic laryngitis, asthma, and neck cancer,

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can be used to evaluate human’s degree of health as the voice characteristics transition from healthy to unhealthy over time [10]. Since the early twentieth century, psychiatrists have been investigating this area of study. As indicated in [11], numerous studies demonstrate the strong influence of emotional arousal on vocal emission. Speech variations in depression have been observed in several studies, beginning with [12], and examining the voice functions of depressed patients is one way to evaluate psychomotor activity in depression. (a) Depressive Speech Normal individuals and depressed patients show different facial expressions, bodily postures, verbal signals, psychological signals, and auditory cues, which leads to researchers utilising these elements to evaluate the severity of depression [7]. From these elements, the study of depression detection can be categorised as audio-based, video-based, and multimodal-based methods. Several studies have demonstrated that a video-based approach has a greater detection rate for depression, but that patient privacy and data confidentiality are major concerns [13]. A person suffering from a depressive disorder is likely to have impaired acoustic speech qualities. Depression can therefore be detected by studying perceived changes in the acoustic qualities of speech. Numerous studies have examined the connection between depression and acoustic parameters of voice signals [14]. Moreover, speech can be used to identify patients with major depression as well as minor depression, which may be less severe yet have similar effects such as a decrease in function and comorbid disease. In short, speech can be used for early detection of depression to prevent deterioration that turns into major depressive disorder (MDD) [15]. The study by [15] indicated that voice changes in depressive episodes may accurately distinguish non-depressed groups from those with minor and major depression using machine learning. Speech activity in depressed patients has been discovered to differ as a function of the negativity of conversational content [16], and emotional activity is required to achieve this [16, 17]. Clinicians often define the speech of a depressed individual as flat, monotonous, and lifeless [18]. In a pilot study of severely depressed patients, [19] found that listeners could detect significant differences in the prosody of depressed voices. This research is related to speech production in a way that allows for the detection of emotion. There are two types of speech production: voiced speech and unvoiced speech, or a combination of the two in a single speech [20]. Voiced speech has large peaks, quasi-periodic waveforms, and a lower zero-crossing, whereas unvoiced speech has multiple random small peaks, a noise-like waveform, and a greater zero-crossing. In addition, the signal produced by the vocal cords versus the vocal tract is the distinguishing feature of speech production. To articulate sounds, the vocal cords and vocal tract are used, and the vocal cords provide the mechanism with more chances to detect the fundamental frequency, F0 . In contrast, the unvoiced operation does not utilise the vocal cord. Only the length of the glottal cycle is determined by the F0 . The glottal flow waveform has been proven to be altered as a result of extreme strain or loss of control in the laryngeal musculature during emotional stress [21, 22], and it has been proven that this alteration is associated with a decrease in

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vocal F0 variability [23]. In addition, physiological factors such as improvements in breathing rate and muscular tension are anticipated to have a global impact on the development of the voice [11, 24]. Moreover, “pull effects”—external concerns such as commonly accepted “display rules,” the listener’s preferences, the speaker’s selfpresentation, etc.—limit the spontaneous expression of affect [24, 25]. Therefore, language-based cultural factors can play a role in determining whether or not it is appropriate to convey or to suppress the effect [24, 26]. (b) Cross-language Production Cross-language speech perception and production (CLSP) is the study of how speakers perceive and produce sounds, sequences, prosody, and tones not present in their native language [27]. Production relates to how humans create sounds, whereas perception describes how speech is comprehended. In some studies, it was proposed that the basic emotion cannot be generalised at least based on the acoustic parameters measured. The value of high F0 and high intensity during anger state was found in German [28] and English [29] but not in Estonian, where the value of F0 and the intensity was recorded to be low in anger expression compared to neutral state [24, 30]. Different languages’ auditory evaluations of the same emotion indicate discrepancies in the speech features employed. German speakers were observed using a falsetto voice to express anxiety [31, 32], a whispering voice by an English actor [33], and a breathy voice in Italian [34]. Similarly, the same voice quality may have slightly different connotations for speakers of different language backgrounds. In English and German, a creaky voice is synonymous with monotony. Although these results may be indicative of language-specific vocal manifestations of emotion, such blatant differences should be taken with caution. It is unclear whether a given impressionistic voice quality mark in different investigations refers to the same acoustic-auditory phenomena. The remainder of this section will review several studies examining the use of cross-language in speech recognition. In [35], the author encoded each other’s emotional speech using English and Japanese speech. The study examines the phonetic correlation between psychobiological and social practices (different cultures). The thesis compares the crosscultural encoding performance and phonetic correlations of emotional voices of native English and Japanese speakers. The results indicate that English-speaking subjects are 60% accurate in decoding the emotion of their native language’s speech, but Japanese-speaking subjects are only 35% accurate. Due to the influence of diverse cultures, one of the hypotheses in the study is proven that there are challenges in encoding the correct emotion between people with different cultural backgrounds. Pell et al. [36] studied how distinct emotions are recognised and acoustically discriminated using Arabic, German, English, and Hindi to better understand how language affects emotional vocal expression. Each speaker made pseudo-utterances (“nonsense speech”) that mimicked their native language to articulate each emotion type, and the recordings were reviewed by a jury of native listeners for their perceived emotional relevance. Acoustic and discriminant feature evaluations in all languages confirmed the importance of speaker F0 (i.e., relative pitch level and variability) for conveying voice emotions. Although their findings show that various emotions have

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many cross-linguistic correlations in their acoustic-perceptual properties, they do not state how much these characteristics lead to pragmatic interpretations of spoken languages, which include auditory, verbal, visual, and other contextual parameters that influence a speaker’s emotion or attitudes. Study by [37] investigates the effectiveness of various acoustic and/or prosodic features in monolingual and multi-lingual contexts. They consider how to specify an optimal set of features for building a multilingual emotion classification scheme that can handle more than one input language. Because cross-lingual emotion detection has low identification rates, they examined specific features on both an American English and a German database. The study found that the performance of multilingual anger recognition system is very similar to the mono-lingual system, at least for this simple case. In addition, [38] examined performance in six factors, one of which is cross-language emotion identification from speech. The Gaussian Mixture Model (GMM) and the Hidden Markov Model (HMM) were the classification models employed in the experiments. The rate of emotion recognition is higher in the speakerbased scenario than in the speaker-independent situation. The ability to perceive emotions varies according to language. In comparison to Telugu, Hindi has a greater acceptance rate. Cross-language emotion perception is quite low when compared to both speaker-based and isolated scenarios.

2.2 Features Indicators of Acoustic Signal Classification Several new researches have revealed that acoustic speech processing can be utilised to detect depression symptoms effectively. The purpose of the acoustic front-ends is to remove distinctive qualities from the spoken utterance. It typically collects a frame of the speech signal every 16–32 ms and updates it every 8–16 ms to do spectrum analysis [39]. Algorithmic blocks in the daily front-ends include Fast Fourier Transformation (FFT), Logarithm Calculation (LOG), Discrete Cosine Transformation (DCT), and Linear Discriminate Analysis (LDA). Speech is a responsive performance mechanism, and even minor physiological and cognitive modifications can result in audible variations. Changes in a speaker’s affective state, which is common in depression, as well as changes in the nerve systems of the somatic and autonomic types, could theoretically modify the processes involved in speech development, such as phonation and muscular articulation [22]. Cummins et al. [40] studies the hypothesis that depression affect in speech manifestation as a reduction in the spread of phonetic events in acoustic feature space. Using a variety of acoustic variability measures specific to the task; Average Weighted Variance (AWV), Acoustic Movement (AM), and Acoustic Volume measures, this study concluded that depression is affected by speech motor control. Muscle tension and control will influence the prosody and constancy of the speech produced; interruption in muscle tension may affect vocal fold motion, whilst variations in respiratory muscle tension may influence subglottal pressure. Depression has been found to influence both the prosodic and source qualities of a speaker’s

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speech [41]. Patients with depressed speech had prosodic speech defects, such as a lower pitch, a narrower pitch range, a slower speaking rate, and articulation errors, as determined by early paralinguistic examination. (a) Prosodic Prosodic patterns of voice, such as rhythm, tension, and intonation, convey essential details of the personality of a spoken language. Prosody is driven by changes in observable parameters such as F0 , length, and energy, and is associated with linguistic units such as syllables [42]. Each speaker has distinct physiological characteristics of speech development and speaking style, which are mirrored in their prosody. It is generally acknowledged that human listeners can better comprehend speakers with whom they are familiar than those with whom they are unfamiliar. This enhanced capacity is owing to the listener’s conscious or unconscious understanding of speakerspecific prosody and idiosyncrasies [43]. Pitch is a perceptible quality of sound. The F0 of vibrating vocal folds is the physical correlate of pitch. The most common types of intonation are attitudinal, accentual, discourse, and grammatical roles [42]. Speech parameterization is the process of transforming a raw speech signal into one that is more conceptual and contains less redundancy. On a short-term time scale, features commonly used in speech signal analysis are extracted from a speech sample using alternating frames of 10–40 ms duration, but longer-term extractions are also possible. The F0 contour was chosen for these listening tests because it incorporates information from a wide range of prosodic data, including F0 variability, speech rate, and pause time. It’s not surprising that studies have found associations between a F0 spectrum and a F0 average, and increasing levels of depression severity, as this prosody provides useful information in identifying depressed individuals [44–48]. (b) Spectral The frequency range of a voice signal at a given moment is represented in highdimensional form by spectral features, which define the speech spectrum. Power Spectral Density (PSD) and Mel Frequency Cepstral Features (MFCC) are two often employed spectral features. Multiple studies have demonstrated that spectral-based properties, particularly formants, change with a speaker’s mental state, although the nature of the effect is still debatable [41]. Some research on depression have discovered a shift in frequency from lower to higher frequency bands [22, 49–51], whilst others have discovered a reduction in sub-band energy fluctuation [9, 49, 52]. However, because spectral features provide more accurate information about vocal tract action, they are descriptive of all the verbal and paralinguistic information contained in speech, which may limit their application in classification systems. The Mel-Frequency Cepstral Coefficients (MFCC) known as Mel-scale cepstral processing, is the most widely used tool for auditory speech analysis [39] when examining how the human ear functions. According to psychophysical studies, the human ear resolves frequencies around the audio spectrum in a nonlinear fashion. Mel-scale, which is modellable with triangular filters equally spaced around Melscale is used to infer non-linear frequency resolutions. It is a frequency domain

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Table 1 Examples of prosodic and spectral features Low level description

Features

Spectral [57]

Loudness Zero crossing rate Spectral roll-off points Spectral flux Harmonicity Variance

Prosodic [58]

F0 Probability of voicing Logarithmic harmonic: noise (LogHNR) Jitter

feature that performs significantly better than time-domain features [53]. MFCC reflect the actual cepstral of a windowed short-time signal resulting from its Fast Fourier Transform (FFT). The adoption of a nonlinear frequency scale that approximates the behaviour of the auditory system differentiates it from the real cepstral. In addition, these coefficients are stable and precise despite variations in speakers and recording conditions. According to [53–55], the foundation of the entire process is the separation of the speech signal into time periods containing an integer number of samples. In most of the programmes, frame overlapping is employed to produce a seamless transition between frames. To eliminate discontinuities at the ends, a Hamming window is applied to each time frame [56]. After windowing, the Fast Fourier Transformation (FFT) is employed to eliminate frequency components of a signal in the time domain for each frame. FFT is used to increase processing speed. The Fourier-transformed frame is processed through the Mel-Scaled logarithmic filter bank. This scale is linear up to around 1 kHz and logarithmic above that [8]. The MFCCs utilise a Mel-scale filter bank in which the higher frequency filters have a wider bandwidth than the lower frequency filters but the same temporal resolution. The final stage involves computing the Discrete Cosine Transformation (DCT) of the outputs of the filter bank. For each speech frame, an MFCC range is computed. An acoustic vector is a sequence of coefficients that describes phonetically important aspects of speech and is incredibly useful for speech recognition study and processing. Table 1 summarizes spectral and prosodic features that can be utilized in acoustic signal classification.

2.3 Speech Signals Classification Classification is a supervised learning theory used in machine learning which separates data sets into categories. The most critical concerns are those related to speech recognition, facial recognition, and other classifications. It could be a binary sorting

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issue or a case with multiple classes. Machine learning has a variety of different identification and classification algorithms. In a study by [59], 170 Chinese natives participated in the automated classification of stressed speech. The participants consisted of 85 depressed patients and 85 healthy controls. The classification performance of prosodic, spectral, and glottal speech features were investigated to detect depression. The ensemble logistic regression model (ELRDD) for identifying indicators of depression in speech was suggested. Logistic regression was chosen as the basic classifier as it was more effective at identifying depression. In this study, ELRDD-E, a strategy for identifying depression based on ELRDD was proposed and tested. It yielded positive results with a precision of 75.00% for females and 81.82% for males. The results also provided a favourable sensitivity to specificity ratio of 79.25%:70.59% for females and 78.13%:85.29% for males. The study [60] uses support vector (SVM) regression to demonstrate the performance of multimodal affect and depression recognition using four distinct segmentation methods. In addition, a speaker detection strategy has also been introduced in order to construct the speaker-specific emotion or depression detection systems. The performance comparison is based on a subset of 150 videos from Audio/Visual Emotion and Depression Recognition Challenge (AVEC 2014) dataset where two tasks from a human-computer interface scenario were chosen from the dataset. Using both the training and testing sets, the SVM-based regression model was trained and tested. The observed RMSE and MAE values, 10.826 and 8.327, outperformed the baseline performance by a slight margin (10.859 and 8.857). In [61], the study aims to determine how emotional information provided in speech (such as arousal, valence, and dominance) affects the level of mildly, moderately and severely depressed individuals. Using AVEC 2014, studio-quality speech recordings of 84 male and female speakers were captured. Support Vector Machine (SVM) instruments from MATLAB 2016b were utilised to classify depression. SVM linear kernels with default hyper-parameter settings were used in all experiments. In conclusion, the provided tests reveal that automatically predicted emotion ratings can be included in a completely automatic depression classification to produce a 5% improvement in accuracy over a baseline system solely based on acoustics features. Paper in [62] analysed and compared the distribution and predicted factors of dementia and depression patients. The machine learning layer includes a collection of functions using the LASSO technique and support vector machine (SVM) using the linear kernel as the prediction model. In addition, the model performance was evaluated using datasets that were not used, and it still performed excellently. These results imply that dementia and depression can be detected and differentiated based only on auditory parameters. Automated screening is also possible due to the great precision of machine learning data. In [63], a new speech segment fusion method based on decision trees is proposed to improve depression identification accuracy and validated on a population of 52 individuals (23 depressed patients and 29 healthy controls). Support vector machine (SVM) and random forest (RF) are the only two basic classifiers utilised to test parameters with 4-fold cross-validation for model evaluation in this research. On

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gender-dependent models, the accuracy of male and female identification is 758.2% and 68.5%, respectively. Based on the findings, it can be inferred that the suggested decision tree model can improve the accuracy of depression classification. In [14], the auditory sources of depression in 170 individuals were investigated (85 depressed patients and 85 healthy controls). Based on speech styles and emotions, a novel computer technique for predicting depression (STEDD) was created and validated. Compared to GMM and KNN, SVM offers the highest classification accuracy and the greatest stability for both males and females. With a sensitivity: specificity ratio of 75.00%:85.29% for males and 77.36%:74.51% for females, this novel approach showed a high accuracy rate of 80.30% for males and 75.96% for females. In terms of depression prediction, these findings are intriguing and provide direction for future research. It can be observed that speech datasets from multiple languages have been utilised and evaluated using various extracted features. For example, in [64], MFCC and modulation spectral (MS) features were used as features, while Multivariate Linear Regression (MLR) and SVM were employed as classifiers to compare their performance with the proposed Recurrent Neural Network (RNN). To classify seven types of emotions in a Berlin and Spanish datasets, the study concluded that feature combination of MFCC and MS portrayed the highest accuracy on both Spanish emotional database with RNN as classifier 90.05% and Berlin using MLR with 82.41%. Additionally, the summarized comparison is depicted in Table 2. The features employed for classification, the classification model, the outcome of the study, and its limitations are depicted in the table of comparison. This demonstrates how different algorithms perform on a particular subset of dataset to detect emotions, which can indicate depression significantly. We can observe speech datasets in different languages that have been used and verified with various feature extraction techniques. For instance, [64] used MFCC and modulation spectral as the extracted features and MLR and SVM as the classifier. The study reaches 90% accuracy, but the limitation is that it uses fewer feature combinations, which do not fully represent the robustness of feature combinations. Two things that can be extracted from the comparison are the use of fewer feature combinations and silos. The limitation motivates to the investigation of the effect of feature combinations on the classification effectiveness. Secondly, most of the studies used Support Vector Machine as the classifier. Other than this, the majority of studies employed SVM as the classifier. As demonstrated in [1], SVM has demonstrated achieved up to 90% accuracy in predicting depression. It ideally distinguishes the two classes by generating the hyperplane between them.

3 Analysis of Feature Selection for Malay Language From the preceding assessment of previous work, it is clear that the classification algorithm successfully demonstrates that several models can classify depressive speech with high accuracy. In addition, [66] claims that speech is a good objective

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Table 2 Table of comparison for speech classification Features extraction

Classifier / approach

Result

Drawback

References

Low level descriptors (LLDs)—MFCCs, pitch, loudness, jitter and shimmer

SVM and VAD

72.9% accuracy to classify healthy and depressed case using DAIC dataset

Uncertain in the duration of speech recording that can give best efficiency to the classifier

[65]

COVAREP repository (MFCC, TE-MFCC, glotta/voice quality)

SVM with RBF Kernel

MFCC give mean error of 48%, VQ gives 82% for neutral and 73% for emotion

MFCC [6] classification biased toward emotion class

Achieve 90% accuracy for depression classification model for DAIC-WOZ and AVEC2016 dataset

Unable to justify [1] using statistical measurement whether increase in number of features can increase the accuracy of the classification model

73 LLD (prosodic (2), Logistic voice quality (8), and regression, SVM, Spectral (63) features) random forest combined with the statistical measurement (mean, median, skewness)

Frequency, formants, One-Dimensional Using The performance energy, normalized CNN DAIC-WOZ does not improve amplitude quotient, 24 dataset, achieve MFCC) 58.5%, 30% and 10.2% of F1-score

[5]

biomarker for classifying depression. This is because individuals who are depressed have a longer reaction time, higher speech rate and longer delay time than stable individuals, all of which suggest psychomotor slowness. However, the personality and speech patterns of each participant can alter speech attributes. Moreover, [24] reported that language relatedness can affect the precision of cross-language emotion attribution. In a large-scale analysis, listeners from varied linguistic backgrounds evaluated simulated emotions generated by German actors in artificially formed sentences. The less similar the vocabulary was to German, the less accurate the identification of emotions. Moreover, in their successful classification efforts [1], the authors underline the need to expand the dataset to include many languages in order to train the machine learning model. In the absence of a strong basis for identifying the combination of features in speech and emotion recognition, prosody is predominantly used. Consequently, this review sees void in the classification of depression speech utilising local Malay Language speech and a combination of acoustic measurements of prosodic, spectral, and MFCCs as the techniques for prediction or classification.

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4 Conclusion Based on the comparison study, there have been several studies on Acoustic Measurement and MFCC for detecting depression through speech. Its effectiveness has been proven especially for emotion detection using different types of languages, algorithms, and additional feature combinations. There is a scarcity of studies that investigated the role of acoustic measurement and MFCC in detecting depression among Malay speakers. Other researchers have demonstrated the efficacy of various features for emotion identification [67, 68]; however, prosody is the most prevalent method for establishing emotion prediction parameters. In conclusion, acoustic measurement and MFCC can be used to predict depression, however, their efficacy on Malay speakers remains to be determined.

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Enhancing User Acceptance of E-Exam Systems: A Comprehensive Model and Empirical Analysis Gulsah Hancerliogullari Koksalmis , Pasa Ciceklidag , and Ibrahim Arpaci

Abstract Computer-based evaluation systems offer several advantages, such as providing flexibility in terms of time and space and reducing costs. This study aimed to develop a research model that integrates the key factors predicting user acceptance of the E-Exam system into the Technology Acceptance Model (TAM). The data was collected from 245 actual and potential users of the E-Exam system via an online survey. The Partial Least Squares-Structural Equation Modeling (PLSSEM) approach was conducted to assess the model. The outputs showed that perceived usefulness, perceived ease of use, subjective norms, and security significantly predicted behavioral intention. Furthermore, computer self-efficacy, user interface design, and computer anxiety significantly influenced perceived ease of use. Additionally, perceived ease of use, practicality, and subjective norms emerged as significant determinants of perceived usefulness. Keywords E-Exam · Security · Self-efficacy · Practicality · User interface design · Computer anxiety

G. H. Koksalmis (B) · P. Ciceklidag Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey e-mail: [email protected] G. H. Koksalmis College of Engineering and Computer Science, University of Central Florida, Orlando, FL, USA I. Arpaci Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Bandirma, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_18

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1 Introduction Computer-based exam refers to measuring and evaluating in an electronic environment, E-Exam systems, also known as integrated systems, represent a novel approach to measuring and evaluating learning, distinct from traditional methods. These systems are primarily designed to streamline institutional evaluation processes and enhance their efficiency. The adoption of information technology (IT) and communication technologies is believed to establish a meaningful connection between learning, teaching, and assessment. E-assessment techniques are particularly noted for their ability to simplify administration, save time, offer flexibility, and enhance accessibility. Furthermore, the feedback provided by these systems contributes to creating a superior learning environment and has positive outcomes for students [1]. E-exams, which are computer-based assessments, can be undertaken not only on computers but also on a wide range of technological devices, including smartphones and tablets. The evolving accessibility of information has expanded the scope of competency assessment in line with our increasingly globalized world. Consequently, many national and international institutions have incorporated online exams alongside traditional examination formats, owing to the ease of exam preparation, administration, and evaluation. Notable examples of such exams include international tests like TOEFL-IBT, GRE, GMAT, and SAT. In Turkey, nationally recognized e-Exams such as e-YDS and e-Driving License are conducted. Furthermore, numerous educational institutions employ various online examination systems for their assessment processes. Despite the growing usage of E-Exam systems including Computer-Based Exams, Internet-Based Exams, Web-Based Testing, and Online Testing, studies on the acceptance of E-Exam systems in Turkey are limited. Therefore, our research’s objective is to examine several dynamics that impact the acceptance of E-Exam systems. The main aim of our research is to observe the factors of the E-exam system usage in Turkey while utilizing the TAM. To explore the opinions of the users, we collected data from the potential e-exam system users, especially students. The primary contribution of our research is that the theoretical structure and practical applicability of the technology acceptance model are implemented in the e-exam system setting in Turkey. To the best of my knowledge, no research takes into account the variables considered in this study simultaneously. “How well is the e-exam system accepted by the users in Turkey?”, “To what extent do the predictor variables explain the e-exam system usage in Turkey?” questions are being answered. Moreover, this research has several useful implications in practice especially for investors government organizations, and system developers. The remainder of the study is structured as follows: Sect. 2 delves into related literature and research models. Section 3 presents the methodology used in this study. In Sect. 4, we delve into a detailed discussion of the results. Finally, Sect. 5 offers the discussion and conclusion of our findings.

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2 Research Model and Hypotheses Development To investigate the antecedents of the E-Exam system used in Turkey, the current research proposes a theoretical model, called the “Technology Acceptance Model which was derived from the theory of reasonable action (TRA) and the Theory of Planned Behavior (TPB)” [2, 3]. It assesses attitudes toward using a new technology. TAM has been practiced in various types of research and it has been utilized for various information technology to date. Some of the studies where TAM models are extended to consider individuals’ actions towards the latest technologies can be summarized as follows: “Internet banking [4, 5], enterprise resource planning [6], ecommerce [7], cryptocurrency [8], e-learning and m-learning [9–11]).” Furthermore, researchers have done several works on the end-user’s behavior towards the E-Exam system. Table 1 summarizes the publications related to the acceptance of the E-Exam system. The TAM is a popular method to assess the acceptance of novel tools in various other systems; for example, healthcare, social networks, wearable technologies, etc. [16–23]. The TAM consists of several basic structures: “actual system use, behavioral intention to use, attitude towards use, perceived usefulness, perceived ease of use, Table 1 Studies on acceptance of e-exam systems References

Variable(s)

Method

Sample

Sample size

Country

[12]

Perceived competence, perceived autonomy, perceived relatedness

Structural equation modeling

Students who senior-level secondary school participated from Europe

140

Greece

[13]

Social influence, effort expectancy, performance expectancy

CBA acceptance model and unified theory of acceptance and use of technology (UTAUT) model

Third-year undergraduate students at a Taiwanese university

186

Taiwan

[14]

External The technology variables, acceptance model attitude towards using

Coaching business courses at a southeastern, private university, and northeastern private university

28

USA

[15]

Practicality, reliability, security, validity

Students of business administration and commerce program in India

289

India

Structural equation modeling

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Subjective norms

Experience

H6

H4b

Perceived usefulness

H4a

H7 H2 Practicality Behavioral intention

H3b

H1

Actual usage

H3a H5

Computer self-efficacy

H8

Perceived ease of use

Security

H9 H10 User interface design

Computer anxiety

Fig. 1 Research model

and external variables.” Based on the TAM, when individuals’ perceived usefulness and perceived ease of use increase, attitude towards use and behavioral intention to use will affect positively which will increase the actual system. The research model is provided in Fig. 1.

2.1 Actual Usage (AU) Actual usage “is a plumb of the rhythm of the usage of a novel tool specified in one’s job and the intensity of use at that frequency” [24].

2.2 Behavioral Intention (BI) BI is “the degree to which a person has formulated conscious plans to perform or not perform some specified future behavior.” Literature shows that BI is affecting AU significantly [24]. Hence, we hypothesize as follows: H1. “BI would be positively related to the AU.”

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2.3 Perceived Usefulness (PU) It is “the degree to which an individual believes that using a particular system would enhance his or her job performance” [25]. Many scholars have demonstrated a positive link between PU and BI [26]. Therefore, we hypothesize as follows: H2. “PU would be positively related to the BI.”

2.4 Perceived Ease of Use (PEOU) Perceived ease of use means “the degree to which a person believes that using a particular system would be free of effort” [2]. PEOU is used to assess E-exam system users’ perception of the simplicity and ease. Literature indicates a positive link between PEOU BI and PU [27]. Hence, we hypothesize as follows: H3a. “PEOU would be positively related to the BI.” H3b. “PEOU would be positively related to the PU.”

2.5 Subjective Norms (SN) Subjective norms are defined as “one thinks that people who are important to him should or should not do the behavior in question” [3]. Earlier research showed that SN was affecting BI and PU in a positive way [28]. Hence, we hypothesize as follows: H4a. “SN would be positively related to the BI.” H4b. “SN would be positively related to the PU.”

2.6 Security (SEC) Security is defined as the state of legal order in public life; people can live without fear and live safely. In E-Exam systems, the SEC stands at an important place in terms of personal privacy. It was indicated that there is a link between SEC and BI [29]. Hence, we hypothesize as follows: H5. “SEC would be positively related to the BI.”

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2.7 Experience (EXP) Experience refers to briefly encountering any situation or similar. In such cases, it enables situations that enable more accurate behavior in subsequent situations with the accumulation of previous gains. It was stated that “Individuals who have more experience using computers, the Internet, and e-mail, saving and finding files on the computer tend to have more positive feelings about the ease-of-use and usefulness of an e-learning system” [26]. Previous studies show the positive impact of EXP on PU [30]. So, we developed the following hypothesis: H6. “EXP would be positively related to the PU.”

2.8 Practicality (PRAC) Practicality means convenient and easily applicable. The traditional examination system, which is based on paper and pencil, includes many long and labor-intensive processes ranging from the printing of exam papers to conducting the exam, to reading and explaining the exam papers. In the E-Exam system, transactions that do not require such qualifications can be performed more easily through computers or these-based systems. Previous research indicates a positive link between PRAC and PU [31]. Hence, it was hypothesized as follows: H7. “PRAC would be positively related to the PU.”

2.9 Computer Self-efficacy (CSE) Computer self-efficacy is “a judgment of one’s capability to use a computer” [32]. Self-efficacy has been extensively considered in learning and teaching environments, and it has been shown that high self-efficacy leads to better learning performance [33]. Earlier research showed the impact of CSE on PEOU, which is positive [34]. Therefore, we proposed the following hypothesis: H8. “CSE would be positively related to the PEOU.”

2.10 User Interface Design (UID) User interface design usually points to the visible organization of items a user can communicate with on a computer-based system. Information software with a userfriendly design is another essential matter that needs serious attention [35]. Earlier

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research indicated a positive relationship between UID and PEOU [36]. Hence, we proposed the following hypothesis: H9. “UID would be positively related to the PEOU.”

2.11 Computer Anxiety (CA) Computer anxiety is described as “the concern of using a computer or the chance of using it when using a computer” [37]. It expresses concerns about the effects of using computer-based technology; for instance, fear of losing crucial data, or other possible mistakes. It was discovered that “computer anxiety has a significant impact on computer use” [32]. The previous proposal found that CA affects PEOU negatively [38]. Therefore, we developed the following hypothesis: H10. “CA would be negatively related to the PEOU.”

3 Methodology Totally 245 questionnaires were gathered, and Partial Least Squares-Structural Equation Modeling (PLS-SEM) was applied [39]. 84.5% of the participants are male, 60.8% of them are 15–20 years old, 49.4% of them are going to high school or have a high school degree, 96.7% of them are single, 88.2% of them are students and 69.8% of them have not used any E-Exam system before. The demographic features are provided in Table 2. PLS-SEM enables the examination of measurement and structural models at the same time [40]. SmartPLS 3.2.7 software is used to conduct the analyses. To quantify the factors, the “five-point Likert scale” was utilized. The measurement instrument consists of 13 scales and 40 items; the scale items are provided in Appendix.

4 Results To evaluate the proposed framework, we used Confirmatory Factor Analysis (CFA). Convergent validity and discriminant validity were performed to check the reliabilities and validities of the constructs. A construct’s reliability at the item level was assessed via factor loadings, which should be a minimum of 0.6 [41, 42]. Table 3 indicates that the factor loading values are higher than 0.6 which indicates the items’ convergent validity.

368 Table 2 Demographics

G. H. Koksalmis et al.

Gender (%) Female

15.5

Male

84.5

Age 15–20

60.8

20–29

36.7

30–39

1.6

40–49

0.8

Educ. St. (%) Prim. Sch

2.4

H. Sch

49.4

Und. Grad

40.4

P. Grad./Ph.D.

7.8

Profession Student

88.2

Employer

0.4

Worker

11.4

Computer experience period (years) 10+

55.9

5–10

28.6

1–5

11.4

0

4.1

E-Exam experience Yes

30.2

No

69.8

Constructs’ convergent validity was evaluated via “Cronbach’s alpha”, “composite reliability (CR)”, and “average variance extracted (AVE)” values. Cronbach’s alpha “measures the internal uniformity of constructs”. The minimum rate of Cronbach’s alpha value is 0.7 The CR denotes “how well the items measure a construct”, and its threshold value is 0.7. AVE is “the variance captured by the construct about the amount of variance attributable to measurement error”, and the recommended threshold value is 0.5 [41]. The Cronbach’s alpha, CR, and AVE measures of constructs are higher than the threshold values, which indicates convergent validity is satisfied (Table 4). After assessing convergent validity, to check the discriminant validity, the “Fornell and Larcker criterion” and “cross-loadings” were taken into consideration. To have a reasonable discriminant validity, the “Fornell and Larcker criterion” requires that diagonal factors representing the square root of AVE values be greater than nondiagonal factors representing the relationships between the structures in the same

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Table 3 Construct validity Construct

Item

Mean

Standard deviation

Factor loadings

T statistics

Actual usage

AU1

0.959

0.007

0.959

132.654

AU2

0.956

0.009

0.957

108.472

AU3

0.908

0.025

0.908

36.896

BI1

0.931

0.014

0.931

66.639

BI2

0.937

0.012

0.938

80.968

BI3

0.898

0.019

0.900

48.395

BI4

0.877

0.022

0.878

39.210

CA1

0.927

0.008

0.928

123.374

CA2

0.832

0.021

0.831

39.439

CA3

0.773

0.030

0.775

25.430

CSE1

0.761

0.037

0.762

20.718

CSE2

0.827

0.029

0.828

28.402

CSE3

0.886

0.018

0.885

48.598

CSE4

0.841

0.027

0.844

31.433

CSE5

0.744

0.038

0.744

19.406

CSE6

0.876

0.021

0.877

40.918

Behavioral intention

Computer anxiety

Computer self-efficacy

Experience

CSE7

0.856

0.027

0.858

32.172

EXP1

0.935

0.012

0.936

75.555

EXP2

0.939

0.011

0.940

88.898

0.884

0.022

0.887

39.699

Perceived ease PEOU1 of use PEOU2

EXP3

0.864

0.025

0.866

34.210

0.851

0.023

0.851

36.303

PEOU3

0.819

0.029

0.821

28.771

PEOU4

0.911

0.014

0.912

63.281

PEOU5

0.812

0.043

0.818

19.129

PR1

0.949

0.005

0.949

189.832

PR2

0.827

0.024

0.827

33.939

PR3

0.831

0.024

0.831

35.346

PU1

0.890

0.023

0.891

39.000

PU2

0.895

0.020

0.895

45.493

PU3

0.917

0.017

0.916

55.475

SEC1

0.862

0.031

0.864

28.089

SEC2

0.894

0.015

0.893

58.309

SEC3

0.635

0.060

0.639

10.620

SN1

0.851

0.032

0.853

26.422

SN2

0.921

0.017

0.921

53.633

SN3

0.897

0.020

0.896

45.568

Practicality

Perceived usefulness

Security

Subjective norms

(continued)

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Table 3 (continued) Construct

Item

Mean

Standard deviation

Factor loadings

T statistics

User interface design

UID1

0.935

0.007

0.934

142.555

UID2

0.836

0.023

0.836

36.732

UID3

0.842

0.025

0.845

33.732

Table 4 Construct reliability Construct

Cronbach’s alpha

CR

AVE

CA

0.802

0.883

0.717

CSE

0.924

0.939

0.689

EXP

0.911

0.944

0.849

PEOU

0.907

0.931

0.730

PR

0.843

0.904

0.759

PU

0.884

0.928

0.811

SEC

0.734

0.846

0.651

SN

0.870

0.920

0.793

UID

0.845

0.905

0.762

AU

0.936

0.959

0.886

column [43]. For the discriminant validity, a novel gauge which is the HeterotraitMonotrait ratio (HTMT) is proposed. It is the average of “all indicators’ correlations across variables measuring different variables” [44]. For a reasonable discriminant validity, HTMT should be lower than 0.85 [45]. According to Tables 5 and 6, the discriminant validity is satisfied. Table 5 Fornell-Larcker criterion BI BI CA CSE

CA

CSE

EXP

PEOU PR

PU

SEC

SN

UID

AU

0.912 − 0.410

0.847

0.678 − 0.500 0.830

EXP

0.434 − 0.371 0.473 0.921

PEOU

0.772 − 0.566 0.686 0.552 0.854

PR

0.379 − 0.231 0.308 0.228 0.341

0.871

PU

0.739 − 0.379 0.612 0.375 0.692

0.518 0.901

SEC

0.601 − 0.352 0.573 0.440 0.550

0.306 0.523 0.807

SN

0.554 − 0.227 0.506 0.184 0.422

0.248 0.495 0.448 0.891

UID

0.348 − 0.551 0.416 0.235 0.488

0.215 0.378 0.247 0.160

AU

0.334 − 0.025 0.168 0.015 0.167

0.244 0.353 0.287 0.261 − 0.005 0.941

0.873

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Table 6 Heterotrait-Monotrait Ratio (HTMT) BI

CA

CSE

EXP

PEOU

PR

PU

SE

SN

UID

AU

BI CA

0.465

CSE

0.730

0.573

EXP

0.471

0.424

0.515

PEOU

0.837

0.640

0.744

0.611

PR

0.394

0.272

0.326

0.256

0.363

PU

0.813

0.440

0.677

0.416

0.769

0.564

SEC

0.694

0.437

0.667

0.523

0.648

0.375

0.645

SN

0.614

0.271

0.562

0.205

0.471

0.264

0.559

0.573

UID

0.379

0.645

0.454

0.254

0.529

0.236

0.421

0.281

0.171

AU

0.354

0.048

0.184

0.045

0.181

0.262

0.387

0.362

0.287

0.076

The path coefficient, t-statistics, p-values, and coefficient of determination were collected and analyzed; the t-value has to be over 1.65 at a confidence level of 95% [46]. The output of our model is summarized in Table 7; moreover, it is illustrated in Fig. 2. Out of twelve hypotheses, eleven of them were supported. BI was significantly related to the AU (β = 0.334, p < 0.05). PU was significantly related to the BI (β = 0.288, p < 0.05). PEOU was significantly related to the BI (β = 0.422, p < 0.05) and PU (β = 0.511, p < 0.05). Subjective norms were significantly related to the BI (β = 0.169, p < 0.05) and PU (β = 0.209; p < 0.05). SEC was significantly related to the BI (β = 0.169, p < 0.05). Whereas EXP was not significantly related to the PU (β = − 0.013, p > 0.05). PRAC was significantly related to the PU Table 7 Path coefficients Relationship BI → AU CA → PEOU CSE → PEOU

Path coefficient

Standard deviation

T statistics

P values

0.334

0.061

5.481

0.000

− 0.227

0.049

4.611

0.000

0.509

0.062

8.178

0.000

− 0.013

0.055

0.235

0.815

PEOU → BI

0.422

0.054

7.855

0.000

PEOU → PU

0.511

0.058

8.830

0.000

PR → PU

0.295

0.039

7.647

0.000

PU → BI

0.288

0.061

4.692

0.000

SEC → BI

0.142

0.053

2.668

0.008

SN → BI

0.169

0.046

3.651

0.000

SN → PU

0.209

0.055

3.791

0.000

UID → PEOU

0.151

0.051

2.928

0.004

EXP → PU

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G. H. Koksalmis et al.

Experience

Subjective norms -0.013

0.209*

Perceived usefulness

0.169*

0.295* 0.288*

Practicality Behavioral intention

0.511*

0.334*

Actual usage

0.422*

0.142*

Computer selfefficacy

0.509*

Perceived ease of use

Security

0.151* -0.227*

User interface design

Computer anxiety

Fig. 2 The structural model with hypothesis testing results. “(* p < 0.05, –: insignificant relationship)”

(β = 0.295, p < 0.05). CSE was significantly related to the PEOU (β = − 0.509, p < 0.05). UID was significantly related to the PEOU (β = 0.151, p < 0.05). CA was significantly related to the PEOU (β = − 0.227, p < 0.05). R-squared values indicate the critical strength or “a level of the overall variance of a variable.” For example, PEOU, PU, SEC, and SN explained 71.9% of the variance in the BI (R-Square = 0.719). Further, our model justified 55.2% of the PU.

5 Discussion and Conclusion 5.1 Theoretical and Practical Implications Recently, several public and private organizations have started to be interested in computer-based training and computer-based evaluation systems to keep up with technology. One of the most important reasons for the increase in interest in these systems is to meet the need arising from the evaluation of these qualifications as easily, understandably, and decisively in a globalized education and training environment. To adapt the classical applications of evaluation systems to computers, to make them

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easily accessible, and to reduce costs, the transition to these systems has become inevitable. This study has various theoretical contributions. First, the technology acceptance model is extended with several variables, and the proposed research framework is an integration of theories. It is believed that this study would add value to the literature by evaluating the role of security, practicality, user interface design, selfefficacy, and computer anxiety simultaneously in predicting the actual usage of the E-Exam system. Second, out of 12 hypotheses, 11 of them were supported. The path analysis results are compatible with the prior literature by indicating that PU and PEOU were significantly related to the BI. Further, computer anxiety, practicality, subjective norms, computer self-efficacy, security, and user interface design were also significant in predicting individuals’ behavioral intentions. The analyses verified that PU, PEOU, SN, and security were affecting BI significantly. CSE, UID, and CA were significant determinants of the PEOU. Further, PEOU, practicality, and SN were significant determinants of the PU. Third, the proposed model explained 71% of the total variance in the BI, which is comparatively high, which indicates that the proposed model and variables in the model are successfully constructed and measured. Moreover, this research has several useful implications in practice especially for investors government organizations, and system developers. Computer-based evaluation systems aim to make these evaluations more flexible in terms of space and time and reduce their costs by using the classical examination understanding with a technological device. These systems will contribute to the institutions adapting their evaluation applications to the computer system and the accessibility of the users of this system. For instance, policymakers and developers can research and develop a more user-friendly environment while preparing E-exam systems. Investors can take into account several aspects such as computer anxiety, security, ease of use, and interface design while making investment decisions on E-exam systems.

5.2 Limitations and Future Work Despite the noteworthy contributions mentioned above, our research has some limitations that warrant consideration for future research. Firstly, the research model was exclusively tested in the context of Turkey. It would be valuable to rerun the model in different cultures or nations, as results may vary across diverse cultural contexts. Secondly, the constructs utilized in the study explained only 11.2% of the variance in actual usage. Consequently, there is room for investigating additional constructs that might play a crucial role in explaining the actual usage of the E-Exam system. Thirdly, the demographic data of participants were not integrated as factors within the proposed framework. Incorporating demographic variables into the research model could provide valuable insights and avenues for future work. Lastly, for a more comprehensive understanding of E-Exam system acceptance, future research endeavors may take into account qualitative data.

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Appendix. The Constructs and Scale Items

Constructs

Codes

Items

Experience

EXP1

“I enjoy using the E-Exam system.”

EXP2

“I am comfortable using the E-Exam system.”

EXP3

“I am comfortable saving and locating files.”

SEC1

“E-Exam systems are as secure as paper-based exams.”

SEC2 SEC3

“Grading of E-Exam system will be secure.” “Username and password log-in ensure adequate security.”

SN1

“People who are important to me would think that I should use the E-Exam system.”

SN2

“People who influence me would think that I should use the E-Exam system.”

SN3

“The information system department supported my use of the E-Exam system.”

Security

Subjective norms

Computer self-efficacy

Perceived usefulness

Perceived ease of use

Behavioral intention

“I could complete the job using the E-Exam system.” CSE1

“If there was no one around to tell me what to do as I go.”

CSE2

“If I had never used a package like it before.”

CSE3

“If I had only the software manuals for reference.”

CSE4

“If I had seen someone else using it before trying it myself.”

CSE5

“If I could call someone for help if I got stuck.”

CSE6

“If someone else had helped me get started.”

CSE7

“If I had a lot of time to complete the job for which the software was provided.”

PU1

“Using the E-Exam system to reach my employee information would enable me to accomplish tasks more quickly.”

PU2

“Using the E-Exam system would increase my productivity in reaching my employee information.”

PU3

“Using the E-Exam system would enhance my effectiveness in reaching my employee information.”

PEOU1

“Learning to operate an E-Exam system would be easy for me.”

PEOU2

“I would find it easy to get the E-Exam system to do what I want it to do.”

PEOU3

“My interaction with the E-Exam system would be clear and understandable.”

PEOU4

“It would be easy for me to become skillful at using E-Exam system applications.”

PEOU5

“I would find the E-Exam system easy to use.”

BI1

“I expect to use the E-Exam system.”

BI2

“I expect the information from the E-Exam system to be used.”

BI3

“I intend to increase my use of the E-Exam system in the future.” (continued)

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(continued) Constructs

Actual usage

Practicality

User interface design

Computer anxiety

Codes

Items

BI4

“I plan to use more applications in the E-Exam system in the future.”

AU1

“I use the E-Exam system very intensively (many hours per day, at work).”

AU2

“I use the E-Exam system very frequently (many times per day, at work).”

AU3

“Overall, I use the E-Exam system a lot.”

PR1

“Use of less paper, which is important.”

PR2

“More accessible than paper-based exams.”

PR3

“Enriched exam experience owing to hands-on tools.”

UID1

“The layout design of the E-Exam system makes it easy to read.”

UID2

“The font style, color, and layout of the interface make it comfortable for me to read.”

UID3

“In general, I am satisfied with the design of the interface of the E-Exam system.”

CA1

“E-exam systems do not scare me at all.”

CA2

“E-Exam systems make me feel uncomfortable.”

CA3

“Working with E-Exam systems makes me nervous.”

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Investigating the Relation Between Learning Analytics and Academic Performance at the International Modern Arabic School: A Qualitative Study Muaadh Mukred, Waleed Abdulkafi Ahmed, Umi Asma’ Mokhtar, and Burkan Hawash

Abstract The utilization of Learning Analytics (LA) has emerged as a pivotal instrument for educational institutions seeking to gain a competitive edge in the swiftly changing realm of education. Adopting Learning Analytics (LA) poses a significant challenge for educational institutions, primarily due to a lack of awareness regarding its potential benefits. The primary objective of this study is to ascertain the significance of adopting Learning Analytics (LA) within educational institutions. The research employs a qualitative focus group methodology to foster indepth dialogue among a heterogeneous group of participants at the International Modern Arabic School (IMAS). The focus group sessions yielded significant insights regarding the viewpoints of educators and administrators regarding the potential adoption of Learning Analytics (LA). The results of this study unveiled a significant correlation between the adoption of LA and academic achievement across multiple crucial dimensions of education. The study has an empirical contribution, and by leveraging LA adoption that facilitates benchmarking and comparison, institutions can evaluate their performance concerning global standards and implement evidence-based enhancements. The identification of at-risk students allows for early intervention, thereby enabling the provision of timely and personalized support to promote academic achievement. This research has significant implications highlighting the transformative capacity of learning analytics (LA) in educational institutions. Adopting this technology promotes the development of innovative teaching strategies customized to meet individual needs. M. Mukred (B) Department of Business Analytics, Sunway Business School, Sunway University, 5, Jalan University, 47500 Petaling Jaya, Bandar Sunway, Selangor, Malaysia e-mail: [email protected] M. Mukred · W. A. Ahmed The International Modern Arabic School, Jalan P14k, Presint 14, 62050 Putrajaya Wilayah Persekutuan, Putrajaya, Malaysia M. Mukred · U. A. Mokhtar · B. Hawash Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_19

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Keywords Learning analytics · Education · International Modern Arabic School · Academic performance · Focus group

1 Introduction The field of education has changed dramatically in recent years with the advent of new technologies and pedagogical approaches [1–9]. One such development is LA, which refers to using data and analytics to measure and analyze student learning and engagement [10]. The utilization of LA has the potential to yield significant insights into student behavior, thereby facilitating the enhancement of teaching methodologies and ultimately improving learning outcomes. Further investigation is warranted regarding the influence of learning analytics on scholastic achievement, particularly within educational institutions [10–12]. When high-quality data and computing tools are accessible, LA typically follows a straightforward trajectory. Consequently, when such tools are not utilized within educational environments, students’ actions are not inherently recorded [13]. Frequently, learners’ actions that take place within computer-based systems but cannot be adequately documented tend to be ignored. Consequently, students are more likely to be disregarded and exhibit perplexity when confronted with a problem or manifest signs of yawning during a lecture, commonly interpreted as indications of boredom or disengagement [14]. According to the research conducted by Chen et al. [15], their study results indicated a need for scholars to explore the potential application of artificial intelligence (AI) within physical classroom environments. This highlights the significance of investigating the utilization of technology in non-virtual educational spaces. The preceding literature demonstrates the imperative to examine the phenomenon of LA in education. The swift progression of technology and the extensive incorporation of digital tools in the educational sphere have led to substantial amounts of student learning data [1, 16]. In the context of LA adoption in educational institutions, challenges remain numerous and rampant, the top among which are lack of awareness regarding its advantages, lack of dedicated research, and lack of published empirical studies on the subject. It is surprising how educators and institutions are generally unaware of the potential benefits that can be reaped from LA, and such lack of awareness has been the primary reason for the low adoption level. In addition, the lack of research on the applications of LA and its effect still needs thorough examination, as a literature gap exists concerning the topic. Such gap contributes to the ambiguity surrounding the issue calling for evidence-based findings to shed light on the validity and efficacy of LA. In order to resolve the challenges, the transformative power of LA needs to be unraveled; awareness needs to be increased, and efforts towards its examination need to be precipitated along with the dissemination of the obtained findings. The wide range of data presents a distinctive opportunity to acquire valuable insights into

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students’ interaction with the educational content, their advancement, and potential areas where supplementary assistance may be needed. The correlation between the utilization of LA and academic performance has not been extensively investigated [13]. Consequently, this study’s primary objective is to examine LA’s impact on academic achievement. The present study investigates the relationship between learning analytics and benchmarking and comparison, early intervention, enhanced teaching strategies, effective resource allocation, data-driven decision-making, and quality assurance. The present study sought to examine the relationship between learning analytics and academic performance within the context of the IMAS. The implications of the study’s findings hold significance for implementing LA systems within educational institutions, specifically those operating internationally. This research endeavor has the potential to offer significant contributions to the field of education by shedding light on the utilization of learning analytics systems and their correlation with academic achievement. It can be a valuable resource for educators and administrators aiming to enhance teaching and learning outcomes. Moreover, this study has the potential to make a valuable contribution to the expanding field of research concerning the utilization of learning analytics in the realm of education, as well as its potential influence on academic achievements.

2 International Modern Arabic School (IMAS) Established in 2008 as a non-profit international school, IMAS is wholly owned by the Alnoor Foundation and strives to blend quality British education with Arabic and Islamic education from Preschool to Secondary levels. With a current student population of over 2100 from 45 nationalities, IMAS fosters a culture of ambition, encouraging students to reach for their dreams and explore new opportunities [17]. The highly qualified and passionate teaching staff at IMAS provide individualized attention to each student, complementing the academic program with various extracurricular activities such as sports, music, drama, and community service. The school is dedicated to creating a safe, supportive, and inclusive learning environment, offering various support services to ensure students succeed academically and emotionally [17]. Beyond academic excellence, IMAS promotes global citizenship and cultural awareness, embracing its diverse community to develop empathy and understanding among students. The school actively fosters a global perspective, encouraging students to appreciate different cultures and contribute positively to the world [17]. IMAS showcases its commitment to technology integration by adopting Google Classroom and an Enterprise Resource Planning (ERP) system very early. The school’s proficiency with online platforms facilitated a seamless transition to remote learning during the Covid-19 pandemic. Additionally, offering Google Chromebooks for all students has enhanced the digital learning experience as an alternative to traditional books. The school’s focus on technology integration nurtures digital literacy

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and equips students with essential 21st-century skills. This paperless approach aligns with sustainability efforts, promoting responsible resource use. Moreover, the integration of LA in 2020 represented a significant achievement in IMAS’s technological progression, facilitating the advancement of tailored and pragmatic educational approaches. The IMAS program equips students with the necessary skills and knowledge to navigate the ever-changing digital landscape. By utilizing data-driven insights, IMAS establishes a strong foundation for future advancements in education.

3 Methodology The focus group methodology is a qualitative research approach employed to collect comprehensive insights and comprehension from a limited number of participants with common characteristics or experiences [18, 19]. This approach promotes the exchange of ideas and fosters active engagement among participants, collecting comprehensive and intricate data on a particular study area [20]. Focus group studies are valuable for investigating participants’ attitudes, perceptions, beliefs, and experiences within a social context [4, 20]. The researchers possess suitable qualifications for examining the relationship between learning analytics and academic achievement within the context of IMAS. The methodology employed by the study is depicted in Fig. 1. The rationale behind utilizing the focus group methodology at IMAS is rooted in its ability to capture students efficiently’ and teachers’ collective perspectives and diverse viewpoints. The study seeks to investigate the complexities surrounding the implementation of learning analytics and its impact on academic performance within a specific international school setting by forming a diverse cohort of individuals with diverse backgrounds and experiences. The interactive nature of focus groups facilitates the collaborative exchange of ideas among participants, thereby fostering a more comprehensive comprehension of the research subject matter. Identify the purpose and Rationale

Participant Selection

Focus Group Composition

Findings Synthesis

Data Analysis

Data Collection

Fig. 1 The methodology of the study

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In order to optimize the efficacy of the focus group study, meticulous participant selection is of utmost importance. The research methodology used a purposive sampling strategy to identify individuals, specifically students and teachers, who possess pertinent experiences and knowledge about using learning analytics and its impact on academic performance. The selection of participants will encompass a range of grade levels and subject areas, thereby ensuring representation of the school’s diverse population. The focus groups will be categorized into two distinct groups: one comprising teachers and the other consisting of administrators. Each focus group for teachers will consist of approximately 6–10 individuals, while the focus groups for administrators will include employees from various departments. This composition will enable open and honest discussions within each group, allowing participants. The data collection process within the focus group methodology entails engaging in interactive discussions guided by a pre-established focus group guide. The guide will comprise open-ended inquiries and prompts specifically designed to delve into the perspectives of students and teachers regarding learning analytics, their encounters with its implementation, and its potential influence on academic achievement. The moderator will adeptly guide the discussions, ensuring equitable participation from all participants and maintaining a coherent focus on the research objectives. The data obtained from the focus group discussions was systematically transcribed and meticulously organized to facilitate thorough analysis. The methodology employed in this study involved using a thematic analysis approach to identify recurring themes and patterns within the collected data. The themes were identified based on the responses provided by admin staff and teachers, facilitating a thorough analysis of the perceived advantages and obstacles associated with using learning analytics at IMAS. Ultimately, the results were deliberated upon and integrated consistently with scholarly discourse.

4 Findings and Discussion This section presents the study’s primary findings, obtained by analyzing data gathered from the focus group sessions. The focus group, comprising ten participants with diverse backgrounds from the IMAS, including educators and administrators, yielded valuable insights regarding the effects of adopting learning analytics on different facets of education. The study findings revealed a significant correlation between the implementation of LA and several crucial factors that contribute to enhancing education. As illustrated in Fig. 2, LA possesses numerous advantages. Previously, researchers highlighted the role of technology in enhancing education in general [1–3, 21–26]. This section is dedicated to elucidating and consolidating the findings of each impact. LA provides numerous advantages for international schools, enhancing the educational experience. Several notable advantages include.

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Benchmarking and Comparison

Early Intervention

Improved Teaching Strategies

Learning Analytics Adoption

Efficient Resource Allocation

Data-Driven Decision Making

Quality Assurance

Fig. 2 Learning analytics and its benefits

4.1 Benchmarking and Comparison Educational institutions can utilize LA to compare the academic performance of their students with other schools at a local or global level. This enables schools to gain insights into their competitive standing and pinpoint specific areas that require enhancement [27]. Martin and Ndoye [28] highlight the potential of learning analytics to improve student engagement and performance in online courses. Their study reviews various categories of online assessments and identifies relevant data sets for analysis. Martin and Ndoye [28] utilized Tableau for quantitative data and Many Eyes for qualitative data. By using learning analytics, instructors can gather and analyze data about students, leading to improved instructional design and delivery that is more meaningful for learners [29]. Similarly, this research has important implications for instructors, instructional designers, administrators, and educational researchers who utilize online assessments, promoting data-driven decision-making in online education. The focus group findings came up with the same and enriched the understanding. According to this study, learning analytics improve benchmarking and comparison by providing objective performance metrics for students and groups. It helps identify learning trends, strengths, and weaknesses, allowing personalized benchmarking for each student. Educators can compare their school’s performance with peers, assess teaching strategies, and monitor progress toward academic goals.

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4.2 Early Intervention According to the findings of this study, learning analytics improve early intervention by identifying students who may be struggling or at risk of falling behind. Educators can detect learning gaps or difficulties by analyzing student performance, engagement, and behavior data. This enables them to provide timely and targeted support and intervention strategies to help students overcome challenges and improve their academic performance. This aligns with previous works [30, 31], where researchers confirmed the role of Learning analytics in improving early intervention. Early intervention through learning analytics ensures students receive the assistance they need to succeed before problems escalate, fostering a more positive and practical learning experience [32, 33].

4.3 Improved Teaching Strategies LA improves teaching strategies by analyzing students’ learning behaviors and performance patterns, enabling personalized interventions, early alerts for struggling students, and real-time feedback for instructors to adjust teaching methods [34, 35]. LA data informs the design of more effective instructional materials and curricula, leading to optimized student engagement, performance, and learning outcomes [36]. As evidenced by Amida et al. [37], a mixed-methods study that was to explore the factors influencing faculty members’ motivation to use LA for teaching improvement. In their quantitative phase, 107 faculty members completed an online survey, showing that cost, utility, attainment value, and competence significantly predicted intrinsic motivation to adopt LA, with positive introjected motivation and motivation serving as indirect predictors of faculty LA usage. The qualitative phase involved ten faculty members participating in focus groups, revealing themes such as tracking learning activities, early alert systems, and institutional support as motivating factors, while time constraints, lack of data competence, and unstructured datasets were identified as demotivating factors. In this regard, the findings of this study confirmed the relationship between LA and improved teaching strategies. The respondents highlighted the pivotal role of LA tools in enhancing the strategies followed by IMAS.

4.4 Efficient Resource Allocation Learning analytics can revolutionize educational resource allocation by analyzing student data to provide insights for optimizing resource distribution. Real-time information on student progress allows personalized support and interventions, enhancing student success and increasing institutional efficiency [38–40].

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The focus group study conducted at IMAS shed light on the manifold advantages of utilizing LA with academic achievement. The study results indicated that the utilization of LA played a crucial role in facilitating the allocation of resources, thereby facilitating the focused distribution of resources to address specific academic requirements effectively. The identification of high-need students was conducted by IMAS through data analysis, followed by the implementation of personalized interventions to ensure their academic success. Additionally, the curriculum enhancements implemented by LA have contributed to developing a more personalized and immersive learning environment.

4.5 Data-Driven Decision Making Within the LA field, educational institutions can evaluate the efficacy of diverse educational resources, curricula, and teaching methodologies. Using data facilitates the decision-making process, thereby enhancing the effectiveness of teaching and learning by enabling individuals to make well-informed decisions. [41, 42]. This is confirmed by the findings of this study where participants mentioned that LA is paramount in facilitating data-driven decision-making within IMAS. By utilizing a range of data sources, such as student performance, engagement levels, and learning patterns, LA provides the necessary tools for IMAS to make well-informed decisions to enhance the effectiveness of teaching and learning. Through comprehensive data analysis, administrators and educators at the IMAS can obtain valuable insights into the efficacy of educational resources, curricula, and instructional approaches. More specifically, using data-driven methodologies allows IMAS to strategically allocate resources in most critical areas, thereby facilitating targeted student support and interventions. Furthermore, the application of LA assists in identifying students who require additional support, thus enabling the implementation of tailored and timely interventions to promote their academic progress. Through data-driven feedback, educators at IMAS can improve their instructional practices, leading to improved student outcomes. Integrating LA fosters an atmosphere emphasizing continuous improvement, allowing the IMAS to refine its educational approaches, enhance student engagement, and optimize academic performance.

4.6 Quality Assurance Using data from LA can improve quality assurance and accreditation endeavors, thereby supporting international educational institutions in achieving established educational standards and maintaining their esteemed reputation [43, 44]. The study findings confirmed this and showed that IMAS demonstrated its commitment to excellence and continual improvement in educational practices by harnessing data-driven insights. LA data provides concrete evidence of the

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school’s effectiveness in teaching methodologies, curriculum design, and resource allocation, aligning with established benchmarks. Regular monitoring and evaluation of student academic progress and engagement enable timely interventions and improvements, ensuring that educational standards are consistently met. Additionally, learning analytics data validates the impact of personalized learning initiatives, showcasing the school’s dedication to addressing the unique needs of each learner. By utilizing this data, IMAS can enhance its curriculum, demonstrate transparency in decision-making, and implement adequate student support and retention strategies. Leveraging LA data empowers IMAS to showcase its progress, growth, and commitment to evidence-based practices, securing accreditation and preserving its reputation as a reputable and forward-thinking educational institution.

5 Future Works Recommendations Future research should focus on further exploring and expanding the potential of learning analytics in educational institutions like IMAS. One crucial area of investigation is long-term data analysis, delving deeper into academic data spanning several years to uncover hidden trends and patterns. By gaining a more comprehensive understanding of long-term educational outcomes, schools can make evidence-based decisions and continuously improve their practices to adapt to changing student needs and expectations. Predictive analytics also holds immense promise for future research; understanding how historical data can be effectively utilized to identify students at risk of academic challenges or disengagement is essential. This research can lead to developing targeted intervention strategies that provide personalized support, empowering struggling learners and promoting a more inclusive and supportive learning environment. Enhancing student engagement through learning analytics is another area that merits further investigation. By analyzing participation, collaboration, and active learning data, researchers can identify the most effective strategies for sparking curiosity and motivation in students. This knowledge will enable educators to design tailored engagement techniques that foster a dynamic and vibrant learning atmosphere, ultimately boosting student performance and satisfaction. Integrating formative assessment data into learning analytics should also be a focal point of future research. Understanding how real-time insights can guide instructional approaches and personalize learning pathways will empower educators to better cater to each student’s unique strengths and challenges. By fine-tuning the feedback loop, researchers can help create a more efficient and effective learning experience for all students. Furthermore, benchmarking against global educational standards remains an area worthy of exploration. Future research should examine how institutions like IMAS can effectively utilize international comparisons to identify areas for growth and continually strive for educational excellence. By adopting best practices worldwide,

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schools can enhance their educational quality and maintain their position as leaders in their respective fields. Future research should focus on data privacy and protection to ensure responsible and ethical use of learning analytics. Investigating the most effective ways to implement strict measures and obtain informed consent from all stakeholders will ensure that trust and transparency remain integral to the learning analytics process. Comprehensive training and professional development for educators are vital components of successful learning analytics integration. Research should explore innovative and effective methods to empower teachers with the necessary skills and knowledge to leverage learning analytics. Continuous development opportunities will equip educators with the tools to navigate the ever-evolving landscape of data-driven education. Inclusive and collaborative research involving all stakeholders is crucial. Thus, future studies should emphasize the importance of gathering feedback from students, parents, teachers, and administrators. Understanding diverse perspectives will help tailor learning analytics approaches to meet the unique needs and aspirations of the entire educational community. Finally, future research should explore innovative data sources beyond traditional academic metrics. Researchers can obtain comprehensive insights into student well-being and development by examining sentiment conveyed in student feedback and observing extracurricular involvement. Implementing a multi-faceted approach allows educators to provide comprehensive support, thereby promoting the holistic development of each student.

6 Contribution This study has two types of contributions as follows.

6.1 Theoretical Contributions In addition to contributions to practice, the study also contributes to theory in the field of education and LA by providing insight into the transformative capacity of LA adoption and highlighting the paradigm shift towards a new strategy. The research emphasizes the ethical considerations relating to data-driven technology usage in education, adding to the ongoing discourse concerning responsible technology integration in education. The study also shed light on the need for stakeholder involvement and educator support as prerequisites for LA implementation, in that collaboration among the relevant stakeholders has a hand in adoption success. The study is consistent with the literature on promoting ongoing improvement in teaching and learning processes and best practices. The proposed theoretical framework advocates the role of datadriven insights in the development of innovation and in meeting students’ needs.

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Overall, the study contributes to enriching theoretical underpinnings regarding LA adoption in educational transformation, directing researcher and practitioner circles toward forming a better educational landscape for the current and future generations.

6.2 Practical Implications The study has implications to practice, specifically to the practical running of the IMAS and other similar educational institutions. The qualitative focus group findings guide the administrators and educators of IMAS which can be practically adopted. The finding supporting a significant relationship between LA adoption and academic achievement can be used as a robust basis for IMAS to adopt evidencebased strategies for improving students’ achievements. Also, LA use in identifying at-risk students can bring about interventions at a timely manner, enabling the provision of tailor-made support and targeted resources. Furthermore, the importance of benchmarking and global standards to IMAS lies in its usefulness in performance assessment based on a global scale and in promoting a culture that works towards ongoing improvement. More importantly, the practical implications of the study and its findings also apply to other institutions similar to IMAS within and outside of the country. In other words, LA integration into the institutions’ frameworks can enhance students’ achievements, promote teaching strategies and establish relevant and effective tools in a dynamic educational landscape.

7 Conclusion Learning analytics offers a transformative pathway for institutions like the IMAS to enhance educational practices and create dynamic, student-centered learning environments. Embracing long-term data analysis, predictive analytics, enhanced student engagement metrics, and global benchmarking, IMAS is poised to shape a brighter future in education. IMAS will continue excelling in its journey toward educational excellence and continual growth through ethical considerations, educator support, stakeholder involvement, and innovation. The study highlights the immense potential of LA Adoption and its transformative impact on education. As technology advances, institutions must embrace LA as a strategic tool to unlock the full potential of their educational endeavors. By leveraging data-driven insights, schools can create inclusive, personalized, and dynamic learning environments that empower students to succeed in an increasingly competitive world. The adoption of Learning Analytics represents a significant step forward in pursuing educational excellence and reflects the commitment of educational institutions to continually enhance teaching and learning practices for the benefit of students and society as a whole.

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The Role of Accounting Information Systems in Strengthening Organizational Resilience: An Empirical Investigation Using the SEM-ANN Approach Ahmed Saleh Al-Matari, Mohammed A. Al-Sharafi, and Mohammed A. Hajar

Abstract This chapter examines the influence of Accounting Information Systems (AIS) on organizational resilience. Anchored in a positivist framework and using a deductive methodology, this empirical research utilizes the SEM-ANN Approach to investigate the effects of AIS-related Human Competency, Complementary Business Intelligence System, and Flexible AIS on organizational resilience. The study is based on a sample from large Malaysian organizations listed on the Bursa Malaysia stock exchange, selected for their rich data availability and high level of engagement with dynamic AIS systems. Key outcomes indicate that all three variables significantly impact organizational resilience. Of these, Flexible AIS stands out as the most influential, followed by the Complementary Business Intelligence System and AIS-related Human Competency. The research emphasizes the vital role that AIS plays in fortifying organizational resilience and provides valuable insights for professionals aiming to use AIS for sustaining organizational effectiveness. Future research avenues include examining possible moderating factors and conducting detailed analyses of the sub-dimensions of each contributing factor, thereby adding more nuance to our understanding of AIS’s role in organizational resilience. As such, this study acts as a foundational piece for further research into how AIS impacts organizational resilience across various settings and temporal dimensions. Keywords Accounting information systems (AIS) · AIS flexibility · Human competency · Complementary business intelligence system · Organizational resilience · SEM-ANN approach A. S. Al-Matari Faculty of Commerce and Economics, Sana’a University, 15542 Sana’a, Yemen M. A. Al-Sharafi (B) Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia e-mail: [email protected] M. A. Hajar N-Wave Technologies (Malaysia) Sdn. Bhd, Petaling Jaya, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_20

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1 Introduction The world of business is characterized by constant change, volatile environments, and unpredictable challenges, creating a need for organizations to remain resilient [1]. Organizational resilience, defined as the capacity of an organization to survive, adapt, and grow in the face of turbulent change [2], has become a key objective for businesses around the globe. In the digital era, the role of information systems in organizations has significantly evolved, becoming central to decision-making processes and business operations [3]. Among these, the Accounting Information System (AIS) is a fundamental component that collects, stores, and processes financial and accounting data, serving as a critical tool for managing an organization’s operational and strategic needs [4]. In the contemporary, volatile business landscape, organizations face inevitable crises necessitating effective mitigation strategies. Economically, disasters have engendered implications amounting to $1.2 trillion in developing countries since 1980, with anticipated escalations due to burgeoning global risks [5]. The United Nations Trade and Development Agency further posits that the economic ambiguity propagated by the coronavirus pandemic could impose a $1 trillion burden on the global economy in 2020. This underlines an urgent need for organizations to recalibrate their focus toward daily operational needs and digital business resilience [6]. This context has fueled an academic pivot towards the examination of organizational resilience (OR) in the face of both natural and anthropogenic disasters [7]. Recent literature has highlighted the role of Dynamic Accounting Information Systems (AIS) in enhancing organizational resilience. Viewed as a dynamic capability, AIS enables organizations to identify environmental shifts, capitalize on opportunities, and stay aligned with their strategic goals [8]. Building on this conceptual foundation, the current study seeks to investigate how dynamic AIS capacity affects organizational resilience. It particularly concentrates on three key aspects: AISrelated human competency, the complementary Business Intelligence (BI) system, and the flexible AIS system. Each of these elements is posited to make a significant contribution to organizational resilience. The importance of organizational resilience has increasingly come to the forefront in today’s fast-paced and uncertain business environment. With rapid technological advancements, changing consumer behaviours, and increasing global risks, the ability of organizations to recover quickly and adapt in the face of adversity is vital for their sustainability and success. In this context, the role of robust Accounting Information Systems (AIS), AIS-related human competency, and complementary Business Intelligence (BI) systems becomes imperative. AIS have the potential to serve as a crucial component in enhancing organizational resilience. A flexible AIS, capable of adjusting to changing information needs and environmental demands, is integral to a firm’s ability to maintain financial stability and operational efficiency under stress. However, the influence of AIS on OR is not unilateral; it is further shaped by the human competency related to AIS. This encompasses the skills, knowledge, and abilities of individuals to effectively use, interpret, and respond to the data and

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information generated by AIS. In addition, complementary Business Intelligence (BI) systems, providing analytical capabilities to process large volumes of structured and unstructured data, have the potential to equip organizations with insights for informed decision-making and strategic planning. These systems, when working in synergy with AIS and a competent workforce, can contribute significantly to organizational resilience. Despite a theoretical linkage between Accounting Information Systems (AIS) flexibility, AIS-related human competency, complementary Business Intelligence (BI) systems, and organizational resilience, empirical investigations into these associations are limited in existing literature. This study addresses this gap in literature by quantitatively examining the influence of these elements on organizational resilience. For the purpose of empirical testing, this study adopts a quantitative research approach, utilizing a sample of large-scale Malaysian companies. These companies were carefully chosen due to their demonstrable ability to actively employ AIS, thereby enabling the investigation of AIS’ impact on organizational resilience. The findings from this study will not only enrich the existing academic discourse but also offer practical recommendations for organizations aiming to boost their resilience. This research utilized a novel SEM-ANN approach, providing a powerful tool for capturing both linear and nonlinear relationships, in addition to non-compensatory interactions, between the exogenous variables (AIS flexibility, AIS-related human competency, and complementary Business Intelligence System) and the endogenous variable (organizational resilience). This robust analytical framework facilitates a comprehensive and nuanced examination of the complex interplay among the study’s variables, thereby enabling a holistic understanding of their collective impact on organizational resilience [1, 9–12]. The paper proceeds as follows: Sect. 2 provides a literature review, building a theoretical foundation for the research. Section 3 presents the methodology, detailing the approach and data collection process. Section 4 illustrates the results and findings from the data analysis. Section 5 includes a discussion of the findings, their implications, and how they contribute to the existing body of knowledge. Finally, Sect. 6 concludes the paper, outlining its limitations and suggesting potential avenues for future research.

2 Theoretical Background 2.1 Flexible AIS and Organizational Resilience Duncan [13] defines flexibility in Information Technology (IT) as comprising three specialized components: connectivity, modularity, and compatibility. Connectivity refers to an IT component’s ability to interact with other components both internally and externally. Modularity represents the capability of IT technical components to

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undergo easy reconfiguration (addition, modification, or removal) with minimal to zero impact. Compatibility, on the other hand, denotes the ability to disseminate all types of information to any technological component. Modularity within the IT infrastructure can foster IT agility, a critical aspect of organizational adaptability. Accordingly, a flexible IT infrastructure is integral to facilitating continuous restructuring in response to changing external conditions [14]. Connectivity not only enables the necessary processes for alliance formation by exposing shared capacities between partners and creating opportunities for joint development [15], but it also facilitates the discovery and utilization of services provided by different departments within an organization, thereby enhancing data flow and knowledge sharing [16]. The compatibility of an Accounting Information System (AIS) plays a pivotal role in structuring the exchanged information, enhancing communication flow between various applications, and enabling organizations to collect, analyze, and generate insights from multiple data sources such as data repositories and business networks [17]. This improved flow of information can lead to actionable ideas and recommendations, thereby driving organizational efficiency and growth. AIS subsystems process financial and nonfinancial transactions that directly affected the processing of the transactions. Hence, an AIS must be a flexible system such easily for organizations to introduce, remove, or alter the system’s elements since its key element is to identify issues and swiftly reorganize the AIS to adapt to the dynamic demands of a dynamic environment [18]. A flexible IS will contributes to overall organizational agility, consequently improving competitive advantage [19, 20]. To cope with rapidly changing environments requires having the resistance capacity to withstand unfavourable and stressful conditions [21], as well as capabilities to integrate, build, and reconfigure internal competencies [22]. A dynamic AIS capability is anticipated to meet the ad hoc requirements of accounting information for decision-makers in a rapidly changing environment. It is posited that accounting information derived from a flexible AIS would exhibit a dynamic nature, enabling enhanced analytics on the reported information [23]. A flexible AIS-IT infrastructure equips an organization with the necessary agility to respond to environmental shifts [24]. Organizations demonstrating high flexibility also typically exhibit superior environmental control. According to [25], an organization’s ability to control its environment is directly proportional to its competitive standing. Thus, higher environmental control, facilitated by a flexible AIS, can contribute to a stronger competitive position. Adjusting to changes in both organizational needs and external information demands is crucial, and should be done without requiring expensive system upgrades. A versatile is capable of effectively handling specialized organizational tasks and industry-specific demands, as well as adapting to changes in regulations or other external conditions. This enables organizations to respond quickly and accurately in crisis situations [26]. Additionally, emerging internet commerce models like B2B, B2C, and more recently, C2C, require a smooth exchange and dissemination of customer data across all platforms. To maintain data consistency, it is essential to synchronize information across multiple databases [27]. In this scenario, a flexible AIS allows for easy adjustments and reconfigurations in organizational procedures

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[28], thereby increasing an organization’s ability to innovate and adapt quickly to changing conditions. As a result, it’s suggested that organizations with greater IT infrastructure flexibility, made possible by a flexible AIS, are likely to have higher levels of organizational resilience. This leads us to put forth the following hypothesis: H1: A flexible AIS has a positive impact on organizational resilience.

2.2 Complementary Business Intelligence System and Organizational Resilience (OR) A Business Intelligence (BI) system is characterized as a voluntary process by which an organization can seek and assimilate information from a dynamic environment. This enables the identification of available business opportunities while mitigating associated uncertainties and threats [29]. Given the rapid and dynamic changes in today’s environment, organizational agility is of paramount importance. Consequently, accounting information should be readily available to decision-makers on an ad hoc basis, with varying levels of detail [30]. Generic accounting systems that lack the capacity to analyze (‘slice and dice’) accounting data may fail to meet these complex demands [31]. As a remedy, organizations can complement their AIS with a Business Intelligence System to enhance data analysis capabilities. Thus, BI, as a component of Dynamic AIS (DAIS) capability, plays a pivotal role in improving Organizational Resilience (OR) through its related capabilities [32]. These capabilities are deemed essential assets, fostering the effective integration of Business Intelligence within an organization’s operational framework [33]. Recently, Business Intelligence Systems have seen growing adoption to provide advanced analytical features, augmenting the capabilities of existing AIS systems that manage comprehensive information related to the organization [34]. The positive influence of business intelligence on organizational agility is corroborated by existing literature on strategic IT alignment [35]. Organizations can leverage knowledge sharing across business units to promptly respond to dynamic market environments, thus enhancing their adaptability [36]. The use of BI, a component of dynamic AIS capability, is crucial for augmenting organizational value [37]. A study conducted by the Massachusetts Institute of Technology, as cited by [38], highlights that Business Intelligence Systems persistently contribute to improved organizational performance. This finding aligns with the research conducted by [39], which established a significant relationship between BI use and organizational performance. According to [40, 41], BI serves as a strategic component contributing to organizational performance through enhancing strategic agility. With a flexible AIS in place, a Business Intelligence System can contribute to the dynamic AIS capability by offering the necessary analytical skills to appropriately analyze and present information. Learning remains a pivotal aspect of any dynamic capability [19]. Moreover, strategic management can anticipate changes based on

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the available information. If a Business Intelligence System is incorporated into the organization’s dynamic AIS, strategic management can access comprehensive data about all units from a singular database [42, 43]. Given the nature of Organizational Resilience (OR) and its intrinsic capacity for adaptability, organizations ought to be capable of reacting swiftly to unforeseen shifts. Therefore, they should treat the information generated by Business Intelligence (BI) as a vital resource for effectively managing such changes [44]. Since BI encompasses both internal and external data, along with market insights and analysis [45], it empowers managers to make rapid decisions that bolster resilience. Additionally, BI provides strategic decision-makers with critical knowledge regarding current trends and likely future shifts [46]. In the realm of strategic decision-making, employing novel methodologies and harnessing emerging information resources is crucial, particularly when addressing aspects like strategic resilience [47]. Specifically, organizations functioning within high-risk environments can derive substantial benefit from integrating risk management strategies with Business Intelligence Systems [30]. Based on these considerations, we analyzed the relationship between the Business Intelligence System and organizational resilience, considering the innovation capability and information provision by the Business Intelligence System to address existing problems and respond to challenges. This leads us to propose the following hypothesis: H2: A complementary business intelligence system has a positive impact on organizational resilience.

2.3 AIS-Related Human Resource Competency and OR Several theoretical constructs and research strands have illustrated the pivotal role of human resources in the design and implementation of organizational strategies, including resilience strategies [30]. The role of accounting professionals has evolved significantly, transitioning from manual to computerized and cloud accounting [48]. Possessing the appropriate IT-related skills for AIS can create a unique synergy between accounting processes and IT resources, thus enhancing overall business operations [49]. These IT-related competencies are critical for accounting professionals to perform their tasks effectively [50], and they serve as a cornerstone of competitive strategy [51]. Recent literature suggests that strategic human resource management activities can and should enhance strategic agility [52]. Human competencies play an integral role in developing dynamic capabilities and fundamentally revamping the resource base [53]. Organizational staff can only meaningfully align their actions with the organization’s goals, such as Organizational Resilience (OR), if they are well-informed [54]. Hence, human resource competency significantly contributes to the development of dynamic AIS capabilities [55]. Professionally trained staff can orchestrate real-time responses to unforeseen events, either directly or through the

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utilization of a shared reference framework, such as dynamic Adaptive Information System (AIS) capabilities. This approach facilitates strategic consistency, ensuring the organization’s ability to adapt effectively to unexpected innovative actions [56]. The technical operations team, responsible for designing and maintaining the organization’s technical infrastructure, necessitates a specific set of technical skills to implement activities at the expected performance level [57]. The AIS human resource capability is primarily designed to facilitate the dissemination of technical capacity, ensuring its efficient and effective performance [58]. The two key characteristics of AIS human resource capability [59] are skills and specificity. Skills pertain to the availability of AIS personnel with requisite business and technical competencies, while specificity denotes the personnel’s understanding of the organizational culture and routine. The AIS human resource skills also encompass technical proficiencies, such as system design & analysis, and competency in emerging technologies [60]. Additionally, experts can assist organizations in strengthening their capacity to scrutinize their external environment [61] thereby improving their aptitude for detecting evolving changes. This serves as a catalyst for enhancing organizational efficacy [62]. As previously mentioned [63], proficiency and specialization are the core attributes of human resources related to AIS. These qualities enable effective dialogue between AIS professionals and other business personnel. Based on this, we put forth the following hypothesis: H3: Competency in human resources related to Accounting Information Systems positively affects Organizational resilience.

3 Methdology The study uses a deductive approach and adheres to a positivist philosophy to evaluate the relationships between empirical evidence and observable phenomena. Specifically, it employs a quantitative methodology to explore the influence of dynamic AIS capacity on organizational resilience. Large-scale companies in Malaysia were selected as the research sample for their demonstrated ability to employ AIS and leverage resources towards organizational resilience [64]. Being publicly traded and demonstrating competitive capabilities at an international level, these organizations are equipped with the necessary AIS capabilities and financial resources to enable the synergies that facilitate dynamic AIS capacity. These organizations are presumed to have extensive experience with dynamic AIS, likely having reached process stability that contributes to organizational resilience. Two primary factors guided the selection of large organizations in Malaysia for this study: (1) these organizations typically have greater data accessibility, and (2) they often possess more comprehensive information systems and demonstrate a higher

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level of interaction with adaptive AIS [65]. Moreover, these organizations facilitate the most expansive use of IT-based AIS within an enterprise resource planning context, covering all critical business operations [66]. The sample set was drawn from companies listed on Bursa Malaysia (https://www.bursamalaysia.com/), which represent a variety of sectors among large Malaysian organizations. The study concentrates on participants who occupy high-level management roles such as Chief Executive Officers, Chief Finance Officers, Directors of Information, Directors of IMIS, Senior System Analysts, Directors of Database Administration, IT Managers, and Senior Accountants. The reason for targeting these specific roles is their expected direct participation in the processes of resource allocation and innovation within the organization. Their thorough grasp of the organization’s resources, processes, capabilities, and performance in innovation lends credibility and precision to their responses. During a data collection window spanning ten months, from December 2019 to September 2020, we distributed 984 survey questionnaires to companies that are listed in Malaysia. The protracted data gathering phase was necessitated due to complications stemming from the Covid-19 pandemic. Out of the disseminated questionnaires, we received 162 completed ones, marking a response rate of 16.5%. After eliminating respondents who didn’t fulfill the selection criteria and surveys with incomplete data, we finalized a sample set of 144 completed surveys. A majority of the respondents held the position of Chief Financial Officer (45.8%), while other staff, including staff accountants, senior accountants, and senior auditors, comprised 29.2% of the responses. The rest of the respondents filled positions such as Chief Information Officer, Director of Management Information Systems, Senior System Analyst, and Database Administration Director. This respondent distribution aligns with the hypothesis that frequent users of the AIS, such as CFOs and accountants, would be more inclined to participate in the survey.

4 Results 4.1 Assessment of the Reflective Measurement Model In this study, Partial Least Squares Structural Equation Modeling (PLS-SEM) was utilized to scrutinize both the structural and measurement models. The measurement model’s validity was confirmed through tests for reliability and validity, encompassing both convergent and discriminant validity assessments. To gauge the data’s reliability, we employed Cronbach’s Alpha (CA) and Composite Reliability (CR) measures, following well-established methods [67–69]. According to the information shown in Table 1, both CA and CR surpassed the minimum benchmark of 0.7 [70], thus validating the data’s reliability. Upon examining convergent validity, it was revealed that all factor loadings exceeded the suggested threshold of 0.6 [71], as depicted in Table 1 and Fig. 1. Additionally, the Average Variance Extracted

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Fig. 1 Measurement model

(AVE) values consistently met or surpassed the minimum criterion of 0.5, thereby confirming the data’s convergent validity. These threshold values align with those used in previous information systems research (e.g., [70]), further justifying their application in our study. As a result, the data collected showed sufficient convergent validity.

4.2 Assessment of the Structural Model Hypotheses Testing Following After confirming the reliability and validity of the measurement model, which includes both convergent and discriminant validity tests, for the collected data, we moved on to evaluate both the structural and measurement models. This assessment was performed using the bootstrapping method, a non-parametric resampling technique that involves drawing repeated random samples from the initial data set. This method was selected because it is the exclusive approach for assessing the statistical significance of path coefficients within the PLS framework [72]. The evaluation of the structural model relied on coefficients of determination (R2 ), tvalues, and the statistical significance of path coefficients [11, 73–80]. The R2 value measures the degree to which the independent variables can predict or explain the

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Table 1 Measurement model assessment results Constructs

Items

Factor loading

CA

rho_A

CR

AVE

Fixable AIS

AISF1

0.871

0.899

0.906

0.923

0.667

AISF2

0.794

AISF3

0.842

AISF4

0.836

AISF5

0.845

AISF6

0.697

AISRH1

0.838

0.877

0.881

0.910

0.670

AISRH2

0.837

AISRH3

0.767

AISRH4

0.832

AISRH5

0.816

Complementary business intelligence system

CBI1

0.855

0.865

0.870

0.918

0.788

CBI2

0.899

CBI3

0.908

Organizational resilience

OR1

0.737

0.941

0.942

0.948

0.532

OR10

0.747

OR11

0.748

OR12

0.697

OR13

0.728

OR14

0.652

OR15

0.742

OR16

0.731

OR2

0.761

OR3

0.765

OR4

0.662

OR5

0.725

OR6

0.734

OR7

0.772

OR8

0.721

OR9

0.732

AIS-related human competency

dependent constructs. A higher R2 value indicates a stronger predictive power of the model. As presented in Table 2, the R2 value for Organizational Resilience is 0.679. This suggests that 67.9% of the variation in Organizational Resilience can be explained by AIS-related Human competency, Complementary Business Intelligence System, and Flexible AIS. To verify the hypotheses, significance tests were conducted on the path coefficients. The precision of the PLS estimates was assessed through

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Table 2 Structural model results H

Structural path

Path coefficient

t-value

P values

R square

VIF

1

Fixable AIS → organizational resilience

0.365

4.639

0.000

0.679

2.260

2

Complementary business intelligence system → organizational resilience

0.317

4.890

0.000

1.816

3

AIS-related human competency → organizational resilience

0.257

3.633

0.000

2.289

bootstrapping analysis. The results for the path coefficients, as shown in Table 2, confirm the significant impact of AIS-related Human competency, Complementary Business Intelligence System, and Flexible AIS on Organizational Resilience.

4.3 Artificial Neural Network Analysis The analytical process of this study was staged, initially employing PLS-SEM to investigate the relationships and ascertain the influence of flexible AIS, AIS-related human competency, and complementary Business Intelligence (BI) on organizational resilience (OR). The subsequent stage applied an ANN analysis approach for the prioritization of these influences. The selection of ANN was influenced by its superior performance over traditional statistical tools like multiple linear regression, binary logistics regression, and SEM in identifying both linear and nonlinear relationships, as indicated in prior studies [1, 9–11, 81]. A conventional neural network is composed of an input layer, one or more hidden layers, and an output layer. For the activation function in both hidden and output neurons, we employed the sigmoid function. To optimize the performance of the model, the value ranges for both input and output neurons were kept within [0, 1] [10]. Additionally, to mitigate the risk of overfitting, we applied a tenfold crossvalidation approach to the Artificial Neural Network (ANN) models, allocating 70% of the data for training and the remaining 30% for testing [82, 83]. The research model designed for this study included one endogenous variable: organizational resilience. To gauge the accuracy of the ANN model, we used the root-mean-square error (RMSE), a commonly used accuracy indicator in prior research [1, 12, 81, 83]. The RMSE offers an understanding of the level of error in both the training and testing phases. As indicated in Table 3, the average RMSE values for the neural network model were 0.0813 for the training set and 0.0788 for the testing set. These figures suggest a high level of predictive precision across different endogenous factors,

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Table 3 RMSE values Network

Number of neuron’s in hidden layer

RMSE (training)

RMSE (testing)

1

1

0.081

0.082

2

1

0.075

0.072

3

5

0.075

0.071

4

3

0.082

0.072

5

3

0.078

0.094

6

2

0.079

0.070

7

1

0.091

0.077

8

3

0.084

0.082

9

3

0.085

0.071

10

1

0.083

0.097

Mean

0.0813

0.0788

Standard deviation

0.004877385

0.009874771

leading to the conclusion that the ANN model developed for this investigation has yielded both reliable and precise outcomes. The method for calculating normalized importance entails contrasting the average value of each predictor with the highest mean value, subsequently expressed as a percentage. Table 4 displays both the mean and normalized importance for all predictors used in the ANN modeling. The results of the sensitivity analysis, as presented in Table 4, indicate that Flexible AIS is the most pivotal variable when it comes to organizational resilience. This is followed by the Complementary Business Intelligence System, which has a relative importance of 87.46%. On the other hand, AIS-related Human competency has the least impact on organizational resilience, registering a relative importance of 51.04%.

5 Discussions and Implications The hypotheses proposed in this research are decisively confirmed by the empirical findings, thereby validating the significance of AIS-related Human competency, Complementary Business Intelligence System, and Fixable AIS as integral factors influencing Organizational Resilience. The first hypothesis stated that AIS-related Human competency exerts a substantial effect on Organizational Resilience. This hypothesis found considerable support in the data analysis. The path coefficient value of 0.257 signals a positive and meaningful association between AIS-related Human competency and Organizational Resilience. The t-value of 3.633, surpassing the customary threshold of 1.96, offers convincing statistical affirmation of this relationship. Furthermore, the low VIF value of 2.289 insinuates negligible multicollinearity, bolstering the reliability of

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Table 4 Sensitivity analysis Network

AIS-related human competency

Complementary business intelligence system

Fixable AIS

1

0.259

0.239

0.502

2

0.217

0.394

0.389

3

0.197

0.444

0.360

4

0.209

0.394

0.397

5

0.185

0.379

0.436

6

0.196

0.376

0.428

7

0.208

0.374

0.417

8

0.160

0.428

0.412

9

0.340

0.346

0.314

10

0.168

0.322

0.509

Mean importance

0.214

0.370

0.417

Normalized importance (%)

51.04

87.46

96.72

Ranking

3

2

1

this outcome. Consequently, the evidence underscores that the proficiency of human resources in handling the AIS system is a pivotal determinant of organizational resilience. The second hypothesis proposed that a Complementary Business Intelligence System has a significant influence on Organizational Resilience. The statistical results from the structural model provide persuasive confirmation for this proposition. The path coefficient of 0.317 demonstrates a potent positive association between the Complementary Business Intelligence System and Organizational Resilience, with the corresponding t-value of 4.890 unambiguously attesting to the statistical relevance of this relationship. These findings imply that the integration of a complementary Business Intelligence System markedly augments an organization’s resilience. Lastly, the third hypothesis posited that Fixable AIS profoundly impacts Organizational Resilience. This conjecture found substantial empirical validation. The path coefficient of 0.365 exhibits a robust positive connection between Fixable AIS and Organizational Resilience. The t-value of 4.639 confirms the statistical significance of this association. The additional insights from the ANN sensitivity analysis affirm that Fixable AIS is the primary variable linked with organizational resilience, thus further substantiating this hypothesis. As such, the ability to modify or correct the AIS system emerges as a critical catalyst for organizational resilience. In summary, the evidence strongly validates all of the proposed hypotheses, highlighting the critical contributions of AIS-related Human competency, Complementary Business Intelligence System, and Flexible AIS to Organizational Resilience. However, based on the analyses from both PLS-SEM and ANN, Flexible AIS is

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identified as the most influential factor, succeeded by the Complementary Business Intelligence System and AIS-related Human competency. These insights have important ramifications for both scholarly comprehension and real-world strategies aimed at bolstering Organizational Resilience.

5.1 Theoretical Implications The current study employed PLS-SEM and ANN approaches to assess the impact of three factors, namely Fixable AIS, AIS-related Human competency, and Complementary Business Intelligence System, on organizational resilience. The findings confirmed that these factors significantly contribute to Organizational Resilience, thus, providing important theoretical implications for the field of information systems. The results demonstrated the importance of the capability to adapt and modify the AIS (Fixable AIS), aligning with the previous work by [69]. The relatively high influence of this factor underscores the significance of adaptability and flexibility in AIS for organizational resilience, especially in an ever-evolving technological environment. AIS-related Human competency also plays a crucial role in organizational resilience. This supports the theoretical framework proposed by [67], emphasizing the importance of human competency related to AIS, extending the perspective that organizational resilience is not merely about robust systems, but also about the skills and competencies of the individuals operating them. Lastly, the impact of the Complementary Business Intelligence System on organizational resilience resonates with the proposition of [84] that Business Intelligence System have the potential to bolster organizational resilience by providing insightful, accurate, and timely information for decision making. While all three factors proved to be significant, the ANN analysis provided additional insight into their relative importance. It was found that Fixable AIS was the most influential factor, followed by Complementary Business Intelligence System, and lastly, AIS-related Human competency. This nuanced understanding enhances the theoretical richness by enabling a more detailed comprehension of the relative impacts of these factors on organizational resilience.

5.2 Practical Implications For practitioners, the findings of this study highlight the necessity of not only having a robust AIS in place but also ensuring it is adaptable and flexible (Fixable AIS). This points to the importance of continuous updates and improvements to the AIS to meet the changing demands of the environment, which in turn, contributes to organizational resilience.

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Furthermore, AIS-related Human competency underscores the need for regular training and skill development of the employees who operate the AIS. Organizations should invest in capacity building initiatives to enhance their human capital in terms of AIS-related skills and competencies. Lastly, the significance of the Complementary Business Intelligence System indicates the utility of implementing or enhancing Business Intelligence System that can provide critical decision-making support, thereby enhancing organizational resilience. Implementing effective Business Intelligence System can help organizations make data-driven decisions and respond more effectively to environmental changes.

6 Conclusion This study presented a comprehensive examination of the impact of Fixable AIS, AIS-related Human competency, and Complementary Business Intelligence System on Organizational Resilience using PLS-SEM and ANN analysis. The results underscored the significance of all three factors in contributing to organizational resilience, providing robust theoretical and practical implications. It was found that while all three factors are important, Fixable AIS has the most influence on organizational resilience, followed by the Complementary Business Intelligence System, and then AIS-related Human competency.

6.1 Limitations and Future Research The insights yielded by this study, while valuable, should be considered within the framework of certain constraints that point towards opportunities for future research. To begin with, the investigation was confined to examining three factors, Fixable AIS, AIS-related Human competency, and Complementary Business Intelligence System that potentially influence organizational resilience, thereby excluding the possible effects of additional variables. Therefore, subsequent studies should consider a broader range of factors to attain a more comprehensive understanding of their collective influence on organizational resilience. Additionally, the methodological approach of the study was cross-sectional, capturing the interplay between the variables at a single point in time. This approach overlooks the potential shifts in these relationships over a more extended period. Thus, to better apprehend the evolving dynamics, future research could adopt longitudinal designs. This study also narrowed its context to information systems. While this provides an in-depth understanding of the variables in this particular area, it restricts the external validity of the results. Consequently, replication of this study across different contexts would be valuable to confirm the broad applicability of the findings.

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Building on this research, potential moderating variables that could influence these relationships should be explored to understand more intricate patterns in the interplay between the variables. Furthermore, future research could delve into a more granular analysis of each factor by investigating their sub-dimensions, thereby affording a more detailed understanding of the contributions of specific aspects of Fixable AIS, AIS-related Human competency, and Complementary Business Intelligence System to organizational resilience. Such endeavors would contribute to both the theoretical literature and practical applications by providing nuanced insights for stakeholders aiming to bolster organizational resilience.

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Deep Dive into the Augmented Reality Customer Experience and Adoption Research: A Bibliometric Study Prio Utomo , Tat-Huei Cham , Chuen-Khee Pek , and Shukhrat Mamatkulov

Abstract The study aims to systematically understand Augmented Reality (AR) technology user experience and adoption studies in recent years. AR technology has garnered significant attention due to its technological advancements, diverse application development, and validated use cases. This research utilises citation and structural knowledge analysis through Biblioshiny to uncover current trends, impacts, and critical, well-developed themes from 186 journal papers, conference papers, and book chapters retrieved from the Scopus Database. Research interest in the AR topic has grown, although the citation rate has remained relatively stable, indicating that technology is still evolving. The study has identified seven important and well-developed research themes, which include user experience and various AR adoptions in cultural heritage, healthcare (especially in rehabilitation and cognitive assessment), the teaching and learning process, gamification, automated vehicles, and the pedagogical and educational domains. Additionally, the study has identified several challenges that may hinder future progression, such as the intangible characteristics of the AR experience, research preparation (including training, participant diversity, and engagement), and the maturity of technology and use-case scenarios. Despite the increasing volume of research on AR, limited research has focused on AR user experience and acceptance, a gap addressed by this study. P. Utomo Department of Technology Management, Universitas Multimedia Nusantara, Tangerang, Banten, Indonesia T.-H. Cham (B) UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia e-mail: [email protected] T.-H. Cham · S. Mamatkulov Tashkent State University of Economics, Tashkent, Uzbekistan T.-H. Cham Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia C.-K. Pek Faculty of Business and Law, School of Management and Marketing, Taylor’s University, 1, Jalan Taylors, Subang Jaya, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_21

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Keywords Augmented reality · Bibliometric analysis · Customer experience · Customer acceptance

1 Introduction Since the first Augmented Reality (AR) head-mounted display system was introduced in 1968 [1], AR technology has garnered increasing attention in recent years. This heightened interest can be attributed to advancements in mobile technology, faster internet connectivity, enhanced processing power, robust application development, innovative and pervasive sensors, and improved see-through displays for visualising 3D content [2]. These technological advancements came to the forefront in 2016 when the AR-supported game Pokémon Go introduced users to a new form of gaming [3]. Subsequently, numerous AR devices have been developed and utilised in various scenarios. For instance, Head-Mounted AR devices have been employed in user acceptance studies [4], while projection-based AR has been used to explore the experience of driving with a see-through cockpit [5]. With the growing prevalence of mobile devices, AR applications have been examined for user interaction in retail [6], Spatial AR has been applied to smart city information visualisation [7], smart glasses have supported communication and care coordination among distributed medical teams [8], and AR dashboards have been studied in the context of automated vehicle research [9, 10]. These technological advancements continue to inspire promising developments and innovations in Metaverse technologies [11, 12]. While the technology itself is not new, the level of interest and the extensive research development surrounding it has been steadily increasing, and acceptance is still in its early stages [4, 13]. Projections suggest that by 2027, neither augmented nor virtual reality will fully evolve into the next computing platform [14]. It is crucial to understand the current trends and acceptance of augmented reality, particularly from a user experience and acceptance perspective, in order to accelerate its technological adoption. Given the existing gap in the literature regarding augmented reality’s impact on customer experience and acceptance, a systematic research approach is necessary. The results of this study are expected to drive innovation, leading to sustainable competitive advantages. This can be achieved by delivering superior customer value, resulting in higher customer satisfaction and loyalty [15], increased sales and productivity through product and services [16–18], and enhanced employee engagement and satisfaction [19–21]. Furthermore, emerging technologies have the potential to foster social and emotional development [22, 23], promote partnerships and collaboration within specific industries for sustainability [24, 25], and contribute to environmental sustainability on a global scale [26].

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To meet this requirement, this study was conducted using a systematic approach to comprehensively investigate the current state of research to advance our understanding of Augmented Reality (AR) in terms of customer experience and acceptance. The objectives of this study are twofold: (1) to discern the trends and impact of published research in this field, and (2) to gain insights into the knowledge structure of the study by comprehending and analysing the primary research themes in this area.

2 Research Method The study utilised bibliometric methods to map the latest trends in Augmented Reality (AR) user experience and acceptance. Bibliometric methods are a qualitative approach for describing, evaluating, and monitoring the most influential works within a specific research field with minimal subjective bias over time [27]. This approach enables researchers to base their findings on aggregated bibliographic data from various sources and document types (such as articles, conference papers, book chapters, and lecture notes). The research procedure, as illustrated in Fig. 1 (adapted ˇ from Zupic and Cater [27] and Turulja et al. [28]), comprises five key steps: 1. Developing the research design: This step involves identifying the need for a systematic review, selecting the appropriate database(s), and developing and evaluating a review protocol. 2. Data collection and selection: Utilising PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to determine data identification, screening, eligibility, and the criteria for inclusion in the analysis, as shown in Fig. 2. 3. Conducting citation and network analysis: This involves performing citation analysis to identify the top-N most cited authors, journals, and institutions. Additionally, network analysis measures similarities and relationships between documents, authors, and journals. 4. Data analysis and synthesis: This step focuses on understanding the content obtained from the previous steps. 5. Reporting and disseminating the results through publication. Research questions are formulated in the research design phase, and appropriate research methods are selected to address these questions. This study aims to answer two primary research questions: (1) What are the trends and impacts of studies

ˇ Fig. 1 Research procedure. Source Adapted from Zupic and Cater [27] and Turulja et al. [28]

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Fig. 2 PRISMA systematic records flow

on augmented-reality user experience and acceptance? (2) What is the conceptual structure of studies on augmented-reality user experience and acceptance? To gather bibliometric data, the study utilised the Scopus database, the largest abstract and peerreviewed literature database providing a comprehensive overview of research from various disciplines [29]. It offers extensive citation coverage in Science, Technology, and Management [30]. The citation analysis examined trends and impacts in articles, conference papers, book chapters, and lecture notes. This analysis sought to identify publication trends over the years, the number of cited documents, and the mean citations per year. It also aimed to determine the most impactful publication sources, influential authors and their articles and globally cited articles. The network analysis included the creation of a thematic map using co-occurrence keyword network analysis. This map categorises thematic clusters within the domain into typological themes, visualised on

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a two-dimensional map based on Centrality and Density, employing the Walk Trap Clustering method [31]. For conducting this analysis, the study utilised the opensource Biblioshiny tool in conjunction with an R package. These tools offer a wide range of statistical and graphical techniques for bibliometric studies [32–34].

3 Results and Discussion 3.1 Research Trend and Impact The study analysed 186 publications from 144 journal outlets (sources) authored by 665 authors between 2006 and 2023. This dataset includes seventy-nine articles, two book chapters, 103 conference papers, and two reviews. The publications exhibited an Annual Growth Rate of 16.79%, with an average age of 3.88 years and an average of 15.55 citations per document. The content of these documents includes 571 authorgenerated keywords and 1141 additional keywords. Regarding author collaboration, there are five single-authored documents, with an average of 3.9 co-authors per document. Referring to Fig. 3, there are several notable publications with a high average total citations per article in 2009, 2010, and 2013, despite no publications in 2007 and 2008 in this domain. The total number of publications grew significantly from 2008 until 2022 despite the declining trend in the average total citations per article (mean TCP per Art) and year (Mean TCP per Year). Five publication sources have a similar h-index of 3, as depicted in the top 5 source outlets in Table 1, while only two of them have a g-index of 5 (ACM International Conference Proceeding Series and Advances in Intelligent Systems and Computing Journal). The Lecture Notes in Computer Science is the source with the most published articles, but the most cited source is the Journal of Personal and Ubiquitous Computing. Interestingly, these top ten most impactful sources include five proceeding conferences, four journals (Advances in Intelligent Systems and Computing, Personal and Ubiquitous Computing, Applied Science, and Computers in Human Behavior), and one lecture note. Table 2 describes the most influential authors based on the highest h-index, gindex, m-index, and yearly average cited articles. All the authors have the same h-index and g-index, except for Riener A., who has a g-index of four. Olsson T. published three articles with a total of 424 citations. The most cited article among these authors is “Expected User Experience of Mobile AR Services: A User Study in The Context of Shopping Centers” by Olsson et al. [35]. This article elaborated and formalised the construction of customer experience within the context of shopping centres. There are four articles published by these seven authors, either as sole authors or collaborators. As an example, Kaasinen and Olsson T. jointly published their research on AR intelligence environment acceptance and UX expectation. Their study reveals

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Fig. 3 Annual scientific production 2006–2023

that for users to start using intelligent environments and to continue using them, six main classes of experiences need to be considered: 1. Instrumental experiences: These are intended to demonstrate a sense of accomplishment, a feeling of technology support in enhancing one’s tasks, and pragmatic and utilitarian service use perspectives. 2. Cognitive and epistemic experiences: These pertain to cognitive processes, conceptualisation, and rationality. The study revealed that individuals were strongly inclined towards heightened cognisance of digital content related to the environment and a desire for intuitive interaction with said content. 3. Emotional experiences: These pertain to the subjective and emotional reactions of the user. Services incorporating AR interaction are often anticipated to elicit emotions such as amazement, positive surprises, and playfulness. 4. Sensory experiences: These are associated with sensory-perceptual experiences that are conceptually processed. These experiences are derived from the multimodal AR stimuli provided by the service and impact the user’s perception of the surrounding environment. 5. Social experiences: These pertain to interactions between individuals facilitated by technology and its features, which enable self-expression and uphold the social values of users. 6. Connectedness and collectivity: These pertain to the anticipation that mobile AR (MAR) will provide innovative avenues for socially mediated interaction and

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Table 1 Top 10 most impactful publication outlet (source) Source outlet

hi

gi

mi

TC

NP

26th IEEE conference on virtual reality and 3D user interfaces, VR 2019—proceedings

3

4

0.6

20

4

ACM international conference proceeding series

3

5

0.333

25

6

Advances in intelligent systems and computing

3

5

0.5

28

5

3 Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)

4

0.231

21

10

Personal and ubiquitous computing

3

3

0.2

404

3

Adjunct proceedings—11th international ACM conference on automotive user interfaces and interactive vehicular applications, automotiveui 2019

2

2

0.4

16

2

AIP conference proceedings

2

2

0.25

16

2

Applied sciences (Switzerland)

2

2

0.5

42

2

Computers in human behavior

2

2

0.2

287

2

Conference on human factors in computing systems—proceedings

2

3

0.286

57

3

hi = h_index; gi = g_index; mi = m_index; TC = Total citation; NP = Total publication

Table 2 Most influential author with their Top Publication Author(s)

hi

gi

mi

PY

TC

TCpY

References

Olsson T.

3

3

0.23

2013

248

22.5

[35]

Kaasinen E.

3

3

0.27

2013

36

3.3

[36]

2022

25

12.5

[37]

2019

66

13.2

[38]

Krouska A.

3

3

1

Papakostas C.

3

3

1

Sgouropoulou C.

3

3

1

Troussas C.

3

3

1

Riener A.

3

4

0.6

hi = h_index; gi = g_index; mi = m_index; TC = Total citation; NP = Total publication; TCpY = Total citation per year

communication based on reality. The development of social experiences can be attributed to AR content’s collective utilisation and creation. This is evident in the socially aggregated AR information utilised for personal purposes and in the co-located and collaborative use of AR services.

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Additionally, Augmented Reality (AR) serves as a tool for communication, contributing significantly to the development of social experiences. Motivational and behavioural experiences are engendered by using or possessing an AR service, which elicits specific behaviours in its users [36]. Several research studies have been conducted in the field of Mobile Augmented Reality (MAR). For example, AR applications have also been explored in automated driving cars for trust calibration [9], automated driving simulators for research on windshield displays [39], and the potential, constraints, impact, role, and adequacy of mixed reality (AR and VR) in driving applications and simulations [40], including AR user acceptance in education [41], user experience, usability, and interactivity of Personalised Mobile AR training systems for firefighters, as well as AR in Welding Simulator [37] The most globally cited articles, as depicted in Table 3, provide the foundational theories and practices for AR research. These include the work of Olsson et al. [35] and Olsson and Salo [42], which establish the core theory of expected user experience. Additionally, they explore dimensions related to AR acceptance research, encompassing aspects like information quality, system quality, cost of use, recommendations, personal innovativeness, risk, and facilitating conditions [43]. Using the Technology Acceptance Model (TAM), the theoretical framework incorporates perceived innovativeness, enjoyment, usefulness, and ease of use, all influencing attitudes toward AR and behavioural intentions to use AR [6, 44]. Further studies delve into AR tools’ usability in conjunction with user participation and their impact on academic performance [45] and the evaluation of AR performance [46, 47]. Finally, there is research exploring barriers and customer behavior in the context of AR [48, 49].

Table 3 Most global cited article Author source

TC

TCpY

NTC

References

Olsson T., 2013, Pers Ubiquitous Comp

248

22.55

2.41

[35]

Tom Dieck M. C., 2018, Curr Issues Tour

232

38.67

9.04

[43]

Rese A., 2017, Technol Forecast Soc Change

204

29.14

6.41

[6]

Fonseca D., 2014, Comput Hum Behav

171

17.10

2.83

[45]

Olsson T., 2011, IEEE Int Symp Mix Augmented Real, ISMAR

140

10.77

2.64

[42]

Arvanitis T. N., 2009, PERS Ubiquitous Comp

130

8.67

1.59

[46]

Jetter J., 2018, Comput Hum Behav

116

19.33

4.52

[47]

Balog A., 2010, Stud Inform Control

96

6.86

1.00

[44]

Alkhattabi M., 2017, Int J Emerg Technol Learn

72

10.29

2.26

[49]

Do H.-N., 2020, Heliyon

69

17.25

7.47

[48]

TC = Total citation; TcpY = Total citation per year; NTC = Normalized total citation

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Fig. 4 Thematic map analysis (words in the map above are overlapping)

Conceptual Structure Analysis The bibliometric analysis of the Scopus database regarding AR user experience and acceptance revealed the presence of seven primary research themes (Fig. 4). These research themes are clusters generated through keyword co-word centrality and density analysis as part of the thematic map analysis. These are considered “motor research themes” due to their significant centrality and high density. The first research cluster explores AR applications’ impact, usability, and acceptance. One of the most cited research areas in AR applications is in the Mobile Augmented Reality (MAR) domain and the expected user experience it offers. MAR has the potential to facilitate various technological capabilities such as ambient intelligence, location awareness, and sensory technologies, enabling a paradigm shift towards mobile and ubiquitous computing [35]. The initial concept of user experience can be adapted from earlier research on product experiences. Hekkert and Desmet [50] argued that these experiences are shaped not only by user characteristics (such as personality traits, competencies, cultural backgrounds, values, and motives) and product characteristics (including product shape, colour, behaviour, and texture) but also by the interactions between these two elements within specific contexts (physical, social, and economic). These characteristics manifest into three primary aspects of user-product experience: emotional experience (such as joy, sadness, and anger), aesthetic experience (involving sensory modalities and delight), and the experience of meaning (comprising semantic interpretation and symbolic associations, such as notions of luxury and attachment).

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The study conducted by Olsson et al. [35] on the anticipated user experience before using MAR suggested twelve critical characteristics of the MAR experience, as detailed in Table 4. Further research is needed to explore the most AR-specific experiences, their operationalisation and manifestation in the real world, and the development of evaluation metrics, which should be considered as future research directions. The second cluster discusses the potential benefits, challenges, and user acceptance of AR applications, specifically within the context of cultural heritage. A quantitative study employing the Technology Acceptance Model (TAM), conducted by Wu et al. [51] at the Cultural and Creative Park in Taipei, explores the relationship between external variables such as personal factors, stimulating factors, and situational factors and the technology perception of AR. It also investigates users’ attitudes, behavioural intentions, and experiential evaluations. The results indicate that factors such as increased visual effects, ease of use, diverse and engaging technological content, a friendly atmosphere within the exhibition area, and on-site staff assistance are all crucial in facilitating users’ behavioural intentions. The study highlights the inability to differentiate between intangible emotional components (attitudes, perceptions, and beliefs) and technology usage readiness as the main reasons individuals avoid recent technology use. It further suggests that researching respondents with varying social backgrounds may reveal different subjective concerns and recommends exploring these emotional components through qualitative approaches. Another experiment involving natural interaction and AR, using Head-Mounted Devices (HMDs) in a cultural heritage museum, aimed to overcome inaccessibility and non-interactivity with museum artefacts. The results indicate that natural interaction in AR is well accepted within a cultural heritage environment, despite the limitation that most participants were in the 25–34 age group [52]. Enjoyment was a dominant factor for acceptance, with participants expecting the system to have the same interaction methods (touchscreen and buttons) they were accustomed to. Training was required to help users transition from their previous usage paradigm and use their hands naturally. There was also a noticeable relationship between individuals with video game experience and the time needed for training due to their eye-hand coordination, enthusiasm for new technology, and ability to learn new and complex control methods. Another research study focused on using context-aware mobile augmented reality (MAR) with smart glasses (ARSG) to explore and enhance visitor experiences at outdoor cultural heritage sites. ARSG provided visitors with context-aware information about points of interest (POIs) within their field of view (FOV). Participants in the experiment confirmed the perceived usefulness, ease of use, and enjoyment of using ARSG. However, they suggested improvements such as better implementation of AR content (well-rendered textures and more intuitive interfaces) and addressing outdoor lighting conditions. The third cluster’s theme revolves around evaluating and accepting immersive technologies, specifically VR and AR, in the context of rehabilitation and cognitive assessment. Two cases related to this theme include (1) a non-pharmacological intervention-controlled trial comparing treatment using traditional mirror therapy

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Table 4 Mobile augmented reality characteristic UX characteristic

Description of the experience

Technological components

Experience categories

Experience originating from service functionalities Efficiency and accomplishment

The enhanced efficiency AR, embeddedness, and convenience in context-sensitivity performing daily tasks result in time and effort savings that improve information acquisition

Aesthetic

Empowerment

The experience of being Embeddedness, capable of attaining context sensitivity goals by new ways that amplify user abilities

Exp of meaning, symbolic significance

Increased awareness and knowledge

Increased awareness of one’s surroundings and the digital affordances in it, thus creating feelings of discovery and insight, including one’s won prior behaviour

Experiences of meaning

Inspiration

The experience of being AR encouraged to try new things or repurpose new services

Aesthetic

Motivation

The experience or feeling to do tedious tasks as results of innovation founded in mixed and AR

Emotional

Surprise

Positive attitude through Context-sensitivity, amazement from embeddedness surpassing expectation and reception of extraordinary information

AR, embeddedness, context-sensitivity

AR, context-sensitivity

Emotional

Experience originating mainly from interaction with information content Connectedness

The subjective Mobility, experience in social embeddedness interaction and mobility through MAR utilisation

Exp of meaning or symbolic significance

(continued)

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Table 4 (continued) UX characteristic

Description of the experience

Technological components

Experience categories

Collectively and participation

The perception of part Embeddedness, of community, due to mobility participation, and belongingness so users have new ways of social interaction

Emotional, experiences of meaning

Creativity

The experience of originality, self-expression, and artistic inclinations due to AR creation experience by combining digital and real-world element

AR, embeddedness

Emotional, experiences of meaning, aesthetic

Liveliness

Feeling of constantly acquiring new and updated content that is socially constructed, thus making the service feel vivid and dynamic

AR, embeddedness, mobility

Emotional

Meaningfulness

AR content is personally significant and relevant that perceived as reliable, up-to-date and align with real world objective

Context-sensitivity, AR

Emotional

Playfulness and entertainment

The experience of joy, amusement, and playfulness that arise from engaging, novel and productive content interaction

AR, embeddedness

Aesthetic, emotional

Experience originating from interaction with the AR Captivation

Deep engagement and fascinating experience interacting with AR

AR

Aesthetic

(continued)

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Table 4 (continued) UX characteristic

Description of the experience

Technological components

Experience categories

Intuitiveness

The experience of being AR, embeddedness natural and resemble human interacting with AR while at the same time aligned with real world

Experiences of meaning

Tangibility

The experience of being AR, embeddedness concrete and coherent to AR content, leading to sense of presence and unity with the surroundings

Emotional, aesthetic

Source Olsson et al. [35] and Hekkert and Desmet [50]

versus AR mirror therapy for patients with phantom limb pain [53]. The user experience analysis indicated that most patients recognised the potential benefits of teletreatment in AR and expressed their intention to continue using it after the trial. Key facilitating factors included the expectation of technical problem-free usage, sufficient training, and support. Limitations of the study included challenges related to treatment frequency, adherence to the treatment procedure, and patient participation. (2) The second study focused on the feasibility and acceptability of Multimodal Adaptive Social Intervention in Virtual Reality (MASI-VR) aimed at improving social functioning and clinical outcomes in individuals with schizophrenia [54]. Preliminary results indicated a significant reduction in overall clinical symptoms after ten sessions of MASI-VR, supporting the feasibility and acceptability of virtual reality social skills training for individuals with schizophrenia. Similar to the study by Rothgangel et al. [53], participants did not complete the overall training program, although they were enjoying it and expressing satisfaction. The fourth cluster theme is related to usability evaluation in teaching and learning processes. Several key studies in this theme include: (1) Botella et al. [55] employed the principles of ISO 9241-210 Ergonomics of human-system interaction—Part 210: Human-centered design for interactive systems to measure effectiveness, efficiency, and user satisfaction within a specific context of use. The experiments involved twelve participants tasked with measuring the time and completeness of Chemistry problems to understand how students synthesise what they’ve learned in their classes through interactions with an Android application. The results indicated that students displayed great enthusiasm and fascination with the teaching process using AR despite slow initial acceptance. Gender did not significantly impact usability performance. However, there were performance differences based on participants’ mobile phone platforms, with Apple iOS users completing tasks more quickly than Google Android users. Researchers argued that iOS users might be more adept at manipulating objects in 3D. Variability in usability responses was attributed to user

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interface design, suggesting potential improvements for future system enhancements. (2) In the context of education and training, the application of the Technology Acceptance Model (TAM) revealed that usability was a dominant predictor, either directly or indirectly, of firefighter behavioural intentions to use AR systems in Greece. This was followed by perceived interactivity and perceived personalisation. Firefighters used head-mounted displays (HMDs) to assess the acceptance of AR technology within the “Naval Fire Fighting Training and Education System” (NAFTES) (https://etrain ing.naftes.eu/). Future research recommendations include involving more countries with different standards and user commonalities. (3) Another study on AR usability focused on map reading and users’ understanding of geographic data in New Zealand. The map comprised an outline of New Zealand, altimetry information derived from Shuttle Radar Topography Mission (SRTM) data, and population data based on the most recent census of each territorial authority. Participants were presented with three scenarios: using a physical printed map, using the outlined map and AR systems, and using both the physical printed maps and AR systems. While user responses indicated that the scenario involving AR systems was slower, it was preferred due to the richer information the combination of physical printed maps and AR systems provided. Additionally, AR systems reduced participants’ effort in integrating information from different maps, making problem-solving easier. Future research should consider comparing the use of traditional printed maps, GIS, and AR systems with other forms of visualisation, such as virtual reality and multimedia presentations. Assessments are also recommended to measure motivation, efficiency, accuracy, and precision in using these technologies. Teaching and learning processes play a crucial role in driving social and emotional development, fostering partnerships and collaboration, and promoting sustainable education [24]. The fifth theme revolves around AR gamification, which assesses AR applications in various domains to enhance user experience, motivation, and acceptance. Key findings in this theme include: (1) The first AR application research in industrial production yielded two significant results: (a) gamification led to a trade-off between speed and accuracy when quality-related feedback was not required, and (b) there was a significant increase in acceptance when the system utilised a straightforward visualisation approach [56]. (2) In a related study by Korn et al. [57] in the same field, an attempt was made to compare two visual processes: visualising processes (i.e., work steps) and visualising sequences (i.e., a sequence of steps resulting in a pre-product). The results indicated that gamified designs with low cognitive loads led to significantly higher perceived happiness after work, implying that users were more motivated. Several potential developments in this domain include the need to detect the emotional impact of gamification, which could open new avenues for future research. Additionally, this study did not establish the quantitative implications of gamification on task completion time and error rates. An essential next step would be a long-term study to determine whether the positive effects and high acceptance rates observed will endure in the workplace. This underscores the ethical implications of using gamification and raises questions that should be the subject of long-term investigation.

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The sixth theme of research explores the application of AR in transportation, specifically in automated vehicles. An experiment conducted by Maruhn et al. [58] aimed to simulate pedestrian behaviour when crossing the street in relation to automated vehicles in urban traffic. The study utilised the Cave Automatic Virtual Environments (CAVE) and Head-Mounted Devices (HMDs) to evaluate new concepts for communicating with driverless vehicles through simulators. Several challenges arose from this research, such as the inability to identify vehicles at greater distances, given that the pedestrian simulator car’s velocity did not exceed 30 km per hour. Nevertheless, the study concluded that AR usage is feasible in this scenario. Another study by Oliveira et al. [59] focused on automated vehicles and explored different interface configurations for communicating potential hazards in the environment (vulnerable road users) and vehicle behaviour (intended trajectory). Prior to the experiment, participants were asked to complete four questionnaires measuring trust, intention to use the pod, perceived system transparency, and perceived technical competence of the pod. After each interaction with the AR, participants answered the same questionnaires, along with additional measures of user satisfaction and the usefulness of the interface. Four AR interfaces were used in this experiment: (1) A baseline human–machine interface that presented weather, time, estimated time of arrival, and destination. (2) Third-person animation that simulates pedestrian hazard objects moving around in its environment. (3) Camera feed overlaid with information showing a live camera feed from the front of the vehicle overlaid with hazard detection and intended behaviour information. (4) AR windscreen that used a 42'' transparent LCD with electronic control from inside the vehicle. The results indicated an increase in trust, perceived system transparency, and technical competence after viewing the animation, camera feed, and AR windscreen, with the exception of the Baseline screen. The study faced both technological and non-technological limitations. Technological limitations included hardware and software constraints affecting real-time environmental alignment and functionality, leading to imprecise alignment of objects and images from the user’s point of view. Non-technological limitations were related to participant demographic diversity. A longitudinal study was suggested to evaluate the evolution of user experience and trust, which could lead to design recommendations for system transparency interfaces, including addressing the risk of motion sickness. The seventh theme explores the role of AR in the education domain. The most cited studies in this domain focus on AR in healthcare education [46, 60–62], pedagogical evaluation [4, 63–65], and automotive manufacturing education [66, 67]. For example, a study by Arvanitis et al. [46] explores technology-enhanced learning for individuals with physical disabilities using a mobile AR technology-based system, where users wear a head-mounted display (HMD) and interact with a computermediated learning platform. This system supports learners in visualising nontypically tangible physical phenomena when interacting with real objects in formal and informal science learning settings, such as during a visit to a science museum. The study emphasises the importance of incorporating technology-enhanced learning in special needs education and proposes six future research directions that focus on examining the importance of special needs education design, extending beyond

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computer usage to improve students’ lives, and exploring how technology can overcome the boundaries of disabilities, alleviate isolation, increase self-esteem, and support integration. Another study by Ordaz et al. [66] explores the impact of the gaming experience on manufacturing environments for training operators in manual tasks. The research reveals that the gaming experience positively influences training completion time, despite some work-related mistakes due to unfamiliarity with manufacturing operations. Usability issues emerged, including varying points of view and reducing complexity, prompting the need for enhancements related to manufacturing procedures. Furthermore, research in the AR field has advanced towards mixed reality applications in education. Allcoat et al. [63] conducted comparative research between traditional learning methods, virtual reality (VR), and mixed reality (MR) in terms of knowledge testing, emotional and physical experiences (interest, amusement, surprise, elatedness, sadness, anger, fear, anxiety, disgust, and contempt), learning experience, and technology evaluation using the UTAUT approach. The findings showed no evidence suggesting that VR and MR provide significantly higher learning quality than traditional methods. However, VR offers emotional benefits and higher relatedness scores compared to traditional learning. Both VR and MR engage learners more effectively than traditional methods, but a challenge that emerged is simulator sickness. The Unified Theory of Acceptance and Usage of Technology questionnaire suggested that there is no significant difference between the system usability scale and perceived quality between VR and MR, although VR environments exhibit a slight increase in positive emotions. VR also produced higher reports of presence than MR, suggesting that VR offers greater immersion. Longitudinal research examining the long-term effects of technology usage is recommended, and the individual benefits of MR and AR are areas of focus for future research.

4 Conclusion This paper summarises the trends and impacts of Augmented Reality (AR) user experience and acceptance from 2006 to 2023. The results of this comprehensive bibliometric study have shed light on the current trends, impacts, and thematic maps of existing research in the Scopus database on this particular topic. The publication exhibits an upward trend in terms of publication volume, with a somewhat steady number of total citations. Additionally, it highlights the most impactful outlets, authors, and articles in this field. Thematic map analysis revealed seven research clusters with strong centrality and high density. These clusters revolve around three core experiential concepts: emotional, aesthetic, meaning, and twelve AR characteristics. These concepts and characteristics find applications in various domains,

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including cultural heritage, healthcare (especially in rehabilitation and cognitive assessment), teaching and learning processes, gamification, automated vehicles, pedagogical methods, and the education domain. The intangible nature of experiential characteristics, research preparation (involving training, participant diversity, and engagement), and the perceived maturity of technology and use cases in most research related to user experience and acceptance present challenges that may hinder future progression.

4.1 Theoretical Contribution The study addressed several research gaps in the current customer experience trends and augmented reality adoption. First, the study identified three prominent experiential aspects of user-product interaction: emotional, aesthetic, and meaning. The emotional aspect relates to emotional psychology, such as feelings of sadness, happiness, fear, and love. The aesthetic aspect is linked to human sensory modalities, encompassing how it looks, sounds, feels, tastes, and sounds. The meaning aspect pertains to cognitive elements, including memory retrieval, association, and interpretation, all of which are related to the personal or symbolic significance of products. Second, the study unveiled technological aspects of AR that influence user experience and adoption. Twelve characteristics of expected user experiences and six design requirements impact and facilitate AR application experiences. Third, the study also revealed the utilisation of the Technology Adoption Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to gauge users’ subjective perceptions and self-reported assessments. These models incorporate new constructions such as perceived interactivity, personalisation, perceived enjoyment, cognitive workload, spatial representation, cost factors, pressures, technological literacy, and more.

4.2 Practical Implication The present study has uncovered growing practical implications for enhancing customer experience and driving customer adoption by improving the quality of product-user engagement through interactivity and immersive experiences in various industries. In retail, customers can virtually try on and compare wearable products, boosting their confidence in making purchases. In the tourism sector, visitors can access historical information, translations, or virtual reenactments of historical objects. In education and pedagogy, AR can make learning more interactive and engaging, thereby enhancing the quality of education and engagement. In the context of emerging driverless vehicles, AR plays a crucial role in safety simulations. In the field of gaming and entertainment, AR is employed to deliver more immersive experiences. In healthcare, AR can be used for learning and conducting medical procedures.

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As experiential technology continues to advance and as head-end devices become more affordable, the adoption and user experience of AR is expected to expand across various industries with the introduction of new and diverse AR applications.

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Blockchain and IoT in the Modern Digital Age Reinaldo Padilha França, Rodrigo Bonacin, Ana Carolina Borges Monteiro, and Rangel Arthur

Abstract In blockchain technology, there is no central authority controlling what happens, but there is a need for a consensus between more than half of the participants who check and validate the transaction. The IoT enables intelligent interconnection between these devices, everywhere through the internet. The use of IoT enhances the use of solutions based on Blockchain guarantee that certain information or transaction is really “true” to be recorded in the Blockchain, comes through verification, and in that sense IoT sensors can generate the evidence necessary that a certain fact happened, guaranteeing the veracity of each transaction registered. The blockchain’s differential, makes these devices communicate without the need for a central unit to identify them, eliminating the need for this “central agent” to approve and validate transactions. Therefore, this chapter aims to provide an updated overview of Blockchain and IoT technologies, showing the fundamentals of this disruptive technology, demonstrating where integration between IoT and Blockchain is important, and featuring the potential of technologies. Keywords Blockchain · IoT applications · IoT environment · Protocols · Public blockchain · Private blockchain · Consortium blockchain · Permissioned blockchain · Blockchain storage · Decentralized database · Hash · Applications · Digital security · Cybersecurity R. P. França (B) · R. Bonacin (B) · A. C. B. Monteiro Renato Archer Information Technology Center (CTI), Dom Pedro I Highway (SP-65), Km 143, 6—Chácaras Campos Dos Amarais, Campinas, SP 13069-901, Brazil e-mail: [email protected] R. Bonacin e-mail: [email protected] A. C. B. Monteiro e-mail: [email protected] R. Arthur School of Technology (FT), University of Campinas (UNICAMP), Paschoal Marmo Street, 1888—Garden Nova Italia, Limeira, SP 13484-332, Brazil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_22

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1 Introduction Blockchain is a technology based on blocks that are created all the time and connect to the previous block, forming a chain of blocks. Considering that for each transfer that happens, these new transactions and information are recorded in blocks, it works like a book that keeps, immutably, the record of all these transactions. All network participants have access to the immutable distributed transaction record, which is shared, and transactions are recorded only once, eliminating the duplication of efforts typical of traditional transaction networks [1, 2]. Blockchain technology is in constant sync between all computers and devices that participate in the registered transaction and information transfer network. Still considering that the tampering that occurred, in case of fraud or technical failure in any of the copies, it will be immediately identified and this copy will be invalidated. The technology maintains the transaction history, being available and secure to all network participants. The immutable property derives from the characteristic that no participant can change or corrupt a transaction after its shared record. If there is a transaction record that includes an error, a new transaction must be added to reverse that error and both transactions will be visible to the entire network [3, 4]. As well as considering that Blockchain technology has security and decentralization as some of the main pillars, involving the processing of mathematical calculations and the participation of countless computers and devices scattered around the world. Since, based on the concept that no network participant has more power than another, i.e., that there is no central authority controlling what happens, there is a need for a consensus between more than half of the participants who check and validate the transaction [5, 6]. The blockchain generates trust because it represents a shared record of the facts, that is, reliable data that allows boosting other new technologies that dramatically increase efficiency, transparency, and digital trust. Also considering that there are several ways to develop a blockchain network, which can be public, private, permissioned, or developed by a consortium [7, 8]. The operation of blockchain technology and transactions registered in blocks are by means of a kind of fingerprint, i.e., hash, which is obtained from a mathematical function, which transforms the received information into an encrypted numeric chain. In this way, the occurrence of a new operation is performed resulting in another block created, then this hash is produced by joining its own numerical combination with that of the previous block, giving rise to a new series of numbers [9, 10]. This property allows any user of the network to confirm the veracity of an operation, considering that only the numerical sequences of each transaction are visible and known. This operating logic makes attempts to digitally violate the blockchain structure difficult, considering that it would be necessary to break the sequence of all blocks successively. Also, this makes any intermediary unnecessary, since the reliability of the transactions is guaranteed by the members of the network [11, 12]. It is worth noting that Blockchain technology is not a brand or even a type of product, this is a concept of data structuring and can be applied in cryptocurrencies,

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making transactions of digital assets faster, more convenient, and more secure, or even it can be used in any transaction, agreement or digital contract that requires the certification of those involved. This helps in the authentication of signatures, digital documents, web pages, brands, and properties, whether virtual or physical, among many other applications, for example. The most recent possibility of technology involves increasing digital security in digital transactions, which may be financial, international, or between different banks since usually involve complex intermediation, settlement, and custody systems, which are expensive and time-consuming [13–15]. The technology can also be applied for example to track fresh seafood from the moment it is caught, as this generates transactions, which increases confidence in the entire food supply chain, tracking the product of each fishery from the very beginning a moment when it leaves the water to supermarkets and restaurants, improving visibility and accountability at each stage of the food supply. Blockchain can be applied to healthcare by changing the way the data and results transmission ecosystem occurs, guaranteeing trust, source of data, and efficiency to improve patient care and even the profitability of the healthcare institution [16, 17]. In this context, Blockchain is understood as a distributed network, with no intermediaries to carry out and validate a digital transaction, much less an entity that performs a collection of operation fees. It is important to mention that all computers and devices within the Blockchain network are known as nodes, and these nodes need to recognize a transaction for this to become valid. In the same way that each unit of information on the Blockchain is known as a transaction, this does not necessarily represent money or even financial assets, this can be anything from music, even property [18–22]. The IoT has the properties of transforming the interactions between electronic appliances, electrical appliances, and even mechanical appliances, considering that this technology enables intelligent interconnection between these devices, everywhere through the internet. Also, the properties of this technology have changed the relationship of people and companies with their environments over the past few years, considering the presence of this from monitoring with cameras and sensors to the management of spaces and production processes. From this, the potential for IoT applications is almost unlimited, however, it is important to emphasize the need to operate at increasingly larger scales, i.e., scalability [23, 24]. In this context, Blockchain presents a robust security model with possible features to overcome and expand the IoT’s scale of operation, considered the main obstacles to exponential growth, even considering that Blockchain offers a standardized method to accelerate the data exchange between devices IoT, without an intermediary. Just as Blockchain operates as a central server to do this “middle of the field” for IoT, according to connection requests, overcoming the bottleneck usually generated by expanding the scale of IoT solutions for many devices, since other difficulties of IoT are also about handling large amounts of data collected by a large network of sensors, and at the same time, reducing transaction processing latency, i.e., the time between the sending of a command and its execution [25, 26].

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Unlike current IoT systems that include machines, sensors, cameras, and several other intelligent computing devices, this depends on computing at the cutting edge, whose digital architecture is based on a centralized server, however, at a single point in the industry network, this makes them vulnerable to failures. In this, Blockchain works as it does in a distributed way of transaction between the “nodes” of the IoT networks, storing this in an encrypted list of transactions, ensuring that each transaction is verified and accepted as a valid transaction by most of the nodes in the network, preventing fraudulent transactions, on several servers participating in the network, considering that from these factors increases digital security and reliability. In this sense, the combination of Blockchain and IoT technologies, offers a promising “new digital world”, helping to solve both digital security and scalability and cost problems [27, 28]. Therefore, this chapter demonstrates a landscape view of the applied aspect, as well as key concerns and challenges, aiming to provide an updated overview of Blockchain and IoT technologies and your technologies, with a concise bibliographic background featuring the potential of technologies, showing the fundamentals of this disruptive technology. Although advanced blockchain has many advances and has been applied in many areas, it is important to highlight the research gaps, providing more explicit statements or questions about what has been overlooked or not yet fully explored in previous studies. In this context, it is worth highlighting that there are still some challenges and areas of development that are being explored [2–5, 11, 13]. Some of them include Scalability: The ability of the blockchain to handle a large number of transactions per second is a concern. The most popular blockchain networks like Bitcoin and Ethereum face scalability issues, leading to longer transaction times and higher transaction fees. Solutions such as sharding and sidechains are being developed to address this challenge. Energy Efficiency: Cryptocurrency mining, especially Bitcoin, is criticized for its high energy consumption. As awareness of environmental issues grows, there is a push to make blockchain operations more energy-sustainable. Interoperability: Currently, many blockchain networks operate in isolation, making it difficult to transfer assets and information between them. Interoperability solutions are being sought to allow for more fluid communication between different blockchains [4–6, 11, 13–15]. Privacy: While transactions on a public blockchain are generally transparent, there are situations where privacy is important. Developing ways to maintain transaction privacy without compromising security and trust is an evolving challenge. Governance: Since blockchain networks are decentralized, making decisions about updates and changes can be complex. Effective governance mechanisms that involve the user community are needed to avoid bifurcations and conflicts [3–5, 11, 13–15]. Regulation and Institutional Adoption: Regulation around cryptocurrencies and blockchain is still evolving in many jurisdictions. Institutional adoption is also a development point, where companies and governments are starting to explore the use of blockchain in their operations. Usability and User Experience: Many blockchain interfaces are still complex and not user-friendly. Improving usability and user

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experience is critical to making blockchain more accessible to a wider audience [13–20]. Security and Attacks: Although the blockchain is highly secure due to its cryptographic and decentralized nature, new types of attacks and vulnerabilities can emerge. Staying ahead of cybercriminals and improving security is an ongoing effort. Industry-Specific Applications: Every industry has its own needs and challenges. Continuing to develop blockchain solutions that meet the specific demands of sectors such as healthcare, logistics, and energy is an ongoing focus [14, 17–20]. Education and Awareness: Public understanding of blockchain and its applications is still developing. Continuing education is important to promote understanding and responsible adoption of this technology. These are just some of the many aspects in which development and innovation in the field of blockchain are taking place. As technology advances and new challenges arise, the blockchain community works to find effective and sustainable solutions [5, 11, 13–20]. The novelty in work concerning the discussion of thematic and your technologies, having as motivation focused on conceding a major scientific contribution. Also is worth mentioning that this manuscript differs from the existing contribution of various previous works since it offers a new perspective on an element missing in the literature, is a scientific collection around the topic addressed, while a survey or review describing and explaining the theoretical aspects, it is often employed in science documenting how each discovery aggregated to the store of knowledge fitting into a theoretical model. Therefore, this type of research work exemplifies the most recent research, applications, and technological developments, which are scarce in the literature. This chapter treats tools and approaches focused with an updated discussion summarizing technological techniques.

2 Blockchain and IoT Fundamentals The paradigm shift introduced by Blockchain is in the fact that it is a distributed network where the participants (the “nodes” of the network) are responsible for the validation and registration of transactions, that is, it is not a proprietary system, but a solution jointly in which everyone can participate in an ecosystem. Business flourished in a centralized context, where a given entity held greater power (centralizer), Blockchain technology represents a break in that model, considerably reducing such asymmetry of power [29–33]. IoT transforms the interactions between electronic, electrical, and mechanical appliances, by enabling intelligent interconnection between them, everywhere, and this should also change the relationship of people and companies with their environments, from monitoring with cameras and sensors to the management of spaces and production processes. The lack of a robust security model and the difficulty in expanding the scale of operation are considered the main obstacles for IoT to grow exponentially, considering Blockchain technology capable of solving these

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problems, offering a standardized method to accelerate the exchange of information. Data between IoT devices, without an intermediary [34–37]. The use of IoT enhances the use of solutions based on Blockchain, since the guarantee that certain information or transaction is really “true” to be recorded in the Blockchain, comes through verification [38–41]. One of the difficulties of IoT is dealing with large amounts of data collected by a large network of sensors, and reducing transaction processing latency, i.e., the time between sending a command and executing it. Since the IoT operates with a central server to do this task, considering that according to connection requests, this creates a bottleneck when estimating the scale of IoT solutions for many devices [42, 43]. From the Blockchain, the database collected by the IoT, allows a copy of this list of transactions can be accessed for reading by users at any time, and, once entered, the information can never be deleted, considering these characteristics prevent there are fraudulent transactions, ensuring that each transaction on a Blockchain is verified and accepted as a valid transaction by most network nodes [6, 44–46]. The combination of blockchain and IoT technologies offers a new and promising world and can help solve security, scalability, and cost issues. Also considering that the current IoT ecosystem is entirely based on connectors, which can be a smart device or object (home appliance, car, or automation equipment in factories) [47, 48]. The potential of combining blockchain with IoT lies in guaranteeing the digital record of pure, reliable, and distributed data, given the gap in the relationship with the physical world, that is, a way to connect, in fact, the physical object with the digital representation. Exemplifying the case of shipping, it is necessary to ensure that a particular container is exactly the container to be selected for proper tracking and not the one next to it, and this example is valid for any other object, given the need to make this link between the physical world and the digital world [48, 49]. This technological union can be used through Blockchain encryption IoT devices glued or placed inside objects to facilitate the link between the physical and digital worlds. This context can become viable when employing this type of solution for the management of the physical part of a bank, such as branches or self-service tellers since this can be used to obtain data in real-time, which will then be transmitted to a blockchain network to be shared [50, 51].

3 Types of Blockchain The public blockchain is a technology with no entry restrictions, decentralized, and with equal participation among all members, i.e., it is open to access by new users, that is, anyone can become a node in the network and contribute to the validation of all transactions, it is important to note that these members do not know each other, this means that the level of trust between them is very low. This aspect naturally slows down the agility in the approval of transactions, after all, the process of analysis and acceptance is a little slower [52, 53].

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As it is a decentralized method, therefore, there is no controlling entity, or individual responsible for accepting members, listing transactions, or the like. It is also considered the type with the most transparency, whose security and monitoring are carried out by the members themselves, with no classification of the more or less qualified. On the other hand, it is not highly recommended for companies that want to apply blockchain technology internally, after all, the data would be accessible to the public, including its competitors [54, 55]. Private blockchain goes against the public, it is, therefore, more suitable for companies that want to take advantage of blockchain technology, but still, preserve the publicity of their data. The idea is that it be more centralized, at least in the possibility of access to the network and information and processes. After all, for new members to join is needed the consent of an individual or a single organization, i.e., a group is responsible for controlling the entry of new nodes. Transactions carried out on the network are also private, meaning that only members who have been granted permission to enter can have access to them. In a way, the chain remains decentralized in terms of validation, but there is a centralized representative who qualifies the network’s participants [56, 57]. This means that the network nodes can still perform exchanges, and make data transactions and everyone has the right to participate in a linear way, but without these processes being public. It is a great way to control the entry and governance, and it increases the agility of the transactions, After all, all the nodes know each other and this increases the reliability between the members in order to decrease the analysis and monitoring time of each transaction [52, 58]. Consortium Blockchain (Federated) emerged with the need to maintain transparency, decentralization, and ease of the public model and still maintain some controlling power. After all, in the blockchain consortium model, there is a set of entities, or organizations, that control the access and privacy of transactions. This means that this group can determine whether viewing and sending will be exclusive to members, or publicly available. This is one of the types of blockchain most used by companies due to malleability in relation to centralization [59, 60]. Semi-private blockchain follows the consortium’s idea regarding the flexibility of permissions, but, unlike, there is a single organization, or company, that manages the entry of members. And, unlike the private blockchain, the set of criteria is preestablished and, if followed, allows the entry of any new members. Therefore, more decentralized than the private, but not to the point of becoming public. This is one of the types of blockchain most used by governments and in business-to-business transactions. This is a private model in which only users with predetermined requirements (computational power for example) are accepted, it is recommended for government applications and for B2B businesses [61, 62]. Both public and private blockchains have the following similarities with respect to peer-to-peer decentralization, validation and registration of encrypted information, and even data inalterability, and both networks remain decentralized, have the same consensus mechanism, and confer high information security and transactions [63]. The differences are related to permissibility, transaction speed, and relative centralization. Considering that in public blockchains, anyone can participate in the

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network, with an incentive for users who contribute with validations, while in private ones, there are two mechanisms for the entry of new nodes in the network, and there is usually no incentive whatsoever. Or even with regard to the aspect that can be registered by voting between all the existing nodes in the network or by choosing commissioned nodes. Finally, as the number of nodes in the private blockchain is lower, transactions require less time to register, unlike public chains, with thousands of participants [64, 65]. The main negative points of public blockchains are the demand for high computational capacity, transparency, and reliability, with respect to the intrinsic trust within the network, illustrating an applicable scenario of hundreds of users from various parts of the world connecting to guarantee the execution of a transaction such as depositing a salary. With regard to a private blockchain, the main disadvantages are node hierarchy, and questionable decentralization, in contrast, the advantages are faster changes, security (even with a reduced number of nodes), and low cost [66, 67]. The most suitable types of blockchain for the supply chain are those intermediate ones, which balance the level of centralization. B2C model companies mostly use the consortium or federated blockchain model. On the other hand, in the B2B model, the preference is for the semi-private [7, 8, 67–69].

4 Blockchain and IoT IoT is the creation of a unique environment that gathers information from various devices and applications, using connected sensors to obtain information, allowing a refrigerator, for example, to be programmed or even learn from the user’s habits, when linked to some algorithm of Artificial Intelligence. The IoT can also include people, animals, or other objects with built-in sensors, such as a heart rate monitor to prevent heart attacks, collars that can track a dog’s location and health, or even sensors on farm equipment to detect crop problems [47, 69]. In this context, “thing” is literally anything that has some intrinsic value that justifies its accompaniment in an information network, which can be something physical (real) or virtual. In the conventional model, the technology is called “humancentric”, that is, focused on generating content that can be consumed by humans and generally also produced by humans. Whereas IoT technology becomes “machinecentric” since the main producers and consumers of content are the things themselves [70]. In addition to the connectivity between its nodes, another characteristic that defines IoT is that each element must also have some computational power, thus becoming somehow intelligent. Such characteristics allow the emergence of a large number of applications that make use of distributed processing and high connectivity, presenting three basic requirements related to receiving data from sensors and/or sending commands to actuators (interaction with the physical environment);

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present connection to an external network to the thing; and even have the ability to process data automatically (without human intervention) [71]. Devices are the physical elements of the network (“things”) and comprise one or more types of sensors and/or actuators; local processing elements (CPU MCU); local information storage elements (non-volatile memories) and some type of M2M communication (Machine-to-Machine). It is usually powered by batteries, but this is not a rule and their power source can vary from power harvesting systems to simple connections to the power line [72–74]. With regard to communication, in IoT, this layer is responsible for creating a network joining all the devices of an application and the internet, which can be divided into short and long-range. Communication over short distances can be used for the connection between several devices forming a mesh-type network where the information percolates until it reaches a concentrator. This packs the information and makes the long-distance connection to the internet, which can be used only for configuring things or as part of a scheme for locating objects or indoor navigation. Long-distance communication can also occur directly between things and a base station, eliminating the need for a hub [75–77]. The security layer for IoT is necessary to implement measures of physical protection (anti-tampering), local encryption of the data before it is sent, access authentication measures, and secure boot, among other characteristics, considering that this is a fragile aspect of IoT. It is also mandatory to use cryptography to transport data and measures to validate information (digital signature), as well as access authentication can also be implemented, since Blockchain can be effective in this respect, providing and providing an adequate level of data security [78]. However, there are challenges in this type of partnership considering the need to resolve challenges such as transaction velocity, data limits, and even verification process, which are crucial to effectuate Blockchain broadly applicable. Even considering that Blockchain represents a paradigm shift for a decentralized network that requires the use of strong data encryption (hash), whether, in public or private Blockchains, there are still aspects of cybersecurity that need to be better addressed if public Blockchains are to be fully trusted with respect to personal data [1, 5, 8, 78]. The benefits of IoT include superior automation that makes time and resource efficiency available, as well as more informed decision-making based on larger amounts of data and better visibility of them. Possible concerns related to the Internet of Things include security risks, invasion of privacy, and challenges related to the administration of such a complex device system. The essential strengths of Blockchain derive from the elimination or reduction of intermediaries and lack of confidence since two parties are able to make a transaction (data is complete, consistent, dated, accurate, and available) without the supervision or intermediation of a third party since it is validated by all participants [10, 12, 78]. Regarding the digital aspect of the Blockchain, practically any document can either be described in code form as well as encapsulated by a ledger entry, far beyond just financial transactions. Greater security and ease of monitoring and data transactions generate more intelligence and control for managers and other departments of a company [13, 77, 78].

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The integration between Blockchain and IoT tends to make the best use of the inherent characteristics of each technology, considering that the fusion of such technologies is very promising for many sectors such as industries, retailers, finance, means of payment, insurance, health, and even content delivery [58, 78]. In this context, Blockchain acts as a large distributed database, where information about digital processes will be recorded in a transparent, immutable way as well as for the entire network of commercial partners, from the cell phone number, the purchase intentions, the chosen channel, the better time and consumer feedbacks throughout the process, exemplifying the retail scenario. In this context, both technologies used together, allow the entire network of partners to share data on the consumer life cycle. A practical application in the retail sector aims to track the supply chain from the implementation of the Blockchain feature, consumers will be able to scan a QR Code with their mobile devices to know exactly the path taken by the specific consumer goods, as well as their journey to the specific chain store. This is based on shared data, offering users greater transparency, which helps to strengthen customer-company relationships. Still relating to digital security in direct transactions, since the entire chain can be verified since it is public, direct transactions between customers and companies are more secure and transparent, without the need for intermediaries, such as banks, for example. And with blockchain encryption and sequential identification, it’s easier to preserve consumer data and history, and faster identification of possible fraud attempts [33, 70, 78]. The IoT can be used to monitor certain consumer behaviors, this can be done with the use of beacons and other physical sensors within a store, for example, to identify and record (on the Blockchain) data from the consumer’s smartphone, ensuring monitoring this purchase intention and other interactions with the consumer. Additionally, Analytics/Big Data and even Artificial Intelligence can be used to enrich the information about this consumer, obviously, this strategy must be aligned with the new guidelines of the General Data Protection Law [79].

5 Security Aspects in Blockchain and IoT IoT devices are an attractive target for attackers because the security of this is belatedly considered, as well as being more difficult to keep up to date, leaving gaps to exploit accuracy, several large-scale attacks targeting IoT devices, and a collective of vulnerabilities [4, 79]. Considering that IoT technology has several applications, including smart grids, smart cities, smart contracts, and health management. In this scenario, the underlying communication network must offer data processing and dissemination in a transparent, intense, and pervasive manner, which raises concerns related to aspects of security, privacy, and scalability [4, 5]. Blockchain technology allows relatively improved scalability of IoT implementations and reduces the risk of compromising IoT devices through a forming consensus group on suspicious behavior [4].

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Relating the challenges in blockchain integration in the IoT it is seen that the blocks generated by the blockchain chain take a significant time to be created. As a single block is difficult to generate, changing it would be equally difficult [28]. Relating to the issue of security in IoT, users and organizations should look for multi-layered security, from the gateway to the endpoint, with end-to-end protection [36]. With regard to the cybersecurity of the IoT network, this implies changing default credentials, since notably this already compromises devices connected through IoT botnets. Or even strengthen the security of the router, since that device becomes vulnerable and creates a vulnerable network. As well as monitoring traffic on the network, performing unusual behavior checks on the network can help users prevent malicious attempts [52]. Exemplifying an IoT applicability case, it is possible to consider a credit transaction between IoT devices regulated by an intelligent contract in a peer-to-peer (P2P) communication network, so the nodes of the IoT devices store consistent copies of that contract. One of the devices can request the update of the contract status to assign credit to another device to provide a service, so the nodes that make up the network are informed of this update when they individually receive the new contract status. Eventually, the credit recipient’s IoT device checks this state on its node and then releases the service to the requesting device. The purpose of this process is, above all, to ensure that each device observes the same status as the smart contract. The crucial point of this IoT architecture is to ensure that each IoT device remains synchronized with the latest version of the contract, in addition to ensuring security, privacy, and scalability. Thus, it is possible to highlight that regardless of the nature of the IoT application, the solution to the digital security challenge in this example of the application case lies in the choice of technology to manipulate the information of transactions between communicating devices. In that sense, Blockchain consists of important disruptive technologies for this [44, 50, 52]. With the use of Blockchain in the IoT, customer transactions are grouped into interconnected blocks forming a linked list, i.e., the blockchain. Each block is linked to only one previous block, considering that mining (the process is performed by a peer, or by a group of peers (mining pool) is the mathematical process that needs to be performed before the block is added to the blockchain [52]. In practice, mining consists of successive attempts to determine a nonce value that resolves the cryptographic hash (SHA256 algorithm, for example) of the transaction block, meeting a certain criterion. The difficulty of this criterion is adjusted based on the frequency with which the blocks are added to the blockchain. The nonce (proof of work) determination is that the block has been verified and can be added. In addition to the transactions, each block has a header with metadata, whereas the identification of each block is made by the cryptographic hash of its header. Each block refers to only one previous block, that is, each block contains the hash of the previous header within its own header. The sequence of hashes that links each block creates a chain of blocks that makes its way back to the first block in the system. Changing the identity of block B would have a ripple effect of changing the identity

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of all blocks subsequent to this. This cascade effect forms the basis of the security of the system with respect to the inviolability of the recorded information [5]. The IoT has allowed the ability to connect to the internet or to networks of devices that share information, i.e., the creation of a hyperconnected world, giving society. This connectivity has provided higher levels of efficiency and productivity, in addition to strong interaction and exchange of information between personal computers, smartphones, other devices, and companies [36]. Considering that a Blockchain makes IoT more secure by the possibility of anonymity, since IoT devices are in a point-to-point mesh network (decentralization of the transactions accomplished), being able to authenticate transactions (enhanced security features) and without the need for a central server based on predetermined rules [50].

6 Discussion With the speed that which technology evolves, it is already possible to enjoy practical applications on a daily basis through IoT. With IoT, it is possible to remotely monitor the elevators and perform the service before any interruption, in addition to being able to schedule planned maintenance, based on time (quarterly, monthly, yearly) for predictive maintenance. Through machine learning technology, IoT platforms can identify trends in sensor readings, which will alert the service provider to the imminence of a problem before this actually occurs. To get the best performance from IoT, a field service management solution is required and must be able to process data in real-time, including the ability to obtain real-time information on-site and on schedules, among other features. From the updated data, this allows companies to react immediately to handle the repair (or prioritize it as appropriate), as well as to move any appointments that need to be changed. Or even considering the use of Blockchain to increase security, an IoT chip with intelligent software can be incorporated into each IoT device, containing a selfexecuting code that determines actions according to a condition. These actions are only performed when a transaction has been authenticated. Illustrating in an application scenario of a car factory, when a part arrives, this communicates to all the other devices where that part should be allocated. After the piece arrives, the entire network is also communicated, so the new message communicates that work can be started. It is important to emphasize the importance of field service management technology capable of reacting in real-time to alerts and scheduling changes. Many systems are only updated periodically throughout the day, which means that there is no precise view of the location of technical collaborators or the status of jobs. From IoT, it is possible to obtain an updated view of which technical collaborators are available to complete a given service, this impacts not being able to help quickly enough in an emergency situation.

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Even with the use of IoT, it is possible to obtain how the provision of traffic data in real-time can help in the optimization of routes. This type of premise through IoT technology also promotes the ability to automatically reorder appointments, since the company has real information about locations, traffic, and skills. Since the volume of sensors in factories, vehicles, buildings, and even urban infrastructure increases exponentially, Blockchain seems like an excellent option regarding the cybersecurity of these digital communications. IoT is the digitalization of the physical world and the total connectivity between people and objects, this interconnects the objects in the digital universe through the network, which allows the human being to know the state of these same objects. It is possible to observe the IoT in the evolution of medical treatments, in the reconfiguration of traffic, and also in the preservation of the environment, motivated by the constant concern with global warming and the intelligent use of natural resources. Considering that several cities in the world already benefit from IoT to monitor the quality of water, air, and sound emission, when these collected data are monitored, these data can be exposed so that people become aware of our consumption of natural resources, as well as a re-education to maintain and preserve them. The IoT technology not only helps to understand the environmental impact that is caused by each choice that people make but also helps to visualize the use and consumption of some parameters such as water, energy, occupation, air quality and flow of water people and vehicles are some of the monitoring possibilities (through the data generated through the sensors). Illustrating an applicable scenario of drugs that are required to be transported and stored under controlled temperature conditions, however, the process of tracking these medical shipments is highly fragmented, so, with IoT and Blockchain it is possible to even automate a pharmaceutical supply chain. This innovation in the corporate world can bring people closer to the environments with which they interact on a more objective level, since using IoT technology to digitize the physical world to record information regarding the consumption of resources such as water, energy, and air quality, this shows the state of occupation and use of space. Communication of this consumption to the public takes place in real-time, enhancing the visitor’s interaction with the space. In this sense, IoT can be used in conjunction with an augmented reality interface for the digital personality of the environment in a world where the virtual and the physical come to complement the identity of things. However, it not only records the use of space in detail, but it also contributes to users’ awareness of the impact of their activities on the environment [80–84]. Considering that IoT sensors are getting smaller, cheaper, and more powerful, allowing devices to “see, hear, and feel” digitally beyond human capacity, enabling devices to feel and control the environment as a fundamental part of creating a connected network. As well, Blockchain can offer a standardized method to speed up the exchange and data. Intelligent software can be embedded in any product or IoT solution, allowing its connection to the internet and the cloud, making it more intelligent, as well as

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enabling its integration into a system. It also enables the system to be improved through simple software updates. IoT connectivity is getting faster, and more ubiquitous, and going further to reach the full network potential of the cloud, IoT devices must be connected through highspeed, low-cost, and wide-ranging internet to handle denser networks designed to be more flexible and faster. Thus, the premise of Blockchain is that there is no central agent (user or agent approving and validating all transactions). Analysis software is using the cloud to make data collected by the IoT more accessible, useful, and increasingly valuable, considering that when two IoT devices communicate, it is essential that the cloud exists to store, process, and analyze the data obtained. The cloud also ensures that data is stored and consulted remotely, in addition to allowing the creation of integrated and intelligent systems that leave devices increasingly smart. Intelligent analysis of information linked to IoT devices results in a much more powerful network than simply adding them in isolation. The more connected IoT devices are part of people’s lives, the more technologies need to continually renew themselves to ensure the safe use of the network, as well as to ensure that devices stay connected and data is protected even as threats evolve. Blockchain-derived encryption, for example, is already used to ensure that only enabled devices are connected to the network and protect data in transit and stored in the cloud. Comparatively, using a Blockchain platform, it is possible to apply cybersecurity on peripheral gateways within various IoT components in the substations of an energy utility, for example. A meshed Blockchain network can allow remote access to devices controlling substations of an energy utility, from viewing maintenance data to forwarding information. Considering that adding thousands of smart meters to a wireless network ends up hampering the digital invasion, due to the huge number of connected devices. While on a conventional network, the more units added, the greater the exposure to digital hacking, still comparing that Blockchain has an immutable and encrypted table of security credentials, allowing professionals to connect to a device, even though the substation is offline from the datacenter due to an accident, such as an earthquake or even another natural disaster. Blockchain also provides a scalable solution, so that security does not become a huge burden, considering that combining cryptography with blockchain makes the more points (nodes) incremented, the more secure the Blockchain network is, unlike bank systems conventional data, which have only one access point. Blockchain and IoT are interesting partners, as both represent distributed systems without a central point of control. For example, equipment that requires periodic maintenance usually requires paper or electronic records that track activities performed during maintenance, but these records can be modified to show maintenance performed even when it was not. Using IoT in conjunction with Blockchain within the equipment, maintenance records can be generated with periodic sampling of the system’s operation. This provides an immutable and traceable history of equipment maintenance interspersed with its own operational data. Or even in another applicable scenario of both technologies employed together (IoT with Blockchain),

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a leased industrial vehicle can benefit from providing the owner with detailed tamper-proof information about the vehicle’s use and repairs. Or even considering a Smart Cities scenario, surveillance cameras are another useful example of blockchain application with IoT, since a camera (IoT device) can periodically track changes in your field of view, being recorded as still images, including metadata to identify date and time. But instead of relying on images and metadata, trust can be guaranteed through the use of blockchain. The images are securely distributed on a blockchain network as transactions, with metadata consisting of the location of the camera and the timestamp. The immutable nature of blockchain makes it harder to tamper with images and metadata, and easier to trust when the images represent some form of evidence. Another context of Smart Cities is smart houses that can rely on blockchain and IoT to safely store and distribute data from the various IoT sensors scattered around the site, protecting them from violations through public-key cryptography, containing encrypted records that protect a home’s data from tampering. With the proliferation of connected IoT devices and Cloud Computing or even Edge Computing and the facility to buy and connect IoT sensors, the path between idea and prototype and between prototype and product is shortened, making it easier to create connected IoT solutions, this means that innovation is not more limited to large corporations. In this sense, IoT is not just a future, it is a growing disruptive technology, but one of the main challenges related to security and trust in data and the management of a huge and decentralized device network. Thus, Blockchain offers a solution to these IoT challenges to exchange data in real-time and even automate processes.

7 Trends Edge computing is a distributed computing model that brings business applications closer to data sources promoting data flow acceleration, with no latency, including real-time data processing, ensuring that applications can be used efficiently in remote locations from distributed IT architecture featuring decentralized processing power, allowing devices IoT to respond to data almost instantly, as it is created, enabling mobile computing. In edge computing, this proximity to the data at its source can deliver great business benefits such as faster insights, representing data is processed by the device itself, without having to place it in the public cloud adding a useful layer of security to sensitive data, allowing efficient data processing and even response times and improved bandwidth availability, in which large volume of data is processed near the source (IoT device or even local server), instead of transmitted to a processing data center, reducing the consumption of Internet bandwidth. However, the limitations of latency and the complexities of management and even bandwidth in a viable model of effective edge computing must address workload management (processing) across all edge locations reliably and the diversity of equipment and devices available, as long as the right workloads are on the right

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machine (device) at the right time, and continuously, maintain flexibility to adapt involving a multi-cloud platform and a comprehensive portfolio of services designed to increase scalability and operate more safely and confidently. This can be achieved through the adoption of a massively decentralized computing architecture. Considering that a variety of edge computing should manage software distribution on a massive scale, extract the greatest value from all of these devices, saving associated costs, unlock the full potential of the vast untapped data that is created by connected devices, and deploy software wherever and whenever needed, accelerating performance and strengthen digital security in edge implementations. Through in-depth analysis with Big Data of connected objects, such as sensors, video cameras, vehicles, and medical devices, only possible after IoT, Big Data Analytics can be understood as the analysis of clusters of structured and unstructured data that are generated digitally at all times, it became feasible to generate new sources of information, more accurate, intelligent and efficient. Big Data Analytics aims to extract, store, and analyze data, in order to assist in making the best decisions, both in internal issues such as business analysis, or external as customer behavior, deriving from the concept that data generation becomes more intense, due to the methods of sharing and integration between the media (IoT). A major challenge in the union of Big Data with IoT is the issue of equipment security, which can be met with Blockchain, capable of having a secure and reliable connection and environment to support all connected IoT devices. In general, Big Data Analytics acts to help companies explore their data in order to create new opportunities, through a data analysis process to optimize the understanding of scenarios and standards, developed with the objective of filtering and obtaining all useful information, from a cycle that starts from data extraction (IoT devices) to the organization, treatment, and analysis of the same. The main benefits that the implementation of Big Data Analytics brings to IoT are the identification of precise standards, the possibility to carry out the personalized segmentation of the company’s target audience, for example, being possible to deliver a product to the customer according to their profile. Cost reduction through cloud data storage, making business more efficient and more effective due to the speed of data processing. Faster and better decision-making through the speed and intelligence of data analysis in real-time and in a more assertive way. Development of products and services due to the collection of data (IoT) about consumers, and later the realization of Data mining, used within a large database to recognize patterns (establishing the needs and desires of customers) and rules that can help in making a decision, resulting in product development, and even managing to anticipate the customer’s needs and desires, or through services that serve customers.

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8 Conclusions Blockchain is a technology that records transactions, everyone can see and nobody can change, although it is something simple, it is innovative because its transactions are continuously checked and stored on the network, which in turn are connected to the previous blocks, creating a chain. Each block must refer to the previous block to be valid. IoT through the evolution of sensors in connected devices and advances in big data analysis is making technology in the field increasingly predictive, highly efficient, and less dependent on human intervention. The benefits increase the visibility of assets improving service efficiency and reducing costs, while intelligent analysis of sensor data leads to a better understanding of customer needs. IoT collects a larger volume of data by connecting different equipment in addition to electronics or computer devices, and it is also possible to integrate computers with vehicles, wearables, and interactive screens, among other diverse items. Whereas the information obtained can be grouped into reports and indicators, providing important and relevant content for the user. The greatest benefits of IoT for mobile devices in matters of agility, however, the challenge is to make them increasingly more technologically advanced, from the possibility of having applications with innovative technologies, increasingly focused on the needs and personality of the user, making the mobile devices a fundamental tool to fulfill everyday tasks. However, to get the most out of IoT technology is needed an efficient field service management system capable of issuing alerts in real-time, including the ability to optimize the scheduling of your organization’s service requests, which allows transactions and data sharing to have a high level of security and transparency. In this sense, blockchain is open-source code to develop new tools for managing online transactions, authenticated and leaving a history, that is, a trail of modifications (growing list of records with data distributed and chained using encryption). Blockchain consists of a structure that permanently prevents someone from changing the basic ledger, in that sense, it would be necessary to change half of the entire chain plus one and thus change the network consensus. The security and transparency of the technology are guaranteed by the validation system, which has to be done by all those with access to the block. Each one can see the same database, if your neighbor’s database is corrupted, it is seeing it. This is the control. Validation is by consensus, not by the majority. If there are divergent data for someone, there is no consensus or validation, that is, what is in the block is the same as that of all participants in the Blockchain network, and thus, it is validated. Blockchain’s goal is to simplify transactions, eliminate the need for intermediaries, and generate trust, reliability, and transparency. Just as IoT enables devices to be intelligent and connected, producing data that generate knowledge and make society’s daily lives more efficient, this technology is transporting the full potential of software and the internet to the physical world, revolutionizing the way of life society through sensors, information, cryptography (Blockchain) and clouds.

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Acknowledgements This work is part of a project submitted to Universidade Estadual de Campinas.

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How Does Gamification in an E-Commerce Platform Drive Customer Experience? The Mediating Roles of Perceived Enjoyment and Brand Engagement Jean Paolo G. Lacap, Mark Ivan Leo Ricafrente, Jude Caponpon, Rannie Medina, Ruby Anna Raneses, Zarah Centeno, and Sharifah Nurafizah Syed Annuar

Abstract Grounded on the flow model, the present study aims to examine the significant effect of gamification in an e-commerce platform on customer experience, and the mediating roles of brand engagement and perceived enjoyment in the said relationship. The study participants were e-commerce platform users (n = 314) and were identified using purposive sampling. All hypothesized relationships were examined using path modeling via partial least squares (PLS), and hierarchical component model assessment was performed for higher-order formative constructs—brand engagement and customer engagement. The results revealed that gamification significantly and positively influences customer experience, perceived enjoyment, and brand engagement. Moreover, between brand engagement and perceived enjoyment, only brand engagement shows a significant and positive influence on customer experience. The mediation analysis showed that only brand engagement indirectly affects the link between gamification and customer experience. Unlike past studies on gamification, the current undertaking utilized brand engagement and perceived enjoyment mediators on the link between gamification and customer experience. Furthermore, a hierarchical component model assessment was utilized where brand engagement and customer experience were treated as higher-order formative constructs. Keywords Gamification · Customer experience · Brand engagement · Perceived enjoyment · E-commerce J. P. G. Lacap (B) City College of Angeles, Angeles City, Philippines e-mail: [email protected] M. I. L. Ricafrente · J. Caponpon · R. Medina · R. A. Raneses · Z. Centeno College of Business Administration Graduate Studies, Polytechnic University of the Philippines, Santa Mesa, Manila, Philippines S. N. Syed Annuar Faculty of Business Management, Universiti Teknologi Mara Sabah, Kota Kinabalu, Malaysia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 M. A. Al-Sharafi et al. (eds.), Current and Future Trends on Intelligent Technology Adoption, Studies in Computational Intelligence 1128, https://doi.org/10.1007/978-3-031-48397-4_23

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1 Introduction Massive growth has been observed in online stores in the past years. The vast technological improvements are transforming brick-and-mortar companies into virtual stores and taking advantage of available digital platforms [4, 47, 55]. The increase in the percentage share of electronic commerce (e-commerce) in the global retail trade from 14% in 2019 to 17% in 2020 [78] indicates that buyers and sellers are going digital. More and more shoppers are buying goods and services online, forcing companies to go online. With the changes in consumer needs, wants, tastes, and preferences, e-commerce marketplaces are finding ways to reach a broader market to drive sales [24, 34, 63, 83]. In the Philippines, 73 million online Filipino active users translated to $17 billion in e-commerce sales in 2021, and the Philippine e-commerce sales growth is mainly due to the surge in demand for digital marketplaces [77]. Despite the challenging and low ranking in internet speed, the Philippines placed 59th out of 182 countries in Ookla’s Speedtest Global Index 2022 [29], Filipinos still prefer buying online, primarily through e-commerce marketplaces [73]. The presence of well-known ecommerce platforms forces these digital marketplaces to use innovative and interactive marketing programs, such as gamification, to augment customer engagement and build loyalty [63]. Gamification, or the use of game-playing elements to engage and motivate customers [31], is now being employed in e-commerce platforms. Several wellknown e-commerce platforms that are available in the Philippines are utilizing gamified approaches to drive sales and customer experience [10, 71, 75, 76, 82]. For instance, Lazada’s gamified features include collectible shopping vouchers, coin collection, daily bonuses, and prize giveaways [10]. On the other hand, Shopee’s renowned game is Shopee Shake, where users can earn Shopee coins which can be used to avail of discounts [1, 82]. With the relative importance of gamification, several studies identified its influence on brand engagement [64, 67, 74, 81], suggesting that game-playing elements build connections between consumers and the brand that influences both performances of the business and brand loyalty [5, 36, 37]. Looking at past studies, aside from the effects of game-playing elements on brand engagement, gamification has been studied as an antecedent of customer experience [19, 42, 64]. Despite the several scholarships on the positive influence of gamification on customer experience, there is a scarcity of how brand engagement can indirectly influence the relationship between the two constructs [64]. Moreover, no studies indirectly link gamification and customer experience via perceived enjoyment. A scholarly work by Raman [65] only examined the influence of gamification on perceived enjoyment. Given that the game-playing elements of e-commerce platforms may influence customer experience, the indirect role of perceived enjoyment must be explored. It may mediate the link between gamification and customer experience, especially in

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the context of gamification in e-commerce platforms. Thus, the current study aims to examine the influence of gamification in an e-commerce platform on the customer experience and how brand engagement and perceived enjoyment will act as mediators in the gamification-customer experience relationship.

2 Literature Review 2.1 Theoretical Framework The flow model developed by Hoffman and Novak is used in this study. The flow model is a theoretical framework that describes and explains more about general human–computer interactions. The flow constructs are essential to understand the nature of consumers’ experiences. This means that this model can recognize a helpful variable that explains consumer navigation behavior in online settings [60]. In the present study, gamification is the e-commerce online interactive environment, which positively influences customers’ experience (hedonic and novelty) during navigation. Consumer interaction [46] intent is frequently associated with entertainment and perceived enjoyment. This customer experience has a desirable mental and internal online interaction between firms and consumers through the direct or indirect brand engagement of products and services (cognitive, emotional, and social). Consumers’ adaptation to mobile technology has forced businesses to redesign interaction and services to achieve online user experience [11, 23, 70]. Companies’ competitiveness in diverse environments on online channel formats such as e-commerce must interact with the customers’ online experience at every point. Customers’ responses reflect their mental and internal reactions to the company’s products and services [64]. The numerous channels (e-commerce, m-commerce, and social media websites) and the interaction between customers and businesses provided an opportunity to foster perspectives and ideas about innovations. As online shopping has grown in popularity because of modern life’s ongoing digitalization [62], businesses strive to provide a unique e-commerce experience to elicit an internal and subjective response to the e-website. Firms incorporate game elements into their business processes to increase customers’ reliance on online social connection, making online shopping appealing, anchored with innovative products and low prices [16, 46]. The widespread use of smartphones has created an additional touchpoint that can affect and add to store customer experience [42]. In recent years, advances in online gaming have been made. Gamification has produced an emerging e-commerce trend that will eventually lead to customer engagement and user experience development [7, 70]. The customer experience in this study included two dimensions: hedonic, which occurs when performing activities to balance the challenge and skills of the

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purchase, and novelty focuses on the psychological feeling of newness that comes from having a new experience. Therefore, it is hypothesized that: H1a. Gamification has a positive influence on customer experience. Customers’ virtual interactions with a firm can be mentally stimulating and entertaining, a source of pleasure and enjoyment. Enjoyment is the degree to which the use of the system is perceived as enjoyable, regardless of the consequences of its use [31]. The importance of enjoyment in mobile gaming enhances the customer shopping experience and engagement that nurture a long-term relationship with the customers [3, 58]. Therefore, it is hypothesized that: H1b. Gamification has a positive influence on perceived enjoyment. Brand engagement is defined as the result of a collaborative customer experience in which consumers interact with the brand’s service portfolio and service providers. Emotional, cognitive, and social engagement have all been defined as components of brand engagement [81]. Xi and Hamari [81] further explained these: (a) emotional talks about the effect of the brand’s services on the positive affective states or feelings of the consumer as a result of their interaction, (b) cognitive highlights the degree of brand-related thoughts coming as caused by conscious elaboration and attention in brand interaction and, (c) social refers to the focus of brand engagement to the further development of interaction with others. The proper design of digital platforms, whether offline or online, enables businesses to create, retain, and develop customer engagement, resulting in increased brand importance [64]. Many online shoppers purchase something for entertainment because they have interactive shopping intentions, such as expressing a subjective response as customers. Consumers’ motivation to engage with brands grows as services and products become more game-like [81]. These gamified touchpoints’ game-like experiences have the potential to create customer experiences that lead to brand engagement [42]. Therefore, it is hypothesized that: H1c. Gamification has a positive influence on brand engagement. As customers interact and enjoy the online game, they continue to engage and are more likely to purchase the product and keep coming back. More so, these customers’ virtual interactions with a company can be mentally stimulating, entertaining, and a source of pleasure and enjoyment [57]. Perceived enjoyment is regarded as the intrinsic value of e-commerce and indicates users’ feelings of excitement presented in the place where their desired goals are a vital issue [59]. According to Verkasalo et al. [79], perceived enjoyment significantly affects the intended use of new technologies in the workplace, however, the effect on user behavior is indirect. Studies also show that mobile data services in communication, entertainment, and information, such as gamification, focus on perceived playfulness, a concept related to perceived enjoyment [19, 42]. Social identification, interaction, and diversion experienced by a customer while playing a game may influence perceived enjoyment. According to Hamid and Suzianti [35], the game’s level of quality and relevant monetary value motivates players to

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pay for the in-game products, and gamification demonstrates that all perceived values have a favorable effect on their intent to continue. Furthermore, based on Koivisto and Hamari [54], in general, gamification provides affirmative and pleasing customer experience. Based on past studies, gamification in offline and online commerce provide utility among customers. For example, mobile apps with potentiated connectivity and interactivity increasingly encounter consumers’ need for joy and fun [14, 22]. Accordingly, shopping enjoyment has a crucial role in influencing consumers’ shopping buying behavior [40], as it has been proved that consumers with a positive mood show higher levels of shopping engagement [26, 49]. Therefore, it is hypothesized that: H2. Perceived enjoyment has a positive influence on customer experience. Customer experience is a personal and internal response which is prompted by direct or indirect interaction with the company. The company’s offerings include the quality of customer service, product features, advertisement, comfort, and reliability [56]. Risitano et al. [68] found that brand engagement plays a vital role in creating a unique customer experience and that brand engagement and customer experience influence behavioral purchase intentions. Junaid et al. [45] stated that customers who have a positive experience and are interested in a brand are more likely to engage with the brand. Bilro et al. [12] showed that engaged customers tend to interact with brands, and customer experiences influence how they share their opinions with others. Moreover, Khan et al. [48] confirmed that brand engagement positively influences online customer experience and ultimately leads to brand satisfaction and loyalty. To further enhance the link between brand engagement and customer experience, gamification can increase customer engagement and motivation [46]. Gamification can make the online shopping experience more enjoyable and stimulating, leading to increased brand engagement and ultimately improved customer experience [81]. Therefore, it is hypothesized that: H3. Brand engagement has a positive influence on customer experience. Gamification is a widely accepted approach for enhancing customer experience and engagement in various scenarios. Though the degree to which gamification is effective depends on the person’s experience of enjoyment while doing it. Prior research has shown that perceived enjoyment may indirectly affect the relationship between gamification and customer experience. For instance, Fu et al. [30] argued that perceived enjoyment is a significant factor that can affect an individual’s affective and cognitive processes (including motivation, learning, and attention) based on the cognitive-affective model of game-based learning. Perceived enjoyment also influences what individuals feel and think about the game mechanics, such as the satisfaction of the overall experience, their inclination to participate, and the extent of engagement [17]. Evidence from different studies show that the relationship between gamification and customer experience is indirectly influenced by perceived enjoyment. An analysis of a gamified customer loyalty program by Kim et al. [49] found that perceived

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enjoyment mediates the relationship between gamification and loyalty behavior, such as positive word-of-mouth and repeat purchases. Other studies also revealed that individual characteristics, such as motivation and personality traits, may moderate perceived enjoyment’s influence. In a gamified online course, Hsu and Chen [39] observed that participants with high intrinsic motivation outperformed those with low intrinsic ones based on determining the effect of perceived enjoyment on their learning outcomes. Moreso, there is a need to do further analysis to examine the indirect influence of perceived enjoyment on the relationship between gamification and customer experience; hence the relevant hypothesis is developed and stated as follows: H4. Perceived enjoyment indirectly influences the relationship between gamification and customer experience. In numerous scenarios, gamification has been proven to improve customer experience. For instance, Shen et al. [72] discovered that gamification strengthened online shoppers’ experiences. Moreover, research has shown that gamification can boost customer loyalty [41]. Customer experience has been found to benefit from brand engagement. For instance, Hollebeek et al. [38] discovered that brand engagement raised customers’ tendency to recommend brands to others through their excellent experience. Also, it has been discovered that brand engagement is a significant indicator of customer loyalty and satisfaction [18, 38]. Customer experience and gamification have been linked by brand engagement as a proposed mediating factor. In online fashion buying, Guo and Barnes [33] discovered that brand engagement mediates the relationship between gamification and customer experience. Similar findings were also made by Javornik et al. [44] relating to augmented reality, where they discovered that brand engagement indirectly affects the relationship between gamification and consumer experience. Studies have revealed that brand engagement might magnify the effects of gamification on customer experience. In the context of mobile gaming, Shen et al. [72] discovered that brand engagement affects the link between gamification and the customer experience. As a result, it is assumed that: H5. Brand engagement indirectly influences the relationship between gamification and customer experience. The operational framework was formulated from the established hypothesized relationships (see Fig. 1). The conceptual framework shows how gamification was integrated with the present study. It is postulated that gamification positively influences customer experience, brand engagement, and perceived enjoyment (H1a, H1b, and H1c). Moreover, it is hypothesized that perceived enjoyment has a positive influence on customer experience (H2), as well as a positive effect on brand engagement (H3). Lastly, the research framework shows how perceived and brand engagement indirectly influence the relationship between gamification and customer experience (H4 and H5).

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Fig. 1 As what has been noted in precedent studies, despite the several scholarships on the positive influence of gamification on customer experience (e.g., [19, 42, 64]), there is a scarcity of how brand engagement can indirectly influence the relationship between the two constructs [64]. Thus, the proposed model (Fig. 1) bridges the research gap in the literature where brand engagement and perceive enjoyment were identified to indirectly influence the link of gamification and customer experience, in particular, in the context of e-commerce

3 Methods 3.1 Participants of the Study The study’s respondents were individuals of legal age who experienced the gamification feature of Shopee, a well-known e-commerce platform in the Philippines. The sampling method used was purposive sampling. Data was gathered through a survey questionnaire in an online set-up, which was distributed to various groups using the said e-commerce platform. Out of 326 responses, only 314 answered the survey questionnaire thoroughly. The survey questionnaire was floated in December 2022 until February 2023. Each respondent (18 years old and above) was asked whether they were an e-commerce platform user and whether they had experience playing or participating in Shopee games (e.g., Shopee shake, Shopee candy, spin and win, Shopee bubble, Shopee farm, Shopee claw, and Shopee pets). Those who answered any of the gamification features of the said e-commerce platform were considered participants of the study. To measure whether the sample size is sufficient to support the results of the devised structural model, the Gamma-exponential method and inverse-square root [51] were utilized. With a minimum absolute beta value of 0.157, 50% level of significance, and 80% statistical power, the Gamma-exponential method recommends 238

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minimum sample size, while the inverse square root is 251. Since the present study obtained 314 valid responses, this signified the robustness of the proposed model.

3.2 Research Instrument This paper utilized an online survey questionnaire as the research instrument primarily sent to various groups of e-commerce platforms. Table 1 reflects the constructs used in the study, the items for each construct, and the sources where these items were adapted. All indicators were gauged using a 7-point Likert scale, where seven signifies strongly agree and one strongly disagree.

3.3 Data Analysis The current undertaking utilized predictive research design and path modeling via partial least squares (PLS) to investigate the hypothesized relationships. The use of PLS structural equation modeling (SEM) is an appropriate approach to examining complex interrelationships between latent constructs and items [8] and is attuned to predictive studies [21]. Moreover, since the study involves the use of not only lowerorder latent constructs (gamification and perceived enjoyment) but also higher-order constructs (brand engagement and customer experience) and the measurement of not only direct effects but also mediating effects, PLS-SEM is a fitting statistical approach [69]. All PLS-SEM analysis in the present study was evaluated using WarpPLS 8.0 [52].

4 Results 4.1 Measurement Model Assessment The validity and reliability of the lower-order reflective latent constructs are established in assessing the measurement model. To say that the reflective latent constructs exhibit internal consistency of items, composite reliability (CR) must be at least 0.70 [61]. As seen in Table 2, all lower-order reflective latent constructs have CR coefficients of greater than 0.70, signifying that gamification (CR = 0.917), perceived enjoyment (CR = 0.965), brand engagement (emotional [CR = 0.918], cognitive [0.923], and social [0.892]), and customer experience (hedonic [CR = 0.961] and novelty [0.898]) are reliable.

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Table 1 Constructs, items, and sources Construct

Indicator

Source

Gamification

GA1. Participating in Shopee games allows me to receive tangible rewards (e.g., coins, cashback, vouchers)

Jami Pour et al. [43]

GA2. When participating in Shopee games, the platform provides tangible rewards based on task behaviors (e.g., inviting friends to join the Shopee game) GA3. Tangible rewards through games are a popular incentive mechanism for consumers to use the Shopee platform GA4. As I participate more in Shopee games, I tend to receive more tangible rewards from Shopee GA1. Participating in Shopee games allows me to receive tangible rewards (e.g., coins, cashback, vouchers) Perceived enjoyment

PE1. I feel excited when participating in Shopee games

Wang and Li [80]

PE2. Participating in Shopee games is fun PE3. Participating in Shopee games brings me enjoyment Brand engagement—emotional

EM1. I love the brands in the Shopee app

Jami Pour et al. [43]

EM2. I feel happy when I use the Shopee app Brand engagement—cognitive

CO1. The Shopee app stimulates my interest in learning more about the brands on the platform CO2. When using the Shopee app, I think a lot about the products/brands on the platform

Brand engagement - social SO1. I share with others about the products I buy on the Shopee app SO2. I feel good when I share to others my Shopee shopping experience (continued)

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Table 1 (continued) Construct

Indicator

Source

SO3. I am friendly with others who also use the Shopee app SO4. I participate in the activities of Shopee brand communities Customer experience—hedonic

SO1. I share with others about the products I buy on the Shopee app

Bilro et al. [12], Cheng and Lu [20], Dedeoglu et. al. [27]

SO2. I feel good when I share to others my Shopee shopping experience SO3. I am friendly with others who also use the Shopee app SO4. I participate in the activities of Shopee brand communities SO1. I share with others about the products I buy on the Shopee app Customer experience—novelty

NO1. Purchasing from the Shopee app differs somewhat from buying in a traditional store NO2. Purchasing from the Shopee app is a unique experience NO3. I experience something new when I purchase using the Shopee app NO4. Purchasing from the Shopee app increases my knowledge NO5. Shopee app offers a variety of products

Regarding convergent validity, the requirements are as follows: average variance extracted (AVE) and factor loading must be at least 0.50, and the corresponding pvalues must be significant [6, 53]. Based on the results in Table 2, all lower-order reflective latent constructs passed the convergent validity requirements. Table 3 shows the discriminant validity test performed using the heterotraitmonotrait ratio of correlations (HTMT). To claim that latent constructs demonstrate discriminant validity, the HTMT ratios must be at most 0.90 [32]. Based on the results, all lower-order reflective latent constructs passed this requirement.

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Table 2 Convergent validity and reliability measures of lower-order reflective constructs Lower-order reflective construct

Item

Factor loading

GA1

0.871

GA2

0.842

Gamification

GA3

0.819

GA4

0.895

Perceived enjoyment PE1

0.932

PE2

0.958

PE3

0.959

EM2

0.921

EM3

0.921

CO1

0.925

CO2

0.925

SO1

0.876

SO2

0.914

Emotional

Cognitive

Social

SO3

0.797

SO4

0.684

HE1

0.914

HE2

0.913

Hedonic

HE3

0.893

HE4

0.922

HE5

0.919

NO1

0.778

NO2

0.909

Novelty

NO3

0.856

NO4

0.796

NO5

0.634

Average variance extracted

Composite reliability

0.735

0.917

0.902

0.965

0.849

0.918

0.857

0.923

0.676

0.892

0.832

0.961

0.640

0.898

Emotional, cognitive, and social are the dimensions of brand engagement; hedonic and novelty are the dimensions of customer experience. All items are significant (p < 0.001)

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Table 3 Lower-order reflective constructs’ discriminant validity using HTMT ratios GA

PE

EM

CO

SO

HE

NO

GA PE

0.696

EM

0.415

0.506

CO

0.508

0.538

0.878

SO

0.496

0.560

0.737

0.797

HE

0.467

0.484

0.830

0.776

0.731

NO

0.570

0.481

0.746

0.830

0.722

0.799

GA gamification; PE perceived enjoyment; EM emotional; CO cognitive; SO social; HE hedonic; NO novelty

Table 4 Hierarchical component model assessment Higher-order formative construct

Indicator

Factor weight

p-value

VIF

FCVIF 3.206

Brand engagement Emotional

0.378

< 0.001

2.262

Cognitive

0.387

< 0.001

2.541

Social

0.367

< 0.001

1.936

Hedonic

0.541

< 0.001

2.022

Novelty

0.541

< 0.001

2.022

3.227

Customer experience

VIF Variance inflation factor; FCVIF Full collinearity VIF

Since the present study has two higher-order constructs, we employed a hierarchical component model (HCM) assessment for brand engagement and customer experience using a disjoint two-stage approach [2, 9]. Using this approach, the following must be satisfied—(1) factor weights and their corresponding p-values must be significant [66], (2) variance inflation factor (VIF must be at most 3.30 [15], and (3) full-collinearity VIF (FCVIF) must be at most 3.30 as well [50]. Based on the results in Table 4, brand engagement and customer experience, as higher-order formative constructs, passed the hierarchical component model assessment.

4.2 Structural Model Assessment Assessment of the structural model involves evaluating the beta coefficients, the corresponding p-values, standard errors, and effect sizes. Figure 2 and Table 5 summarize the results of the structural model assessment.

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Fig. 2 The study’s structural model with parameter estimates

Table 5 Assessment of direct and mediating effects Hypothesis

β

p

SE

f2

Decision

Direct effects H1a. GA → CE

0.157

0.002

0.055

0.087

S

H1b. GA → PE

0.636

< 0.001

0.051

0.404

S

H1c. GA → BE

0.460

< 0.001

0.053

0.211

S

H2. PE → CE

0.012

0.412

0.056

0.006

NS

H3. BE → CE

0.756

< 0.001

0.050

0.620

S

Mediation analysis H4. GA → PE → CE

0.008

0.421

0.040

0.004

NS

H5. GA → BE → CE

0.347

< 0.001

0.038

0.173

S

GA gamification; CE customer experience; PE perceived enjoyment; BE brand engagement. β value of the path; p p-value; SE standard error; f 2 effect size. S supported; NS not supported. Effect sizes were assessed based on the following: 0.02—small; 0.15—medium/moderate; 0.35—large/ substantial [25]

The findings showed that gamification has a significant and positive effect on customer satisfaction (β = 0.157, p < 0.01, f2 = 0.087), perceived enjoyment (β = 0.636, p < 0.001, f2 = 0.404), and brand engagement (β = 0.460, p < 0.001, f2 = 0.211) small, large, and medium effect sizes respectively. Hence, H1a, H1b, and H1c are supported.

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On the other hand, perceived enjoyment was found to have no significant effect on customer experience (β = 0.012, p = 0.412). In contrast, the link between brand experience and customer experience was found to be significantly and positively related (β = 0.756, p < 0.001), with a large effect size (f2 = 0.620). Thus, H2 is unsupported while H3 is supported. Concerning mediation analysis, perceived enjoyment was found to have no indirect effect on the relationship between gamification and customer experience (β = 0.008, p = 0.421). On the one hand, brand engagement was found to have a mediating effect on the link between gamification and customer experience (β = 0.347, p < 0.001) with a medium effect size (f2 = 0.173). Hence, H4 is unsupported while H5 is supported.

5 Discussion The present study explored how gamification in an e-commerce platform influences customer experience, an important factor of competitive advantage [13]. Moreover, it investigated the indirect role of perceived enjoyment and brand engagement on the relationship between gamification and customer experience. The results revealed that gamification significantly influences customer experience, perceived enjoyment, and brand engagement. The finding suggests that incorporating game elements into e-commerce platforms and providing tangible rewards because the customer participated in the games can benefit businesses in promoting good customer experience, improving perceived enjoyment, and stimulating brand engagement of the consumers. This incentive mechanism of getting tangible rewards, such as cashback or vouchers, from participating in games of online shopping apps turned out to affect customers to have an exciting and unique experience, feel fun and enjoyment, and stimulate interest in learning more about the brands in the platform. These results are consistent with the findings of numerous prior studies [28, 39, 46, 64, 81], which showed that gamification could lead to customers increased pleasant and exciting shopping experience, enjoyment, and engagement with the brand. However, perceived enjoyment was found to have no significant influence on customer experience. The result indicates that no matter how much the games in e-commerce platforms bring pleasure to the customers, it does not necessarily translate to a good customer experience. In other words, entertaining customers in online shopping apps with games does not mean giving them an exciting and unique experience. This situation can mean that customers do not necessarily look forward to using online shopping apps to have fun participating in the games, and there might be other factors that businesses should focus more on. This result contradicts a previous study [46] that stated that making an online shopping experience enjoyable positively enhances customer experience. Unlike perceived enjoyment, brand engagement was found to have a significant positive influence on customer experience. The finding shows that developing the consumers’ emotional, cognitive, and social aspects of brand engagement can help

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promote good customer experience in terms of the hedonic or dynamic element and the novelty dimension or the newness aspect. The result further implies that the pride, interest, and desire to share positive feedback, i.e., being engaged with the brand, makes the customers look forward to using the e-commerce platform. This finding is in accordance with the results of several earlier studies [36, 45, 48, 68], which asserted that brand engagement positively influences customer experience and that these two variables are directly related to each other. Between the two studied mediators, which are perceived enjoyment and brand engagement, only brand engagement was found to have a significant positive indirect influence on the relationship between gamification and customer experience. This outcome suggests that the resulting perceived enjoyment of the consumers in the gamification efforts in an e-commerce platform does not necessarily lead to an indirect enhancement in customer experience. Thus, it can be interpreted that the customers do not need to enjoy the games; if they receive tangible rewards, they will have a good customer experience. This result negates the findings of various preceding studies [49, 72], which claimed that perceived enjoyment, in the context of gamification, indirectly influences the customer experience. On the other hand, the mediation analysis suggested that the resulting brand engagement due to gamification in online retail has a significant positive indirect influence on customer experience. This link is supported by past studies, which showed that gamification increased brand engagement, resulting indirectly in better customer experience in the context of a fitness app [49] and online fashion shopping [33].

6 Conclusions The present study confirms the mediating role of brand engagement on the relationship between gamification and customer experience. The customers’ interaction with the brand through word-of-mouth, loyalty programs, and shop visits, as triggered by gamification, can positively impact their experience in general. On the other hand, perceived enjoyment was found to not influence customer experience and does not mediate the relationship between gamification and customer experience. From a customer’s perspective in the context of gamification, discerning enjoyment, an intrinsic value of e-commerce, indicates users’ excitement [59] does not mean that it will consequently improve their customer experience.

7 Practical Implications The findings of this study have important implications for marketing practice. These imply that integrating gamification components into an e-commerce platform can help businesses improve customer satisfaction, foster brand loyalty, and raise consumers’ perceptions of enjoyment. However, not all games can earn enough

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engagement. As such, businesses should consider implementing gamification strategies responsive to the current trend, where customer engagement will increase (which will result in an enjoyable and exciting purchasing experience). Another perspective is that games may not be intended for a particular market as the value of rewards is relative to the target market. Certain socioeconomic classes will most likely patronize these games but not others. Companies can give incentives and prizes that are more enticing and financially rewarding relative to all customers.

8 Theoretical Implications For the implications to the theoretical framework, the flow model was used and insinuated the idea that gamification can result in a fun and exciting purchasing experience that benefits the customer. According to the study, brand engagement, as opposed to perceived enjoyment, mediates the association between gamification and consumer experience. This research emphasizes how crucial it is to consider more variables than just perceived enjoyment when analyzing how gamification affects the consumer experience, such as brand engagement.

9 Limitations and Future Research Direction The present study also has limitations. Initially, the respondents were mainly from Metro Manila and its neighboring provinces in the Philippines; other online shoppers from other parts of the Philippines were not included. Second, the study focused on gamification in the context of a well-known brand for an online shop platform. It did not include other online shop platforms. Third, it covers only brand engagement and perceived enjoyment as mediating variables in the relationship between gamification and customer experience. And finally, the dominance of Generation Z and Millennials as participants is also a limitation. Thus, the following are recommendations for the future proponents of this study: (a) consider respondents from metro cities in the country, such as Cebu, Davao, and their neighboring provinces; (b) consider other known online shopping platforms that can be explored further and lastly; (c) consider obtaining more responses from more matured groups such as Generation Y and Baby Boomers.

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