Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions: Case of Vietnam and Sri Lanka 9811950164, 9789811950162

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Charitha Harshani Perera Rajkishore Nayak Long Van Thang Nguyen

Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions Case of Vietnam and Sri Lanka

Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions

Charitha Harshani Perera · Rajkishore Nayak · Long Van Thang Nguyen

Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions Case of Vietnam and Sri Lanka

Charitha Harshani Perera Faculty of Business and Law Northumbria University Newcastle upon Tyne, United Kingdom

Rajkishore Nayak School of Communication and Design RMIT University Ho Chi Minh City, Vietnam

Long Van Thang Nguyen School of Communication and Design RMIT University Ho Chi Minh City, Vietnam

ISBN 978-981-19-5016-2 ISBN 978-981-19-5017-9 (eBook) https://doi.org/10.1007/978-981-19-5017-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Declaration

I certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research programme; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and ethics, procedures, and guidelines have been followed. February 2022

Charitha Harshani Perera

v

Acknowledgements

I wish to express my sincere gratitude to all who supported me on this long journey towards completing this thesis. First and foremost, my heartfelt gratitude and appreciation go to my supervisors, Assoc. Prof. Rajkishore Nayak and Dr. Long T. V. Nguyen, for their patience, encouragement, and helpful insights to make me who I am today. I am very thankful for their scholarly guidance, constructive comments, and critical revision throughout this process, making it possible for me to complete my Ph.D. journey. Second, I am grateful to the academic and admin staff at RMIT University for their kind support and advice, in assisting me during these three Milestone reviews to make it successful. Further, I would like to thank Assoc. Prof. Robert McClelland for his enthusiastic teaching, and guidance in research method courses which provided me with a better understanding of the research knowledge and skills. I sincerely thank my colleagues, and it was a pleasure to share these years with them who helped and encouraged me in various ways. Third, I express my gratitude to Assoc. Prof. Jodie Conduit (University of Adelaide, Australia) for her contribution, constructive criticism, invaluable comments, and guidance in completing this task. Without her advice as my external examiner for 3 years, this thesis could never have been completed. My sincere thanks also go to my family for always being there for me. I thank my mom for her never-ending love, support, and encouragement. Special thanks to my dad, who always believed in me and supported all my decisions. Without them, I would not be the person I am today. Last but not the least, I thank my brothers for always being there for me through all the good and bad times. Their patience, support, strength, and love made me complete this programme successfully. I am grateful for having you all in my life.

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Research Overview

Higher Education Institutes (HEIs) have identified the need for developing branding strategies to be competitive in the marketplace in an increasingly competitive environment. Accordingly, HEIs have started to focus on brand equity to attract prospective students. With the tremendous growth of social media recently, many marketers and academia have focused on examining the impact of social media marketing on brand equity. The majority of the recent studies on social media marketing and brand equity have focused on the products and developed countries. Limited studies have been conducted to examine the extent to which social media marketing influences brand equity for services in emerging countries. Accordingly, the main objective of this study is to examine the extent to which social media marketing influences the customer-based brand equity of HEIs in emerging countries. This study seeks to develop a theoretical framework by drawing from signalling theory and social information processing theory, to examine the role of social media marketing in building customer-based brand equity for HEIs. The present study considers the mediating effect of brand credibility and the moderating effect of location (Sri Lanka vs. Vietnam), and brand usage experience (junior students vs. senior students) in developing the relationship between social media marketing and customer-based brand equity. For addressing the research objectives, a survey-based quantitative approach involving 993 undergraduates, four each from the Sri Lankan and Vietnamese higher education sectors, were recruited. Descriptive and Inferential statistical analyses were employed to show the associations and predictive abilities of the independent variables within each construct. Two statistical software packages, namely the Statistical Package for Social Sciences (SPSS) version 23, and Analysis of Moment Structures (AMOS) version 23, were used to analyse the quantitative data. The conceptual model was tested based on the analyses mentioned above.

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About This Book

Higher education institutions operate in a strong competitive environment due to the homogenous nature of their services and always look for new marketing strategies to be competitive in the marketplace. Therefore, building customer-based brand equity has become crucial for higher education institutions to differentiate themselves from others to attract prospective students. Social media-based marketing facilitated prospective students to communicate and collaborate to gather information relevant to higher education institutions and their respective brand equity. However, many models on customer-based brand equity received limited support in the higher education sector, particularly in emerging Asian countries. Further, the relationship between social media marketing and customer-based brand equity has received limited attention. A dearth of literature remains in the context of higher education in emerging countries. As such, drawing from social information processing theory, this study empirically investigated how higher education institutions can develop customer-based brand equity by using social media marketing and subjective norms mediated by brand credibility, taking cross-country comparisons between Sri Lanka and Vietnam. The quantitative findings from 993 undergraduates from Sri Lanka and Vietnam indicated the indirect effect of social media marketing, and subjective norms on customer-based brand equity via brand credibility. Compared with their Sri Lankan counterparts, Vietnamese undergraduates’ perception of institutions’ brand equity is more strongly influenced by subjective norms and brand credibility. Further, Vietnamese are relying more on firm-generated content, whereas Sri Lankans are focusing on user-generated content. The findings of this study enrich the higher education literature and have implications for higher education providers in developing branding strategies through social media platforms.

xi

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Problem Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Significance of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Research Aim, Objectives, and Questions . . . . . . . . . . . . . . . . . . . . . 1.7 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 1 5 9 12 13 14 14 15

2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Brand and Branding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Branding in the Service Sector . . . . . . . . . . . . . . . . . . . . . . . 2.3 Brand Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Definition of Brand Equity . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Brand Equity in Higher Education . . . . . . . . . . . . . . . . . . . . 2.3.3 Benefits of Brand Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Different Perspectives on Brand Equity . . . . . . . . . . . . . . . 2.4 Brand Equity Perspective of This Study . . . . . . . . . . . . . . . . . . . . . . . 2.5 Overview of the Antecedents and Possible Outcomes of Customer-Based Brand Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Measuring “Customer-Based Brand Equity” in the Higher Education Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7.1 Social Media Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7.2 Social Media Marketing in Higher Education . . . . . . . . . . 2.8 Subjective Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Brand Credibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25 25 25 28 30 30 33 37 38 46 48 50 55 56 63 64 67

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Contents

2.10 Higher Education in South Asia and South-East Asia . . . . . . . . . . . 2.10.1 Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10.2 Brand Usage Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 69 75 76 77

3 Research Model and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Hypotheses Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Hypothesis H1: User-Generated Content and Brand Credibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Hypothesis H2: Firm-Generated Content and Brand Credibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Hypothesis H3: Subjective Norms and Brand Credibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Hypothesis H4: Brand Credibility and Customer-Based Brand Equity . . . . . . . . . . . . . . . . . . . 3.2.5 Hypothesis H5: Mediating Effect of Brand Credibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.6 Hypothesis H6: The Moderating Effects of Location . . . . 3.2.7 Hypothesis H7: The Moderating Effects of Brand Usage Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Development of the Conceptual Framework . . . . . . . . . . . . . . . . . . . 3.4 Definition of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Theoretical Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Signalling Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Social Information Processing Theory . . . . . . . . . . . . . . . . 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115 115 115

4 Methodology and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Research Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Ontology and Epistemology . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Philosophical Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 The Current Study’s Philosophical Stance . . . . . . . . . . . . . 4.3 Research Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Survey Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Questionnaire Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.3 Questionnaire Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.4 Questionnaire Translation . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 137 137 138 139 144 145 147 149 151 151 152 153

116 117 118 119 121 123 125 127 127 128 128 129 131 131

Contents

4.6

Data Collection for the Pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Validity and Reliability of the Piloted Questionnaire . . . . 4.6.2 Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.3 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Sample Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.1 Sample Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.2 Sampling Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.3 A Sampling of the Questionnaire Respondents . . . . . . . . . 4.8 Statistical Analysis Techniques Used . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Ethical Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Quantitative Data Presentation and Analysis: Descriptive Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Preliminary Data Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Response Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Data Screening and Cleaning . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Background and Demographic Profile of the Study Sample . . . . . . 5.3.1 Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Respondents’ Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Sri Lankans and Vietnamese Demographic Observations on Dependence . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Descriptive Analysis of Respondents’ Responses . . . . . . . . . . . . . . . 5.5.1 Firm-Generated Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 User-Generated Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Brand Credibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.4 Customer-Based Brand Equity . . . . . . . . . . . . . . . . . . . . . . . 5.5.5 Subjective Norm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Quantitative Data Presentation and Analysis: Inferential Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Exploratory Factor Analysis (EFA) . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Test for Common Method Bias . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Test of Sampling Adequacy and Data Sphericity . . . . . . . . 6.2.3 Performing EFA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 EFA Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.5 EFA for the Final Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 The Measurement Model: CFA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 The Measurement Model Evaluation: Goodness-of-Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 CFA for Path Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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154 154 155 157 158 159 160 160 162 163 164 164 169 169 169 169 171 176 176 178 178 183 184 184 184 185 185 185 185 187 187 188 188 188 189 189 194 194 195 196

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Contents

6.4 6.5

The Measurement Model Enhancement . . . . . . . . . . . . . . . . . . . . . . . The Measurement Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Reliability Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 The Measurement Model Evaluation: Construct Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 The Structural Model: Structural Equation Modelling (SEM) . . . . 6.6.1 The Structural Model Evaluation: Goodness-of-Fit . . . . . . 6.6.2 Testing Research Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . 6.6.3 Testing the Mediating Effect of Brand Credibility . . . . . . . 6.6.4 Moderating Effect of Location and Brand Usage Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Covariance Among UGC, FGC, and SN . . . . . . . . . . . . . . . . . . . . . . 6.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

198 199 199

7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Overview of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Discussion and Implications: Research Question 1 . . . . . . 7.2.2 Discussion and Implications: Research Question 2 . . . . . . 7.2.3 Discussion and Implications: Research Question 3 . . . . . . 7.3 Comparative Analysis Between Sri Lanka and Vietnam Based on Their Demographic Observations . . . . . . . . . . . . . . . . . . . 7.4 Theoretical and Managerial Contribution of the Study . . . . . . . . . . 7.4.1 Theoretical Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

217 217 217 218 222 224

8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Brief Overview of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Limitation of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Suggestions for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

247 247 247 248 249 249 275

201 203 203 204 206 208 212 213 214

226 229 230 236 239 240

Abbreviations

AMOS 23 BC CBBE CFA EBBE EFA eWOM FBBE FGC HE HEI KMO SEM SIPT SMM SN SNS SPSS 23 UGC

Analysis of Moment Structures (Software—Version 23) Brand credibility Customer-based brand equity Confirmatory Factor Analysis Employee-based brand equity Exploratory Factor Analysis Electronic word-of-mouth Finance-based brand equity Firm-generated content Higher education Higher education institutes Kaiser-Meyer-Olkin Structural Equation Modelling (quantitative data analysis technique) Social Information Processing Theory Social Media Marketing Subjective norms Social Networking Sites Statistical Package for Social Science (Software—Version 22) User-generated content

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Chapter 1

Introduction

1.1 Introduction This chapter starts by presenting the background to the examination topic and continues to layout the research problem. It then highlights the general aim of the study, provides a clear explanation of the objectives and research questions, and explains the methodology adopted to address the research questions to accomplish the overall aim of the study. Finally, it discusses the study’s significance in detail and provides an overview of the thesis structure.

1.2 Research Background The higher education sector faces several challenges due to the increased competitiveness to protect its reputation, image, and position in the marketplace (Bunce et al., 2017; Lomer et al., 2018). In the global competitive environment, where higher education institutions (HEIs) are created to meet the growing demand, the HEIs marketers are thus left to embrace the marketing strategies (Epple et al., 2017; Pizarro Milian & Davidson, 2018). While witnessing the strong performance of higher education markets, the rising number of students triggers competition among nations and individual HEIs (Marginson, 2018a, 2018b). The similarities among the HEIs and the identical nature of the degree programmes diminish the potential for the aspiring students to differentiate while comparing the HEIs in a clustered market space (Rutter et al., 2017; Tomlinson, 2017). Similar to customers’ confusion in the clustered marketplace, the prospective students found the decision-making process of selecting an HEI confusing. The intangible, heterogeneous, perishable, and inseparable services of the higher education sector highlight the similar offerings of the HEIs as it is predominately considered as a service industry (El Alfy & Abukari, 2019; Heckman & Montalto, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_1

1

2

1 Introduction

2018). Prospective students are increasingly searching for various factors to assess the HEIs’ quality to decide whether the HEI fulfils their requirements (Panda et al., 2019). However, the prospective students cannot gauge the true quality and the consistency of the services provided by the HEIs until they are enrolled and engaged in the HEI’s experience (Asim & Kumar, 2018; Sharabati et al., 2019). Therefore, this intangible nature of higher education, like any other professional service, featured the high perceived risk associated with consuming it (Ferreira et al., 2017; Heckman & Montalto, 2018; Hersh & Merrow, 2015). To overcome these challenges, HEIs have facilitated the prospective students to visit the HEIs physically and get the experience before enrolling to reduce the perceived risk (Rudd, 2018; Swani & Milne, 2017; Tsiotsou & Wirtz, 2015). This reduces physical intangibility rather than the mental intangibility of the HEIs’ services (Kimmons et al., 2017; Lovelock & Jochen, 2007). Therefore, Mourad et al. (2019) suggested that the perceived risk associated with mental intangibility could be minimised by creating and managing the brand equity for HEIs to signal a higher level of quality of their services. Although HEIs’ administrators have recognised the importance of brand management (Amzat, 2016; Endo et al., 2019; Mampaey et al., 2015) and strong brand equity to achieve strategic goals, only limited studies are available on developing a theoretical model of brand equity for HEIs (HerreroCrespo et al., 2016; Mourad et al., 2020; Pinar et al., 2014; Williams Jr and Omar, 2014). As the emerging stream of higher education research focuses on brand equity, it may represent a powerful basis to differentiate the HEIs and influence the prospective students’ selection process, while acting as a risk reliever (Eldegwy et al., 2018; Mourad et al., 2020). Conceptualising and understanding the brand equity from the customers’ perspective is significant as it provides specific guidelines and marketing strategies for creating a strong customer base (Bose et al., 2018) and the management’s decisionmaking process (Mardani et al., 2016). Customers’ perception of the brand image, brand awareness, brand associations, loyalty in repeat purchases, and perceived quality of the brand enhances the brand equity (Foroudi et al., 2018; Kim et al., 2018a, 2018b). Also, customers’ ability to recognise and recall a brand amid competitive brands reflects the values of a specific brand, which increases brand equity (Filieri et al., 2019). Since the customers are the reason for any firm’s existence, customers’ attitudes and perceptions towards the brands have become vital in developing brand equity (Augusto and Torres, 2018). Therefore, in the competitive environment where the firms are increasingly focusing on developing marketing strategies to strengthen their relationship with existing and prospective customers, the one-time transaction has shifted to a long-term relationship (Eriksson and Hermansson, 2018), while highlighting the importance of customer-based brand equity (CBBE) in attracting and retaining the customers with firm’s brands (Youssef et al., 2018). CBBE shows the extent to which the customers are attached to a brand that leads to the success or failure of a specific brand (Shafaei et al., 2019). As HEIs are focusing on their current and prospective students and attempting to fulfil their needs and wants through coherent marketing strategies, they have initiated their focus on technology-enabled digital marketing communication strategies

1.2 Research Background

3

(Badwan et al., 2017; Newell et al., 2019). The advancement of technologies has made the customers tech-savvy while increasing their familiarity with the new communication methods (Foltean et al., 2019; Ostrom et al., 2019). The importance of digital marketing as a means of communication has become critical in reaching the largely distributed target audience (Dahiya & Gayatri, 2018; Yoga et al., 2019). Recently, social media has become an important platform to conduct digital marketing activities while communicating with a large audience (Adeola et al., 2020; Ferdous, 2019). Therefore, digital marketing communication activities have increasingly started on various social networking sites such as Facebook, YouTube, and Instagram. This manifested the marketing activities on social media while highlighting the importance of social media marketing (SMM) for businesses in today’s competitive market space (Al-Rawi, 2019; Gavino et al., 2019). With these advanced digital platforms, the success and existence of any business are mainly based on the attributes of its marketing activities, and the HEIs are not an exception (Au-Yong-Oliveira et al., 2018; Isaac et al., 2019). As a result, social media’s popularity has led the HEIs to conduct their marketing activities on social media while motivating the researchers to examine the role of SMM in higher education sectors (Chugh & Ruhi, 2018; Rivera-Rogel et al., 2019). Peruta and Shields (2018) noted that social media has an incredible potential to be utilised as a marketing communication tool in reaching the current and prospective students in HEIs. They are increasingly using social networking sites to search and gather the necessary information. Social media usage among undergraduates, in particular, has grown rapidly from 12% in 2005 to 99% in 2017 worldwide (Sutherland et al., 2018). This has further motivated the HEIs to integrate social media into their marketing communication (Brech et al., 2017; Peruta & Shields, 2018) while inducing the prospective students to make a better-informed decision about the HEI brands and their equity (Bolat & O’Sullivan, 2017; Peruta & Shields, 2017). Thus, SMM has started to play an ever-increasing role amid the prospective students’ decision in selecting an HEI while enhancing their positive perception towards the HEI’s brand. SMM has received increased attention in the higher education sector to have and maintain a quality relationship with the students (Clark et al., 2017). It is reasonable to assume that engagement with SMM could enhance the communication between student–student and student–HEI (Chugh & Ruhi, 2018), which helps the students to make their study decisions and HEI selection (Pringle & Fritz, 2018). Studies on SMM have been conducted previously in various sectors such as automobile (Chatterjee and Joshi, 2018; Ulas and Vural, 2019), luxury fashion brands (Lee et al., 2018; Liu et al., 2019; Park et al., 2018), and tourism (John et al., 2018; Liu et al., 2019). Their primary focus was to identify the relationship between customer satisfaction with SMM communication (Sano, 2015; Seo & Park, 2018). Although several exploratory studies have examined the effect of SMM from the perspective of branding, the number of empirical studies is limited in identifying its impact on brand equity (Godey et al., 2016; Seo & Park, 2018). In this regard, limited attention has focused on developing and enhancing brand equity in the context of SMM (Raji et al., 2019). Besides, the importance of SMM in enhancing CBBE has received less

4

1 Introduction

attention in the service context, particularly in the higher education sector. Therefore, the present study focuses on identifying the importance of SMM in creating CBBE in the context of the higher education sector. Moreover, prospective students’ enrolment decision depends on the perceived social pressure and the opinions of others whom they believe as important (Utami, 2017). This perception has been labelled as that students’ normative beliefs, and behaviour resulted in perceived social pressure, or so-called subjective norms (Belgiawan et al., 2017). Undergraduates’ behavioural intention to follow others’ opinions in decision-making is mainly determined and influenced by their attitudes towards the subjective norms (Maloshonok & Shmeleva, 2019; Mishkin et al., 2016). Subjective norms are influencing the students’ perceptions while inducing them to behave in a specific manner and comply with other people’s views (Park et al., 2009). Despite the noteworthiness of subjective norms on customer behaviour, there is a shortage of empirical studies that explore the significance of subjective norms for students’ decision-making process and HEIs’ branding (Felgendreher & Löfgren, 2018). There is a notable scarcity of studies on subjective norms’ adoption in an online environment. Limited studies have focused on identifying the importance of subjective norms in creating HEI’s brand equity on the social media platform (Hung et al., 2018). Thus, this study empirically tests the effect of subjective norms on HEI’s branding and its brand equity. In this increasingly competitive environment where an HEI’s quality is not easily observable, students generally make their selection decisions amid feelings of uncertainty (Habibov & Cheung, 2017). In this sense, intensive efforts from the HEIs are needed to reduce the risk associated with the students’ decision-making process while becoming the top among the competitors (Ballarino & Panichella, 2016). Therefore, HEIs aim to justify their best ability and willingness to provide promised superior services than their competitors to increase the credibility of their brands among the prospective students (McLaughlin et al., 2018). The credibility signalled by an HEI brand is considered as important as it decreases prospective students’ uncertainty and economises the decision-making costs (Nguyen et al., 2016). However, despite the significance of brand credibility and its benefit to prospective students, the effect of brand credibility to create HEI-based branding strategies has received limited attention in higher education literature. Therefore, the importance of brand credibility to create and enhance brand equity for HEIs has remained unclear. For filling the above research gaps, this investigation has provided a comprehensive model including SMM, subjective norms, and brand credibility to create and enhance CBBE for HEIs to enrich the higher education and marketing literature. The study also focused on identifying the mediating effect of brand credibility between the relationships of the study constructs, with the hope of enhancing the generalisability. In particular, the present study focuses on emerging Asian countries, Sri Lanka and Vietnam, which are striving to attract prospective students to increase the enrolment rate to expand their higher education sectors while being competitive in the market space (Tran & Villano, 2017; Weerasinghe & Fernando, 2018;). Among the emerging

1.3 Problem Outline

5

markets in Asia, Sri Lanka and Vietnam strive to have remarkable growth in their higher education sectors (Nguyen, 2017; Wickramasinghe, 2018). Sri Lanka, as a South Asian country, is focusing on increasing the enrolment rate of the higher education sector (Weerasinghe & Fernando, 2018), while Vietnam, as a South-East Asian country, is projecting to expand the higher education sector to meet the growing demand in enrolment rate (Tran & Villano, 2017). The number of studies on SMM and CBBE in emerging countries is inadequate, and the comparison between the emerging Asian countries is also limited. Subsequently, Sri Lanka and Vietnam were chosen as two contrasting countries with distinctive cultures, and diverse maturity levels in the higher education sector to form a comparative analysis in adopting SMM, subjective norms, brand credibility, and CBBE among the emerging Asian countries. To provide a broader insight into this research area, the researcher surveyed a wide cluster of students currently studying in the private HEIs in both countries and recognised the undergraduates’ experiences about HEI brands based on their brand user experiences in an online environment. Social information processing theory suggests that an individual’s interaction in the online environment helps to create and maintain healthy relationships with the firms and brands though there is a lack of face-to-face communication (Ramadan et al., 2018). Therefore, using social information processing theory as the theoretical foundation, we argue that the SMM and the norms-based social pressure the people receive from the social media interaction could motivate the online users to rely on the credibility of a brand to create and enhance CBBE for the preferred brand. Further, this study makes a comparative analysis based on location (Sri Lanka vs Vietnam), and undergraduates’ brand usage experience (junior students vs senior students) relating to HEIs as moderators to identify the different perceptions of HEI brands and their brand equity among the emerging countries.

1.3 Problem Outline Any country’s economic growth is highly influenced by the higher education sector (Marginson, 2018a, 2018b; Sanders, 2019). Therefore, developing a proper higher education sector is vital to foster innovation and increase intellectual skills (Iqbal et al., 2019). In recent years, higher education sectors have pulled the government to pay a special consideration since it trusted that HEIs could generate higher income for the country while dealing with potential foreign investments (Mughal & Vechiu, 2018). Therefore, Sri Lanka and Vietnam are increasingly focusing on establishing new HEIs, and thus, a higher level of competition in performance, quality, and prices of higher education services has emerged in Sri Lankan and Vietnamese (Brankovic, 2018). Both Sri Lanka, and Vietnam are categorised as developing countries in the Asian region (Gunarathne et al., 2020; Long, 2021). Vietnam is one of the fastestgrowing and most vibrant economies in Asia whereas Sri Lanka is classed by the World Bank as a lower middle-income country. The growth of higher education in

6

1 Introduction

Asia has been much more rapid than in other parts of the world (Hai et al., 2020). This growth has helped to expand access to higher education and to reduce the burden on governments to finance higher education through public funds. Despite the availability of a reasonably good number of HEIs, there is a disappointing level of enrolment rate in the higher education sector in Sri Lanka. Therefore, the Sri Lankan higher education sector is struggling to develop a range of strategies to increase its enrolment rate while identifying a variety of marketing strategies to attract prospective students (Weerasinghe & Fernando, 2018). In the Vietnamese context, in tandem with the developments, Vietnam is projecting to expand its higher education sector (Tran & Villano, 2017) to strengthen its relationship with the developed countries (Nguyen & Tran, 2018). Transnational education (TNE) is booming in Vietnam so that Vietnam is striving to attract national and international students for their HEIs (Bilsland et al., 2019). Therefore, Vietnamese HEIs attempt to develop marketing strategies to attract more international students while reducing the outward flow of domestic students and being competitive in the marketplace (Whitney et al., 2018). The external flow of Vietnamese students is rising since 2005/2006, reaching 94,662 in 2017 (UNESCO, 2020a, 2020b). The governments’ visions of Sri Lanka and Vietnam for 2025 have stated to transform each country into the hub of Asia, with a knowledge-based highly competitive, social-market economy (Department for International Trade, 2017). Previously, both countries had only public universities, but, later, the number of HEIs increased in both counties with the establishment of private HEIs (Van et al., 2020). Even though there are several HEIs in both countries, students’ outbound mobility in HE has increased drastically (Ilieva et al., 2017). With that caveat firmly in place, the UNESCO numbers, which tend to capture mainly students going abroad for higher education, still reflect a near doubling of outbound students over the past decade with just under 18,000 Sri Lankans studying abroad in 2016 (ICEF, 2018). Sri Lanka is expected to have one of the fastest-growing territory enrolments in the world, with average annual growth of about 4.5% through 2027 (ICEF, 2018). However, British Council also projects that outbound mobility from Sri Lanka will exceed 32,000 students by 2027, a roughly 80% increase over the current UNESCO benchmark (British Council, 2018). Over the past six years, the number of Vietnamese students studying abroad has increased by 69% (StudyPortal, 2020). According to Vietnam’s Minister of Education and Training, there were about 170,000 Vietnamese studying abroad in 2019. According to UNESCO IUS, Japan, the United States, and Australia are among the top destinations of Sri Lankans and Vietnamese (StudyPortal, 2020). Due to their high-quality brand power, these three destinations’ popularity for higher education has grown rapidly (Arbidane & Tetereva, 2020; Casidy & Wymer, 2015). Since, Sri Lankans and Vietnamese are considered to be the most brand-conscious consumers worldwide which are beneficial for the other places as it is regarded as a prestigious place to study (Ismail, 2017; De Silva et al., 2020). Therefore, Sri Lanka and Vietnam are looking forward to reducing the outbound student mobility to increase their enrolment rate in local HEIs by introducing a variety of marketing strategies (Brankovic, 2018; Tran & Villano, 2017). Branding is one such strategy the HEIs need to focus on as it connects students’ emotional

1.3 Problem Outline

7

connection with the respective HEI (Balmer et al., 2020). To achieve this, HEIs in Sri Lanka and Vietnam is looking into the provision and development of strong brands for HEIs as it is difficult to evaluate their quality in advance, and students usually perceive the selection of the education service as a risky decision (Wilkins et al., 2018). The vision reiterates the need for HEIs to reduce brain drain, suggesting that the state education system has failed to highlight their values among prospective students (Siekierski et al., 2018). As a result, there was not much concern about the HEIs’ brands and consequently no motivation to build unique brands to achieve a differentiated position in the market. Accordingly, this study is focusing on Sri Lanka and Vietnam to provide a holistic view of the HEI brand and its brand equity to identify whether the brand equity has the ability to reduce students’ outbound mobility in HE. Even though both countries have been classed as emerging countries, the maturity of higher education sectors is different, and that has provided the basis for this study in conducting a comparative analysis between the countries. The higher education sector is a complex and challenging one due to its multiple layers. Due to the growing number of HEIs and the enrolment rate in Sri Lanka and Vietnam, there has been furious competition among HEIs, which stresses, more than ever, the importance of brand equity in this field. The Sri Lankan market includes about 20 public and 17 private HEIs and institutions that grant post-secondary degrees. In Vietnam, according to the higher education ministry, the market is composed of 171 public and 65 private HEIs. According to the UNESCO statistics in 2020, the gross enrolment ratio of undergraduates has increased from 24.9 to 28.6% during 2011–2019. In contrast, the undergraduate enrolment rate in Sri Lanka has reached close to 20% in 2019, placing it among the lowest rates of all emerging countries (UNESCO, 2020a, 2020b). One major difference between both markets is that data on public expenditure revealed that Vietnam spent about 5.3% of its GDP on education whereas the proportion of Sri Lanka’s public investment (recurrent and capital) on higher education—0.3% of GDP and 1.5% of total government expenditure—was way below that of its South Asian neighbours (Asian Development Bank, 2016). Such a low level of investment not only results in the deterioration of quality and relevance but also constrains the future expansion of higher education in the country. In Vietnam, policies are in place to encourage tuition deductions and exemptions, as well as financial aid schemes for low-income students (World Bank, 2020). The introduction of tuition fees appears to be a feasible financing option for Sri Lanka, given its unmet demand for higher education, the willingness of students’ families to pay, and the potential benefits of education to both the public and individuals. Another profound difference between both countries is the paying students. In Viet Nam, about 29% of the students enrolled at the HEIs are supported by a loan scheme. But for Sri Lanka, an estimate of the potential costs of a proposed loan scheme for disadvantaged students at HEIs revealed that the government would incur annual incremental costs of SLRs 700 million ($5.33 million for 1,000 students) (Asian Development Bank, 2016). Understanding the students’ perception of brand equity from the two distinctive countries will be the main pillar of any strategic marketing plan in HEIs, seeking

8

1 Introduction

to maintain, improve, or even revamp their brand equity (Mourad et al., 2020). This study offers a road map for HEIs to distinguish themselves from their peers and develop a sustainable competitive advantage for those who are in the same geographical regions with different cultural maturity levels in higher education. With emerging countries now accounting for almost three-fifths of the world’s GDP and the spending power of emerging economies on the rise, the developing higher education sector will further strengthen their existence in the competitive market (Cie´slik & Tran, 2019). The developing countries those who are struggling to develop the living status of the people can gain several benefits by branding their local HEIs as well. Each year, the government has to allocate funds to provide scholarships for the students to pursue their higher studies abroad. If branding HEIs helps to reduce outbound mobility, it will help the developing countries to invest the grant allocated for scholarships to develop their own HEIs. Moreover, the developing economy can focus on transnational education by creating positive brand equity for HEIs to develop partnerships with international universities and to attract foreign students to HEIs in developing countries to increase their visibility among the other regions. By reducing the outflux of local students through branding HEIs, developing countries can retain the educated youth with them to develop their countries further. As emerging countries in South Asian and South-East Asian regions, Sri Lanka and Vietnam have focused on upgrading their technological communication as information technology reforms have been introduced (Le et al., 2019; De Zylva and Wignaraja, 2018). Therefore, most Sri Lankan and Vietnamese firms have started focusing on social media to implement their marketing activities on diverse social media platforms (Fernando & Fernando, 2019; Krishen et al., 2019). Even though social media usage and SMM are well-established in developed countries, this concept is relatively new in emerging countries such as Sri Lanka and Vietnam (Hagg et al., 2018). Hence, this study extends the SMM usage to the emerging countries by considering samples from Sri Lankan and Vietnamese higher education sectors. The difference between the Sri Lankan and Vietnamese users based on their level of social media usage, the brand usage experience on social media, and the maturity of higher education sectors has provided the basis for this study in conducting a comparative analysis between the countries. The need to examine the factors that encourage SMM adoption while enhancing CBBE in the higher education sector has become a crucial factor. Even though previous researchers have made considerable attempts to examine the usage of SMM and CBBE as independent constructs, the relationship between SMM and CBBE has received little attention, particularly in higher education sectors in emerging countries. The present study, therefore, is centred on the question “What is the extent to which social media marketing influences undergraduates’ perception of customer-based brand equity in Sri Lankan and Vietnamese higher education sectors”?

1.4 Research Gap

9

1.4 Research Gap The following research gaps have been identified from the previous discussions, which provided the impetus for this research: Even though CBBE has risen as a most crucial topic for managerial and theoretical bases, many of these measures are applied to goods dominant brands and not services (Iglesias et al., 2019; Šeri´c et al., 2018). While CBBE studies have mainly focused on products such as automobiles and electronic equipment (Tasci, 2018), limited attention has been paid to the service sectors (Çifci et al., 2016). Sarker et al. (2019) noted that the service sector increasingly requires a proper conceptualisation model of CBBE. Due to the services’ intangible nature, the previous researchers did not pay much attention to building brand equity for the service sector (Cano Guervos et al., 2020; Ou et al., 2020). As a result, the higher education sector also received limited attention in building a strong brand and brand equity for HEIs. The importance of brand management for HEIs and brand building has become one of the main strategic objectives of HEIs (Amzat, 2016; Endo et al., 2019; Mampaey et al., 2015), yet, the higher education literature is lacking a comprehensive theoretical model of CBBE for HEIs (Herrero-Crespo et al., 2016; Mourad et al., 2020). The literature has not presented an in-depth discussion of the critical factors for developing CBBE for HEIs (Endo et al., 2019; Pinar et al., 2014). In this regard, the notion of CBBE in HEIs is limited in the higher education and marketing literature (Endo et al., 2019; Papadimitriou & Ramírez, 2018; Spry et al., 2018), and there is a dearth of empirical studies on the CBBE determinants in the higher education sector (Mourad et al., 2019). In addition, most of the previous empirical and theoretical researchers have focused on international marketing for higher education (Lomer et al., 2018; Wilkins, 2017; Wilkins et al., 2018); therefore, research work on branding for higher education is relatively scarce (Mourad et al., 2019). Several earlier researchers have focused on identifying the importance of websites (Barcellos et al., 2017; Ismailova & Inal, 2018; Saichaie & Morphew, 2014), blogs, and emails (Balroo & Saleh, 2019) to market HEIs in the online space. However, the literature pays less attention to students’ engagement with the social media content and its importance for HEIs’ marketing (Stathopoulou et al., 2019). More importantly, Brech et al. (2017) highlighted two findings from their studies, which indicated a critical gap relating to SMM relating to the service sector. First, SMM findings are strongly influenced by the type of industry, making it difficult to generalise findings from one industry to another (Corstjens & Umblijs, 2012; Schulze et al., 2015). Therefore, the previous studies’ conclusions related to SMM could not be applied to the higher education sector. Second, though the prior researchers have highlighted the importance of social media to communicate with current and prospective students (Al-Rahmi et al., 2018; Chugh & Ruhi, 2018), limited attention has been given to understand the underline mechanisms of SMM for HEIs (Brech et al., 2017; Shields & Peruta, 2019).

10

1 Introduction

Developing HEI’s brand equity and communicating it with the stakeholders through social media is vital, yet HEIs often struggle to find the appropriate balance (Peruta & Shields, 2018). While several studies have been performed on SMM and CBBE in the marketing literature, these two research streams have remained mostly independent. Therefore, the relationship between SMM and CBBE requires further investigation (Ebrahim, 2020; Koay et al., 2020), particularly in the higher education sector. The previous studies related to subjective norms have primarily centred on recognising the customers’ behavioural outcomes (Alnaser et al., 2017; Kashif et al., 2018; Liang & Shiau, 2018). Subjective norms as an antecedent to customers’ behavioural intention have been well-established in a technology-based environment (Konietzny et al., 2018; Petrescu et al., 2018). However, there is a notable shortage of studies conducted to identify the importance of subjective norms on customers’ behaviour towards brand-related activities in the online environment. There is a scarcity of research conducted to investigate the influence of subjective norms on brands and their equity in the social media sphere (Sijoria et al., 2019). While subjective norms have received much attention from practitioners and academics, limited studies revealed cross-cultural differences in subjective norms (Minton et al., 2018), particularly in the higher education sector (Malmström & Öqvist, 2018). Several studies have attempted to identify the direct effect of brand credibility on perceived value (Chakraborty & Bhat, 2018; Jahanzeb et al., 2013; Vera, 2015) and brand choice intention (Wang et al., 2017) by reducing the perceived risk associated with the brand (Chin et al., 2019; Martín-Consuegra et al., 2018). Some studies have recognised brand credibility as a mediator between brand experience, endorser credibility, and customers’ purchase intention (Chin et al., 2019; Dwivedi et al., 2018). However, the impact of brand credibility in building CBBE has received less attention in the literature, and limited studies are available to identify the relationship between SMM and CBBE with brand credibility dimensions as intervening variables (Chakraborty & Bhat, 2018). The influence of brand credibility on the linkage between subjective norms and CBBE is sparse in the literature. Therefore, limited studies have focused on identifying the mediating effect of brand credibility among SMM, subjective norms, and CBBE in the service context, particularly for HEIs. The social information processing theory explains that customers should be able to make instant choices by referring to their internal information sources (Bhaduri & Ha-Brookshire, 2017) to make judgements about the brands (Yan & Wang, 2018). However, though this theory has been studied in marketing for many years (Colicev et al., 2019; Nasution et al., 2011; Papadopoulos & Heslop, 2014; Sagan, 2016), potentially impacting the process through which customers’ experience unfolds (Hsu & Lawrence, 2016), its role in social media marketing is under-explored. While SMM is receiving increasing attention in the developed countries, the adoption of social media and conducting marketing activities on social networking sites are relatively new in emerging countries such as Sri Lanka and Vietnam (Hagg et al., 2018). Choi et al. (2018) argued that social media attitudes, adoption, and behaviour

1.4 Research Gap

11

might differ based on cross-cultural differences. However, a comparative study to identify the factors which motivate the customers to use social media in emerging countries, particularly in Sri Lanka and Vietnam is still lacking in the literature. Besides, the various models of CBBE developed to study the customers’ attitudes and behaviour in developed countries may not be relevant in emerging countries due to cultural differences (Mourad et al., 2019). Several researchers have argued that CBBE models employed in developed countries where they originated are not suitable to apply in the emerging country context (Hudson et al., 2016; Rambocas et al., 2018). Social media usage in emerging countries is still in the development phase (Rahman et al., 2020). One of the main reasons why social media is not wellestablished in emerging countries is that research and development related to social media have been primarily conducted in developed countries, and limited studies have focused on emerging countries (Olotewo, 2016). Therefore, the benefits of using social media marketing have not been discussed in detail in the emerging country context (Ahmad et al., 2019). In addition, many firms in emerging countries are still conducting their marketing activities in traditional media due to a lack of trust in social media channels (Catalán-Matamoros & Peñafiel-Saiz, 2019). The inadequate social media professionals in such organisations undermine the benefits of using social media for marketing activities. The lower adoption of social media marketing among the emerging countries resulted in lower engagement with the customer and the brand (Crittenden et al., 2020). But, in contrast, developed countries have already identified the importance of using social media, and they were able to reap the benefits doing marketing on social networking sites by boosting their sales while increasing their customer base (Kvasniˇcková Stanislavská et al., 2020). The social media professionals in developed countries focused on converting their followers on social media pages into customers (Olotewo, 2016). This is the main reason for the firms in developed countries to succeed in their marketing activities on social media. Social media marketing in developed countries is well-established among the many sectors such as automobile (Jena, 2020), luxury fashion brands (Ananda et al., 2019), and real estate (Ullah et al., 2019), and they are further expanding it to the other sectors as well (Dwivedi et al., 2020). Sobaih et al. (2016) purport that higher education institutes in emerging countries suffer from a lack of communication technology to connect with the students. Therefore, the technology-based formal communication method is not widely used by the HEIs in emerging countries. The HEIs in emerging countries are still using traditional communication methods such as emails, websites, and blogs to connect with the students (Elvestad et al., 2018). But, it communicates a relatively limited amount of students, and the majority of the students’ requirements could not be addressed. The lack of online presence of HEIs in emerging countries made a lack of interactivity with the students. Eventhough the previous researchers have argued that social media can be used as a communication platform for academic-related purposes, HEIs in emerging countries did not use their full capabilities in engaging with students in the online sphere (Dennen et al., 2020). Accordingly, marketing on social networking sites in emerging countries did not receive much attention in theory and practice.

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1 Introduction

Therefore, this study took another attempt to enrich the literature by identifying the importance of social media as a marketing communication tool for HEIs in emerging countries. In addition, previous studies have mainly focused on developed countries, and limited studies were conducted on the emerging country context in identifying the importance of social media marketing (Jamali & Karam, 2018). The findings of this study contribute to the socio-cultural perspective of student brand engagement in emerging countries, shedding light on why students depend on social media marketing and norms in selecting an institute to pursue their higher studies.

1.5 Significance of the Study This study focuses on CBBE, which is conceptualised as a multidimensional construct to understand the undergraduates’ perceptions of the higher education sector. Thus, the present study aims to fill the existing gap by contributing an underdeveloped area in higher education literature related to CBBE, while highlighting its importance for HEIs in emerging countries in the following ways: (1) it allows assessment of the equity at the brand level, (2) it capitalises on the high acquaintance of marketing managers with customer-based measures, and hence easily interpreted and used in practice, and (3) it leverages the abundance of marketing scholarly research that capitalises on customer-based equity measures. This study further contributes to the service brand equity by corroborating and clarifying findings from the higher education sector to enrich the service branding literature. Besides, there is a notable shortage in the higher education literature relating to SMM. Therefore, this study reviewed existing academic and industry knowledge on SMM to provide a comprehensive understanding of the theoretical and practical knowledge of SMM in the higher education context. Thus, this research will verify the extent to which SMM can make a significant impact on the prospective students’ decision-making process in selecting an HEI. The importance of subjective norms and their impact on CBBE for HEIs is discussed in this study which has received limited attention in the higher education literature. It filled the gap by examining the mediating role of brand credibility in developing CBBE through SMM, and subjective norms to offer a comprehensive understanding of enhancing CBBE for HEIs. Again, this study contributes to the social information processing theory in general and in the higher education sector. The importance of social information processing theory in relationship marketing had been well documented (Davis & Agrawal, 2018). We empirically demonstrated and extended this theory’s usefulness to the social media environment, integrating brand-related commutation activities on the social media sphere. Thus, a social information processing lens was used to advance our understanding of branding activities in the social media marketing phenomenon. The present study took another significant approach to investigate the interplay between the constructs of the study based on the students’ brand usage experience,

1.6 Research Aim, Objectives, and Questions

13

categorising them as junior and senior students, to understand students’ behaviour on the social media platform in an emerging country context. The present study makes a comparative analysis of the higher education sectors in Sri Lanka and Vietnam, which received limited attention in the marketing and higher education literature. Thus, this study further advances the literature while having a cross-cultural analysis based on the customers’ social media adoption and brand engagement in emerging countries. From the managerial perspective, the findings of this research will further help the marketing managers to figure out how the content generated in social media platforms could be used more effectively in creating branding strategies for HEIs. Finally, highlighting the SMM and CBBE adoption in Sri Lankan and Vietnamese undergraduates, this study’s findings pave the way for further in-depth research in this field, and other service sectors in Sri Lanka or Vietnam, and possibly other countries with a similar cultural heritage.

1.6 Research Aim, Objectives, and Questions This study aims at enhancing the understanding and knowledge concerning the impact of social media marketing on CBBE in higher education sectors in Sri Lanka and Vietnam by incorporating subjective norms and brand credibility to develop and validate an integrated model, which includes location and brand usage experience of the undergraduates. Three research questions (RQ) were identified from this broad aim as follows: ● RQ1: What is the extent to which social media marketing, subjective norms, and brand credibility are related to customer-based brand equity, as perceived by prospective undergraduate students? ● RQ2: What is the extent to which brand credibility influences the relationship between social media marketing, subjective norms, and customer-based brand equity? ● RQ3: Are there any differences in adopting social media marketing, subjective norms, brand credibility, and customer-based brand equity based on location and undergraduates’ brand usage experience? Three research objectives were formulated as follows to address the above research questions: (1) To evaluate the undergraduates’ perception of social media marketing, subjective norms, and brand credibility in the higher education sector, and its causal relationship with customer-based brand equity; (2) To examine the mediating effect of brand credibility among social media marketing, subjective norms, and customer-based brand equity; and

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(3) To evaluate the moderating effect of location (Sri Lanka vs Vietnam) and undergraduates’ brand usage experience (junior student vs senior student) from the perspective of undergraduates in Sri Lanka and Vietnam.

1.7 Proposed Methodology Initially, this study conducted a literature review to identify the gaps to form a strong foundation for existing knowledge to develop the proposed model. It particularly focused on the existing theories to establish logical relationships among the study constructs to provide a foundation for the proposed model. Primary data were collected via quantitative approaches, followed by an empirical study using undergraduates from private HEIs in Sri Lanka and Vietnam, to address the research questions.

1.8 Structure of the Thesis The thesis consists of eight chapters, as follows: Chapter 1 has introduced the research background, the nature of the research problem, the research gap, and the significance of the research. Further, this chapter discusses the research aims, objectives, and research questions and provides a summary of the methodology. Chapter 2 has reviewed the existing literature related to SMM, CBEE, and the other constructs of the study. It critically reviews some models associated with SMM and CBBE to identify the existing gaps to provide a novel contribution through this study. Chapter 3 has developed a theoretical model using existing theories to address the study’s research objectives. The hypothesis was then developed based on the logical relationships shown in the framework. Chapter 4 provided a detailed description of the methodology adopted in this study to address the research objectives. In this chapter, the researcher examined the philosophical position in detail while highlighting the methods used for data collection via a questionnaire. The sampling strategy of the study is described, and the relevant techniques for data analysis are presented. Further, the ethical considerations made in the study are discussed. Chapter 5 has reported the findings of the descriptive data analysis. The demographic profiles of the respondents have been discussed in detail, and all the steps, starting from data screening to factor analysis, are explained. Chapter 6 has discussed the results of confirmatory factor analysis and tested the hypothesis using Structural Equation Modelling (SEM). Finally, this study made a comparative analysis between Sri Lanka and Vietnam to identify the differences in adopting social media and brand practices.

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Chapter 7 has discussed the main findings of the study, considering the literature reviewed in Chap. 2. It concentrates on how these findings provide answers to the research questions and thereby satisfy the study objectives. This chapter presents theoretical and managerial implications and highlights the contribution to the existing body of knowledge. Chapter 8 draws a conclusion based on research findings and discusses the research limitations. Finally, it provides some valuable suggestions for future research directions.

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Sanders, J. S. (2019). National internationalisation of higher education policy in Singapore and Japan: Context and competition. Compare: A Journal of Comparative and International Education, 49(3), 413–429. Sano, K. (2015). An empirical study the effect of social media marketing activities upon customer satisfaction, positive word-of-mouth and commitment in indemnity insurance service. In Proceedings International Marketing Trends Conference (Vol. 27, No. 3, pp. 21–32). Sarker, M. M., Mohd-Any, A. A., & Kamarulzaman, Y. (2019). Conceptualising consumer-based service brand equity (CBSBE) and direct service experience in the airline sector. Journal of Hospitality and Tourism Management, 38, 39–48. Schulze, C., Schöler, L., & Skiera, B. (2015). Customizing social media marketing. MIT Sloan Management Review, 56, 8. Seo, E.-J., & Park, J.-W. (2018). A study on the effects of social media marketing activities on brand equity and customer response in the airline industry. Journal of Air Transport Management, 66, 36–41. Šeri´c, M., Mikuli´c, J., & Gil-Saura, I. (2018). Exploring relationships between customer-based brand equity and its drivers and consequences in the hotel context. An impact-asymmetry assessment. Current Issues in Tourism, 21, 1621–1643. Shafaei, A., Nejati, M., & Maadad, N. (2019). Brand equity of academics: Demystifying the process. Journal of Marketing for Higher Education, 29, 121–133. Sharabati, A.-A. A., Alhileh, M. M., & Abusaimeh, H. (2019). Effect of service quality on graduates’ satisfaction. Quality Assurance in Education. Shields, A. B., & Peruta, A. (2019). Social media and the university decision. Do prospective students really care? Journal of Marketing for Higher Education, 29, 67–83. Siekierski, P., Lima, M. C., & Borini, F. M. (2018). International mobility of academics: Brain drain and brain gain. European Management Review, 15, 329–339. Sijoria, C., Mukherjee, S., & Datta, B. (2019). Impact of the antecedents of electronic word of mouth on consumer based brand equity: A study on the hotel industry. Journal of Hospitality Marketing & Management, 28, 1–27. Sobaih, A. E. E., Moustafa, M. A., Ghandforoush, P., & Khan, M. (2016). To use or not to use? Social media in higher education in developing countries. Computers in Human Behavior, 58, 296–305. Spry, L., Foster, C., Pich, C., & Peart, S. (2018). Managing higher education brands with an emerging brand architecture: the role of shared values and competing brand identities. Journal of Strategic Marketing, 1–14. Stathopoulou, A., Siamagka, N.-T., & Christodoulides, G. (2019). A multi-stakeholder view of social media as a supporting tool in higher education: An educator-student perspective. European Management Journal. Studyportal. (2020). Beyond China: The next Asian international student recruitment countries. Retrieved July 11, 2021, from https://studyportals.com/blog/beyond-china-the-next-asian-intern ational-student-recruitment-countries/ Sutherland, W., & Jarrahi, M. H. (2018). The sharing economy and digital platforms: A review and research agenda. International Journal of Information Management, 43, 328–341. Swani, K., & Milne, G. R. (2017). Evaluating Facebook brand content popularity for service versus goods offerings. Journal of Business Research, 79, 123–133. Tasci, A. D. (2018). Testing the cross-brand and cross-market validity of a consumer-based brand equity (CBBE) model for destination brands. Tourism management, 65, 143–159. Tomlinson, M. (2017). Student perceptions of themselves as ‘consumers’ of higher education. British Journal of Sociology of Education, 38(4), 450–467. Tran, C.-D.T., & Villano, R. A. (2017). An empirical analysis of the performance of Vietnamese higher education institutions. Journal of Further and Higher Education, 41, 530–544. Tsiotsou, R. H., & Wirtz, J. (2015). The three-stage model of service consumption. In J. R. Bryson & P. W. Daniels (Eds.), Handbook of service business-management, marketing, innovation and internationalisation (pp. 105–128).

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Chapter 2

Literature Review

2.1 Introduction This chapter reviews and critically analyses the extant literature to explore various aspects of branding and social media as the foundations to develop the theoretical framework to identify the relationship between customer-based brand equity and social media marketing in the Sri Lankan and Vietnamese higher education sectors. This chapter is divided into four main sections. The first section provides a detailed discussion on brand, evaluation of branding, branding in the service sector, brand equity, different perspectives of brand equity, the view of brand equity in the context of this study, overview of the antecedents and possible outcomes of CBBE, brand equity in the higher education sector, and the measurements of CBBE in the higher education sector. The second section of this chapter critically reviews the theoretical background of social media, social media marketing, firm-generated content (FGC), user-generated content (UGC), and social media marketing in higher education. Section 2.3 focuses on subjective norms and brand credibility, and Sect. 2.4 discusses the moderators of the proposed framework, i.e., location and students’ brand usage experience. Further, this section compares and contrasts social media usage among the Sri Lankans and Vietnamese to make a comparative analysis between the countries.

2.2 Brand and Branding In marketing literature, a brand provides a primary point of differentiation between firms’ offerings (Benoit et al., 2017) to help the firms’ success (Chang et al., 2018). The brand starts with a distinctive name (Chao & Lin, 2017) and is then endorsed by the corporate reputation of the sign of ownership (Biraghi et al., 2018). The brand © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_2

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is a signal to disclose the hidden qualities of the product or service, inaccessible to contact (Chang et al., 2016), and possibly readily available to access through experience (Arya et al., 2019), but where the customers refuse to take the risk of purchasing it (Mohseni et al., 2018). Brand sometimes can be defined from the customers’ perspective (Tasci, 2018) and/or from the brand owners’ perspective (Bachmann et al., 2018). Kotler (2002) identified the brand as “a name, trademark, logo, or another symbol, a brand is essentially a seller’s promise to deliver a specific set of features, benefits, and services consistently to the buyers”. Later, several scholars expanded this concept and suggested that the brand is a combination of complex symbolic attributes and ideas (Liu & Liu, 2018; SadikRozsnyai & Bertrandias, 2019). The brand name is much more than a mere label (Geyskens et al., 2018), which enables the customers to be a part of the context of complex buying situations (Foroudi et al., 2018). However, a brand is a guarantee of a firm that it will continuously and consistently deliver on its promises (Piha & Avlonitis, 2018), including explicitly or implicitly made on intangible features (Kindermann & Schreiner, 2018), specific quality thresholds, benefits, and convenience to customers (Yan & Cao, 2017). At one end of the spectrum, brand constitutes a name, a logo, a symbol, an identity, or a trademark (Japutra et al., 2018), while at the other end, the brand embraces all tangible and intangible attributes of the business (Ding & Keh, 2017; Naidoo & Abratt, 2018). A strong brand provides endless benefits to customers and firms (Chang et al., 2018). A successful brand offers a signal to the customers (Anees-ur-Rehman et al., 2018) that it protects them from competitors who attempt to provide similar products with different qualities (Biedenbach et al., 2019). Kotler (2000) explained that a brand depicts the producer’s value and suggests the kind of customers who buy or use the product. Later, Keller (2003) noted that a brand could create an optimal position in the customers’ minds. Kapferer (2012) stated that the brand has a mental association with the customers, which adds to the perceived values of a product or service. Yang et al. (2019) argued that the brand is not an end in itself, and it needs to be managed since it is an instrument for a firm’s growth and profitability. However, the brand helps the customers differentiate the products and services when they have asymmetric information about their quality and performance (Boeuf & Darveau, 2019). The best brands convey a warranty of quality, which reduces customers’ perceived risk in the purchase (Hazée et al., 2017). A brand does not only act as an information provider but also performs certain other functions that justify its monetary return or attractiveness when the customers value them (So et al., 2017; Sánchez-Casado et al., 2018). The brand further creates powerful and effective communication between the customers and the firms (Yang et al., 2017), which provides greater exposure to the firm and brand. Also, the brand helps firms to build customer recognition (Iglesias et al., 2019a, 2019b), create a competitive edge in the market (Massara et al., 2018), and easily introduces the firm’s new products (Ahn et al., 2018; Iglesias et al., 2019a, 2019b), develops greater customer loyalty (Cheng et al., 2018), enhances credibility (Dwivedi et al., 2018), and enhances the

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Table 2.1 Functions of a brand for the customers (adapted from Kapferer, 2008) Function

Customer benefit

Identification

To be seen, to quickly identify the sought-after products, and to structure the shelf perception

Practicality

To allow savings of time and energy through identical repurchasing and loyalty

Guarantee

To be sure of finding the same quality no matter where or when you buy the product or service

Optimisation

To be sure of buying the best product in its category, the best performer for a particular purpose

Badge

To have confirmation of your self-image or the image that you present to others

Continuity

Satisfaction created by a relationship of familiarity and intimacy with the brand you have been consuming for years

Hedonistic

Enchantment linked to the attractiveness of the brand, its logo, its communication, and its experiential rewards

Ethical

Satisfaction linked to the responsible behaviour of the brand in its relationship with society (ecology, employment, citizenship, and advertising which does not shock)

purchase intention (Moreira et al., 2017). Kapferer (2008) identified the benefits the customers can receive through a brand’s functions (Table 2.1). The brand is the direct consequence of the market segmentation strategy, which provides the basis for product or service differentiation (Gengler & Mulvey, 2017). The term “branding” is more than a name or definition that provides a signal to the external world, which has been stamped with a mark and imprint of a firm (Brusch et al., 2018). Branding consists of transforming the product or service category, which requires long-term involvement, high-level skills, and resources (Vinayak et al., 2017). It is one of the key elements in marketing strategies (Du Preez et al., 2017), which is increasingly viewed as a powerful tool to obtain sustainable competitive advantages (Geurin & Burch, 2017) and to fully utilise available resources (Odoom et al., 2017). Branding is a challenge to develop a set of positive associations for the brand (Boisvert & Ashill, 2018). It involves the process of endowing products and services (Yang et al., 2019) with the advantage that accrues to build a strong brand (Odoom et al., 2017). In the process of branding, all different kinds of information may be linked to a brand, including awareness, attributes, benefits, images, thoughts, feelings, attitudes, and experiences (Yang et al., 2017; Ahn & Back, 2018; Yang et al., 2019). The recent branding literature emphasises the importance of building a strong brand by making an emotional connection between the brand and the customer (So et al., 2017; Moliner et al., 2018). Studies have confirmed that modern customers are no longer simply buying a product or service (Gebauer et al., 2017); instead, they buy the wonderful and emotional experiences surrounding the sold item (Ou & Verhoef, 2017; Ruiz-Mafe et al., 2018). Therefore, today, branding has become an integral part of the marketing strategy to offer valuable experience (Rangarajan et al., 2017) and differentiation sources (Yakimova et al., 2017).

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The increasingly competitive environment, experienced by both the manufacturing and service sectors, has led the branding to be increasingly used as a strategic resource (Flikkema et al., 2019) to achieve a competitive advantage (Punjaisri & Wilson, 2017). More specifically, the service sector focuses more on branding than the manufacturing industry because of the intangible nature of the services (Odoom et al., 2017). In the competitive, interconnected, and transparent business environment, service branding activities have to offer memorable brand image and brand experiences to their customers (Sepe & Pitt, 2017) which differentiate themselves and build a stable competitive position in the marketplace (Hunt, 2019). This is a challenge to the service sector due to its intangibility nature. While focusing on the higher education sector, which is one of the fastestgrowing and competitive sectors under the services, this study aims to understand the importance of branding for the service sector contributing to the service brand literature.

2.2.1 Branding in the Service Sector The desire to create a distinct mental picture of the firm and the services provided by the firm manifests itself in presenting a strong service brand (Iglesias et al., 2019a, 2019b). Strong service brands enable customers to visualise better and understand the service’s nature (Zhang et al., 2017; Granados & Velez-Langs, 2018). Service brands are “the surrogates when the company offers no fabric to touch, no trousers to try on, no watermelons or apples to scrutinise, and no automobile to test-drive” (Berry, 2000). It reduces customers’ perceived social and monetary risk in purchasing the services (Chang & Ko, 2017), which is difficult to evaluate before purchase because the service sector is often associated with a higher risk than the manufacturing sector (Sichtmann et al., 2017). It is often suggested that marketing in the service sector is relatively challenging due to the unique characteristics of the services like intangibility, perishability, heterogeneity, and inseparability (Popli & Rizvi, 2015; Çifci et al., 2016; Legner et al., 2017). A particular concern is that services are more difficult to evaluate (Wang et al., 2017) than manufactured goods because the perceived risk is generally higher in selecting a service (Janssen et al., 2018; Walsh et al., 2017) in advance of purchases (Nollet et al., 2017; Kumar et al., 2019). The heightened competition among service providers further adds the need to develop a unique identity for the service brands besides the role of customers’ involvement and participation in the service process (Abney et al., 2017; Gummerus et al., 2017; Jung & Seock, 2017). The strengthening of the service brand is primarily driven by attributes of the firm such as the quality of the service (Markovic et al., 2018), the people standing behind the service (Sahin ¸ et al., 2017), and the relationship between customer and supplier (Ahn & Back, 2018). Service brands must assure customers of a consistent and uniform level of service quality (Sierra et al., 2017), which is essential for market offers, and is characterised by experience and credence attributes (Chocarro et al.,

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2018). Unlike the manufacturing sector, the service brand is associated with the “firm” in the service sector (Makanyeza & Chikazhe, 2017; Sierra et al., 2017). In the manufacturing sector, the packaged goods or products are considered as the primary brand (Berry, 2000); but in the service sector, the service offering company is considered as the primary brand (Berry, 2000). This is due to the service sectors’ inability to package, label, and display their services in the same way as the products (Biemans & Griffin, 2018). These differences do not implicate that brand development is less appropriate or less critical for the service sector (Leckie et al., 2018). The intangibility nature of services tempts the customers to purchase the services from a safe place (Chang & Ko, 2017). A strong service brand is a safe place for customers to buy the required service (Nobar & Rostamzadeh, 2018). From the customer’s viewpoint, a strong service brand is a promise of future satisfaction which blends what the firm says about its services, how the firm performs the services, what others say about the firm’s services, and how others evaluate the firm’s services versus their competitors (Weitzl & Hutzinger, 2019). The service sector must perform its service effectively to maintain a strong service brand (O’Reilly et al., 2017). Service providers must focus not only on filling the customers’ needs but also on fulfilling theirs as well. Executing a needed service and performing it better than the competitors is a powerful brand-building strategy for companies (Borchardt et al., 2018). However, due to the intangibility and the perceived risk associated with services (Chang & Ko, 2017; Hussain et al., 2017), customers’ perception of the branded service is particularly crucial (Liu et al., 2017; Markovic et al., 2018). In this situation, a branded service can play an essential role as a risk reliever while increasing customers’ trust (Cambefort & Roux, 2019; Geyskens et al., 2018) and ensuring customers’ confidence in their purchase decision (Oghazi et al., 2018; Shang et al., 2017). Furthermore, a branded service facilitates the customers to select the most promising service among the competitive brands (Ibrahim et al., 2017; Molina et al., 2017; Mourad et al., 2019). Thus, the branded service has been increasingly recognised as a significant determinant of customer choice in the service sector (Rubio et al., 2017; Manhas & Chauhan, 2017; Boukis et al., 2017). In essence, a service brand provides a promise or a signal to the customers about the service (Anees-urRehman et al., 2018; Karanges et al., 2018) delivered by enabling the customer to better visualise the service offerings (Kokina et al., 2017; Khodadad Hosseini & Behboudi, 2017). While several researchers hinted that services pose challenges to marketers and brand managers, very few have investigated service branding at all (de Noronha et al., 2017; Coffie, 2018; Endo et al., 2019). Even though branding plays a crucial role in the service sector since strong service brands increase customers’ trust in the invisible purchase, the higher education sector has not embraced this new ontology (Endo et al., 2019). However, to date, marketing research has focused on issues ranging from customer evaluations of services (Pai et al., 2018), measuring service quality (Lee & Cheng, 2018), service failures (Trianasari et al., 2018), and service

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switching (Bergel & Brock, 2018). It has not been scrutinised, particularly in the education sector. Since a brand represents the totality of feelings, and perceptions existing that prospective students have towards the particular HEI, building a brand is ideal for standing out and creating an emotional connection (Endo et al., 2019). It is generally argued that branding HEIs have become increasingly important as it develops and articulates a unique, clear, and coherent brand to respond to and satisfy the students’ needs (Clark et al., 2020). Branding in the higher education sector has, therefore, been identified as vital for future brand theory and practices (Broucker et al., 2020), but inadequate attention has been made. The assumption is that the intended marketing outcome would differ even for the same product or service if it were unbranded (de Almeida et al., 2017). In these situations, customers are automatically generating the perception and association with the brand (Iglesias et al., 2019b). Marketers must emphasise brand equity to build a strong brand, as it allows them to earn a greater margin and volume than without the brand name (Keller & Brexendorf, 2019). A brand with positive brand equity facilitates to recognise and recall the brand easily while creating a substantial distinction among the competitive brands (Maanda et al., 2020). Brand equity is the added value of a brand that produces uniqueness among other competitive brands. It is, therefore, not possible for a brand not to have any brand equity. If a strong brand is considered to be the most valuable asset to a firm (Rahman et al., 2018), managers must measure the brand equity built upon their brand. Since the brand has turned into the most important asset for an organisation, measuring the strategic value of a brand, i.e., brand equity, has become vitally important (Keller & Brexendorf, 2017), as discussed in the following section.

2.3 Brand Equity 2.3.1 Definition of Brand Equity Brand equity is one of the most popular and conceivably vital promoting concepts, which both academicians and practitioners have widely discussed over the past few years (Girard et al., 2017; Thammawimutti & Chaipoopirutana, 2018). The term “brand equity” was first used widely by US advertising practitioners in the early 1980s (Hunt, 2019). They viewed brand equity as the value endowed on a product due to the firm’s marketing effort, which compromises the percentage of the total value of many industries (Troiville et al., 2019). The idea of brand equity further developed in the mid-1990s to cross over the gap between short- and long-term marketing accomplishments indicating non-financial, market-based intangible assets, which resulted from the firms’ past marketing activities (Chatzipanagiotou et al., 2016; Christodoulides et al., 2006; Peters & Taylor, 2017). A decade later, brand equity became an integral component of marketing performance measurement (Anabila,

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2020), and was considered an essential source of competitive advantage (Liu & Jiang, 2020). Scholars stated that a brand is influential and manifests its importance in three interconnected markets: customer, product, and financial markets (Cal & Lambkin, 2017; Fischer & Himme, 2017; Winzar et al., 2018). Thus, the values created by these markets may be designated as brand equity (Fischer & Himme, 2017). Nevertheless, Weiger et al. (2017) and Sinclair and Keller (2017) argued that the adoption of brand equity resulted from the informational requirements of the following groups of people: (a) marketers, who are seeking to increase the credibility of their brands by demonstrating the value of a brand from the financial terms, (b) accountants, who set the price of a brand to be sold or purchased, and include the brand in the company balance sheet, especially in the mergers and acquisitions, and (c) shareholders and financial analysts, who verify the financial performance associated with the firm’s brands. Brand equity is considered as a signalling phenomenon (Šeri´c & Gil-Saura, 2017; Singh, 2018) where the brands with high equity convey quality signals that can reduce customers’ uncertainty (Efanny et al., 2018; Heinberg et al., 2018). Furthermore, Salazar-Ordóñez et al. (2018) argued that brand equity plays a vital role in reducing risks, especially during failure episodes of the firms’ brands. Hazée et al. (2017) noted that customers are more likely to forgive failures caused by the brand with high equity than the losses caused by the brands with a lower level of brand equity. A high-equity brand helps firms offset the potential negative consequences resulting from a brand’s failure (Guèvremont & Grohmann, 2018; Hazée et al., 2017; Lambert et al., 2018). The brand equity paradigm has been extensively discussed in marketing literature, and many researchers have offered a wide array of definitions for the brand equity concept, as presented in Table 2.2. Farquhar’s (1989) conceptualisation has prompted several questions that remain unanswered, including “what are the adequate strategies for leveraging brand equity and what are the possible determinants of brand value?” (Davcik et al., 2014). However, the works of Farquhar (1989) has preempted future research in identifying the possible ways to determine the strategic aspects of brand equity formation, to leverage brand equity, and to identify how brands act as aggregators of value to core product functionality (Davcik et al., 2014). However, in the branding literature, Farquhar’s (1989) pioneering work laid the foundation for later research on brand equity (Donat, 2018). More recently, several scholars conceptualised brand equity as a relational marketbased asset (Bendle & Butt, 2018; Rahman et al., 2018) generated through continuous interactions and effective relationships between brand and their customers (Cheng et al., 2019; Iglesias et al., 2019a). Despite the availability of numerous definitions for brand equity in the literature, the scope of brand equity and its measurements have little consensus (Cheng et al., 2019; Keller & Brexendorf, 2017). The academic discussion is inconclusive about the conceptual foundations, sources, essence, and measures of brand equity (Liu et al., 2017; Su & Chang, 2018). For example, there is no consensus in the literature

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Table 2.2 Key research of brand equity concept and definitions Author and year

Definition

Farquhar (1989, p. 24)

The added value with which a given brand endows a product; thus, rendering the development of a strong brand is imperative for organisational strategic thinking

Aaker (1992, p. 28)

“A set of brand assets and liabilities linked to the brand’s name and symbol, can subtract from, and add to the value provided by a product or service and provide value to customers and a firm”. The assets and liabilities are divided into the following five categories: brand awareness, perceived quality, brand associations, brand loyalty, and other proprietary brand assets. The first three are perceptual components of brand equity, and brand loyalty is classified as a behavioural component

Keller (1993, p. 1)

The differential effect of brand knowledge on customer response to the marketing of the brand, which is of high importance for firms’ competitive position and performance

Simon and Sullivan (1993, p. 1)

The incremental cash flows that accrue to branded products resulted from the sale of unbranded products. This definition explains brand equity in terms of financial impact

Park and Srinivasan (1994, p. 271) The added value endowed to the product as perceived by a customer to measure the difference between the individual customer’s overall brand preference and brand preference based on objectively measured product attribute levels Feldwick (1996)

The total value of a brand as a separable asset when it is sold or included on a balance sheet

Yoo et al. (2000)

The difference in consumer choice between a branded and an unbranded product with the same level of features

Ailawadi et al. (2003, p. 1)

The “marketing effects or outcomes that accrue to a product with its brand name compared with those that would accrue if the same product did not have the brand name”

Clow and Baack (2005)

A set of characteristics that make a brand unique in the marketplace, which allows the firm to charge a higher price and retain a greater market share than would be possible with an unbranded product

Wang and Finn (2012)

The added value to the existing brand name, which enriches the firm’s marketing endeavours

Hazes et al. (2017)

The strength of a brand the customers hold in their minds

Chow et al. (2017)

An aspect of perceived value in customers’ minds such that branded products and services cause customers to be biased towards the brand and/or the given products and services versus an unbranded equivalent

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33

on whether brand equity refers to the value of a brand name or the value of a brand (Guzmán & Davis, 2017; Bettencourt, 2017; Round & Roper, 2017); or what is the theoretical delineation of brand equity (Wang et al., 2019). Although brand equity has no universally accepted definition, most authors agree and provide a description similar to Aaker’s (1992), which is a set of brand assets and liabilities that gives value to the customers and the firms. Most scholars agreed that Aaker (1992) provided one of the most generally accepted and comprehensive conceptualisations of brand equity phenomena (Brochado & Oliveira, 2018; Kladou et al., 2017; Nyadzayo et al., 2016). Although scholars have defined brand equity in several different ways, brand equity should be defined in terms of the marketing effect uniquely attributed to a brand (Keller, 2009; Yu et al., 2018; Naidoo & Abratt, 2018). In Aaker’s (1992) definition, both the perceptual and market behaviour-related measures to capture customers’ responses were considered for the branding of a product or service. Aaker (1992) highlighted human characteristics associated with a brand’s assets and liabilities in creating brand equity. Since the present study is based on brand equity from students’ perspectives, it must consider a relationship between human characteristics and branding to create a brand value for HEIs. Accordingly, the present study is based on Aaker’s (1992) definition of brand equity since it is the most comprehensive and widely accepted definition of brand equity available in the marketing literature. The brand should always have better contact with the customers to develop positive brand equity (Soler & Gémar, 2017; Iglesias et al., 2019a). Theurer et al. (2018) suggested that the firms must create better communication channels to facilitate direct customer contacts with the firms’ brands to develop brand equity. Therefore, the firms must develop effective marketing strategies to enable communication between the customers and the brands (Seyyed Amiri et al., 2017). In the current hyper-connected environment, this is not only about inspiring their customers through communications but also about offering a consistent, valuable experience to the customers (Iglesias et al., 2019b; Peruta & Shields, 2017). Brand equity must be properly maintained since it tends to reduce over time unless the firms keep it well (Altaf et al., 2017).

2.3.2 Brand Equity in Higher Education The increasing competition among HEIs has driven the higher education sector to focus on clearly articulating HEI brands (Cattaneo et al., 2017; Lomer et al., 2018; Hu et al., 2018). Further, globalisation (Tight, 2019; Hromcová & Agnese, 2019) and increased effort to recruit students have fuelled the branding of the higher education sector (Lim et al., 2018; Lomer et al., 2018). An HEI brand is defined as a manifestation of the institution’s features that distinguishes it from others, reflects its capacity to satisfy students’ needs, engenders trust in its ability to deliver a certain type and level of higher education, and helps potential recruits to make wise enrolment decisions (Bennett & Ali-Choudhury, 2009; Fazli-Salehi et al., 2019; Priporas &

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Kamenidou, 2011). Managing the HEIs’ brand equity is vital as it is the source of creating HEI’s value (Spry et al., 2018), which generates a higher degree of HEI brand preference (Simiyu et al., 2019), favourable choice intention (Sultan & Wong, 2019), increased market share (Brech et al., 2017; Lomer et al., 2018), and higher revenue (Chapleo & O’Sullivan, 2017). HEIs are urged to employ a strategic approach for addressing ongoing challenges (Aleixo et al., 2018; Yáñez et al., 2019), which might lead to adopting a student-centred branding strategy (Guilbault, 2018). Since the students face a daunting and complicated decision when choosing which HEI to attend (Sultan & Wong, 2019), branding has simplified the selection process (Lim et al., 2018). Wilkins et al. (2018) purport that HEIs can create a strong feeling through branding, i.e., to create a unique communicative identity. Balmer and Wang (2016) noted that HEIs are talking about differentiation through branding, but they fail to “practice what they preach”. Branding HEIs bestows a certain level of social status, affording graduates a sense of identification and a way to define themselves, not merely as students, but as life-long HEIs’ members of a corporate “brand community” (Lim et al., 2018). Since HEI branding simultaneously focuses on internal and external stakeholders (Spry et al., 2018), HEIs are externally considering marketing (Vieira-dos Santos & Gonçalves, 2018), and internally focusing on promoting the HEI’s value (Sagy et al., 2018). As a service sector, higher education (Galeeva, 2016; Endo et al., 2019) is mostly focusing on its stakeholders by establishing a lengthy and formal relationship (Bischoff et al., 2018; Vargas et al., 2019) to continuously deliver the promised HEIs’ services (Upadhyaya & Ahuja, 2019). Panda et al. (2019) claim that HEIs must pay attention to the intangibility and inseparability nature of the higher education services in branding. The complexity of HEI brands has been increased due to numerous factors such as diverse stakeholders (Spry et al., 2018), institutional resistance to change (Barrett et al., 2019), the information gap between choice factors identified by students and HEIs (Heffernan et al., 2018), internal structures (Blanco-Portela et al., 2017), and communication mechanisms (Lee et al., 2018). Friess and Lam (2018) noted that, as a service brand, HEIs require a greater emphasis on establishing effective communication with the stakeholders, since all HEIs become stakeholders’ touchpoints. A strong HEI brand carries with it a promise of a particular level of service and students’ outcomes (Friess & Lam, 2018). HEIs are actively pursuing branding campaigns (Matongolo et al., 2018) to evoke a favourable position in the minds of their stakeholders (alumni, government, parents, community, etc.). As education is a credence good (Jongbloed et al., 2018), sometimes, even post-consumption evaluation becomes challenging (Manzuma-Ndaaba et al., 2018), making the selection of an HEI risky (Hersh & Merrow, 2015). The HEIs focus on developing positive brand equity for them to mitigate the risk endowed with the institutes (Dennis et al., 2017; Mourad et al., 2019). The intensification of market-based pressure facing higher education providers has led

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many to adopt the brand equity concept for their institutes (Fazli-Salehi et al., 2019; Rutter et al., 2017). HEIs focus on establishing positive brand equity to (a) enhance the HEI brand awareness among the stakeholders (Dennis et al., 2017), (b) attract many students (Lim et al., 2018), (c) enable the HEIs to recruit high-calibre faculty and administrators (Yusoff et al., 2019), (d) differentiate themselves from rival new and existing HEIs (Mourad et al., 2019), and (e) gain a higher market share (Chee et al., 2016). Although HEIs have many stakeholders such as academic and administrative staff, funding bodies, boards of trustees, private donors, and national and local government agencies, students are the most important ones because the HEIs ultimately rely on them for their financial well-being (Hodge et al., 2018). Students strive for rationality when selecting an HEI, but rationality is bound by certain constraints such as incomplete and vague information (Erman, 2017). In that situation, HEIs’ brand equity plays a vital role instead of assessing all HEI credentials (Roy et al., 2019), and students select the HEI brand most appealing to them. However, the HEIs expect to minimise the perceived risk by developing brand equity (Mourad et al., 2019), which provides a signal of the quality of HEI brands (Altaf et al., 2018) to influence the students’ HEIs’ selection process as it acts as a risk reliever and a differentiation tool (Balaji et al., 2016). Undoubtedly, strong brand equity is vital for HEIs to maintain their competitiveness in the marketplace (Lomer et al., 2018). In this sense, to become a top-of-mind service provider during the decision-making process of students (Royo-Vela & Hünermund, 2016), intensive strategies must be implemented to enhance HEI’s CBBE (Prabowo et al., 2017; Šeri´c et al., 2018). Thus, all HEIs attempt to present their best image, facilities, and staff to attract prospective students (Hillman & Baydoun, 2018; Lee et al., 2018), which is considered as their main challenge. Therefore, HEIs are diversifying (Stout et al., 2018), customising (Perera-Rodríguez & Moriña Díez, 2019), and adding value to the marketing programmes to build a strong brand and increase their brand equity (Dennis et al., 2017; Kalafatis et al., 2016). It is suggested that HEIs need to develop and manage their brands (Lim et al., 2018; Lomer et al., 2018; Spry et al., 2018). However, several studies have examined the brand equity for HEIs (Heffernan et al., 2018; Schlesinger et al., 2017). Thus, the notion of CBBE for HEIs has barely made its mark on higher education marketing literature (Eldegwy et al., 2018; Mourad et al., 2019). While the branding literature suggests that successful brands are necessary to achieve a firm’s marketing goals (Chapleo & Simpson, 2019; Rutter et al., 2017), little evidence of any work is available that establishes the precise advantages of CBBE in the higher education context (Berndt & Hollebeek, 2019; Lim et al., 2018; Wilkins et al., 2018). Eldegwy et al. (2018) noted that HEIs are inherently more complex, but positive brand equity can simplify this complexity, and promote attraction and loyalty among the students. However, there is a notable shortage of empirical research focusing on brand equity determinants in higher education markets (Mourad et al., 2019, p. 3). Therefore, Mourad et al. (2019) suggested that HEIs have a long way to go in terms of understanding and incorporating the brand equity concept for them. Brand equity

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became more important for HEIs, as it often influences HEI ranking (Kalafatis et al., 2016). Despite the growing research attention focusing on the importance of students’ perception in branding HEIs (Endo et al., 2019; Wilkins et al., 2018), still, a research gap exists in empirically connecting students in developing HEIs’ brand equity (Rutter et al., 2017; Hailat et al., 2019). Most previous empirical and theoretical research have focused on international marketing for higher education (Wilkins, 2017; Wilkins et al., 2018; Lomer et al., 2018); however, branding and CBBE for higher education are relatively scarce (Mourad et al., 2019). Although HEI administrators increasingly recognise the need for brand management (Mampaey et al., 2015; Amzat, 2016; Endo et al., 2019), and brand building is becoming a strategic goal, the higher education sector lacks theoretical models of brand equity for HEIs connecting the students (Pinar et al., 2014; Williams Jr & Omar, 2014; Herrero-Crespo et al., 2016; Mourad et al., 2019). Moreover, the branding literature offers no prior research examining the issues and factors important for developing strong brands and brand equity for HEIs (Endo et al., 2019; Pinar et al., 2014). Therefore, limited attention is paid to identifying the critical elements of CBBE within the higher education sector (Gwavuya & Kamuriwo, 2019). This necessitates an inexorable mandate for HEIs to progressively adapt the student-as-customer strategy for their branding activities (Budd, 2017; Guilbault, 2018). Su and Chang (2018) noted that HEIs’ adoption of the student-as-customer concept leads to improved CBBE within HEIs. The “student-as-customer” made the students the most important stakeholders within the higher education sector. In addition, Bolat (2017) purports that students are online customers of HEIs. However, the reason for the dearth of research on CBBE in the higher education sector may be that the higher education services are high-credence quality services (Pergelova & Angulo-Ruiz, 2017) with complex and unique characteristics (Feldman, 2017). The authors argued that commercially focused activities, such as CBBE, are inherently difficult for HEIs, and articulating real differentiation is often a challenging task (Datta et al., 2017). Scholars emphasise three fundamental factors present within CBBE of HEIs, namely (a) a collection of promises concerning the HEI brands’ benefits (Eldegwy et al., 2018), (b) a set of distinctive features that define the HEI brands’ inherent nature and reality (Foroudi et al., 2017), and (c) external communication (Rutter et al., 2017). In line with that, Karaosmanoglu and Gultekin (2019) suggested identifying the new communication channels that became critical at present to develop and enhance CBBE in HEIs. Since the students in the higher education sector are using permanent and universally accessible communication methods such as social media, HEIs try to adopt social media for their branding activities (Bolat & O’Sullivan, 2017). In this context, social media-based interactive communication could play an ever-increasing role in students’ decision-making process, which may increase CBBE (Berndt & Hollebeek, 2019; Royo-Vela & Hünermund, 2016). In this study, the CBBE in the higher education sector has expanded the research area, which can be segregated into three major research streams. The first research stream encapsulates the antecedents of HEIs’ CBBE from the students’ perspectives.

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The second research stream includes the guiding frameworks to assess the effectiveness of CBBE for the higher education sector. The last research stream is to identify an effective communication method to develop CBBE for HEIs.

2.3.3 Benefits of Brand Equity Brand equity is the outcome of the marketing of a product or service because of its brand (Seo & Park, 2018), as compared to the same product or service, not identified by that brand (Keller, 2009; Mohan et al., 2017; Narteh, 2018). Since brand equity arises from the “added value” endowed to a product (Theurer et al., 2018; Troiville et al., 2019) because of investment in marketing for the brand (Seo & Park, 2018; Su & Chang, 2018), it provides a common base for interpreting marketing strategies (Bose et al., 2018; Chi et al., 2020) and assessing the value of a brand (Cheng et al., 2019; Šeri´c et al., 2018). Thus, brand equity can be manifested or exploited for its benefits to both firms and customers, as discussed in the following section. Brand equity creates a brand with a competitive advantage to make strategic decisions by providing a platform for new products and licensing while dominating the market to create entry barriers (Theurer et al., 2018; Veselinova & Samonikov, 2018; Hasan, 2019). The study of Septyanti and Hananto (2018) revealed that brand equity helps a brand to widely leverage its distribution. Most firms aim to have positive brand equity to improve the perceptions of product performance (Liu et al., 2017), gain greater customer loyalty (Juga et al., 2018), reduce their vulnerability to competitive marketing actions (Winzar et al., 2018), and overcome marketing crises (Thaler et al., 2018). Since brand equity creates larger margins while providing more significant trade or intermediary cooperation and support, it extends brand extension opportunities (Ahn et al., 2018; Cheng et al., 2019; Rouziou et al., 2018). In addition, Keller and Brexendorf (2017) noted that brand equity receives a more elastic customer response to price decreases and an inelastic customer response to price increases. Brand equity also provides value to the firm to enhance efficiency (Kashif et al., 2018) and the effectiveness of the firm’s marketing programmes (Schmitz & VillaseñorRomán, 2018). Further, brand equity makes the brand unique to have a price premium (Masuda & Kushiro, 2018), inducing the customers to pay more on the brand, while attracting and retaining current and prospective customers (Theurer et al., 2018). According to Guitart et al. (2018) and Rahman et al. (2019), high equity in brands can increase a firm’s advertising efficiency. In addition, brand equity facilitates the firms to enhance the efficiency and effectiveness of their marketing activities through promotions and advertising (Kumar et al., 2018; Seo & Park, 2018) to attract potential customers who have a high-quality perception of the brand (González-Mansilla et al., 2019). Accordingly, brand equity provides a higher margin to the firm’s brands by permitting premium pricing (Foroudi et al., 2018; Rambocas et al., 2018; Shuv-Ami et al., 2018) and reducing reliance on promotions (Fam et al., 2019). In contrast, if brand equity exhibits a negative impact on customers, then the firm needs to invest more in promotional and advertising activities to maintain its position

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in the distribution channel (Efanny et al., 2018; Guitart et al., 2018). As brand equity provides the platform for the firm to grow by brand extensions, a distinctive position for the brand would be created (Ahn et al., 2018; del Barrio-García & Prados-Peña, 2019; Ozretic-Dosen et al., 2018) while having a sustainable development (RuizMolina & Lavorata, 2018). Therefore, the firms can leverage a brand with high equity by using the brand extension and reduce the likelihood of brand failure (de Oliveira et al., 2018). Phung et al. (2019) found that brand equity continuously leverages the firms’ distribution channels. The channel members perceive that they have less uncertainty in dealing with a proven brand name. Similarly, Kirchoff et al. (2019) discussed how brand equity could be used to attract prospective customers, which creates strong recognition and association related to brands in the customers’ minds. Further, de Oliveira and Caetano (2019) revealed that brand equity helps firms to create barriers for competitors to enter the market and the customers to switch to competitors’ brands. Brand equity offers several other services to the customers while providing a wide range of benefits to the firms. Brand equities shape the quality and quantity of customers’ association with the brand (Keller & Brexendorf, 2017), implying that brands with more detailed knowledge structure in customers’ minds by possessing greater equity to brands as compared to the competitive brands (Hariharan et al., 2018). Furthermore, brand equity creates value for the customer by enhancing the customer’s interpretation of processing information while increasing their confidence in the purchasing decision and satisfaction (Zhou et al., 2017). Ailawadi et al. (2003) suggested that measuring brand equity is vital as it (i) guides marketing strategy, a tactical decision, (ii) assesses the extendibility of a brand, (iii) evaluates the effectiveness of marketing decision, (iv) tracks the brands’ situation compared with the competitors over time, and (v) assigns a financial value to the brand in the financial transaction and balance sheet. Brand equity could not be conceptualised and easily measured as it is a broad concept that can be discussed from different perspectives depending on its purposes (Bose et al., 2018). The advantage of conceptualising brand equity from different perspectives is to enable the firms to improve their brand’s value in customers’ minds in different ways (Chow et al., 2017). Therefore, the following section discusses the different perspectives of brand equity.

2.3.4 Different Perspectives on Brand Equity According to Vinh (2017), Khanna et al. (2019), and Rahman et al. (2019), brand equity can be described from two different perspectives: financial and customer perspectives. Similarly, Cal and Lambkin (2017) viewed brand equity from financial and marketing perspectives. Here, the “marketing perspective” covers the customers’ responses and decision-making process towards the firms’ brand, which is related to the customers’ perspectives suggested by Cheng et al. (2019).

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Chen and Chen (2008) and Martínez and Nishiyama (2017) described brand equity from a financial-based perspective, customer-based perspective, and combined perspective. The integrated perspective incorporates both CBBE and financial-based brand equity (FBBE) (Vijayakumar et al., 2018). This approach has appeared to make up for the insufficiencies that may exist when emphasising only one of the two understandings (Kim et al., 2003). Therefore, scholars have already ignored the combined approach from a brand equity perspective (Cal & Lambkin, 2017; Chi et al., 2019). The brand equity literature is strengthened through the empirical validation of the third perspective of brand equity, namely employee-based brand equity (EBBE) (Theurer et al., 2018). The employee-based perspective focuses on the firm’s internal brand management (Du Preez et al., 2017). The firms develop internal branding to fortify their EBBE. The key thrust of internal branding is to align the employees’ behaviour towards the customers and other stakeholders within the firm. Recognition must be given to the benefits derived from internal brand management as encapsulated in EBBE (Raj, 2018), and discussed in the following section.

2.3.4.1

Employee-Based Brand Equity

The EBBE is an internal brand equity type (Raj, 2018), while both the FBBE and CBBE are external brand equity (Donat, 2018). The principal concept behind this perspective is employees internalise the brand value, and then deliver and fulfil the brand with promised quality to the customers (Boukis & Christodoulides, 2018). Therefore, firms are increasingly encouraging employees to embrace their roles as brand ambassadors (Schmidt & Baumgarth, 2018). They are considered essential to the brand-building process (Quaratino & Mazzei, 2018). The EBBE has been developed in a human resource setting in brand equity (Poulis & Wisker, 2016). This refers to the value provided by employment to existing or potential employees (Boukis & Christodoulides, 2018). So, the firms promote and educate the brands to the employees through internal branding, which helps employees to clarify their roles in building and delivering brand attributes associated with the products or services (Terglav et al., 2016). EBBE reflects the “uniqueness of company brand associations, brand consistency, brand creditability, and brand clarity” (Supornpraditchai et al., 2007). In this vein, King and Grace (2009, p. 130) defined EBBE as “the differential effect that brand knowledge has on an employee’s response to their work environment”, which requires translating the brand identity to the meaningful context which exhibits the employee’s roles and responsibilities. Furthermore, King et al. (2013) noted that EBBE incorporates brand endorsement, brand-consistent behaviours, and brand allegiance, where employee behaviours reflect the consistency of the firms’ brand equity. EBBE captures the added value the employees receive due to employee-based brand-building effort (Christodoulides, 2018). It constitutes the central tenet in internal branding (Iyer et al., 2018; Lee et al., 2019) that delivers the brand promise to

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the customers (Boukis & Christodoulides, 2018; Kashif et al., 2018) without the internalisation of stakeholders in developing value to the firm (Lee et al., 2019). EBBE captures the added value (Boukis & Christodoulides, 2018; Theurer et al., 2018) the employees receive due to employee-based brand-building effort (Christodoulides, 2018; Lee et al., 2019). Further, employees’ work-related behaviour is centred around delivering a highquality brand experience to the customers (Xiong & King, 2019). Because the customers are not only interested in purchasing the product and service, but also trying out to put their investment for gaining supreme experiences (Iglesias et al., 2019a, 2019b), thus, EBBE helps the firm to develop its relationship with the customers (Lee et al., 2019). Then the firms become able to make the brand meaningful and relevant to the customers through the employees (Karanges et al., 2018), and see the value that exhibits positive consumption behaviour (Sürücü et al., 2019). The firms also need to make the brand meaningful and relevant to the employees (Murillo and King, 2019), to see the brand’s value and exhibit positive work-related behaviours (Gill-Simmen et al., 2018), which in turn manifests itself in brand equity (Boukis & Christodoulides, 2018). Therefore, firms must have employees with varying degrees of knowledge related to their brands (Piehler, 2018; Piehler et al., 2019). If the firms are operating in a market where employees lack sufficient knowledge, this will create an adverse effect on the firm’s branding process (Boukis & Christodoulides, 2018). The firms should be confident that the outcome of their employees’ work behaviour contributes to the realisation of the brand promise (Murillo & King, 2019), as evaluated by the consumer, as opposed to detracting from it. Previous studies emphasise that EBBE is essential to measure employment quality to create employment opportunities (Poulis & Wisker, 2016; Wilden et al., 2010). EBBE assists in developing a strong brand image (Boukis & Christodoulides, 2018), providing executive rewards, increasing the firm’s performance (Wisker & Kwiatek, 2018), increasing financial/non-financial performance (Yang & Basile, 2019), and enhancing CBBE (Hasni et al., 2018). The EBBE is considered the cornerstone of creating the CBBE (Bataineh et al., 2017) since the employees’ brand knowledge will enthusiastically motivate them to serve customers and achieve the firms’ objectives (Chiang et al., 2018).

2.3.4.2

Financial-Based Brand Equity

From the financial perspective, it is possible to give a monetary value to the brand, useful for the firms in a merger, acquisition, or divestiture purposes (Ansary & Hashim, 2018). Such monetary values could be measured per year based on the profit that a firm received from the brand compared to another brand, with the same product and price but with minimal brand-building efforts (Datta et al., 2017; Nyadzayo et al., 2016). The financial perspective is based on the incremental discounted future cash flows (Bian et al., 2018; Lanier Jr et al., 2019) that would result from a branded product’s revenue over (the revenue of) an unbranded product (Hepola et al., 2017). Furthermore, this financial-based perspective also adapts to identify the financial

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market value-based technique (Fischer & Himme, 2017), which is important to estimate a firm’s brand equity (Dirsehan & Kurtulu¸s, 2018). Based on the financial market value of the firm (i.e., the firm’s brand equity), the FBBE is extracted from the value of a firm’s other assets (Nguyen et al., 2019). There is no precise definition for FBBE, but Barwise (1993) defined it in practical terms as the brand’s long-term customer franchise, and its financial value. In his definition, Barwise (1993) argued that brands are financial assets so the top management and the financial market need to identify the value of those financial assets. Since the monetary value of the brand is dependent on its brand strength (Lin et al., 2018), i.e., the strength of its customer franchise (Nyadzayo et al., 2018), recognising the values to develop the brand strength is crucial (Jiang et al., 2018). Simom and Sullivan (1993) and Biel (1997) define FBBE in terms of cash flow differences between a scenario where the brand name is added to a company product and another scenario where the same product does not have a brand name. Further, the financial perspective defines brand equity as the “total value of a brand which is a separable asset—when it is sold or included in a balance sheet” (Atilgan et al., 2005, p. 238). Measurement of brand equity from this perspective is solely articulated in monetary terms (Cottan-Nir, 2019), in which a brand provides a financial benefit to the firm (Davcik & Grigoriou, 2019). In turn, financial benefits can be realised by the owner of such brands (i.e., firms) (Wang & Sengupta, 2016), which offers the ability to deliver what the brand promised consistently (Rehman & Kausar, 2016). Brands are considered value generators for businesses (Barradas et al., 2020; Golob & Podnar, 2019). A financial-based perspective once proposed FBBE as measuring, analysing, and predicting a brand’s equity in a product market (Srinivasan et al., 2005). Thus, Srinivasan et al. (2005) defined FBBE as “the incremental contribution ($) per year obtained by the brand in comparison to the same product (or service) at the same price but with no brand-building efforts (hereafter, base product), which is consistent with the ‘added value’ notion of ‘brand equity’”. Wang et al. (2016) identified FBBE as the current and future ability of a brand to attract and retain potential and existing customers and then increase the shareholders’ value. Several studies attempted to explain brand equity from the financial perspective as the increase in profit (de Oliveira et al., 2015; Martínez & Nishiyama, 2019) or the quantity of cash flow in the future (Basgoze et al., 2016), which is the cost of replacing the brand (Vomberg et al., 2015) or its liquidation value (Liu et al., 2019). Wang (2010, p. 336) defined FBBE as “the additional economic value a brand offers to a company in its relative potential to generate future earnings or cash flows”. The ability to generate cash flow should be carefully and continuously managed and invested in to optimise the brand’s financial value (Tasci, 2019). A brand’s financial value is based on the aggregated earning-power of its embedded value, both tangible and intangible (Baalbaki & Guzmán, 2016). Financialbased brand equity from the perspective of financial markets capitalises on the profit value (Tiwari, 2010) resulting from the association the brand builds with a particular product or service (Merz et al., 2018). Since brands have been increasingly considered as primary capital for many businesses (Naik, 2017), financial professionals

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suggest that a brand has equity, which exceeds its conventional asset value (Wang and Yu, 2015). FBBE is mainly focused on evaluating a firm’s brands (Shuv-Ami, 2016; Veloutsou & Guzman, 2017; Chow et al., 2017) since it is the main objective of profit-based organisations (Bontis et al., 2018; Lee & Raschke, 2020). FBBE facilitates the firms to create a competitive edge to gain financial benefits for the firms (Clark et al., 2018; Romero-Hernández and Romero, 2018). Estimating the financial value of a brand is undoubtedly useful for a firm to evaluate the economic return the firm receives from the brand (Paul, 2017), but this does not help the marketing managers to understand the brand equity building process (Crass et al., 2019). Firms must evaluate the FBBE to identify the brand’s ability to generate financial returns in the future (de Oliveira et al., 2015) because the marketing expenditure outcomes are often long-term and substantially delayed (Wang & Sengupta, 2016). Financial perspectives mainly deal with the relationship between the investment in the marketing programme and the brand’s financial value to the stakeholders (Choi et al., 2019; Fischer & Himme, 2017). The brand equity sought by investors in the financial market is related to both the physical assets value (Sardo & Serrasqueiro, 2017) such as the plant and equipment, and the financial worth of the brand in the financial market (Shuv-Ami, 2016a). Typically, FBBE is used to rank the firm’s brand or the competitors’ brands with the firm’s brands (Shuv-Ami, 2016b; ShuvAmi et al., 2018) concerning their financial worth or value (Martínez & Nishiyama, 2017). The brand equity from the financial perspective shows the brand value results (Mizik & Pavlov, 2018; Ansary & Hashim, 2018). Thus, it deals with the investment in marketing programmes (Fischer & Himme, 2017) and then, in the end, translates the brand’s worth or value in the financial market (Narteh, 2018). However, the firm’s investment in developing and enhancing CBBE, in turn, underpins the firm’s FBBE (Fischer & Himme, 2017).

2.3.4.3

Customer-Based Brand Equity

The perceptual equity from the customer, known as the customer-based brand equity, is an important factor affecting the brand’s stability (Šeri´c et al., 2018). As brand equity positively associates with customer equity and brand success (Steenkamp, 2017; Tasci, 2018), CBBE received significant attention from the academics and business community (Pansari & Kumar, 2017). The customers’ viewpoint allows an examination of the brand’s perception in the marketplace and is critical in assessing brand strengthening (Wiedmann et al., 2018). Conceptualising brand equity from the customers’ perspective is useful, as it provides both specific guidelines for marketing strategies (Bose et al., 2018) and the decision-making process of management (Mardani et al., 2016). The groundwork for customer-based brand equity was set in the early nineties by Aaker (1991, 1992, 1996) and Keller (1993, 2003), and has been adopted, modified, tested, and retested over the past decades.

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Aaker (1992) is considered the pioneer in conceptualising CBBE, whose research mainly focused on the customers, rather than the firms or other relevant stakeholder groups (Ahirrao & Patil, 2017; Davcik et al., 2015; Makagiansar et al., 2019). Aaker (1992) viewed CBBE as a set of assets (liabilities) linked to a brand’s name and symbol that adds to (or subtracts from) the value provided by a product/service to the customer. Aaker (1992) developed a model to evaluate CBBE by identifying four main dimensions: brand awareness, brand loyalty, perceived quality, and brand associations. Brand awareness refers to the customers’ ability to recall (Chen et al., 2017) and recognise the brand (Anees-ur-Rehman et al., 2018) in the marketplace. It is the brand’s strength traced in customers’ memory (Bihamta et al., 2017), as reflected by customers’ ability to identify the brand under different conditions (Razak et al., 2019). In other words, brand awareness is the likelihood that a brand name will bring to mind and the ease with which it does so (Arora & Kumar, 2018; Tariq et al., 2017). Brand loyalty is the customers’ attachment to the brand (Adam et al., 2018; Japutra et al., 2018). This attachment creates favourable attitudes towards a particular brand (Phung et al., 2019; Zhang et al., 2018), which resulted in repeat purchases of the same brand over time (Datta & Kaushik, 2019; Khan et al., 2019) over other competing brands (Dwivedi et al., 2018; Ong et al., 2018). Brand loyalty further can be explained as the tendency to be loyal to a focal brand (Cheng et al., 2019; Shen et al., 2017), which demonstrates the intention to buy a specific brand as a primary brand (Mazzucchelli et al., 2018; Song et al., 2019). Perceived quality is another important dimension in measuring CBBE, which focuses on the customers’ subjective evaluation of a brand rather than the actual quality (Mrvelj & Matulin, 2018; Perra et al., 2018). Perceived quality provides the customers with a valid reason to purchase a specific brand by differentiating the brand from competing brands (Su & Chang, 2018). Further, brand associations are believed to contain “the meaning of the brand for consumers” (Keller, 1993, p. 3). Brand personality (Vinyals-Mirabent et al., 2019), and the firm’s association with the brand (Zhang et al., 2016) are the most critical drivers in creating a brand association. Brand association is anything deeply seated in customers’ minds about the brand (Ahmed & Latif, 2019). This creates a unique image (Vogel & Watchravesringkan, 2017) and symbols associated with the brand (Ahmad & Thyagaraj, 2017). The brand association provides and creates value to the customers (Park & Lee, 2019) by emphasising a reason to purchase the brand (Falahat et al., 2018) while providing acquaintance (Rajagopal, 2019) and differentiation that is not replicable (Park & Lee, 2019). Aaker (1991) argued that a brand association has a level of strength. The link to a brand (from the association) will be stronger when it is based on many experiences or exposures to communications, and when supported by a network of other links. However, this model draws attention to brand management in an ideal world, providing a conceptual definition, rather than demonstrating how CBBE should be measured practically (Çifci et al., 2016). Keller (1993, p. 2) disregarded the behavioural aspect and viewed CBBE as “the differential effect of brand knowledge on customer response to the marketing of the

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brand”. Three important concepts are included in his definition: “differential effect”, “brand knowledge”, and “consumer response to marketing” (Keller, 1993, p. 8). The differential effect is the customers’ responses to a firm’s brand (Pansari & Kumar, 2017) with the unnamed or fictitiously named of product or service which follows the same marketing strategies by other competitive firms (Olson et al., 2018). Brand knowledge is the combination of brand awareness and brand image the customers associate with the brand (Han et al., 2018a, 2018b; Kim et al., 2018a, 2018b). Customers’ response to marketing is defined in terms of consumer perceptions (Fan et al., 2018), brand preferences (Topaloglu & Gokalp, 2018), and brand purchasing behaviour (Dumitrescu et al., 2018) arising from marketing mix activity (Stead & Hastings, 2018). In his conceptualisation, Keller (1993) evaluated the concept of CBBE through two dimensions of brand knowledge: brand awareness and brand image. Brand knowledge is conceptualised as “consisting of a brand node in memory to which a variety of associations are linked” (Keller, 1993, p. 3). Brand knowledge affects customers’ response to the brand and increases their awareness of the brand (Divakaran, 2018). Brand awareness is considered as the extent to which the customers’ ability to recall (Chen et al., 2017) and recognise the brand (Anees-urRehman et al., 2018), and is further deemed as the key factor in developing marketing strategies (Ahn & Back, 2018). Similarly, brand image is recognised as an essential concept in increasing brand knowledge (Boronczyk & Breuer, 2019). Brand image is the perception the customers hold in their minds about the brand (Anwar et al., 2019; Mabkhot et al., 2017). This develops over time with the customers’ interaction (Blasco-Arcas et al., 2016) and experience (Altaf et al., 2017) with the brand. However, the main difference between the two conceptualisations of Aaker (1992), and Keller (1993) lies in brand loyalty (Huang & Cai, 2015). Keller (1993) identified brand loyalty as a favourable outcome of the customers’ belief and attitude toward the brand that is manifested in repeat buying behaviour. On the other hand, Aaker (1992) considers brand loyalty as a dimension of CBBE. However, the key questions about Aaker’s (1992) and Keller’s (1993) measurement models remain unanswered; specifically, the structural validity of their measurement remains vague (Yoo & Donthu, 2001). After the conceptualisation of CBBE by Aaker (1991, 1992) and Keller (1993), several scholars suggested different definitions and dimensions for measuring brand equity from the customers’ perspective. Park and Srinivasan (1994) explained CBBE from the perspective of brand association and postulated how brand attributes contribute to CBBE. The association relates to a brand’s characteristics that create an attribute-based component of brand equity (Baalbaki & Guzmán, 2016; Farjam & Hongyi, 2015), which differentiate the subjectively perceived attribute level and objectively perceived attribute level (Schick et al., 2019). If the brand association creates an attribute-based component of CBBE, it affects the overall preference of a brand (George, 2017; Winzar et al., 2018). Cobb-Walgren et al. (1995) measured CBBE based on the conceptualisation of Aaker (1991) and Keller (1993) using a set of four dimensions: brand awareness, brand associations, perceived quality, and brand loyalty. Lassar et al., (1995,

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p. 13) defined CBBE as “the enhancement in the perceived utility and desirability a brand name confers on a product” and incorporated five dimensions in measuring CBBE: performance, social image, value, trustworthiness, and attachment. Lassar et al. (1995) distinguished the perceptual attributes from the behavioural dimension so that the customers’ behaviour is the consequence of the CBBE. Keller (2001) argued that CBBE provides a yardstick for the firms to assess the progression of the brand-building process, and identified brand resonance, consumer judgement, consumer feeling, brand performance, brand imagery, and brand salience in creating a competitive advantage for the firm. Vázquez et al. (2002, p. 28) defined CBBE as “the overall utility that the consumer associates to the use and consumption of the brand; including associations expressing both functional and symbolic utilities”. According to Netemeyer et al. (2004), perceived quality, perceived value for the cost, uniqueness, and willingness to pay a price premium are important in measuring CBBE. Pappu et al. (2005) operationalised CBBE as the combination of customer perception (brand awareness, brand association, and perceived quality), and customer behaviour (brand loyalty, willingness to pay a higher price). According to Tolba et al. (2009), CBBE drives the brand market performance and helps firms to make strategic decisions. Furthermore, Tolba et al. (2009) conceptualised CBBE as incorporating knowledge equity, attitudinal equity, and relationship equity. Bianchi et al. (2014) proposed brand salience, brand association, brand loyalty, brand quality, and brand value to measure brand equity from the customers’ perspective. Favourable customer responses and positive CBBE can lead to enhance revenue (Tasci, 2019), reduce cost (Feiz & Moradi, 2019), and earn greater profit (Narteh, 2018) and higher loyalty (Sürücü et al., 2019). It has direct implications for the firm’s ability to command higher prices with larger margins (Ansary & Hashim, 2018), customers’ willingness to seek out new distribution channels (Yang et al., 2019), the effectiveness of marketing communication (Wong, 2018), and the success of brand extension (Ahn et al., 2018) and licensing opportunity (Brochado & Oliveira, 2018). A firm must create a brand that has a favourable, strong, and unique association with the customers to build positive CBBE (Raji et al., 2019; Schmitz & VillaseñorRomán, 2018). This is achievable by integrating brand identities (Orazi et al., 2017) into supporting marketing programmes that enhance brand communication between potential customers (Dwivedi & McDonald, 2018; Foroudi et al., 2017). As contemporary marketing is shifting from a one-time transaction to a long-term relationship between customers and the firms (Eriksson & Hermansson, 2017), the significance of CBBE in retaining customers with the firm’s brands draws more attention (Youssef et al., 2018). Customers can meaningfully differentiate the brand to create CBBE (Raji et al., 2018), and this differentiation comes from the values attached to a given brand (Iglesias et al., 2017). It can be asserted that the more fulfilled the customers’ expectations are, the more valuable is the CBBE (Liu et al., 2017).

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2.4 Brand Equity Perspective of This Study This dissertation will focus on the CBBE perspective and will not discuss FBBE or EBBE, as these two perspectives lie outside the scope of the proposed study. The rationale behind this decision is based on the following points. This study focuses on the higher education sector and builds based on the perception and ideas of the students in the higher education sector. Recent literature identified students as the customers of the higher education sector (Budd, 2017). Gupta and Kaushik (2018) argued that higher education, as a service sector, has many customers and stakeholders (e.g., future employers, government, and society), but the students are the core customers. As the students are paying for the education, the relationship between institutions and students has shifted from a traditional academic relationship to a more contractual type of relationship, from a customer’s perspective (Garcia-Perez-de-Lema et al., 2017). This study also does not focus on how employees create value for the HEI brands, and how employees are educated in communicating brand value to the stakeholders. Moreover, this study does not seek to address the financial outcomes of the HEI brands and evaluate the financial equity the HEI is receiving from their brand. Since this study focuses on brand equity operations in the higher education sector through marketing activities, customer-based brand equity is the most appropriate approach to examine students such as customers in the higher education sector (Guilbault, 2018). Customers’ perceptions are what customers think (Gong & Yi, 2018), attach (Iglesias et al., 2019a, 2019b), and contribute to creating brand equity (Moliner et al., 2018). The power of the brand depends on the convictions (Gong, 2018; Rambocas et al., 2018) and the perception of consumers (Iglesias et al., 2019a, 2019b; Sreejesh et al., 2018), based on what they have learned, felt, seen, and heard about the brand (Pathak & Pathak-Shelat, 2017; Gong, 2018). The real value of a brand can only be realised when the brand is relevant to the customers (Moliner et al., 2018; Wiedmann et al., 2018). Therefore, the CBBE perspective indicates that brand value creation stems from customer-level outcomes (Fischer & Himme, 2017; Winzar et al., 2018), such as perceptions (González-Mansilla et al., 2019), attitudes (Moise et al., 2019), knowledge (Raji et al., 2019), and behaviour towards a specific brand (Chatzipanagiotou et al., 2019). Thus, the concept of customer-based brand equity was brought up to address the brand equity from the customers’ perspectives, which reflects the power of a brand that lingers in customers’ minds (Martínez & Nishiyama, 2019; Phung et al., 2019). Since numerous differences exist between services and goods, customers evaluate service brands differently from goods brands (O’Reilly et al., 2017; Ramanathan & Velayudhan, 2017). Therefore, a different conceptualisation of customer-based brand equity for the service sector is required (Ande et al., 2017; Kumar et al., 2018; Ahn et al., 2018).

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As CBBE positively associates with the customers’ perception towards the brand and the success of the brand (Boukis & Christodoulides, 2018), it has received significant attention from the academic and business community (Pansari & Kumar, 2017; Seo & Park, 2018). Even though CBBE has emerged as one of the most important topics for management, most conceptual and empirical research on CBBE has focused on products and not services (Iglesias et al., 2019a, 2019b; Šeri´c et al., 2018). As extant literature shows that CBBE models are narrowly focused on brand equity constructs (Tasci, 2018) while ignoring the service experience components (Sarker et al., 2019), their adaptability to the context of service-dominant brands becomes questionable (Çifci et al., 2016). Therefore, applications of the customer-based brand equity measures to service brands are limited (Iglesias et al., 2019a, 2019b; Sarker et al., 2019). The concept of CBBE became increasingly important for the service firms to influence the customers’ choices (Gomes et al., 2016; Zhang et al., 2016; Chakraborty & Bhat, 2018). Strong CBBE transcends specific features and benefits to the customers (Feiz & Moradi, 2019; Hasni et al., 2018) and penetrates customers’ emotions related to the services (Elsäßer & Wirtz, 2017; Saeed & Shafique, 2019). Therefore, the service firm’s real value emerges in the customers’ experience with service brands (Chen et al., 2018; Foroudi et al., 2018). The service brand that connects with the customers emotionally (Hemsley-Brown & Alnawas, 2016; Prentice et al., 2019) is critical in building a favourable service brand equity (Dwivedi et al., 2018; Iglesias et al., 2019a, 2019b). Since most studies focus on individual service brand equity components such as product loyalty (Ahn et al., 2018; Juga et al., 2018), service quality (Altaf et al., 2018), customer loyalty (Nyadzayo & Khajehzadeh, 2016; Zameer et al., 2019), and service loyalty (Ou et al., 2017; Juga et al., 2018), a comprehensive approach is necessary to develop brand equity for the services from the customers’ perspective (Ahn et al., 2018; Kumar et al., 2018). Compared to other services, the concept of brand equity in the higher education sector has drawn limited attention in the literature (Endo et al., 2019; Papadimitriou & Ramírez, 2018). Most studies related to higher education focused on international marketing for HEIs to attract students (Naidoo et al., 2016; Wilkins, 2017; Lu et al., 2018), and the importance of developing customer-based brand equity for HEIs was called off. In line with the critical gaps in the literature, this study recognises the importance of customer-based brand equity for services while focusing on higher education as a specific service provider. While highlighting the definitions, dimensions, and advantages of CBBE, it is essential to highlight the antecedents and the consequences of CBBE, and the next section discusses the antecedents and consequences of CBBE.

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2.5 Overview of the Antecedents and Possible Outcomes of Customer-Based Brand Equity Extant literature suggests that customer-based brand equity has both functional/attribute (objective) (Weng, 2019) and experiential (symbolic/subjective) antecedents (Bairrada et al., 2018). The functional antecedents reflect the brand’s intrinsic utilitarian aspects (Oh et al., 2019) and its ability to satisfy customers’ functional needs and wants (M’zungu et al., 2017). The experimental antecedents reflect the brand’s ability to meet customers’ psychological or social needs (Elbedweihy et al., 2016). Reliability, effectiveness, distribution intensity, price, and style are suggested as functional antecedents (Chinomona, 2016; Agbadudu & Adekunle, 2017; Su & Reynolds, 2017; Dai & Chen, 2017), whereas association, personality, and awareness are considered to be the experimental antecedents (Ansary & Hashim, 2018; Chatzipanagiotou et al., 2016; Frank & Watchravesringkan, 2016). In addition, behavioural loyalty, attitude, and active engagement are proposed as experimental antecedents (Algharabat et al., 2019; Giovanis & Athanasopoulou, 2018; Vogel & Watchravesringkan, 2017). The individual dimensions, brand awareness, perceived quality, brand loyalty, and brand association proposed are considered as the antecedents to CBBE (Foroudi et al., 2018; Šeri´c & Gil-Saura, 2017). Brand awareness, as an antecedent of customerbased brand equity, plays a dominant role in customers’ purchase decision-making process (Algharabat et al., 2019; Phung et al., 2019). According to Rauyruen et al. (2009) and Grant et al. (2014), brand loyalty is the antecedent to brand equity while as per Juntuen et al. (2011), brand loyalty and brand equity are parallel outcomes and not consequential. Further, Šeri´c et al. (2018) argued that brand loyalty is one of the components of customer-based brand equity than the antecedent of brand equity. Netemeyer (2004) stated that further studies are necessary to examine whether the brand association is an antecedent or an outcome of customer-based brand equity. Several other studies suggested that advertising and promotions are antecedents (Ahn et al., 2018; Amoako et al., 2017) since advertising and promotions can create and increase the brand’s awareness, thereby increasing the brand equity from the customer’s perspective (Raji et al., 2018). Liao et al. (2017) noted that marketing mix elements positively influence customer-based brand equity as antecedents. Specifically, perceived advertising spending (Liao et al., 2017), perceived price (Mpinganjira & Maduku, 2019), and firm’s image (Iglesias et al., 2019a, 2019b) have provided empirical evidence for this effect. Alternatively, customers’ perception under experimental marketing exerts effort to create positive value for the brand is considered an antecedent (Foroudi et al., 2018). Performance has been identified as the primary driver of customer-based brand equity in the context of services (Syed Alwi et al., 2016), and brand attachment and attitude are prerequisites of customer-based brand equity (Dennis et al., 2017; Vogel & Watchravesringkan, 2017). Therefore, developing a positive image, strengthening brand attachment, and creating an affirmative attitude among customers should be synchronised to enhance customer-based brand equity (Ansary & Hashim, 2018).

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Stojanovic et al. (2018) suggested that future studies should focus on identifying the role of social media as an antecedent in generating or increasing brand equity from the customers’ perspective. User involvement (Lee & Kim, 2018), user participation (Hu et al., 2016), and self-expressive brands (Algharabat, 2017) are the antecedents of customer-based brand equity within the social media context. In a social media environment, credibility is considered antecedent to customer-based brand equity, which encompasses the customers’ effective engagement with social media applications (Martín-Consuegra et al., 2018). The credibility the customers hold in the brand is a driver where the customers’ cognitive brand-related sense-making leads to generate positive CBBE (Hollebeek & Macky, 2019). Failing to deliver what it promises will negatively affect brand equity (del Barrio-García & Prados-Peña, 2019). Sijoria et al. (2019) have identified electronic word-of-mouth (eWOM) in social media as an antecedent in building customer-based brand equity in service industries. A positive eWOM helps marketers build positive brand equity through information completeness, accuracy, comprehensiveness, relevance, and understandability (Sijoria, 2019). Some researchers argued that customer satisfaction is an antecedent (Algharabat et al., 2019; González-Mansilla et al., 2019), while other researchers view satisfaction as an outcome (Iglesias et al., 2019a, 2019b; Šeri´c et al., 2018). Satisfaction influenced the brand’s value through loyalty and perceived quality, which are considered to be two key antecedents of customer-based brand equity (San Kim et al., 2018a, 2018b; Martín et al., 2018). However, previous studies have suggested that satisfied customers are not always loyal to the brands (Chuah et al., 2017; Erci¸s et al., 2012; Nisar & Whitehead, 2016), and therefore, contradict the notion that satisfaction is a leading antecedent to customer-based brand equity (Biedenbach et al., 2015; Gupta et al., 2017). Several scholars have examined the antecedents, in particular, based on social identity (Boukis & Christodoulides, 2018; Vatankhah & Darvishi, 2018) and supported the idea that group experience (i.e., salient and community group) (Wang & Tang, 2018) and customers’ responses on brand benefits (Godey et al., 2016; Ahn & Back, 2018) act as antecedents of customer-based brand equity (Wang et al., 2018). Customers’ engagement with social media brands (Algharabat et al., 2019; Pansari & Kumar, 2017) and accessing the information provided by the firms on social media (Chahal & Rani, 2017), and social media users’ participation in an online brand community (Weiger et al., 2017) acts as an antecedent to brand value from the customer perspectives (Machado et al., 2019). Similarly, several researchers delineated many consequences of customer-based brand equity. Customer-based brand equity helps customers reduce the anticipated risk related to brand purchase decisions (Soh et al., 2017). This increases the customers’ anticipated confidence in making a purchase decision (Pansari & Kumar, 2017). Typically, all required information about a brand is not readily available for the customers to make a rational and objective purchase decision (Mas-Ruiz et al., 2016), so customer-based brand equity may lead them to a greater level of confidence in customers’ brand purchase intention (Raji et al., 2018). Furthermore, the customers’

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anticipated satisfaction would increase due to customer-based brand equity (Hasni et al., 2018). Brand profitability performance (Rambocas et al., 2018), brand market performance (Liu et al., 2017), and customer value (Jiao et al., 2018) are considered as possible consequences since they provide financial value to both customers and firms. According to Chakraborty and Bhat (2018), and Kim and Lee (2018), the perceived value and revisit intent are the outcomes of customer-based brand equity while the behavioural intentions of the customers (Shim et al., 2017) received more attention from the firms as another consequence. Moreover, price premium (Rambocas et al., 2018), brand extension (Ahn et al., 2018), brand reputation (Güçlü Sözer et al., 2017), and brand resonance are identified (Eldegwy et al., 2018) as another set of the consequence of brand equity from the customer perspectives. Customers’ switching behaviour between the service brands is identified as an outcome resulting from service failures, denied services, and low customer services (Wu et al., 2018; Phung et al., 2019). In the service sector, customer retention strategies simultaneously focus due to robust and powerful customer-based brand equity (Chahal & Bala, 2017). Brand equity on the favourable firm and consumer-related outcomes include profitability (Liu et al., 2017), sales volume (Ahn et al., 2018), higher financial performance (Cal & Lambkin, 2017), and higher sales revenue (Rambocas et al., 2018). Brand preference (Gomez et al., 2018) and brand choice intention (Phung et al., 2019) also include the consequences of customer-based brand equity. When the customers perceive a higher level of positive brand equity, they find the brand holds outstanding equity compared to other alternative brands (Wang et al., 2018). If the customers are highly favouring any specific brand (Kim et al., 2019), it raises the customers’ choice intention related to a particular brand (Foroudi et al., 2018). Phung et al. (2019) also noted that brand choice intention is the most important outcome of customer-based brand equity, linked to customers’ favourable consideration in the brand purchasing process. Creating CBBE leads to the invention and sustenance of the competitive advantage of a brand (Vatankhah & Darvishi, 2018). Multiple studies have reported that the users’ connection with social media platforms affects their attitudes, and satisfaction is a favourable outcome of CBBE in the social media context (Raji et al., 2018; Seo & Park, 2018; Dwivedi, 2019). Taken together, CBBE influences how customers respond in the marketplace (Keller & Brexendorf, 2017).

2.6 Measuring “Customer-Based Brand Equity” in the Higher Education Sector CBBE was conceptualised by offering different insights from previous researchers. Table 2.3 briefly reviews the most used dimensions of CBBE.

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Table 2.3 The most commonly used CBBE Dimensions Author

CBBE dimensions

Aaker (1992)

Brand awareness, brand association, brand loyalty, and perceived quality

Keller (1993)

Brand knowledge, which consists of brand awareness and brand image

Park and Srinivasan (1994)

Brand association (attributed based and non-attributed based)

Lassar et al. (1995)

Brand performance, social/image, price/value, trustworthiness, and identification/attachment

Keller (2001)

Brand salience, brand performance, brand image, consumer judgement, consumer feelings, and consumer brand resonance

Kim et al. (2003)

Brand loyalty, brand awareness, perceived quality, and brand image

Netemeyer et al. (2004)

Perceived quality, perceived value for the cost, uniqueness, and willingness to pay a price premium

Atilgan et al. (2005)

Brand awareness, brand association, brand loyalty, and perceived quality

Kimpakorn and Tocquer (2010) Brand awareness, perceived quality, brand differentiation, core service brand association, supporting brand association, brand trust, and brand relationship Hakala et al. (2012)

Brand awareness, brand association, brand loyalty, and perceived quality

Bianchi et al. (2014)

Brand salience, brand association, brand loyalty, brand quality, and brand value

Nyadzayo et al. (2016), Kladou et al. (2017), and Brochado and Oliveira (2018) noted that Aaker (1992) and Keller (1993) had developed the most widely used models for measuring customer-based brand equity. Although Aaker (1992) and Keller (1993) have developed a model to evaluate brand equity from the customers’ perspective, these models are purely conceptual, and have not been tested empirically within the product or service sphere (Atilgan et al., 2005; Baalbaki & Guzmán, 2016; Tasci, 2018). These models focus more on explaining the underlying theory of the concept than providing practical implications (Çifci et al., 2016). Furthermore, Aaker’s (1992) model ignores the brand’s symbolic consumption essential for brand equity, and primarily focuses only on the functional aspects of brands (Çifci et al., 2016); hence, Aaker’s (1991) brand equity dimensions are questionable (Christodoulides et al., 2015). Similarly, the applications of Aaker’s (1992) and Keller’s (1993) models to service organisations display poor validity, so the existing measurement scales based on Aaker’s (1992) and Keller’s (1993) customer-based brand equity models are not suitable for service-dominant brands because of the inherent characteristics of services (Çifci et al., 2016). Some dimensions in their models seem to differ for services (Huang & Cai, 2015). For example, Aaker (1991) stated that perceived quality is unidimensional, while scholars in service marketing argue that perceived quality is

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multidimensional (Bezerra & Gomes, 2016; Pribeanu et al., 2017). Further, Aaker’s (1991) study recognises perceived quality as one of the components of brand equity but does not specify whether this refers to goods or services (Nam et al., 2011). Further, Aaker’s (1991) study does not state which quality dimensions should be included in the brand equity model and therefore, whether the model is suitable for assessing service-dominant brand equity models (Çifci et al., 2016; Nam et al., 2011). Besides, Num (2011) argued that applications of the goods-based brand equity models show poor validity in the service industry. In addition to Aaker’s (1991) and Keller’s (1993) models, Yoo and Donthu (2001) developed a multidimensional scale to measure customer-based brand equity by utilising Aaker’s (1991) and Keller’s (1993) conceptualisations. Yoo and Donthu (2001) developed a three-dimension brand equity model, comprising (a) brand loyalty; (b) perceived quality; and (c) brand awareness/associations, combined into one dimension. They also proposed an overall brand equity scale, which estimated the construct through four items. Yoo and Donthu’s (2001) scale is perhaps the most robust scale in the literature because they used a comprehensive approach to scale development (Baalbaki & Guzmán, 2016; Christodoulides & de Chernatony, 2010; Çifci et al., 2016). Although applications of Yoo and Donthu’s (2001) brand equity scale confirm its reliability, the validity of their model is questioned (Baalbaki & Guzmán, 2016; Washburn & Plank, 2002). In this scale, two distinct constructs of customer-based brand equity, i.e., brand awareness and brand associations, are combined into one dimension (Washburn & Plank, 2002). Both constructs are correlated, but both Aaker (1991) and Keller (1993), whose frameworks are used as a basis in this scale development, distinguish between awareness and associations. Washburn and Plank (2002), and Çifci et al. (2016) stated that the discriminant validity of Yoo and Donthu’s (2001) measures used to assess three dimensions is inadequate (perceived quality, brand loyalty, and brand associations/awareness), and brand association and brand awareness are not distinct. Furthermore, the measures (or items) in brand loyalty and overall brand equity seem alike since both explain the customer’s intended action towards the brand (Baalbaki & Guzmán, 2016; Chaudhuri & Holbrook, 2001; Sarker et al., 2019). Also, the items loaded in brand loyalty and the overall brand equity were the same. Hence, it should be considered as one rather than defined as two constructs (Sarker et al., 2019). Yoo and Donthu (2001) also used only three different product brands (camera film, athletic shoes, and colour televisions) and failed to include service brands. This is a limitation because service brands rank high in terms of brand equity from the perspectives of present-day customers (Herrero-Crespo et al., 2016). Furthermore, several researchers argue that Yoo and Donthu’s (2001) brand equity measure is not suitable for service-dominant brands (Washburn & Plank, 2002; Molinillo et al., 2015) because service-dominant brands differ from goods-dominant brands (Voorhees et al., 2017) due to the inherent characteristics of services: intangibility, perishability, heterogeneity, and inseparability (Wynstra et al., 2018). To address this deficiency, Nam et al. (2011) introduced three symbolic consumption-related brand equity dimensions: self-congruence, brand identification,

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and lifestyle congruence. The model suggested by Nam et al. (2011) was empirically tested in the UK hospitality industry, but findings are limited to only hotel and restaurant brands (Çifci et al., 2016). Hence, there is uncertainty about whether their findings are valid when the customer-based brand equity model is applied to other service sectors and national cultures (Çifci et al., 2016). However, Berry (2000) and Lassar et al. (1995) have developed two models to measure customer-based brand equity in the service sector. Berry (2000), in his model, explained the service-based brand equity using brand awareness, and brand meaning similar to Keller’s (1993) model. Berry (2000) also proposed some additional service-based dimensions such as the firm’s communication strategy to present their brand in the marketplace, uncontrollable information shared by the external parties about the firms’ brands, and customers’ experience with the brand. Even though this model is to measure customer-based service brand equity, this has some drawbacks. The logical interrelationship between the construct in this model was ignored (Sarker et al., 2019). Besides, Çifci et al. (2016) validated this model in the context of global fashion brands in Turkey and private label brands in Spain. Later, Çifci et al. (2016) and Sarker et al. (2019) suggested that this model can be adapted to the service brand setting after conducting empirical tests. Still, it lacks the generalisability to apply in the context of another service. Since all the services are not equal in the degree of their attributes, Berry’s (2000) model would not be suitable to be adopted in the higher education context. Lassar et al. (1995) have considered forerunners in measuring customer-based brand equity in the service sector. In developing the model, they have focused on the customers’ perception rather than objective indicators and the global value of a service brand (Alexiadou et al., 2017). Several researchers have used the model proposed by Lassar et al. (1995) and empirically tested it in various service settings (Horng et al., 2012; Christodoulides et al., 2015; Kareem Abdul, 2017; Hasni et al., 2018). They suggested that this scale is more appropriate for measuring customerbased service brand equity than other existing scales. In developing the scale, Lassar et al. (1995) incorporated five variables: performance, social image, value, trustworthiness, and attachment. In their model, the performance was regarded as critical for the services (Beeler et al., 2017), since customers would not purchase the service if the service performance is poor (Sousa & da Silveira, 2017) and the brand will receive a low level of brand equity (Juga et al., 2018). Lassar et al. (1995) replaced the quality dimensions with the more focused term “performance”. As an inclusive term, performance referred to the customers’ overall judgement about the brand (Sultan & Wong, 2018), free from faults, and long-lasting flawless related to the services (Oliveira et al., 2017; Schonberger & Brown, 2017). This model has limited the reference of image dimension to the social dimension and named it a social image. The social image refers to the value added to the service as the social reputation (Heinberg et al., 2018), which is associated with owning or using a service brand (Ahn et al., 2018). It is related to the customers’ perception of the esteem (Hur et al., 2018), which they receive within the social groups holding the brand (Friedrichsen & Engelmann, 2018).

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The value was included in measuring service brand equity as it depends on the perceived balance between price and the service level (Hill & Brierley, 2017). The firms need to communicate the service’s value to the customers (Immonen et al., 2018). This is to the position of the service brand in the marketplace (Otubanjo, 2018). When communicating the value of a service brand, it reduces the perceived risk and (Fischer & Himme, 2017) enhances customers’ familiarity (Ahn et al., 2018) and confidence in the service brand (Fazal-e-Hasan et al., 2018). Trustworthiness is to place a higher value on the service brand the customers would purchase. Customers’ trustworthiness reveals the expectancy (Dong & Li, 2017), which is based on the customers’ belief that the service brand has specific qualities that make it consistent (List et al., 2017), competent (Kharouf et al., 2019), honest (Gordon et al., 2019), and responsible (Jalilvand et al., 2017). It is the key characteristic of a successful long-term relationship with the customers and service brands (Kosiba et al., 2018). For example, customers’ trust in Nordstrom has translated into a higher level of service brand equity for Nordstrom stores (Badrinarayanan & Becerra, 2018). Conversely, distrust of the customers’ hold relating to services creates adverse effects on service brand equity (Hilman et al., 2017). Lassar et al. (1995) have adopted the perceptual rather than behavioural dimensions of customer-based brand equity, excluding behavioural or attitudinal dimensions, such as loyalty which differs from Aaker’s (1991) incorporated definition. It distinguishes commitment as feeling versus commitment as action (Indarti et al., 2017). The feeling is viewed as one of the consequences of service brand equity (Raji et al., 2018). The feeling is the interpretation of commitment (Cesário & Chambel, 2017), which is subsumed in this model under the rubric of attachment (Deutschmann et al., 2018). Attachment is defined as “the relative strength of a consumer’s positive feelings towards the brand” (Lassar et al., 1995, p. 13). It is to identify the customers’ sentimental attachment to the service brands (Šeri´c et al., 2018). According to their rationale, customers formulate perceptions about the physical and psychological features of the service brands (Ahn & Back, 2018) through various information sources (Sultan, 2018). Seo and Park (2018), Theurer et al. (2018), and Bose et al. (2018) suggested that marketing activities create customer-based brand equity. However, extant research does not address whether marketing activities directly create customer-based brand equity or any mechanism in the higher education sector. There is a notable shortage of empirical research focusing on the determinants of CBBE in higher education markets (Mourad et al., 2019). Therefore, this study utilises a component-based approach whereby brand equity is conceptualised as a multidimensional construct in the higher education services industry. Further, there is significant confusion over what is precisely meant by brand equity, what constitutes it, and how it should be measured in the social media sphere (Algharabat et al., 2019; Raji et al., 2019). Thus, the literature has not paid attention to the exact relationship between social media marketing and CBBE in the higher education sector. There is a scarcity of studies investigating the influence of subjective norms and brand credibility on CBBE related to services, particularly in the higher education sector. While branding has received much attention from practitioners and

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academics, limited studies revealed cross-cultural differences in CBBE, particularly in the emerging country context. Accordingly, the present research focuses on identifying the importance of social media marketing, subjective norms, and brand credibility in developing customer-based brand equity for HEIs in emerging countries, which remains an under-researched area.

2.7 Social Media The advent of social media has substantially changed how people communicate (Zheng et al., 2018) and interact with each other (van Asperen et al., 2018). Social media gained tremendous popularity globally as a new means of communication (Kamboj et al., 2018) and self-expression (Kim et al., 2018a, 2018b). According to Seo and Park (2018), social media is an online application, platform, or media to facilitate interaction and content sharing. Definitions usually cover the notion of “social media”, referring to the technologies emphasising the content created by the users, and their interaction with the created content on social media (Wang et al., 2017). The widely accepted definitions divided the term “social media” into two parts: “social” and “media”. “Social” includes all the activities such as collaborations and interactions among people, which may be used for individual, professional, and/or entertainment purposes, and leverages social networks cultivated by individuals (Gil de Zúñiga et al., 2018). “Media” denotes all the Internet-enabled tools and technologies that carry out such activities (Edwards et al., 2018). Depending on the different purposes, social media can be categorised into three groups: (i) network-oriented social media, which includes communication between family, friends, and colleagues (Bedard & Tolmie, 2018) [for example, Facebook, Viber, and Pinterest (Peng et al., 2018)]; (ii) collaboration-based social media, which facilitates exchanging non-personal information at home or work-setting (Mehmood et al., 2018; Nohrstedt & Bodin, 2018) [for example, blogs, wikis, and forums like chats (Peinl, 2018)]; and (iii) entertainment-based social media, which is for diversion and interaction, such as digital games, and online contests (Yu & Oh, 2018). Social media includes blogs and micro-blogs (such as Blogger, Twitter, and WordPress), discussion forums (such as Quora and Reddit), digital content sharing platforms (such as Flickr, Instagram, Pinterest, and YouTube), social gaming sites (such as Gree, Mobage, and Zynga), and social networking sites (such as Facebook, Google+, LinkedIn, Mixi, and Orkut) (Li et al., 2017; Dragiewicz et al., 2018; Mkono, 2018; Rout et al., 2018). Social media drives a new set of models to businesses (Bidmon & Knab, 2018) that challenge traditional business processes and operations (Leitch & Merlot, 2018). The main difference is transforming one-to-one mass customisation into a one-to-many multidimensional two-way marketing promotional model (Stieglitz et al., 2018). Customers are increasingly using social media to search for information (Sampson et al., 2018), and turning away from traditional media, such as televisions, radio,

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and magazines (Sung et al., 2018). Since social media provides the platform for the customers to interact with other customers (Dolan et al., 2019), the firms are no longer the sole source of brand communication (Anees-ur-Rehman et al., 2018). Traditional brand communication administered by the firms, brand, and marketing managers has been gradually shaped by social media users (Raji et al., 2018). Communication has democratised with the rise in social media (Aguirre et al., 2019). Communication on the firm’s brands happens with or without the firm’s permission (Gómez et al., 2019). However, for better or for worse, now social media is potent (Walker et al., 2019). It is up to the firms to decide if they want to be serious about social media (Vermeer et al., 2019) and participate in this communication mode (Zanon et al., 2019). Social media allows the users to comment, share, review, and even create content across social media platforms (Peruta & Shields, 2018; Lee, 2018) now appearing in search engine results (Widmer et al., 2019). This creates challenges and opportunities for firms and marketers, where they find the ability to connect with customers in real time (Lee et al., 2018) and manage a significant volume of incoming customer data (Wagner et al., 2017; Hackley, 2018). In the context of higher education, HEIs have been turning more towards social media (Al-Rahmi et al., 2018; Lee et al., 2018) to build and maintain high-quality relationships with stakeholders. With the advent of social media, like other service sectors, HEIs also acknowledge and maintain their presence on different social media platforms (Stathopoulou et al., 2019), which is imperative for student recruitment, retention, visibility, and interaction (Al-Rahmi & Zeki, 2017; Rutter et al., 2017). Many studies showed the benefits the HEIs receive from social media, thereby confirming the increased popularity of social media in higher education (Al-Rahmi et al., 2018; Alt, 2018; Chugh & Ruhi, 2018). The main advantage is the possibility to obtain feedback, collaboration, co-creation of knowledge, student engagement, and cost advantages (Clark et al., 2017; Alt, 2018). Therefore, the increased adoption of social media has led the HEI’s marketing and brand managers to use it for their marketing activities. As a result, HEI has turned into social media marketing instead of traditional marketing activities.

2.7.1 Social Media Marketing SMM is a new way of Internet marketing (Tsakiridou & Karanikolas, 2019) that uses social media networking platforms as marketing tools (Jones & Harvey, 2019). Monitoring marketing activities through social media helps firms generate more effective marketing promotional activities (Westberg et al., 2018), and improve the firm’s operations (Lee, 2018). Marketers can, therefore, leverage social media to craft personalised messages and offer to the target audience (Walrave et al., 2018). Tuten and Solomon (2017, p. 46) defined social media marketing as “the utilisation of social technologies, channels, and software to create, communicate, deliver, and exchange offerings that have value for an organisation’s stakeholders”. Chen and Lin

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(2019) defined social media marketing as a commercial marketing event or process that positively influences the customers’ purchase decisions. SMM is considered to be the latest development in advertising (Shareef et al., 2019), promoting (AlAwadhi & Al-Daihani, 2019), and communicating a brand with the customers (Swani et al., 2019). The robust marketing activities in social media have created an exemplary scope for any firm’s brand to exposure through the attention (Iankova et al., 2018) and perception of the users (Joo et al., 2018), and to develop opinions to create values (Tajvidi et al., 2018). As a result, most fast-growing industries are eagerly striving to adopt viral marketing on social media (Pan et al., 2019; Xu & Pratt, 2018), and marketers have already acknowledged that social media marketing is the fundamental hub to generate consciousness about the existence (Ismail et al., 2018) and the motivation to purchase a brand (Martín-Consuegra et al., 2019). Although SMM offers similar facilities to the firms such as communicating content (Müller & Christandl, 2019) and targeting and engaging customers (Dolan et al., 2019), different social media platforms are perceived as a more specific favourable form of communication (Blankespoor, 2018). For example, Facebook provides a rich means for users’ relation with firms, and other users (Jang et al., 2018); Twitter facilitates the communication of brand messages (Chandler et al., 2018) and mining users’ response in real time (Zou et al., 2018); Instagram shares the image-based content (Valentini et al., 2018); and YouTube for videos (Thelwall, 2018). SMM provides a new landscape for brand marketing communication (Kusumasondjaja, 2018; Gómez et al., 2019), where social media users play a more active role as marketers (Fujita et al., 2019; Shareef et al., 2019), and brands are social currency (Kesgin & Murthy, 2019; Yang et al., 2019). This transforms marketing communication into practice to connect social media users (Berryman et al., 2019; Borges-Tiago et al., 2019), firms (Garcia-Morales et al., 2018; Zheng et al., 2018), and brands in the context of social networking (Gómez et al., 2019; Kamboj et al., 2018). The role of social media in marketing has shifted from message execution to the expansion of understanding social media users (Uzuno˘glu et al., 2017). The higher the marketing on social media, the more unique the users rally the firms’ brands (Gasco et al., 2019; Hsiao et al., 2019). The main advantage of marketing through social media is that marketers can choose different platforms to present their brands to customers (Raji et al., 2019; Roma & Aloini, 2019). The main aim of selecting different platforms is to know the customer needs (Coelho et al., 2018; So et al., 2018) and achieve a sustainable competitive advantage (Liu et al., 2019). Through SMM, the brand seems to offer benefits to the target market. The extant literature has empirically identified the benefits of social media marketing, including stimulating sales (Pan et al., 2019), reducing marketing costs (Jung et al., 2019), and creating user interactivity on platforms by stimulating users to post or share content (Djafarova & Trofimenko, 2018), increase brand awareness (Pantano et al., 2019), and improve the brand image (Gómez et al., 2019). Along with these benefits, firms use social media marketing more reactively (Jung et al., 2019). For example, firms can analyse and monitor users’ conversations on social media (Lee, 2018) to understand how they view a firm or its actions (Andersen et al., 2019). Also, social

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media marketing provides the platform to evaluate the value of the preferred brands (Park et al., 2018) by exchanging ideas and information among the users (Bigne et al., 2018), and create an opportunity to reduce misunderstanding (Ali et al., 2019) and prejudice towards brands (Sharma & Verma, 2018). In some instances, where the brand has been central to the social media marketing campaigns (Arrigo, 2018), the success of the campaign was determined by assessing the brand equity of the desired behaviour (Shay & Van Der Horst, 2019). In a systematic review of the social media literature, Kapoor et al. (2018) found that social media has been widely adopted as a marketing medium. However, despite an overwhelming interest on behalf of researchers in brand management theorisation (Felix et al., 2017; So et al., 2018; Tuten & Perotti, 2019), and in particular, concerning the specific coverage of the brand equity concept in the service sector (San Iglesias et al., 2019a; Martín et al., 2018), the predominant focus has thus far rested in identifying the relationship of social media marketing with customer-based brand equity (Seo & Park, 2018). Accordingly, this study provides a comprehensive conceptualisation of social media marketing, which goes beyond the isolated focus on customers and/or communicative aspects discussed in existing social media marketing literature. The framework and theories from different disciplines, such as psychology and sociology, have been used to emphasise the essential elements of social media marketing. This study’s theoretical framework outlines the crucial factors related to the communication of social media marketing in positioning the user (Pacauskas et al., 2018), and the firm (Pantano et al., 2019), along with each of the key continua. As per Felix et al. (2017) and Galati et al. (2017), the scope of social media marketing can be divided into two categories based on the functions it performs: as a strategic tool and as a communication tool. As a strategic tool, SMM can change the business model (Wu, 2016; Parsons & Lepkowska-White, 2018). It determines the effectiveness of social media investments (Galati et al., 2017; Iankova et al., 2018) regarding return on investment and returns on sales (Wu, 2016). Furthermore, firms use social media marketing to monitor and analyse social media conversations to understand how customers view a firm or its action (Felix et al., 2017). As a strategic tool, social media marketing specifically measures the firm’s economic aspects (Galati et al., 2017). As a communication tool, on the other hand, SMM conducts marketing activities involved in the marketing of goods, services, brands, information, and ideas via social media (Harrington et al., 2019). The rise of social media marketing as a communication tool allows people to share their experiences with firms, goods, and services with others on social media platforms (Choudhary & Jhamb, 2019). Social media marketing as a communication tool allows creating and exchanging the content generated on different social media platforms (Felix et al., 2017; Al-Awadhi & AlDaihani, 2019). Marketing communication on social media has been initiated through firm-generated and user-generated content, which are considered to be the two main communicating methods of SMM (Müller & Christandl, 2019; Sadek et al., 2018). The following section provides a detailed discussion of the importance and difference between firm-generated and user-generated content on social media.

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Firm-Generated Content

The firm-generated content means firm-initiated marketing over social media which enhances the customer-firm relationship (Kumar et al., 2016; Yang et al., 2019b). The flexibility of social media allows firms to create content on brands in a range of forms (Colicev et al., 2018), and to disseminate it through different platforms (Kim & Chae, 2018). It is best described as the communication of information, in any form, created by firms and shared directly through their official social media pages (Kumar et al., 2016; Wang & Kim, 2017; Yang et al., 2019b). Kumar et al., (2016, p. 9) define FGC as “the messages posted by firms on their official social media pages”. Similarly, Colicev et al. (2019, p. 102) described FGC as “content created by marketers on official brand pages on social media channels”. These messages are critically important, as they could enhance the credibility of the brand-related information of the firm through their direct interactions with customers (Bashir et al., 2017; Colicev et al., 2018). FGC is likely to affect the target audience considering the “message sentiment, customers’ response to the message, and customers’ innate disposition” towards the firm’s social media platform (Kumar et al., 2016, p. 9). The goal of creating FGC is to address the customers who cannot be reached with traditional media only (Kumar et al., 2017). FGC helps companies strengthen their relationship with their target customers (Osei-Frimpong & McLean, 2017; Colicev et al., 2019). Yet, customers could react positively or negatively (e.g., increase or reduce the number of likes, comments, and reposts), depending on the vividness (Colicev et al., 2018) and interactivity of FGC on social media about the brands (Viswanathan et al., 2018). FGC can change both customer participation (Dedeoglu, 2019) and brand exposure on social media (Bigné et al., 2019). However, the level of customers’ involvement and customers’ reaction to the FGC is unpredictable (Kumar et al., 2017; Nisar & Prabhakar, 2018; Song et al., 2019) and amplifies the changes in the effectiveness of brands on social media (Pinto et al., 2019; Viswanathan et al., 2018). Raji et al. (2018) asserted that FGC avail firms and brand managers to enhance the opportunity of acceptance of the firm’s brand through the content published on social media. Yang et al. (2019) noted that the information included in the FGC could be classified as informative FGC, and persuasive FGC. Informative FGC is considered as informative shifting about the firm’s brands, price, quality, etc. (VanMeter et al., 2018), in other words, the firm’s official information to notify their brands to external parties (Sinclair & Keller, 2017). Persuasive FGC is connecting with the customers (Dedeoglu, 2019), and promoting the brand personality by forming the relationship on a personal level (Kumar & Möller, 2018) without providing brand information (Müller & Christandl, 2019). Both informative and persuasive FGC are equally important for a firm to market its brand in the social media sphere (Yang et al., 2019). Colicev et al. (2019) identified three key dimensions of FGC: volume, valance, and vividness, where all three dimensions have a significant positive effect on FGC. The volume captures the frequency of brand posts, valance captures the negative

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or positive sentiments, and vividness captures the richness of the texts, and videos (Colicev et al., 2019). Generally, FGC increases the probability that a brand would be incorporated into customers in making a purchase decision (Poulis et al., 2019; Pan et al., 2019), and influences customers’ perception of the credibility of brand-related information (Mishra, 2019). Although FGC is becoming the most crucial channel, through which firms can embrace it, very few studies have addressed this area (Song et al., 2019; Yang et al., 2019; Sheng, 2019). The firms should understand how FGC impacts customer behaviour (Mishra, 2019) and develop insight into customers’ impressions (Kim & Chae, 2018), by influencing behaviour through the information shared by the firms on social media about their brands (VanMeter et al., 2018). Studies in this area have mainly focused on the effect of FGC on purchasing behaviour in various contexts such as new movie releases (Lu et al., 2019; Nisar & Prabhakar, 2018), box office revenue prediction (Song et al., 2019; Viswanathan et al., 2018), and product browse (Zhao et al., 2017). Some studies have examined the underline motivation of people to review the FGC of social media (Nisar & Prabhakar, 2018; Majid & Laroche, 2019), while others have focused on the importance of FGC over traditional marketing (Colicev et al., 2018; Yang et al., 2019). Since FGC serves as a useful source to share information (Hajli, 2018; Dedeoglu, 2019) and an influencer on purchase decisions (Human et al., 2018; Pan et al., 2019), the focus on the effects of FGC in previous research is understandable. Studies so far focused on the general value of FGC in different contexts, and limited studies have focused on identifying the relationship between FGC and CBBE (Mishra, 2019). Furthermore, the reviews on the importance of FGC for higher education is an under-researched area. Therefore, by conducting a detailed FGC analysis, this study aims to provide three contributions to the literature. First, we extend the recent research on social media in the higher education context by investigating the importance of FGC in developing HEI’s credibility. This is a considerable research area that has only received limited attention. Second, we test the effect of FGC on customer-based brand equity and consider the breadth and stability of the student-HEI relationship simultaneously, which also contribute significantly to this developing research area. Third, this study aims to identify the effect of social norms on FGC from the perspectives of students in higher education.

2.7.1.2

User-Generated Content

In the participatory context of social networking sites, firms are not any more than sole resource of brand-related communication (Chari et al., 2016). This has caused a paradigm shift to the user-centric media model (Park et al., 2019). Thus, the emergence of user-generated content is increasingly becoming a new form of brand communication (Colicev et al., 2019; Roma & Aloini, 2019). Although scholars have been unable to agree upon a standard definition of UGC (Grosser et al., 2019), Hollebeek and Macky (2019, p. 503) recently defined UGC as “the media content created or produced by the general public, rather than paid

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professionals and primarily distributed on the Internet”. In line with that, Colicev et al. (2019, p. 102) defined UGC as “brand-related content created by users”. They further identified the most relevant dimensions of UGC as valance (sentiment contained in the posts created by users), and volume (frequency of UGC). Although UGC can be developed for any purpose, a growing number of people create UGC for brands (Liu et al., 2017b; Müller & Christandl, 2019), by providing an opportunity for firms to identify how customers perceive their brands (Jiao et al., 2018; Zanon et al., 2019). Hence, social media users become the co-authors of brandrelated content (Raji et al., 2018), as it provides an opportunity for users to publish (Krumsvik, 2018). UGC allows customers to transmit the product or service information (Yang et al., 2019a) and involvement with marketing activities (Fox et al., 2018). UGC on social media platforms not only acts as an informant giving brand information (Kim & Song, 2018; Roma & Aloini, 2019) but also as recommender providing reviews of experienced customers (Schivinski et al., 2019). UGC Reviews by a strong influencer can help change lead to prospective customers (Jang & Moutinho, 2019; McShane et al., 2019). Compared to FGC, UGC content is often unpredictable (i.e., can be either positive or negative about the brand) (Li et al., 2018; Confente et al., 2019) and inconsistent with firms’ messages (Yang et al., 2018). The pervasiveness of UGC has practically created room for the users’ voices to be heard about the brand (Raji et al., 2018). Thus, users are now integrating their anecdotal comments, thoughts, and perception of a brand (Raji et al., 2018) beyond what the firms cannot ignore and prevent (Ahmad et al., 2018). Thus, social media participation through UGC could have a varying effect on customers’ perceptions of the brands (Busser & Shulga, 2019; Yoo et al., 2019). The customer involvement and responsiveness to UGC provide the platform to view other customers’ opinions and experiences (Lee et al., 2017; Ukpabi & Karjaluoto, 2018), which affects customers’ relationship intensity with the brands (Weiger et al., 2017). Since social media provides a platform for users to create and exchange brand-related UGC (Roma & Aloini, 2019), value creation exists not only between customers and firms but also between customers (Yu et al., 2018). UGC is both “participation and production of content on social media, rather than merely reading and watching the users” content (Stehling et al., 2018; Yoo et al., 2019). Studies on UGC adopt the convention of content creation and dissemination, conceptualising it similarly to eWOM (Kunja & GVRK, 2018; Aichner, 2019). However, the two concepts of UGC and eWOM have differed in whether the content is generated by customers or only conveyed by the customers (Knoll & Proksch, 2017). However, UGC is related to the brand and social media users with no commercially oriented intention (Nachiappan & Sambrani, 2018; Yoo et al., 2019), and not controlled by firms (Poulis et al., 2019). Although firms worry about losing their control towards brand-related UGC and even having negative UGC about the firms’ brands, most have now embraced it as a potentially powerful tool (Salem & Twining-Ward, 2018). If UGC contains negative brand information, it may of course

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afflict a brand with harmful consequences (Yang, 2018), and thus create an adverse effect on the firm’s reputation, and therefore, on brands (Confente et al., 2019). Brand-related UGC shared via social media may have more influence than other sources (Cu et al., 2019) because it is transmitted by a trustworthy information source embedded in a customer’s network (Grosser et al., 2019). Customers are sharing information about brands or products in the form of online reviews (Kawaf & Istanbulluoglu, 2019) or talking about their experience with brands or products on personal social networking sites (Liu et al., 2019). From the service perspective, UGC is an important “authentic” source of information (Bigné & Decrop, 2019; Qi et al., 2018). It is essential for the service firms to identify negative service experiences (Jang & Moutinho, 2019; Yang et al., 2019a), and avoid the crisis (Cheng, 2018). In the social media field, studies mainly focused on the importance of UGC in the construction of destination images (Kim et al., 2017; Wong and Qi, 2017; Qi & Chen, 2019; Sharif & Mura, 2019), and empirically tested the effect of UGC in the fields of tourism and hospitality (Hernández et al., 2018; Ukpabi & Karjaluoto, 2018; Holder & Ruhanen, 2018; Law et al., 2019; Narangajavana Kaosiri et al., 2019). Further, social media studies have primarily focused on the effects of UGC on market outcomes in various contexts such as book sales (Dellarocas et al., 2007), music sales (Duan et al., 2008), movie box office revenues (Chintagunta et al., 2010), music album sales (Dhar & Chang, 2009), travel planning (Mendes-Filho et al., 2018), luxury hotel reservation (Jang & Moutinho, 2019), and video game sales (Brunt et al., 2019). Some studies have examined the motivations that underlie customers’ decisions to contribute content to social media (Fox et al., 2018; Han et al., 2018a, 2018b; Narangajavana Kaosiri et al., 2019), while others have focused on how UGC interacts with traditional media marketing (Laurell & Sandström, 2018; Yang and Stohl, 2019). UGC serves as a useful source of word-of-mouth (Chu et al., 2018; Ramirez et al., 2018) to indicate the quality of a specific product or service (Confente et al., 2019; Jang & Moutinho, 2019). While researchers and industry practitioners have taken advantage of the power of UGC to create value for the firms and the customers (Montecchi & Nobbs, 2018; Liu, 2019), limited studies have been conducted in identifying the importance of UGC in the higher education sector. Despite social media being a vital part of current higher education pedagogical practices (Adams et al., 2018; McCune, 2018), as argued by Gai et al. (2016) and Scullion and Molesworth (2016), the higher education context is slower in recognising the value of usergenerated social media data and the content of social media for the higher education marketing and branding (Bolat, 2017). This study identifies three relevant streams of brand-related UGC research. The first stream of UGC research is to determine how credible students find user-generated content and their goals for engaging with them in the context of higher education. Even though several scholars argue that UGC often includes brand-related subject matter (Roma & Aloini, 2019; Colicev et al., 2019) influencing customers’ purchase decisions (Jang & Moutinho, 2019; Liu, 2019), little empirical evidence exists regarding to what extent the customers’ brand equity perceptions are affected

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by user-created social media content. Therefore, the second stream strand of UGC research focuses on the relationship between UGC and customer-based brand equity. In this sense, this investigation plans to contribute to the literature by shedding new light on students’ intention to involve with HEI-related UGC and its impact on brand equity from the students’ perspective. The third stream is to identify the role of social norms in developing UGC in the higher education context.

2.7.2 Social Media Marketing in Higher Education With the rising popularity of social media across the HEIs, and the students in particular (Al-Rahmi et al., 2018; Chugh & Ruhi, 2018), understanding the kind of impact the HEIs have on their social media marketing effort is becoming more pertinent (Fujita et al., 2018; Uncles, 2018). The most prominent reason why social media has become an important communication tool in HEIs’ marketing landscape is its ability to protect HEIs within the competitive environment (Bolat & O’Sullivan, 2017; Koshkin et al., 2017; Peruta & Shields, 2018; Al Hallak et al., 2019), and connect with target audience instantly (Bellucci et al., 2019; Peruta & Shields, 2017). The benefit of social media marketing extends beyond the student recruitment phase (Peruta & Shields, 2017; Hou, 2017). Social media marketing has been shown to increase students’ sense of connection with the HEIs (Ha et al., 2018; Le et al., 2019). Social media for higher education marketing facilitates the prospective students to receive a better understanding of the HEI including the reputation of the HEI (Panda et al., 2019), location (Feng, 2019; Haffner et al., 2018), quality (Panda et al., 2019), the relevance of the courses offered by HEI (Rambe & Moeti, 2017), and the reputation of the HEIs’ staff (Fearon et al., 2018). These factors influence the prospective students and other social media users to have a significant effect on their final decision of choosing HEI (Fujita et al., 2017; Baker, 2018). HEIs create social media groups to connect with the right audience as a part of the HEIs’ marketing activities (Swart et al., 2019). For example, HEIs have separate Facebook pages such as club pages, different pages for different academic departments, and sports pages to cater to the target audience (Peruta & Shields, 2018). Despite the increased perceived risks of adopting social media in higher education marketing, HEIs seem to appreciate the benefits of using social media for marketing and overcome any negative feelings to be “good HEI” (Bennett, 2017). In the higher education context, students are susceptible to use social media to reinforce the processes of gathering information related to HEIs compared to traditional media (Bekmagambetov et al., 2018; Grau et al., 2019). Specifically, the undergraduates are more oriented towards social media than the others (Wickramanayake & Muhammad Jika, 2018). Undergraduates mainly use social media to positively enhance resource and information sharing (Ha et al., 2018), active participation (Sheeran & Cummings, 2018), interaction (Gupta & Pandey, 2018), collaboration (Al-Rahmi et al., 2018), and critical thinking (Jahn & Kenner, 2018). However,

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HEIs in emerging countries often suffer from poor infrastructure and lack communication technology and formal electronic methods to connect with their students. These countries still depend on one-way communication and do not use the full capabilities of social media in conducting marketing activities to engage with prospective students. Existing studies on social media in higher education have focused on how the social media platforms (e.g., Facebook, Twitter, and YouTube) contribute to HEIs’ recruitment efforts (Rutter et al., 2016; Beech, 2018) and brand awareness (Foroudi et al., 2017; Rutter et al., 2017). Previous studies have mainly focused on understanding how HEIs’ marketing efforts in the online space via their websites (Barcellos et al., 2017; Ismailova & Inal, 2018; Saichaie & Morphew, 2014), blogs and emails (Balroo & Saleh, 2019) influence students’ decision-making processes. Further, studies revealed that content presented on HEIs’ websites have a positive and significant impact on students’ choices of HEIs (Bolat & O’Sullivan, 2017). However, no in-depth evidence into understanding how students engage with social media content created by their peers and HEIs (Bolat & O’Sullivan, 2017; Peruta & Shields, 2018; Stathopoulou et al., 2019) is available. Nevertheless, two general findings from prior research indicate an important gap in the literature. First, results on social media marketing are strongly influenced by the industry, making it difficult to generalise findings from one sector to another (Corstjens & Umblijs, 2012; Schulze et al., 2015). Therefore, the conclusions of the previous studies related to social media marketing could not be applied to the service sector, specifically for higher education. Second, surprisingly, while prior research has investigated online marketing strategies of HEIs (Foroudi et al., 2017; Pizarro Milian & Davidson, 2018) and shown the importance of social media for HEIs (Al-Rahmi et al., 2018; Chugh & Ruhi, 2018), very little is known about the underlying mechanisms of social media marketing for higher education (Brech et al., 2017). Particularly, prior studies did not provide a concise answer to the question “whether FGC or UGC leads to a higher level of interactivity with customer-based brand equity related to HEIs?” With these facts in mind, this study seeks to establish a baseline of social media marketing communication strategies for HEIs and identify what strategy will maximise the customer-perceived HEI brand equity.

2.8 Subjective Norms An individual’s motivation to involve with social actions such as participating in a social group (Taylor-Collins et al., 2019) is determined by the attitude (Han et al., 2019) and behaviour (Bellucci et al., 2018) embedded in the subjective norms. Individuals’ behaviour is not regulated (Guayasamin et al., 2017) and enforced (Friehe & Schildberg-Hörisch, 2018), but is rather voluntary based on individual beliefs (Landon et al., 2018), perceptions (Sedlander & Rimal, 2019), and the collective subjective norms (Lombardi et al., 2019). Subjective norms are not a static phenomenon (Bachrach et al., 2017); they are both effects and are affected by human

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behaviour (Bergquist & Nilsson, 2018), and is one of the most effective ways to influence individual behaviours (Brewis et al., 2019). Subjective norms are expectations of society towards acceptable behaviour (Ho et al., 2017; Minton et al., 2018). Individuals tend to follow subjective norms assuming it is the “right” thing to do according to their perspective (Huber et al., 2018). According to Grimes and Marquardson (2019, p.26), the subjective norm is “the perceived social pressure to perform or not perform behaviour”. When individuals join social groups, they provide a signal about the other group members (e.g., friends, family, and colleagues) and activities (Walumbwa et al., 2017; Raaphorst & Van de Walle, 2018). This is important for individuals to identify the potential consequences of their behaviour (Dumas et al., 2018) and the appropriateness of their actions (Gächter et al., 2017). Subjective norms are considered to be the blueprint of social rules (Huber et al., 2018) of socially accepted behaviour (Randazzo & Solmon, 2018). Individuals, therefore, comply with those rules enforced in subjective norms, either one of the two rationales (House, 2018). First, individuals follow the rules because of the fear of backlash from other group members (Huber et al., 2018), and second, individuals believe individual behaviour is the correct way to behave in a given situation (Salmon & Serra, 2017). Subjective norms, however, refer to the degree to which people perceive what other people consider as important (Chang et al., 2017), and expect to perform the same behaviour (Kaba, 2018). Thus, subjective norms are considered to be acceptable behavioural standards (Carcioppolo et al., 2017), and the common belief shared among social groups (Carcioppolo et al., 2017). Subjective norms comprised descriptive social norms and injunctive social norms (Shafer & Wang, 2018; Ru et al., 2018). Descriptive social norms are vital when individuals have low-monitoring behaviour (Blay et al., 2018; Gren, 2018), whereas injunctive social norms influence individuals with high-monitoring behaviour (Fleckman et al., 2019). With social media activities, subjective norms have been increasingly affected by users’ ability to share and view information quickly. Members of the social media groups are affected by the comments, reviews, and values held by other members, and accordingly, shape their behaviour. Active online relationship with the group members strengthens the adoption of subjective norms among the group members. In the social media sphere, subjective norms suggested individuals would be rewarded if an individual’s behaviour has been accepted by his/her group members as it is. Petrescu et al. (2018) noted that emerging countries, which are collectivist, might feel more pressure to share information on social media. Further, collectivist customers’ tendency to respect norms are significant predictors of digital information sharing (Fu et al., 2017; Gvili & Levy, 2019). Subjective norms can develop brand communities (Gong, 2018) that engage in conversation through social media about the brands (Danylchuk et al., 2018). Subjective norms enable the brand communities to collect information about the firms (Lee et al., 2018b), and the other users to perform a specific behaviour towards the brands (Marder et al., 2018) by negatively influencing the perceived risk endowed with the

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brand (Wang, 2019). However, previous studies indicated that subjective norms motivated to adopt the communication technologies such as e-mail (Makki et al., 2018), but the influence of subjective norms on social media members’ participation and commitment remains limited (Zheng et al., 2019). Yet, there is a lack of studies on subjective norms regarding the use of social media in marketing, and a gap exists to understand the importance of subjective norms for social media marketing (Jacobson et al., 2019; Zheng et al., 2019). Furthermore, when the customers are critically concerned about the value addition in brands (Hung et al., 2018; Yang et al., 2019), and communicating the brand’s added value, the subjective norm becomes a key strategy to fabricate customerbased brand equity (Danylchuk et al., 2018; Wang, 2019). While a few scholarly works have suggested that subjective norms can effectively construct customer-based brand equity, very few have empirically tried it (Shuv-Ami et al., 2018; Rather & Hollebeek, 2019; Mascarenhas, 2019). Hence, building customer-based brand equity using subjective norms is a significant issue, and filling this gap is essential. Even if earlier studies have explored the relevance of brand satisfaction (Weitzl & Hutzinger, 2019), brand image (Wang et al., 2018), brand consciousness (Misra & Panda, 2017), brand awareness (Wang, 2019), and brand trust (Rather & Hollebeek, 2019) with subjective norms discretely, very few have explored the relationship between the subjective norms and customer-based brand equity. Previous studies have identified that subjective norms are significant drivers of highly interactive services (Raymond et al., 2018). Due to the intangible nature of the services, subjective norms would help influence the customers’ behaviour (Muhammad et al., 2018), and induce them to purchase a particular service (Lin & Niu, 2018). Even though the broader literature on the influence of subjective norms on manufactured goods is extensive (Tomintz & Barnett, 2018), relatively little is known about the subjective norms in service sectors (Steinhaus et al., 2019). Since this study is mainly focused on the higher education sector as one of the service sectors in any nation, identifying the importance of subjective norms to HEIs is vital, which remains unclear (Felgendreher & Löfgren, 2018; Lesley, 2018; Riordan & Carey, 2019). In the higher education context, prospective students lack experience with the potential HEIs (Dunnett et al., 2012; Elsharnouby, 2015), and they rely heavily on others to obtain specific information and opinions to select a particular HEI to pursue their higher studies (Nguyen et al., 2018). Students’ motivation to act following the ones superior may function as a risk-reliever method to select an HEI (Sin, 2015). For some students, such norms might be motivating (Darrington & Dousay, 2015; Silva & John, 2017), but for others, these norms may create external pressure to perform (Karikari et al., 2017). Despite the importance of subjective norms for the students’ behavioural intention (Malak et al., 2018; Omura et al., 2019), limited studies have focused on examining the importance of subjective norms for higher education marketing (Felgendreher & Löfgren, 2018). A wealth of previous research related to subjective norms has mainly focused on identifying the customers’ behavioural outcomes (Alnaser et al., 2017; Liang &

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Shiau, 2018; Kashif et al., 2018). Research in this area focuses on behavioural intentions to engage in object behaviour because measuring actual behaviour is often unfeasible (Reypens & Levine, 2018), (Londono et al., 2017). The placement of subjective norms as an antecedent to behavioural intentions is well established, both generally and in specific relation to technology adoption (Konietzny et al., 2018; Petrescu et al., 2018). Despite the significance of subjective norms on customer behaviour, researching the influence of subjective norms on brands and their equity in the social media sphere is inadequate (Sijoria et al., 2019). While subjective norms have received much attention from practitioners and academics, the limited studies revealed cross-cultural differences in subjective norms (Minton et al., 2018), particularly in the higher education sector (Malmström & Öqvist, 2018). Therefore, by combining the gaps identified in the literature, this study tries to determine the effect of subjective norms in higher education in developing customerbased brand equity using social media, which has not been explored in the service marketing literature. This study further contributes to the existing body of knowledge by examining the mediating role of brand credibility on the relationship between user-generated content, firm-generated content, subjective norms, and customer-based brand equity.

2.9 Brand Credibility In the realm of intense competition, brands use cues as signals (Kolbl et al., 2018) to communicate brand-related information (Calderón-Monge et al., 2018) to trigger positive outcomes for creating value for the brand (Park et al., 2018; Wirtz & Ehret, 2019), and increasing the purchase intention (Brunner et al., 2019). Firms deploy brands as an efficient market signal (Dwivedi et al., 2018) when a high level of asymmetric information exists regarding the brands (Boeuf & Darveau, 2019). Although a brand provides a signal to the perceived quality of a product or service (Cheng et al., 2019), the signal must be credible (An et al., 2019). According to brand management researchers, brand credibility refers to the customers’ perception of the brand’s perceived ability (Rather et al., 2019), motivation (Dwivedi et al., 2018), and willingness (Anees-ur-Rehman et al., 2018) to provide accurately (Klawitter & Hargittai, 2018) and truthful information (Visentin et al., 2019), and deliver what the brand promises to its customers (An et al., 2019). Brand credibility is an essential tool to establish a strong customer-brand relationship (Kashif et al., 2018). Brand credibility comprises two components: trustworthiness and expertise (Brunner et al., 2019). Trustworthiness is the brand’s willingness to deliver what it has promised (An et al., 2019), while expertise refers to the brand’s ability to deliver the actual promised value to customers (Palmeira et al., 2019). Since brand credibility is considered as a unidimensional construct (Kashif et al., 2018), the collective impact of trustworthiness and expertise links to other marketing outcomes. Brand credibility is assessed through the holistic exposure of the customers (Dwivedi et al., 2018) to the brand across pre-purchase behaviour (Buchan et al.,

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2018). Furthermore, in today’s digital and interactive age, real-time information influences a brand’s credibility (Pinem et al., 2019). Brand credibility is intricately linked to high perceived brand value (Chakraborty & Bhat, 2018), thereby improving customers’ perception of the brand attributes (Chin et al., 2019). Similarly, higher brand credibility exerts a substantial effect on customers’ brand choice intention (Martín-Consuegra et al., 2018), influences the perception of high quality (Pecot et al., 2018), and low information searching cost (Fan et al., 2019). If the trustworthiness and expertise are not managed correctly, it can lead to brand rejection (Sun et al., 2019). In a service setting, brand credibility provides many benefits for the firms including high relationship equity (Mills et al., 2019), reduced perceived risk (Busser & Shulga, 2019), and positive change in customers’ purchasing and consumption behaviour (Chin et al., 2019), which forms stronger ties between customers and the brand (Kashif et al., 2018). High perceived risk, which arises from the intangible and interactive nature of service (Truong & Hara, 2018) illustrates the importance of brand credibility in the service context (An et al., 2019). Signalling theory explains that brand credibility provides a signal of service quality (del Barrio-García & Prados-Peña, 2019), which is extremely important when a higher level of asymmetric information is available about service providers (Bougoure et al., 2016; Mathew & Thomas, 2018). Service brand credibility acts as a bridge on which trust is built between the service providers and the customers (Bougoure et al., 2016; Sheeraz et al., 2016). Customers meet the brands’ promises by developing confidence in the trustworthiness (Portal et al., 2019; Saju et al., 2018) and expertise of the service brands (Jeng, 2016), which are critical success factors for a service firm (Akturan, 2018). The credibility of a service brand is that it increases the reputation of the service firm (Lou et al., 2018; Martínez-Ferrero & GarcíaSánchez, 2018), which requires consistent investment in and delivery of consistent service over time (Chakraborty & Bhat, 2018; Karanges et al., 2018), expressing the values, beliefs, and identities that a brand stands for with clarity (Dwivedi & McDonald, 2018). Nevertheless, investigations on brand credibility have mostly focused on tangible dominant products (Bougoure et al., 2016) and overlooked the customers’ service domain (An et al., 2019). Several studies have identified the direct effect of brand credibility, and evidence indicates that brand credibility is intricately linked to high perceived value (Jahanzeb et al., 2013; Vera, 2015; Chakraborty & Bhat, 2018). Similarly, higher brand credibility can influence brand choice (Wang et al., 2017) and exert a substantial effect on purchase intention through the perceptions of high quality, low risk, and information cost-saving (Jeng, 2016; Martín-Consuegra et al., 2018; Chin et al., 2019). Some studies have also identified the mediating effect of brand credibility between brand experience, endorser credibility, and customers’ purchase intention (Chin et al., 2019; Dwivedi et al., 2018). However, only a few studies have discovered the relationship between brand credibility and CBBE so there are inconsistent relationships between brand credibility and CBBE dimensions. A few studies have focused on identifying the relationship between social media marketing communications and customer-based brand equity with brand credibility

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dimensions as intervening variables (Chakraborty & Bhat, 2018). Moreover, the influence of brand credibility on the linkage between subjective norms and customerbased brand equity is still scarce in the literature. According to the earlier studies, only a few researchers have identified the mediating effect of brand credibility among FGC, UGC, and CBBE in the service context, specifically in the higher education context. Some recent attempts signify the importance of brand credibility as a construct, but it has been employed with a different theoretical mindset than this study. For example, several studies have focused on positive marketing outcomes of corporate brand credibility (Pratono & Tjahjono, 2017; Balmer et al., 2017; Wang & Lee, 2018; Hasanah & Wahid, 2019), the relationship between innovation (Bairrada et al., 2018; Martín-Consuegra et al., 2018), and purchase intention with brand credibility (Chin et al., 2019; Jeng & Lo, 2019); but still, the context of brand credibility and its influence on the higher education sector is missing. This is important in an era where HEI brands are suggested to employ innovative means to reposition their brands (Lomer et al., 2018), and survive among the threats, and the competition received from the other competitive HEIs. Similarly, when students are confused in selecting the right HEI (Dominguez-Whitehead, 2018), and HEIs are struggling to attract more students (Lombardi et al., 2019), this study provides valuable empirical insight to brand credibility to overcome those challenges. Therefore, the present research explores whether brand credibility has a more significant positive impact on social media marketing and customer-based brand equity in the higher education context. Understanding the factors that positively impact social media marketing and customer-based brand equity is critical for HEIs’ success. Therefore, this study used brand credibility as the mediator to develop the relationship between social media marketing and customer-based brand equity.

2.10 Higher Education in South Asia and South-East Asia According to Sekaran (2009), moderating variables modify the relationship between independent and dependent variables. Therefore, this study uses location (Sri Lanka vs. Vietnam) and brand usage experience (junior vs. senior) as moderating variables among the study constructs.

2.10.1 Location Despite the rapid growth of worldwide social media usage (Dwyer, 2019), adoption and use of social media marketing have been highly variable across countries (Humprecht & Esser, 2018; Boulianne, 2019). It has been reported that various social media marketing activities proliferated in Asian countries, but, they are still in their

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infancy stage (Ashraf et al., 2019; Tsai & Men, 2017; Wood et al., 2018). The differences in social media adoption and usage across countries may be associated with characteristics of country environments such as telecommunications infrastructure (Sobaih et al., 2016), and the cultural influences on social media use (Alsaleh et al., 2019; Marengo et al., 2018). Nations with different cultural settings are driven by different motivations in their use of social media (Krishen et al., 2019; Stamolampros et al., 2019), and thus they form dissimilar social networking relationships (Alsaleh et al., 2019; Sheldon et al., 2017) that are reflective of their respective cultural orientations (Teng et al., 2017; Kizgin et al., 2018; Kizgin et al., 2019). Along the same line, culture may play a critical role in dictating customers’ interactions with social media brands (Jiao et al., 2018; Roma & Aloini, 2019). The adoption of social media for branding activities depends on the national cultures (Choi et al., 2018; Song et al., 2018), identified as a key characteristic underlying consumer behaviour differences (Becker & Lee, 2019). The unique cultural characteristics across the countries, such as shared values, norms, and learned behaviours, could affect the customers’ perception towards the brands (Csaba, 2017; Parida & Sahney, 2017; Xiao et al., 2018). Cross-cultural psychology theories and empirical research suggest that culture impacts everything from attitudes to motivations to social media needs and responses (Krishen et al., 2018). There are cross-cultural differences in social media attitudes, adoption, and behaviours (Choi et al., 2018). Understanding these subtle differences among countries is vital for marketers to build effective communication strategies through social media to connect with customers from different cultures and socioeconomic strata (Alsaleh et al., 2019). The cross-cultural literature has also documented that cultural differences do not influence only the customers’ social media communication preferences and social media marketing behaviours—it also affects the branding strategies and the content of marketing messages (Choi et al., 2018). By recognising the overarching influence of culture on shaping both social media content and customers’ relationship with social networking sites, this study pays special attention to finding how countries with different backgrounds influence the mechanism underlying customer-brand relationship on the social media platform. The aim is to compare two distinct emerging countries, Sri Lanka which is developing (Weerasundara et al., 2018; Dissanayake et al., 2019), and Vietnam which is in a transition in Asia (Kim et al., 2019; Fforde, 2019). In this regard, this study focuses on subjective norms, branding activities, and social media adoption across two countries. Therefore, this study sampled customers from two countries (Sri Lanka and Vietnam), to examine the differences in adopting social media marketing and branding among the emerging countries.

2.10.1.1

Comparison Between Sri Lanka and Vietnam

The present study aims to comparatively analyse the social media marketing communication adoption and branding between Sri Lanka and Vietnam from the students’ perspective in HEIs. This study identifies the similarities and differences between the

2.10 Higher Education in South Asia and South-East Asia Table 2.4 Hofstede’s cultural dimension values of Sri Lanka and Vietnam (adapted from Dissanayake et al., 2015)

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Hofstede’s cultural dimensions

Sri Lanka

Vietnam

Power Distance Index (PDI)

80

70

Individualism versus Collectivism

35

20

Uncertainty Avoidance Index

45

30

Masculinity versus Femininity

10

0

two countries to examine the cross-cultural differences. Individuals from different countries may exert different kinds of social and cultural behaviour values, and such values influence people’s perception of social media activities, customer-based brand equity, and subjective norms. In selecting Sri Lanka and Vietnam as a moderating variable, this study identifies many similarities and differences between the two countries. Geert Hofstede (Guritno et al., 2020) developed one of the most valuable frameworks regarding national cultures. This study mainly asserted four dimensions: (1) power distance index, (2) Individualism versus collectivism, (3) Uncertainty Avoidance Index, and (4) Masculinity versus Femininity in comparing the cultural differences between the countries (Table 2.4). Sri Lanka and Vietnam are categorised under the intermediate score according to the Power Distance Index. Both countries are conscious of the hierarchical order and slow in decision-making (Kim, 2017). Individuals accept that the superiors are powerful, and thus employees take the status quo for granted (Jung et al., 2018). From the individualism and collectivism perspective, both countries hold a lower position. Individualist culture expects to concern more on himself/herself or the immediate family, whereas collectivist culture concentrates more on society rather than own self-interest (Dissanayake et al., 2015). According to Kim (2017), the low index refers to the collective society, and Sri Lanka and Vietnam fall under collectivism where the primary obligation is towards social well-being. Uncertainty avoidance refers to the level of stress in society in the face of an unknown future (Hofstede, 2011). Sri Lanka and Vietnam have a lower score in the uncertainty avoidance index (UAI). Countries with higher UAI follow “rigid codes of belief and behaviour and are intolerant towards deviant persons and ideas, but weak uncertainty avoidance societies maintain a more relaxed atmosphere in which practice counts more than principles and deviance are more easily tolerated” (Kim, 2017, p. 27). Accordingly, Sri Lanka and Vietnam believe that practices are more important than principles if they are ambiguous. Masculinity-Femininity refers to “the distribution of values between genders, which is another fundamental issue for any society, to which a range of solutions can be found” (Hofstede, 2011, p. 12). Lower scores reflect a feminine society where they focus on “working in order to live” (Dissanayake et al., 2015, p. 223). A feminine society values equality, solidarity, and quality in their working lives. Masculinity, as the opposite of femininity, refers to “live in order to work”, and focus more on assertive, competition, performance, and resolving conflicts by fighting (Dissanayake et al., 2015, p. 211). Sri Lanka and Vietnam are at the lowest score in the index, and

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thus they fall under the feminine society. Masculine culture countries strive for a performance society, whereas feminine countries strive for a welfare society. In recent years, social technologies have been widely used by students in Vietnam daily (Krishen et al., 2019). Although it was perceived as a type of technology used mainly for social and entertainment purposes, it has gradually been adapted for their use in their HE (Manca, 2020). Nguyen et al. (2020) have suggested that Vietnamese students who adhere to the collectivist cultural background are benefiting from using social media for educational purposes, such as exchanging information with their peers and discussing academic topics. These uses occurred “often or very often” among 80.7% of students in Vietnam while students at a language university in Vietnam improved their competency in English by using social media (Bich Diep et al., 2021). Tuan (2021) noted that students in Vietnam show positive attitudes on social media use in HE as they believe that social media provides the facility to co-create and share knowledge with global audiences beyond classroom walls. In contrast, despite the fact that the growth and development of social media assisted by the factors that support the growth and usage of mobile devices and the development of ICT, students in Sri Lanka are not realising the full potential (De et al., 2018). Therefore, students’ attitudes and expectations regarding utilising social media technologies to support learning, exchanging knowledge, and gathering information are negative even though Sri Lankans have a collectivist culture. Accordingly, Sri Lankan students are adopting relatively slowly with social media for their educational purposes. Sri Lanka is classified as an emerging country (Asian Development Bank, 2017), whereas Vietnam is a transition country (Spotlight on Vietnam, 2017). Essential demographic and economic indicators of Sri Lanka and Vietnam provide an overview of both countries in terms of population and economy (Table 2.5). Vietnam is increasingly urbanising than Sri Lanka, with estimates that the urban population will reach 50% by 2025 (The World Bank Group, 2017). Further, Vietnam has closed the gender gap with carefully balanced values between females and males (The World Bank Group, 2017). An overview of the higher education sectors of Sri Lanka and Vietnam provides the empirical setting for this research. Table 2.5 Country overview (adapted from Hootsuite, 2019)

Population and economy

Sri Lanka

Vietnam

Total population

20.98 million

96.96 million

Female population

52.0%

49.9%

Male population

48.0%

50.1%

Annual change in population size

+0.3%

+1.0%

Urban population

19%

36%

Median age

34.1

32.6

GDP per capita (ppp)

$12,811

$6,776

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The Sri Lankan higher education sector includes 20 public and 17 private HEIs that provide bachelor’s degrees (Ministry of Higher Education and Cultural Affairs, 2017). Sri Lankan undergraduate enrolment rate is reaching close to 20%, placing it among the lowest rates of all emerging countries by 2016 (Nedelkoska et al., 2018). On the other hand, the higher education sector in Vietnam faces a challenging marketing environment due to the emergence of private HEIs (Khoi et al., 2019). The demand for higher education in Vietnam is rising, and the higher education sector is undergoing considerable change, with a range of new, private providers joining established publicly funded universities. In Vietnam, the higher education sector comprises 171 public and 65 private HEIs (Tran & Villano, 2017). Among the emerging markets for higher education, Vietnam experienced remarkable growth in HEIs number, quality, and student enrolment. The country has witnessed a higher enrolment rate in the higher education sector 22.6–28.79% for 2010–2016 (UNESCO, 2017). Vietnam finds itself a successful mover in Asian transnational education (TNE) (Spotlight on Vietnam, 2017). Among all the Asian countries, Vietnam is in the second place in degree recognition and quality assurance of higher education (Ilieva et al., 2017). Sri Lanka and Vietnam are experiencing remarkable growth in the Internet and social media with the advancement of new technologies. Internet usage and social media adoption show the technological literacy between Sri Lankans and Vietnamese (Table 2.6). Relating to social media usage, Sri Lanka has a lower social media adaption level than Vietnam, which is becoming a country with an increasing number of social media users. The number of domains in Vietnam ranks 8th in Asia, 2nd in South-East Asia, and 18th worldwide (Khuong & Huong, 2016, p. 280). Sri Lanka and Vietnam provide an interesting context for research on customerbased brand equity in higher education sectors. Higher education sectors in Sri Lanka and Vietnam face a challenging marketing environment due to the emergence of private HEIs. While marketing practice suggests an increased focus on brand’s role, there is no systematic academic research to understand the role played by customer-based brand equity within the higher education sector. The demand for HEIs in Sri Lanka and Vietnam is growing. The higher education sector is undergoing considerable change, with a range of new, private providers joining established publicly funded universities. This has created significant uncertainty in the marketplace for private HEIs. The emergence of the new private HEIs introduced the market the concept of competition among HEIs as each HEI had to build brands to communicate their service offer to the marketplace. By considering the similarities and differences between Sri Lanka and Vietnam, this study makes a novel empirical contribution by testing the proposed conceptual framework in Sri Lanka and Vietnam as an example of emerging higher education markets, while focusing on private HEIs. Even though Sri Lanka and Vietnam are sharing similar cultural backgrounds, both countries differ in several aspects such as international mobility of students, research, and education provision (Table 2.7).

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Table 2.6 Internet and social media usage among Sri Lanka and Vietnam (adapted from Hootsuite, 2019) Sri Lanka

Vietnam

Internet usage—total number of active internet users

7.13 million

64.00 million

Internet users as a percentage of the total population

34.0%

66.0%

Total number of active mobile 6.55 million internet users

62.40 million

Mobile Internet users as a percentage of the total population

64.0%

31.0%

Social media usage—total 6.20 million number of active social media users

62.00 million

Active social media users as a 30.0% percentage of the total population

32.6%

Total number of active social media accessing via mobile devices

5.70 million

58.00 million

Active social media users via mobile as a percentage of the total population

27.0%

60.0%

Social media audience—Facebook

6.00 million (Female—32%, Male—68%)

61.00 million (Female—48%, Male—52%)

Instagram

1.10 millio (Female—31%, Male—69%)

6.20 million (Female—59%, Male—41%)

Twitter

182.5 thousand (Female—22%, Male—78%)

684.5 thousand (Female—33%, Male—67%)

LinkedIn

980.0 thousand (Female—37%, Male—63%)

2.60 million (Female—55%, Male—45%)

The above differences lead Vietnam and Sri Lanka to develop their social media strategies to conduct marketing activities. Since Vietnam is showing a higher level of openness which facilitate the mobility of students and researchers can create a huge impact on social media marketing. This will facilitate to HEIs to offer TNE and can attract more students through marketing on social media because of the openness. Compared to Vietnam, Sri Lanka is having a lower recognition of TNE degrees, and they communicate their value to the local labour market, so that Sri Lanka also can implement communication strategies through social media to attracting international students and ensuring sufficient education provision through the means of TNE.

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Table 2.7 Country comparison on international HE (adapted from Ilieva et al., 2017) Structure of the National Policies Framework

Sri Lanka (weights according to Vietnam (weights the National Policies according to the National Framework) Policies Framework)

Countries’ national support for international higher education (openness of higher education systems, quality assurance of higher education provision and recognition of international qualifications, access, and sustainability)

0.5 (lower openness) 0.4 (lower quality assurance) Weak international research engagement

0.7 (higher openness) 0.6 (higher quality assurance) Strong international research engagement

The openness of higher education (facilitate mobility of students and researchers; the ability of HEIs to offer TNE)

0.6 (moderate level of infrastructures which facilitate mobility of students and researchers; the ability of HEIs to offer TNE)

0.8 (better infrastructures which facilitate mobility of students and researchers; the ability of HEIs to offer TNE)

Quality assurance and degree recognition

0.5 (lower recognition of TNE indicates a failure to secure employment that matches their education level)

0.7 (higher recognition of TNE degrees and communicating their value to the local labour market)

International research 0.4 (lower level of support from engagement (countries’ national the government) support for international research engagement)

0.8 (strong government support for international research collaborations. Official Development Assistance countries view research engagement as a means to develop research capacity, and they often co-fund research schemes initiated by other nations)

2.10.2 Brand Usage Experience Brand usage experience is the customers’ brand-specific association with a product or service (Oakenfull & McCarthy, 2010). Customers’ brand usage experience contributes to brand awareness that reveals the critical insight on “experience precedes awareness” in some contexts (Choi, 2014, p. 5). In higher education sectors, students’ brand usage experience depends on their experience with a particular HEI, and it categorised students as juniors and seniors based on their study year (Beneke, 2011). Students’ brand usage experience creates different perceptions related to the same HEIs (Oakenfull & McCarthy, 2010). Therefore, this study focuses on students’ tenure in identifying brand usage experience. Junior students are the freshmen, or up to the second year in the 4-year bachelor’s degree programmes, who create higher market demand for higher education (Subotzky et al., 2008). Junior students have a limited experience with HEIs, whereas senior students possess a higher level of familiarity with HEIs. Since the senior

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students have grown up in the HEI premises at least for 3 years of their higher education life, they demonstrate a high level of familiarity with the HEI than the juniors (Subotzky et al., 2008). The long-term experiences of the seniors differ from the junior students, based on the time they spent in a particular HEI. While junior students need to explore more about the HEIs, senior students are familiar with all the HEI activities. As such, junior students are more likely to gather information about the activities related to HEIs to develop their relationships with a particular HEI (Gunuc & Kuzu, 2015). Short- and long-term experience with juniors and seniors could lead to a different perception of branding with HEIs, based on their experiences at different levels (Wardley et al., 2017). The proficiency level in following social media could differ among juniors and seniors, and the perception of the trust towards the information shared on HEIs on social media could vary. The perceived social pressure and trust endowed with HEI brands are different based on the students’ brand usage experience, which in turn leads to creating different perceptions about the HEIs in their minds. Therefore, the researcher has selected one country from South Asia (Sri Lanka), and another from South-East Asia (Vietnam) to moderate the relationship between social media marketing, brand credibility, subjective norms, and CBBE. Besides, undergraduates’ brand usage experience is (Junior students vs. senior students) considered, being a moderator to strengthen the above relationships. Considering the challenges faced by HEIs in Sri Lanka and Vietnam, this study proposes a moderating effect of location (Sri Lanka vs. Vietnam), and undergraduates’ brand usage experience (junior students vs. senior students) to demonstrate the students’ perception of social media, and branding based on their level of maturity with HEI. Further, this research study analyses the cross-cultural difference in adopting social media marketing and customer-based brand equity for HEIs.

2.11 Summary This chapter has reviewed several empirical studies of social media and brand equity to establish a theoretical framework and identify the factors considered necessary for social media marketing, and customer-based brand equity in the higher education context. This chapter content is mainly devoted to elaborating the theoretical roots and findings of previous studies in respective areas to justify the scope of the study. A detailed overview of the theoretical background of the study variables is presented. The chapter identifies several gaps in the literature and seeks to fill them through the present study. Based on those implications, the next chapter will discuss the research design and conceptualisation of the study.

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Chapter 3

Research Model and Hypotheses

3.1 Introduction Chapter Two made a considerable attempt to elaborate on the theoretical and conceptual background of social media marketing communication, customer-based brand equity, and other related concepts while having an in-depth discussion of the previous research works, thereby exploring the relationship between study constructs. This information has immensely helped to construct the logical connection between the main variables of the present study. In this chapter, the researcher attempts to develop the conceptual framework for demarcating the scope of the study by incorporating the existing theories and developing a hypothetical relationship among the main constructs of the research model. It provides a rational basis on which the research methodology of the next chapter can be discussed. Accordingly, the first section of the chapter presents the relationship between the primary constructs and the study hypotheses. The second section presents the conceptual model and the theoretical foundation of the logical connection between the constructs to address the research problem. Operational definitions are presented in the third section to prevent the study from deviating from its main purpose.

3.2 Hypotheses Development The marketing literature, previous studies on social media, and branding have examined the behaviour of brand communities and the positive outcome of branding on social media. However, even in studies that examined online brand communities, the extent to which social media marketing, subjective norms, and brand credibility influence undergraduates’ perception of customer-based brand equity, particularly in the higher education sector, have not been addressed. Thus, this study was © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_3

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developed based on the following three research questions, and subsequently, relevant hypotheses were developed to address each research question to achieve the objectives of this study. Research Question 1: What is the extent to which social media marketing, subjective norms, and brand credibility are related to customer-based brand equity, as perceived by prospective undergraduate students? Research Question 2: What is the degree of brand credibility influencing the relationship between social media marketing, subjective norms, and customer-based brand equity? Research Question 3: Are there any difference in adopting social media marketing, subjective norms, brand credibility, and customer-based brand equity on location and brand usage experience? Research question 1 will be addressed through hypotheses 1, 2, 3, and 4, Research question 2 will be addressed through hypothesis 5, and Research question 3 will be addressed through hypotheses 6 and 7.

3.2.1 Hypothesis H1: User-Generated Content and Brand Credibility H1: User-generated content on social media will be positively related to brand credibility Social media users tend to heed what other users share and likely to be absorbed by the sentiment disseminated by fellow social media users (Ivanov & Sharman, 2018). The match between different brand-related contents created by different users serves as a good indicator of brand credibility (Pucihar et al., 2018). The brand-related UGC in terms of “Likes”, comments, shares, and rating would help social media users to recognise the brand’s expertise and trustworthiness (Quesenberry & Coolsen, 2018). The rationale of depending on the UGC is its lack of commercial purpose (Oliveira & Casais, 2019), and free of bias (Camacho-Otero et al., 2019), which exhibits a brand’s real nature. The higher level of positive review on the shared videos or status about a brand by social media users resulted in a higher confidence level on the brands and brand manufacturers (Yoshida et al., 2018). The positive perception the users created on social media content is likely to generate positive feedback from their peers concerning the brands (Huang et al., 2018), which could help to influence other social media users’ perception of the credibility of a brand. High involvement of the users in generating brand-related UGC acts as a signal for the potential members (Busser & Shulga, 2019), and provide the basis for the other users to determine whether the particular brand is credible enough to purchase. In the participatory context of social networking sites, firms are not any more than sole-resource of brand-related communication (Chari et al., 2016). This has

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caused a paradigm shift to the user-centric media model (Kaur et al., 2018), which is likely to be more prominent with the users’ sceptics to seek brand information (Dunn & Harness, 2018). UGC on social media platforms acts as an informant giving brand information (Fox et al., 2018) and recommender giving reviews of experienced customers (Roma & Aloini, 2019). Yet, the nature of UGC prompts several concerns related to brand credibility, as it is a tricky task to evaluate the strangers’ opinions (Hernández-Ortega, 2018; Lo & Yao, 2019). Since UGC is a blend of amateur, semi-professional, and professional people (Stehling et al., 2018), the information could be vulnerable. Besides, the posts created by social media users could be dishonest with negative comments and reviews and develop certain brands’ credibility by tarnishing the competitive brands’ credibility (Yelijiang, 2017). Students’ involvement with like-minded social media users helps to gather actual experiences related to the HEIs’ performance and services (Ghasemi et al., 2018) to develop a positive perception towards the credibility of a specific HEI brand. Therefore, students could rely on the UGC to identify HEI’s ability and trustworthiness in delivering what is promised by the HEIs. Any negative comment on the HEIs’ inability to provide a better service to its stakeholders could adversely affect the students’ perception of HEIs’ credibility. Based on the preceding discussion, the researcher has introduced the testable hypothesis, which seeks to determine the relationship between user-generated content and brand credibility relating to HEIs from the perspectives of undergraduates.

3.2.2 Hypothesis H2: Firm-Generated Content and Brand Credibility H2: Firm-generated content on social media will be positively related to brand credibility Brand-related information posted by firms leads social media users to actively engage with firms’ brands (Poulis et al., 2019; Colicev et al., 2019). A positive evaluation of the content generated by firms could positively influence customers’ perception of the brands (Müller & Christandl, 2019). The higher number of followers of firms’ brand pages creates greater influence on other social media users by assuring a brand’s performance (Lee et al., 2018b). Social media users perceive FGC as a credible source of information as the firm has better knowledge about their brands (Mathew & Thomas, 2018). If a firm’s brand managers create a meaningful image through the FGC that fit into the overall aesthetic landscape of the brand (Wang et al., 2017), it could signal the brand’s ability and expertise in providing superior performance than competitive brands. To be famous, or to market a particular brand, the firms no longer need expensive marketing activities; they now gain vast followership enabling more comprehensive visibility on social medial, which induces the user to follow the firms’ brand pages (Scholz et al., 2018).

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Since the customers’ become increasingly tech-savvy, the signals about brands through FGC become even more critical as the customers evaluate information while comparing with the other similar brands on social media (Dedeoglu, 2019). Firms with a higher level of ability in convincing social media users’ by providing signals about their brands could influence the users’ perception of brand credibility. Therefore, the firms should strive to generate useful and complete content for the social media user to develop and enhance credibility for their brands, resulting in a favourable attitude to choose the firm’s brand (Poulis et al., 2019). An effective brand communication shortens the brand decision-making process (Flanagin et al., 2018), enhancing brand credibility by increasing the probability of a brand incorporating into the users’ mindset. In the context of higher education, students’ involvement has drastically increased with social media communities (Al-Rahmi et al., 2018), and the students’ engagement with social media has escalated the HEI’s information through social media (Fujita et al., 2019). The information shared by HEIs’ on their official social media pages might be useful in addressing students’ needs and reducing uncertainties about HEIs (Dedeoglu, 2019). Since the HEIs try to publish the relevant information on their official social media pages, students could rely on that information assuming it is from a credible source, and thus believe the HEI brand’s credibility (Brech et al., 2017). Therefore, it is anticipated that firm-generated content will have a positive influence on brand credibility in the higher education context. Based on the preceding discussion, the researcher introduced the testable hypothesis, which seeks to determine the relationship between firm-generated content and brand credibility relating to HEIs from the undergraduates’ perspectives.

3.2.3 Hypothesis H3: Subjective Norms and Brand Credibility H3: Subjective norms will be positively related to brand credibility Subjective norms can positively or negatively affect people’s perception of the brand (Alnsour and Al Faour, 2019). Negative subjective norms can result in people’s fear of making purchases and making negative judgements about the brand (Perry, 2017). Based on the brand-related norms shared among the group members, the individuals perceive that a specific brand has the ability and willingness to provide better performance continuously. Higher subjective norms about brands by facilitating interaction with like-minded people could significantly impact an individual’s perception of the brand’s credibility. Individuals tend to believe in a brand’s credibility, based on the pressure received from the society to get identified by the society. When no specific information is available, but the direct experience of other people about the brand is known, individuals usually rely on their general brand-related knowledge (Kang et al., 2017) or the availability heuristic to make a judgment about the brand in identifying the brand’s credibility.

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For example, people purchase expensive brands such as Rolex Sea-Dweller that work 1220 m underwater based on the opinions of the peers, friends, or family believing that the quality is significantly high, which reduces the risk of performance failure that poses higher credibility towards the watch (Jiang et al., 2019). When reference groups have negative subjective norms and beliefs, it could reduce the credibility endowed with the brand (Jeong & Jang, 2017). In high-context cultures such as the Asian culture, subjective norms have more power on customers’ behaviour and decisions (Gampe & Daum, 2018), which could be applied to deciding about the brands as well. In this study, subjective norms reflect student’s perceptions of whether the referents accept, encourage, and implement the credibility of an HEI brand. Peer reviews and recommendations could increasingly influence students’ perception of HEIs. Students could be motivated to select an HEI believing that the members of their reference or aspiration group would perceive the HEI as credible. A better opinion of the reference group results in more encouragement to rely on the credibility of certain HEI brands, and a high motivation to comply with it to select an HEI. The recognisable opinions of others who are close to and essential to the students and who maintain influence over decision-making affect the students’ perception towards the HEIs’ ability and willingness to provide the promised service. Subjective norms could influence the student’ normative beliefs by inducing the students to have positive feelings towards the HEIs. Students may receive a feel of identification and acceptance as they follow the referents, and select an HEI, believing that HEI brands’ credibility is based on the referents’ suggestion. Since all students have no opportunity to visit and get experience with the HEI, they rely on the information shared among the society to understand the HEI’s expertise and trustworthiness in providing better performance continuously. Although there are limited studies about the relationship between subjective norm on brand credibility, previous research works have examined the effects of subjective norms on other brand-related variables such as brand loyalty and brand commitment (Ghasrodashti, 2018; Hegner et al., 2017; Mustapha et al., 2019). Therefore, the author developed H3 to propose the relationship between subjective norms and brand credibility.

3.2.4 Hypothesis H4: Brand Credibility and Customer-Based Brand Equity H4: Brand credibility will be positively related to customer-based brand equity The signalling theory suggests that brand credibility increases customers’ perception of the brand’s performance by influencing their psychophysical processes (Bougoure et al., 2016). Brand credibility is different when compared with other constructs used to measure customer-based brand equity because it takes an insider view of a service firm (Taj, 2016). Brand credibility is the combined effect of all prior marketing

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actions consistent over the period to create value to the brand (Kumar & Polonsky, 2019). High information acquisition costs and perceived risk delays the customers’ decision-making process (Rosenbaum et al., 2018). It drives weaker confidence in brands by establishing a weak customer-brand relationship (Kashif et al., 2018a). Brand credibility can be shaped by higher consistency in performance (Anees-urRehman et al., 2017), higher clarity in providing promised service, and higher brand investments over time (Dwivedi et al., 2018), by enabling value creation to the brand. Brand credibility highlights the role of trustworthiness and expertise of a brand, which acts as a signal to the customers to positively contribute to the brand evaluation (Clauss et al., 2019). The present study argues that if the customer pursues the brand as credible, then that positive perception creates a unique position in customers’ minds, which could lead to the development of CBBE. CBBE could be affected when the customer reviews the brand’s ability and trustworthiness in providing promised performance while evaluating them to make judgments about the brand. The clarity of the brand’s credibility by leveraging the trustworthiness with the brand based on past marketing activities could leverage the value endowed with the brands. The higher performance level of the brand is affected when the customers can recognise a brand’s expertise level and trustworthiness. This further influences the customers’ ability to recall the brand when the brand is given a signal. Customers’ positive perception of the credibility of a brand could affect the perceived brand utility, relative to its cost in enhancing CBBE. Higher brand credibility leads to customers’ perception of consistent quality as customers infer that more credible brands are higher in quality, which can create a positive value to the brand. If a brand were unbaled to provide the promised performance continuously, it would make an adverse effect on the customers’ brand perception, which results in tarnishing the value endowed with the brand. Distinguishing a brand among the alternative brands based on its expertise and trustworthiness could subsequently influence the brands’ value and enhances CBBE. Like any other professional service, higher education has unique features with different consequences for developing its marketing strategy (Pizarro Milian & Davidson, 2018). A higher level of perceived risk is associated with HEIs in using it because of higher education’s intangible aspects (Koss, 2018). It could be argued that the best solution to minimise the perceived risk is to develop and manage brand equity in the higher education market to signal the higher credibility of the services provided by the institute. Hence, HEIs’ brand credibility could be a potentially significant element that influences students’ perception of the HEIs’ brand equity as it acts as a risk reliever that simplifies the decision-making process and a differential tool that gives cues to the students to add value to the institutes. Thus, HEIs must increasingly invest in building and maintaining the credibility endowed with their HEIs to distinguish themselves from competitors and create a unique value for the HEI. The perceived credibility regarding the HEI brands will help students to develop a positive attitude towards a specific institute. However, prospective students know very little about the particular characteristics, services, and courses of HEIs.

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In that situation, brand credibility plays a significant role in convincing the students by ensuring the HEIs’ ability to provide high-quality, consistent service because quality assessment takes place after the enrolment. Understanding the features of the higher education services and determining the students’ perception of the HEIs’ credibility to build brand equity from the students (customer) could provide a broader insight into a wide array of stakeholders affiliated with HEIs in Sri Lanka and Vietnam. Based on the above justification, the author developed H4 to determine the relationship between brand credibility and CBBE.

3.2.5 Hypothesis H5: Mediating Effect of Brand Credibility H5a: Brand credibility mediates the relationship between user-generated content and customer-based brand equity H5b: Brand credibility mediates the relationship between firm-generated content and customer-based brand equity H5c: Brand credibility mediates the relationship between subjective norms and customer-based brand equity In the realm of intense competition, brands use cues as signals to communicate brand-related information (Cowan & Guzman, 2018) to trigger positive outcomes such as brand equity (Theurer et al., 2018). In considering the social media marketing communication with customer-based brand equity, the customer’s information acquisition process relies on both internal and external information sources that influence the customer’s overall brand judgement (Piehler et al., 2018). In the social media environment, customers compare the communication stimuli with their stored knowledge of comparable communication activities. Therefore, marketing communication in social media through UGC and FGC could influence the brand credibility formation to add value to the brand. The information on the firm’s official social networking sites disseminates the signal of credibility related to the brands (Poulis et al., 2019), influencing the customers’ future brand consideration (Ismail, 2017). Brand-related FGC tries to affect customers’ believability of the brands by conducting different marketing campaigns to enhance the value endowed in the brand (Dedeoglu, 2019). Social media users typically rely on the content published by firms assuming that firms are well aware of their brands than the others (Mishra, 2019), which could induce users to enhance CBBE. Generally, firm-created social media communication acts as a form of advertising to inform the brand’s characteristics, enabling the customers to evaluate firms’ brand against competitive brands (Müller & Christandl, 2019). When a customer responds to the brand-related content published by the firm, anyone who views the firms’ brand page can see the comments, which could help the prospective customers to gain a better understanding of the brand’s expertise and trustworthiness

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in developing and enhancing the brand equity. Any negative responses on the FGC reveal the brand’s inability to provide a better service which could lead to creating an adverse effect on brand equity. Since social media provides a platform for users to create and exchange brandrelated UGC, value creation for brands exists not only between customers and firms but also among customers (Müller & Christandl, 2019). Compared to FGC, the UGC content is often unpredictable (i.e., can be either positive or negative about the brand) and inconsistent with firms’ messages (Lee et al., 2017). Therefore, UGC could create a varying effect on users’ perception of the brands, thus enhancing brand equity. The users’ involvement and responsiveness on UGC reflect other users’ opinion and experiences on brands (Yang et al., 2019a) that in turn influence users’ relationship intensity with the brands (Roma & Aloini, 2019). This could result in adding value to the brand. If the users are highly engaged with the brand-related UGC activities on social media platforms, their effect on the brand’s value is likely to be more positive (Jiao et al., 2018). Such a positive impact could link to enhance brand equity, and later, purchasing the brand. As people perceive UGC as unbiased without any commercial interest (Camacho-Otero et al., 2019), a higher level of believability about the brands could develop positive brand equity among social media users. The social normative pressure on a person about the brands could lead them to think favourably about the particular brand (Muller & Peres, 2019). Individuals tend to add value to the brand as other members do to gain identification and acceptance among the group (Nikitas et al., 2018). Individuals believe that the opinions about the brand shared by the peers, family, or other influential people are trustworthy and reliable (Tajvidi et al., 2018), which motivate to develop the perceive high credibility towards the brand, which in turn produce positive CBBE. Since the HEIs became highly customer-oriented and students being considered as the primary customers (Budd, 2017), the information shared by the HEIs on their social media pages could help to influence the students’ perception of HEIs against the competitive HEIs (Peruta & Shields, 2018). This would increase students’ enthusiasm towards the particular HEI and motivate prospective students to add value to the HEI brand. Similarly, the information shared by users relating to HEIs on social media could influence the prospective students’ minds seriously as they believe that the users are sharing their experience which helps to develop positive brand equity about the HEIs. Depending on the others’ belief and perception of HEI motivate the students to build the same set of feelings towards the HEIs. Then the students tend to develop positive CBBE about the HEIs while conforming to the HEIs expertise and trustworthiness based on the social pressure received from the influential people, such as family or peer group. Regarding the challenges facing the HEIs and suggestions offered to enrich CBBE for higher education, we propose a mediating effect of brand credibility among the study constructs. The brand credibility construct is empirically tested in isolation of the different branding frameworks, which entails that it has not yet been integrated into a CBBE framework as a mediating variable. This way, we believe the relationship between UGC, FGC, SN, and CBBE could be further explained where customer

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perceives brands to be more credible. Based on the preceding discussion, the author has introduced a group of hypotheses that determine the mediating effect of brand credibility.

3.2.6 Hypothesis H6: The Moderating Effects of Location H6a: Location moderates the relationship between user-generated content and brand credibility H6b: Location moderates the relationship between firm-generated content and brand credibility H6c: Location moderates the relationship between subjective norms and brand credibility H6d: Location moderates the relationship between brand credibility and customerbased brand equity Moderating variables modify the relationship between independent and dependent variables (Sekaran, 2009). Therefore, the researcher selected location (Sri Lanka vs Vietnam) and brand usage experience (junior students vs senior students) among UGC, FGC, SN, BC, and CBBE. This study attempted to identify the differences in adopting social media marketing strategies and branding practices in developing countries, especially Sri Lanka and Vietnam in Asia. Such an attempt is reckoned to strengthen the significance of the understanding of the effect of social media marketing on CBBE in the emerging country context. The current work is also deemed to contribute knowledge to the international practitioners interested in emerging marketplaces in establishing HEIs in emerging Asian countries and would like to benefit from social media marketing in branding their HEIs. Individuals from different cultural backgrounds may inherit different beliefs and values while shaping their characteristics (Berry & Dasen, 2019). The level of engagement with social media activities and technological literacy may differ based on the social-cultural factors and the infrastructure facilities in each country. This would influence people’s perception of brand-related information on social media. Variety of practices within each country could make dissimilar and distinguishable attitudinal judgments towards social media marketing activities, brand’s credibility, and equity. Therefore, the impact of social media marketing and brand credibility on brand equity is further expected to vary in two different contextual settings. The populations in Vietnam and Sri Lanka were selected for comparison in this study due to the following reasons. First, Vietnam is one of the fast-developing emerging economies, which is in the transition stage (Fforde, 2019). On the other hand, Sri Lanka is a developing lower-middle-income country with the lowest

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revenue-to-GDP ratio, and a high declining GDP ratio among the emerging market economies (Athukorala et al., 2017). Second, Vietnam has the fastest developing HE system among emerging countries, consisting of 171 public and 65 private HEIs with a high level of undergraduate enrolment rate (Tran & Villano, 2017). Further, Transnational education is booming in Vietnam, strengthening their relationship with developed countries such as United States, United Kingdom, and Australia (Nguyen and Tran, 2018). Sri Lanka, on the other hand, is still having a lower developing rate in the higher education sectors consisting of 20 public and 17 private HEIs, and the lowest undergraduate enrolment rate which reaches close to 20% in 2016, placing it among the lowest rates of all emerging countries (Nedelkoska et al., 2018). Third, Vietnam has 64% of active social media users, whereas Sri Lanka has only 30% active social media users by 2019 (Vietnam Digital Landscape, 2019). Relating to SM usage, Vietnam has 64% of active SM users and 60% active mobile SM users (Vietnam Digital Landscape, 2019), while Sri Lanka has 30% of active SM users and 27% active mobile SM users (Digital Sri Lanka, 2019), which are less than half of the Vietnamese. The annual growth of active SM users is 13% and 3.3% in Vietnam and Sri Lanka, respectively (Digital Sri Lanka, 2019). Moreover, Vietnam is converting into a country with an increasing number of SM users, and the number of domain ranks 8th in Asia, 18th worldwide, and 2nd in South East Asia (Khuong & Huong, 2016). Fourth, most research conducted to understand factors influencing the intention to adopt social media and branding has been mainly performed in developed countries (Jamali & Karam, 2018). Therefore, the manner and the way in which undergraduates’ perception on the importance of the content generated by users and HEIs through social media could vary across the countries, and, therefore, their standpoint on HEI branding could also be diverse. The notion of subjective norms between countries and perceived importance for HEIs’ brand equity could be differently related to cultural contexts. The fundamental basis of the brand credibility of HEIs may vary in different parts of the globe. Thus, the impact of the HEIs’ credibility could be a function of nationality. Differences among countries could directly affect the customers’ perception of social media strategies for branding while indirectly affecting their overall evaluation of brands through their beliefs. In line with that, Vietnamese and Sri Lankans may exhibit differences in following social media contents, while adopting branding strategies for HEIs based on their cultures and level of maturity of the higher education sectors. Due to the lack of empirical studies on the determinants of brand equity in higher education services in emerging countries, the researchers examined the impact of UGC, FGC, and BC to develop brand equity for HEIs using Sri Lankan and Vietnam higher education sectors. Accordingly, the researchers formulate the following hypotheses. Based on these contentions, this research, therefore, asserts that the relationship between UGC, FGC, SN, BC, and CBBE in the current context will be moderated by “location”, and the following hypotheses were formulated.

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3.2.7 Hypothesis H7: The Moderating Effects of Brand Usage Experience H7a: Undergraduates’ brand usage experience moderates the relationship between user-generated content and brand credibility H7b: Undergraduates’ brand usage experience moderates the relationship between firm-generated content and brand credibility H7c: Undergraduates’ brand usage experience moderates the relationship between subjective norms and brand credibility H7d: Undergraduates’ brand usage experience moderates the relationship between brand credibility and customer-based brand equity Being a student can be a rich and transformative period in a person’s life, but the attachment with HEI brands and attitude towards the HEI creates critical differences that are time-dependent. Students’ brand usage experience depends on their tenure and experience with a particular HEI. Thomson et al. (2005) suggested that strong attachments are developed over time between a student and the HEI. As time passes, the relationship and the attachment between the students and the HEI brand could be increased. The opportunities for longer time interaction naturally increase the positive perception towards the HEI brands, which makes developing or maintaining the attachment challenge. In turn, the students’ time within an HEI can impact their commitment, trust, and loyalty towards the HEI brand. This categorised students as juniors and seniors, based on their year of study. Therefore, the moderating effect of undergraduates’ brand usage experience was considered to identify different perspectives of the junior and senior students in HEIs. Junior students who are considered freshmen, or up to the second year in a fouryear bachelor’s degree, could have different perceptions of social media activities (Subotzky et al., 2008). The limited experience with HEIs soon after the high school education leads the junior students to have a different view on the HEI’s brands and brand equity. Senior students have already grown up in the HEI premises for at least three years of their higher education life, and demonstrate a higher familiarity level with the HEI. The junior students are new in the HEIs and are more likely to engage in university activities to develop their relationships with others (Gunuc & Kuzu, 2015). In contrast, senior students are aware of the HEIs’ capabilities and weaknesses compared to junior students. However, both junior and senior students are equally important for an HEI’s existence (Phan et al., 2018). Short- and long-term experiences with the juniors and seniors lead to the view of the HEIs ability and trustworthiness in providing superior service continuously. The proficiency level in social media could vary between juniors and seniors. The perception of social media for HEIs’ marketing communication could be different from each other. The social pressure gained from the subjective norms could impact

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the junior and senior students in different ways. Since the senior students have been involved with HEIs longer, that can influence their perception of developing and enhancing brand equity to create a differential effect than junior students. Furthermore, the level of proficiency in social media could vary among juniors and seniors. The perception towards the credibility of the information shared about the HEIs on social media could differ. Content generated on social media by the users and HEIs related to HEI brands could be different based on the students’ brand usage experience, creating a different perception in their minds. In view of the challenges facing HEIs to enrich brand equity, we propose a moderating effect of students’ brand usage experience. This way, we believe students’ brand usage experience could further strengthen the relationship between social media marketing, subjective norms, brand credibility, and customer-based brand equity. Thus, the following hypotheses have been proposed: Table 3.1 summarises these hypotheses. Table 3.1 Hypotheses of the study Hypotheses H1

User-generated content on social media will be positively related to brand credibility

H2

Firm-generated content on social media will be positively related to brand credibility

H3

Subjective norms will be positively related to brand credibility

H4

Brand credibility will be positively related to Customer-based brand equity

H5a

Brand credibility mediates the relationship between user-generated content and customer-based brand equity

H5b

Brand credibility mediates the relationship between firm-generated content and customer-based brand equity

H5c

Brand credibility mediates the relationship between subjective norms and customer-based brand equity

H6a

Location moderates the relationship between user-generated content and brand credibility

H6b

Location moderates the relationship between firm-generated content and brand credibility

H6c

Location moderates the relationship between subjective norms and brand credibility

H6d

Location moderates the relationship between brand credibility and customer-based brand equity

H7a

Undergraduates’ brand usage experience moderates the relationship between user-generated content and brand credibility

H7b

Undergraduates’ brand usage experience moderates the relationship between firm-generated content and brand credibility

H7c

Undergraduates’ brand usage experience moderates the relationship between subjective norms and brand credibility

H7d

Undergraduates’ brand usage experience moderates the relationship between brand credibility and customer-based brand equity

3.4 Definition of Variables

127

Fig. 3.1 Conceptual framework

3.3 Development of the Conceptual Framework According to Smyth (2004), a conceptual framework is a structured form of the variables a researcher uses to accurately address the given research problem based on the clearly defined aims and objectives of the study. Consequently, the researcher developed the conceptual framework based on the extensive literature review to demonstrate the relationship between the key variables of the study. As per the nature of this study, firstly it must examine the motives of undergraduates in following UGC, FGC, and subjective norms towards brand credibility, and secondly, the possibility of using brand credibility for developing customer-based brand equity. Thirdly, it identifies the differential effect of location and undergraduates’ brand usage experience on the study variables. Therefore, the conceptual framework is divided into three phases. The first part of the framework demonstrates the relationship between UGC, FGC, subjective norms, and brand credibility. The second part of the framework demonstrates the relationship between brand credibility and CBBE from the perspectives of undergraduates (customers). The third part of the framework demonstrates the moderating effect of location (Sri Lanka vs Vietnam) and brand usage experience (junior undergraduates vs senior undergraduates). Figure 3.1 illustrates the conceptual framework for the study.

3.4 Definition of Variables As the primary constructs of the conceptual framework are psychological phenomena, there are no universally accepted definitions for each construct. Therefore, in this section, the researcher attempts to define the major constructs of the

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3 Research Model and Hypotheses

Table 3.2 Operational definitions of main variables Concept

Definition

User-generated content

Any form of brand-related content posted by the content created by marketers on official brand pages on social media channels

Firm-generated content

The content created by the firms on official brand pages on social media channels to market their brands and create customer-firm relationship

Subjective norms

The individual’s motivation to perform or not perform behaviour based on perceived social pressure

Brand credibility

The customer needs the believability of the brand information contained in the brand to perceive that the brand has the ability (i.e., expertise) and willingness (i.e., trustworthiness) to continuously deliver what was promised

Customer-based brand equity

The brand’s value derived from the customers’ perception and the overall experience that the customer associates with the consumption of a brand, including functional and symbolic utilities

Location

An actual place or natural setting marked by some distinguishing features

Brand usage experience

Undergraduates association with HEI brands based on their tenure and experience with the institution

research model. Those definitions are immensely useful in demarcating the scope of the present study and in avoiding misinterpretation of the constructs in the research model. The author developed all operational definitions based on various perspectives presented in previous literature regarding those constructs. Therefore, the research design is based on the operational definitions demonstrated in Table 3.2.

3.5 Theoretical Foundation The theoretical foundation in developing the logical relationships between variables was explained below, by incorporating signalling theory and social information processing theory.

3.5.1 Signalling Theory Signalling theory was developed to help explain how decision-makers interpret and respond to settings where information is both incomplete and asymmetrically distributed among parties to a transaction (Spence, 1973, 1974). The theory is founded on the premises of whether external parties such as customers required completed information as a signal to rely on the information shared by the firms

3.5 Theoretical Foundation

129

(Plummer et al., 2016). Spence (1974) defines a “signal” as an activity or attribute that alters the beliefs or conveys information to others by design or accident. The concept of brand credibility has emerged from signalling literature (Brunner et al., 2019). According to this theory, firms use brands as signals to convey information when imperfect and asymmetric information is available (Karanges et al., 2018), and the content of the brand signal is considered in term of credibility. In contrast, credibility refers to how effectively information is shared to convey the expertise and truthfulness of brands depending on the truthful information (Wang & Scheinbaum, 2017). Signalling theory indicates that a credible brand can reduce the difficulty in selecting a brand by providing clear and accessible information, thus increasing the purchase intention (Jeng, 2016). Signalling theory also suggested that credibility is a key determinant of a brand to convey information effectively, as it is cumulative of prior marketing communication strategies (Pecot et al., 2018). In the online environment context, the information shared by various users influences the other online users’ perception of the particular brand’s credibility (Qiu and Kumar, 2017). The like-minded people would highly rely on a brand’s credibility based on the experience and knowledge they receive from the content shared on the different social media platforms (Marchiori and Cantoni, 2015). The brand communities motivate people to engage more with their online communities, and therefore, prioritise group interests over their own. Further, people join with the social media brand communities to fulfil their need for identification with the groups by complying with the information shared among the members (Habibi et al., 2014). Therefore, the members of social media brand communities engage in collective behaviour with their beloved brands to help another brand identifies to develop the credibility of a brand. Brand signalling theory suggests that credibility can build brand equity (Erdem and Swait, 1998;Spry et al., 2011; Wang, 2017) where credible brand facilitates low information searching and processing cost while reducing perceived risk (Dwivedi et al., 2018) by increasing the customer expected utility from the brand, thus adding value to the brand (del Barrio-García and Prados-Peña, 2019).

3.5.2 Social Information Processing Theory The Social Information Processing Theory (SIPT) provides the fundamental premises that “individuals, as an adaptive organism, adopt attitude, behaviour, and belief to their social context and the reality of their past and present behaviour and situations” (Salancik & Pfeffer, 1978, p. 226). According to Yan and Wang (2018, p. 200), SIPT maintains “an individual’s behaviour is usually contingent on information-processing patterns”, which involves individuals’ perception of social cues in the virtual opinion community. SIPT suggests that individuals find alternative ways to gather information about the brands (Jahng & Hong, 2017), and make a judgement about the brands through social media communication in the absence of face-to-face interaction and nonverbal

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cues (Yan & Wang, 2018). SIPT further suggests that to form the impression of other social media users, people adapt their perceptions based on whatever information provided on social media platforms (Shan, 2016). Stojanovic et al. (2018) suggested that social information processing affects the advertising activities on social media, leading to the value-adding to a brand. Bhaduri and Ha-Brookshire (2017) purport that customers could judge the brands by referring to the information shared on social media to make instant brand choices. Farrer and Gavin (2009) argued that social media users present and acquire knowledge from different social media mediums by employing the content shared to disclose more about themselves than face-to-face interaction with the counterpart. Taking a user-centric approach based on SIPT, Ramadan et al. (2018) identified the influence of similar users’ relationships with SNS on brand’s relationship in the social media context. They noted that socialising with friends, and the relationship with firms who advertised brands, create them a perceived value. According to SIPT, as pointed out by Yan and Wang (2018), the service providers and community peers are essential sources to create a different social influence based on the social media posts, which differ in terms of content to deepen the knowledge. SIPT further suggests that (Westerman et al., 2012) people use whatever information shared on social media channels to make judgements about other people and follow others to get accepted by the social groups. Based on SIPT, Hollebeek and Brodie (2016) argued that social media community engagement and users’ perception is influenced by entirely relevant community norms and rule familiarity. Hsu and Lawrence (2016) noted that SIPT explains the substantial influence people received on brand attitude and brand choice based on the social pressure they received. SIPT was originally designed to explain people’s interaction in the online environment to accomplish their communication goals (Davis & Agrawal, 2018). This study aims to extend SIPT to the social media environment regarding social media users’ interaction with brand-related commutation activities on the social media sphere. Further, extending the subjective norm from a general context to social media context, the person’s perception about the brands deepened on the social pressure they receive from others. This could significantly contribute to social media literature. Drawing from SIPT (LaBerge & Samuels, 1974), we argue that the content generated on social media platforms, and the social pressure the people receive from the social media interaction, induced users to rely on the expertise and trustworthiness of a brand to add a unique positive value to a preferred brand. Hence, brand credibility takes a mediating role to build customer-based brand equity by demonstrating the social media users’ tendency to accept social media communication by including subjective norms as an additional predictor to depend on the perceived credibility of brands to enhance customer-based brand equity. The cross-cultural comparison among the two selected countries and the undergraduates’ brand usage experience of the selected sample of the study has been used for moderation.

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3.6 Summary The researcher attempted to design a research model and develop research hypotheses in this chapter. Initially, the justifications for the logical relationship between constructs have been discussed using signalling theory and social information processing theory. Subsequently, operational definitions have been allocated to specify the meaning of each construct in the research model. Finally, seven hypotheses have been developed to demonstrate the relationship of the main constructs in the model with the justifications given in the literature. The following chapter discusses the methodology and methods employed to achieve the research aim and objectives.

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Chapter 4

Methodology and Methods

4.1 Introduction According to Eldabi et al. (2002, p. 64), a methodology is “a system of explicit rules and procedures, upon which the research is based and against which knowledge claims are evaluated”. Krippendorff (2018, p. 5) stated that methodology provides a language for talking about the process of research. Accordingly, this chapter discusses the research methodology and methods employed to achieve the aims and objectives of the current study, which are concerned with identifying the relationship between social media marketing and customer-based brand equity, incorporating other marketing constructs related to higher education sectors in Sri Lanka and Vietnam. This chapter begins with a discussion regarding the philosophical stance adopted for the study (Sect. 4.2). The research purpose and approach are discussed in Sects. 4.3 and 4.4, respectively, followed by a detailed discussion of the research methods employed to obtain quantitative data appears in Sect. 4.5. Statistical tests utilised to analyse the collected data are presented in Sect. 4.6, and Sect. 4.7 clarifies the ethical considerations considered by the researcher in the study. Finally, a summary is offered in Sect. 4.8.

4.2 Research Philosophy In any social science research, it is essential to address the research philosophy and paradigm, which refers to the fundamental belief in choices of a method in ontological and epistemological fundamental ways (Guba & Lincoln, 1994). The research philosophy is the critical assumption for the researcher to view the world, and these assumptions become important in identifying the research strategy and the study methods (Saunders et al., 2009). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_4

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In this regard, Easterby-Smith et al. (2012) noted the value of identifying different philosophies. First, it helps the researcher to explore the research design by providing opportunities to achieve the study objectives. Then, the researcher can identify which philosophical perspectives generate which research designs, and which design is suitable, and which is not for the current research. Third, it facilitated the researcher to identify and create further design options beyond their past research experience. Thus, Saunders et al. (2009) suggested that determining the appropriate philosophy is the first thing a researcher should do when starting a research study that will govern the research strategy and research method. They pointed out two philosophical principles: ontology and epistemology.

4.2.1 Ontology and Epistemology The review of previous research studies shows that researchers begin their investigation by making assumptions on what they plan to study, including and developing the knowledge they require. In this respect, two basic philosophical principles, ontology, and epistemology are identified, which have been central in philosophical debates over many years (Guba & Lincoln, 1994; Hathcoat et al., 2019; Saunders et al., 2009). These two philosophical principles provide the research bases to create assumptions and determine the suitable research paradigm for the study. Thus, a better understanding of both philosophical principles is an essential step in any research effort (Hathcoat et al., 2019). Ontology is more than just a metaphor or a convenient figure of thought (Popper, 2012), but it fails to realise that existence can only be asserted of something described, not of something named (Russell, 1993, p. 253). Ontology is “concerned with nature of reality”, which questioned the researcher’s belief and assumptions on how the world operates, and the committee held to particular views (Saunders et al., 2009, p. 110). Therefore, the primary question addressed in ontology is whether there is existing real-world outside of what is already counted as knowledge (Bryman, 2012; Crotty, 1998). Bryman (2012) and Saunders et al. (2009) highlighted two ontological positions: objectivism and constructivism. Objectivism, in an ontology position, means that social phenomena confront external facts beyond our reach (Hornborg, 2006). It reflects social entities independent of social actors concerned with their existence (Saunders et al., 2009). In other words, reality exists autonomously as an immutable structure (Holden & Lynch, 2004). Thus, everybody shares the same reality as it is the only one in existence (Chilisa & Kawulich, 2012). On the other hand, constructivism refers to how social actors continually assert social phenomena and their meanings (Bryman, 2012). It further implies that social phenomena and categories require a constant revision state than social interaction (Carter, 2008). Epistemology is the other philosophical dimension concerned with “what constitutes acceptable knowledge in the field of study” (Saunders et al., 2009, p. 112). It

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refers to the nature of the relationship between the knower and the would-be-knower and what can be known (Guba & Lincoln, 1994). It reflects how people acquire knowledge and their knowledge about the existence of reality (Crotty, 1998). This further identifies people’s assumptions on creation, offers, and utilisation of the domain of knowledge (Nonaka & Teece, 2001). The domain of any social research has three prominent epistemological positions: positivism, post-positivism, and interpretivism, as discussed in the following section (Guba & Lincoln, 1994).

4.2.2 Philosophical Paradigm Guba and Lincoln (1994, p. 116) emphasise that identifying a suitable research paradigm as “paradigm issues are crucial”, and no researcher “ought to go about the business of inquiry without being clear about just what paradigm informs and guide his or her approach”. Every researcher must determine the appropriate paradigm for their research before embarking upon them. Paradigm is the collection of beliefs researchers have in a particular disciple, which influences what should be studied, and how the research should be done (Healy & Perry, 2000). In this respect, a research paradigm works as a framework for the study, which provides guidelines on conducting the research study based on peoples’ perception of reality and their assumptions on the nature of knowledge (Creswell et al., 2003). Similarly, Guba and Lincoln (1994, p. 107) defined a paradigm as “basic belief systems based on ontological, epistemological, and methodological assumptions”. The researcher understands the nature of reality to identify the relationships among the different variables and decide the research methods. Understand the most relevant research paradigm enables researchers to focus their attention on a specific problem and available research methods. Even though many social science studies have exerted an intensive effort in classifying diverse perspectives of paradigm, still, there is no explicit agreement on the best way to categorise the various assumptions related to reality (Prandini, 2015). The most common and traditional classification distinguishes two main paradigms: positivism and interpretivism (Bryman, 2012; Saunders et al., 2009). However, many scholars have used an alternative paradigm, i.e., post-positivism, by incorporating similarities and differences between the two main paradigms (Saunders et al., 2009).

4.2.2.1

Positivist Paradigm

Positivism, an epistemological position, focuses on the importance of objectives and evidence in searching for truth, which is unaffected by the researcher (Creswell et al., 2003; Chilisa & Kawulich, 2012). This means the researcher should not influence the research findings (Tuori, 2017). According to Wildemuth (1993, p. 450), positivism refers to the “systematic, controlled, empirical, and critical investigation of natural phenomena guided by theory and hypotheses about the presumed relations among the

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phenomena” where the reality is objective, transcending an individual’s perspective, and can be expressed in terms of observable statistics. Positivists view reality as objective and independent from the researcher’s control (Arghode, 2012). Hence, the researcher should be neutral in investigating people and isolated from the observed phenomenon (Chilisa & Kawulich, 2012). Positivism holds the scientific method as the only way to establish the truth and objective reality (Yilmaz, 2013). That means a positivist should collect data only via scientific methods (Saunders et al., 2009). Therefore, the researcher should collect the data externally without trying to affect or be affected by the external forces (Sandelowski, 2000). To put it differently, while conducting social research, the researcher should detach from their values (Chilisa & Kawulich, 2012) and assumptions to be unbiased in their objectivity on any research point (Burkard, 2018). The insights provided by the positivist researchers may have high-quality standards of validity and reliability (Beverland & Lindgreen, 2010). Therefore, the outcome can be replicated in the same phenomenon or events for different groups or subgroups of the population in the social context (Bansal et al., 2018). Positivists believe that only one reality exists that is constant across time and setting (Saunders et al., 2009). Therefore, a positivist believes that the right datagathering instruments and tools are important to measure and produce the absolute truth for a given inquiry (Chilisa & Kawulich, 2012). The research approaches are quantitative (Bryman, 2012), and techniques of gathering data are mainly questionnaires, observations, tests, and experiments (Sekaran & Bougie, 2016). The problem statement here specifies the variables to be studied and their relationships (Morçöl, 2001). To illustrate, positivist research in social science follows a systematic problem-solving process. Hypotheses are developed based on the existing theories, related quantitative data collected and analysed to test the proposed hypotheses, and generalisations are made, or new developments to current theories occur (Coetsee, 2010). In social science, this systematic approach is known as deductive reasoning; a research strategy is developed to test the hypotheses (Sekaran & Bougie, 2016). This is placed to testing the theories which have incorporated the practices of the natural scientific model. In particular, it embodies a view of social reality that provides a foundation for quantitative research studies (Bryman, 2012).

4.2.2.2

Interpretivism

Interpretivism is a term given to a contrasting epistemology to positivism, which helps understand the world from others’ experiences (Saunders et al., 2009). It is originally rooted in the fact that understanding the human knowledge related to the humans (Guba & Lincoln, 1994) and social sciences could not be the same because the way the humans interpret the world and acts based on such interpretations are different from one to another (Chilisa & Kawulich, 2012).

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Interpretivism believes that reality is socially constructed, and there are many intangible realities as the way people are building them in different ways (Saunders et al., 2009). Since the interpretive identifies that the reality is mind-dependent, so the knowledge is subjective (Zanetell & Knuth, 2002). Unlike positivist, interpretivism believes the reality is subjective, complex, multiple, and continuously changing (Chilisa & Kawulich, 2012). Consequently, interpretive adopt the relativist ontology (Sandberg, 2005), assuming a single phenomenon may have multiple interpretations (Kelliher, 2011). Interpretive researchers reject the assumption that a researcher should be independent of what is being investigated; instead, they argue that the researcher should be a part of what is happening rather than measure it objectively (Saunders et al., 2009, p. 119). Hence, the researcher does not measure the exact fact but deal with the diverse meanings and perception of the people in a particular social context to gather their experiences as a base to the facts (Crotty, 1998). Interpretivism is called a naturalistic inquiry where the researchers wish to take the fact into account in judging the appropriateness and usefulness of the analysis (Guba & Lincoln, 1994). According to Saunders et al. (2009), interpretivism researchers should be involved in social studies by adopting an empathetic stance to understand people’s views about reality. Interpretivism believes that knowledge is produced by exploring and understanding, but not discovering. Since the interpretive paradigm assumes the absence of objective reality, believing that meanings are not discovered but rather constructed, the use of quantitative methods could not be sufficient. It may produce a false outcome (Bryman, 2012). Instead, the interpretivists believe qualitative methods would be appropriate to study the social phenomenon (Creswell et al., 2003). Although interpretivism became an increasingly important perspective in social research, it received many critics. As interpretivists’ knowledge about the real world is usually subjective, the result will also be built on diverse and subjective judgments about the world, which leave no chance to assess the truthfulness of the knowledge (Marsh & Furlong, 2002; Alvesson & Skoldberg, 2009). Furthermore, the researchers are worried about the lack of ability to generalise the findings to a broader social context. Hence it leaves out a gap in verifying the validity and usefulness of research outcomes (de Souza et al., 2016; Marsh & Furlong, 2002). Since the findings are mostly based on the different people’s perspectives in different settings over different time scope, it makes generalisations deceptive and misleading (Easterby-Smith et al., 2012). The outcomes are unquestionably affected by the researchers’ interpretation, own belief system, ways of thinking, or cultural preference, causing too many biases (Kennedy, 2019). Since interpretivism is focusing on the understanding of current phenomenon rather than problems related to individuals, and society at large, studies based on interpretivism concerns the relationship between the theory and research, thus aligns to emphasis inductive reasoning from the particular to general (Saunders et al., 2009). Tables 4.1 and 4.2 outline the main features and differences between the two paradigms. These two paradigms present different perspectives and methodological choices, and it is the question being asked to determine the suitability of the paradigm

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Table 4.1 Differences between positivist and interpretivist paradigms (adapted from EasterbySmith et al., 2012) Positivism

Interpretivism

The observer

Objectivity

Subjective

Data type

Quantitative data

Qualitative data

Research progresses

Hypotheses, deduction

Induction

Generalisation through

Statistical probability

Theoretical abstraction

Sampling requires

Large

Small

Table 4.2 Ontology, epistemology, methodology, and methods for positivism and interpretivism (adapted from Easterby-Smith et al., 2012; Guba & Lincoln, 1994; Saunders et al., 2009) Orientation

Positivism

Interpretivism

Ontology

– Single reality exists

– Multiple realities constructed using human experience

Epistemology – The researcher must be independent – Verification of hypotheses using scientific approaches

– The researcher is a part of what is being observed – Increased the general understanding of the situation

Methodology – – – –

– Induction reasoning, data to theory – Generating new theories – Use qualitative data

Methods

Deductive reasoning, theory to data Hypotheses testing Establish causal relationships Measure quantitative data

– Mainly questionnaire

– Mainly interviews, focus group, observations, and case study

chosen. Hence, the research questions and objectives of this study are the driving forces in selecting a philosophical paradigm. The prime intention of the study is to identify the impact of social media marketing, on customer-based brand equity towards brand choice intention, through testing the validity of a proposed theoretical framework. It is obvious, for many reasons, that a positivist paradigm would be appropriate. However, it is also recognised that the aim of the study is to explore some unobservable aspects of the research problem, relating to social media and branding; hence, the traditional positivist approach will not be practical. The researcher believes a modified version of the positivist paradigm would be more suitable, and this paradigm is described as post-positivist.

4.2.2.3

Post-positivism

Post-positivism provides another paradigm that moves positivism from a narrow perspective into a more encompassing way to examine real-world problems, as to overcome the criticism, and thus, to make a suitable alternative social phenomenon

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(Creswell & Miller, 2000; Henderson, 2011). Increasing appealing of post-positivism gained higher credibility throughout the social science community (Dedeurwaerdere, 2018). Post-positivism researchers believe in reality but do not abandon conventional positivism (Henderson, 2011). Guba and Lincoln (1994) argue that the real world is independent of the researcher’s world, complicated and intensely exposed to diverse perceptions. Post-positivism directly affects the construction of reality by influencing factors such as culture, gender, and personal belief (Stead, 2004). However, Taber (2017) argues that most contemporary science is no longer positivist but post-positivist. Furthermore, post-positivism differed from both positivism and interpretivism in its ontological, epistemological, and methodological assumptions (Creswell et al., 2003; Guba & Lincoln, 1994; Saunders et al., 2009). On the ontological perspective, post-positivists agree that reality does exist (Joslin & Müller, 2016). However, reality cannot be perfectly maintained due to the researcher’s human limitation (Guba & Lincoln, 1994). Therefore, any effort made to apprehend it falls under probability considerations (Dedeurwaerdere, 2018). Regarding the epistemological position, post-positivists agree with the notion that perfect objectivity is approachable but cannot achieve fully (Chilisa & Kawulich, 2012). Therefore, accepting the probability of being subjective occurs while conducting the research (Clark, 1998). This assumption is known as “dualism”, which incorporates both objectivity and subjectivity in the same study (Denzin & Lincoln, 1994). Hence, the researcher aims to develop a better understanding of the social context by being personally involved in collecting data. Interestingly, Westerman (2006) and Yanchar (2006) argue that post-positivist research can have the quantification position rather than embrace contextualism in qualitative methodologies. In light of this developmental perspective, as proposed by Westerman (2006) and Yanchar (2006), the integration of quantitative methods into the post-positivist paradigm rejects the requisite to search the abstract universal contextualised empirical designs. Kock et al. (2008) suggested that post-positives need to move from interpretation to measurements towards the internal validity of the constructs. They argue that a research method should be selected based on the research questions (Wildemuth, 1993), enabling the researcher to apply the best methods, rejecting the dichotomy of qualitative and quantitative analysis (Turner, 2013). In methodological terms, post-positivist studies are broader and have many different things to quantify in conducting research (Clark, 1998). Post-positivist believe that positivist research methods predominantly mirror the representational ideology of the post-positivist researchers so that they strive to discover the truth objectively hidden in the subject’s mind (Ryan, 2006). They find out the truth by formulating it as a problem and advancing the knowledge as hypothesis-testing. In the positivism paradigm (Strang, 2015), the primary purpose of the research is to test a theory, find the strength of a relationship between variables, or identify the cause-and-effect relationship. According to Guba and Lincoln (1994), this is a refurbished version of triangulation which falsifies the hypotheses rather than verifying.

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Therefore, Post-positivists aim to address the problems in a more natural setting, collecting more situational information.

4.2.3 The Current Study’s Philosophical Stance The ontological belief of this study is that the real world is independent and exists beyond the reachable knowledge to the researcher. The researcher accepts that the socially constructed world is influenced by individuals’ belief, experience, and personal values. This study rejects the positivist’s assumptions about the single reality and adheres to the probability of multiple realities. Additionally, it acknowledges the ability to generalise the findings to the entire study population. As a result, the present study adopts a post-positivist ontological position, considering the existence of multiple realities and the ability to know them. Epistemologically, the study is located in the post-positivist paradigm; it is conducted not only to create knowledge but also to get a better understanding of the Sri Lankan and Vietnamese undergraduates’ behaviour towards social media marketing and customer-based brand equity. Even though the researcher involves in developing the research model and determining the scope of the study, it recognises that the researcher is independent of those being researched and does not influence the undergraduates who participated in the investigation. In summary, this study adopts the post-positivist paradigm for three main reasons. The first is its intentions to identify the impact of social media marketing, subjective norms on the customer-based brand to shed more light on the factors essential to adapt to higher education sectors in Sri Lanka and Vietnam. Since this study adopts brand credibility as a mediator and location, and brand usage experience as moderators in developing the framework, empirical test through quantitative data is used to test the proposed framework’s viability. The post-positivist paradigm is appropriate in such situations in archiving the objectives whilst simultaneously testing the theories using collected data. The second reason is, although the researcher uses statistical analysis to identify the relationships, the researcher is aware that numbers do not speak for themselves and that the empirical results might (or not) suggest different realities in two different contexts (Sri Lanka and Vietnam). Since these two countries represent two different cultural backgrounds while sharing some similarities and differences, the researcher rejects the notion of “one absolute truth” and believe in multiple realities concerning the same topic. Third, the researcher uses a close-ended questionnaire to collect data and distribute the same questionnaire among the selected HEIs in both countries. Even though the researcher is sharing the same culture as the participants, the researcher remains objective.

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4.3 Research Purpose The choice of research strategy, data collection methods, and analysis are greatly influenced by the research objectives, aims, and questions (Saunders et al., 2009). In this regard, Saunders et al. (2009) proposed three forms of research: exploratory, descriptive, and explanatory. An exploratory study is undertaken to discover what is happening and to gather information when not much aware of a specific situation (Gardner, 2013). These studies are useful to understand the unclear problem and need more extensive preliminary work to be familiar with the phenomena and understand the situation (Malhotra et al., 2006; Sekaran, 2003). Hence, exploratory research is usually adopted when the research evidence is insufficient and little is known about the research problem (Miller & Waller, 2004). By generating ideas about the problem, the researcher can address the need for new theory development (Kothari, 2004). Exploratory research is useful at the initial stage of the study to understand the context of the research when the existing research outcomes are vague, the ill effects of the severe impediments and the topic is complex, and when sufficient theories for the development of theoretical framework are lacking (Saunders et al., 2009). In essence, exploratory studies are conducted to gain a better understanding of the problem and determine the required actions. It does not suggest the solution but rather provides the essential information that can provide recommendations for future action (Sekaran, 2003). Generally, an exploratory research design consists of the following three methods, and such studies are focusing on (a) the survey of concerning literature, (b) the experience survey, and (c) the analysis of “insight-stimulating” examples (Kothari, 2004, p. 36). In exploratory studies, interviews, direct observations, interviews with the “experts” in the subject, focus group, and literature analysis are used as data collection techniques to identify a clear pattern of the problem (Saunders et al., 2009; Sekaran, 2003). Flowers et al. (2017) reiterate such studies help to refine research questions by gathering more contextual data on the problem under consideration to develop research concepts and enhance the quality of research design. Descriptive studies, on the other hand, provide an accurate description or a picture of a particular situation or phenomenon (Boulding, 2017). It further describes the characteristics of the variables on interest and provides a clear picture of the phenomena before data collection (Kothari, 2004). This research design helps to describe the topic of interest, and finally, the causal relationship of the variables can be examined through hypothesis (Saunders et al., 2009). According to Kothari (2004), descriptive research studies must be rigid but not flexible, and they focus mainly on the study objectives, method of data collection, selecting a sample, collecting data, analysing data, and reporting the findings. Descriptive studies present data in a meaningful form to provide a better understanding of the characteristics of a group in each situation (Kim et al., 2017a). In these studies, the researcher takes out samples and then wishes to make a statement about the population, based on the sample analyses (Saunders et al., 2009).

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Descriptive studies are recommended when a problem of interest is well-defined and usually adopt quantitative data collection methods to collect descriptive data, which are then subjected to statistical analysis, with findings presented in numbers, figures, and diagrams (Neuman, 2007). The sample is designed using probability sampling techniques and a pre-paled statistical design for analysis (Kothari, 2004). Descriptive studies do not attempt to ferret out a cause-and-effect relationship but to identify and describe the possible relationship between the study variables (Dulock, 1993). Such knowledge formulates the hypotheses and proposes solutions to the identified problem statement (Buell et al., 2018). Descriptive research design is useful in both the initial and the final stages of the investigation into a given area (Sekaran, 2003). Explanatory studies, as the third option, helps identify the different cause relationship between variables in a given situation or phenomenon (Evans, 2004). They enable the researcher to examine and explain relationships between variables, particularly the cause-and-effect relationship (Saunders et al., 2009). Unlike exploratory and descriptive studies, explanatory research aims to answer “why” questions regarding the interaction among research variables (Ahmad et al., 2017). Explanatory research studies go beyond the descriptive studies explaining the relationships among the different variables in each situation (Neuman, 2007). The researcher uses both qualitative and quantitative data to test the hypotheses, developed using existing theories to test the hypothetical relationships (Sekaran, 2003). The current study’s aims and objectives present descriptive motives for the study. The present study describes the topic of interest, and finally, identifies the possible relationship of the variables, which can be examined through hypothesis (Saunders et al., 2009). This study uses quantitative approaches and statistical methods to analyse collected data. This study contains several keywords to search and identify the relevant articles for the study. Table 4.3 contains the keywords to make easier for the other researchers to find this study when they are conducting a search on the similar kind of topic. Given the nationwide population for the current study, this study has first used cluster sampling to derive the HEIs for the study. Accordingly, 17 HEIs from Sri Lanka and 60 HEIs from Vietnam were initially grouped into four different categories Table 4.3 Keywords of the study

Keyword Higher education institutes social media marketing user-generated content firm-generated content brand equity subjective norms brand credibility emerging countries

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based on (a) research-intensive, (b) teaching-intensive, (c) regional-focused, and (d) special interest (Kandiko & Mawer, 2013). All private HEIs in Sri Lanka and Vietnam were categorised into these four groups after an exhaustive study of their websites, social media profiles, and communicating administration office of each HEI. The HEIs that fall into more than one of the four categories were grouped depending on the main purpose for the HEI’s existence. Following that, one HEI was chosen using simple random sampling. Once the required number of HEIs have been selected, systematic probability sampling was used to select the undergraduates for the study. Accordingly, every eighth student who passes the main gate of each HEI was chosen by the researcher and her data gathering team. Due to the confidential nature of the information, a random number table could not be utilised since a list of students’ names, email addresses, student numbers, and other essential contact details could not be obtained. Finally, purposive sampling was used to identify the eligibility of each individual for this study. Two criteria were used: the first was that they had to be a current undergraduate of the chosen HEI because there could be prospective students, visitors, or friends of undergraduates from another HEI, and the second was that they had to have at least one social media profile because some questions were based on undergraduates’ experience with social media. The proposed theoretical framework is tested to identify the relationship between the variables and provide a better understanding of the leading research problem. This study also develops hypotheses based on the existing theories to explain the hypothetical relationships among the variables.

4.4 Research Approach According to Saunders et al. (2009), there are two types of research approaches: deductive and inductive. However, several scholars defined qualitative and quantitative as the two research approaches (Denzin & Lincoln, 1994; Creswell, 2014). The deductive approach helps to logically generalise the known fact (Bryman, 2012). It is considered a rational process in creating assumptions known to be true (Cohen et al., 2002). Within the deductive approach, the research results are shown in the form of numbers presented in figures (Maschi, 2016). In other words, the deductive approach is performed using the numerical data, and measure and analyse them to find the relationships between various data sets (Brannen, 2017). The researcher knows that basics in a particular domain deduce a hypothesis (or hypotheses) that must then be subjected to empirical scrutiny (Bryman, 2012). The researcher deduces the hypotheses and translates them into operational terms. Sekaran (2003) emphasises that a deductive approach is one of the primary methods to conduct scientific research. The deductive approach seemed more formalised and more structured (Cohen et al., 2002). Sekaran (2003) emphasises that deductive or hypothetico-deductive

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research represents one of the primary methods for performing scientific research. Accordingly, the researcher arrives at a rational conclusion for the study based on the reasonable generalisation of pre-existing facts (Lucas, 2003). The deductive research approach presents the nature of the relationship between theories with the research problem and then develops a theoretical framework drawn from the existing theories (Saunders et al., 2009). Therefore, the first step under the deductive approach is theory formulation, where the researcher attempts to integrate what is known about a specific phenomenon (Neuman, 2007). The theoretical framework contains all important variables combined to identify their relevance to the research problem and determine their occurrences to address the research problem (Saunders et al., 2009). The hypotheses are then generated, incorporating the variables included in the theoretical framework based on the pre-assumed relationships among the variables (Kothari, 2004). Those generated hypotheses are translated into operational terms by enabling the researcher to measure through empirical investigations (Cohen et al., 2002). The data are collected using qualitative or quantitative methods and analysed using relevant tools and techniques (Hammersley, 2017). The findings obtained for the analysis help to decide whether to accept or reject the hypotheses (Woo et al., 2017). Finally, the researcher makes recommendations based on the findings relating to the theory under consideration (Neuman, 2007; Sekaran, 2003). Saunders et al. (2009) assert that the deductive research approach is important for three reasons. Firstly, it involves analysing the causal relationship between the variables. The deductive approach dictates that the researcher should be independent of what is being observed to pursue rigorous scientific (Ormston et al., 2014). Secondly, the concepts need to be operationalised, enabling the research to measure quantitatively by holding the research problem as a whole and reducing it to some simplest possible elements to provide a better understanding of the study. Finally, the survey findings generalise to the entire research population if the results are based on a sufficient and representative sample. With the premises, which are always some previously formed theories or conclusions, the process in deduction is more logical and reasonable (Saunders et al., 2009; Sekaran, 2003). Inductive research, in contrast, is the logical process of establishing a general assumption to develop a theory by analysing the collected data based on observable facts (Neuman, 2007). Sekaran (2003) explained that the inductive approach observes specific phenomena and helps to develop theories based on the finding and arrive at a general conclusion. This approach identifies the similarities or patterns in collected data in developing a general statement (Coley et al., 1999). It allows the researcher to establish general propositions considered as proceeding from what is particular to what is general (Fischer & Gregor, 2011). Despite the ability of the deductive approach to identify and establish the causal relationships among the variables, inductive researchers argue that the researcher cannot understand and interpret the participants’ behaviour attached to a situation (Walmsley & Lewis, 2014). Hence, inductive reasoning more closely observes people’s behaviour to uncover more aspects of the social phenomenon (Kothari, 2004). Most use qualitative approaches to collect data, including the participant’s

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emotional experience on what is happening in a given situation (Zhang & Wildemuth, 2009). Therefore, the inductive approach is best used to acquire in-depth information about a problem and to reveal underline motives, feelings, values, and perceptions (Saunders et al., 2009). The nature of the research topic determines the most appropriate approach for the study (Cresswell, 2014). The deductive approach is suitable if the research topic is already researched and provides premises to develop a theoretical framework based on the previous studies and suggest testable hypotheses based on the previous research work (Cohen et al., 2002; Sekaran, 2003; Saunders et al., 2009). In contrast, if the research topic is relatively new, lacks clear theories to govern it, and unable to develop hypotheses based on the previous studies, it increases the appropriateness of inductive reasoning to develop more understanding of the topic and generate new theory by collecting and analysing contextual data (Neuman, 2007; Saunders et al., 2009; Sekaran, 2003). This study focuses on customer-based brand equity as a general phenomenon. Specifically, it concentrates on how it can be utilised for HEI brand building in the Sri Lankan and Vietnamese context. As the primary aim mentioned already, this study develops the theoretical model based on the related literature. Next, quantitative data are collected and statistically analysed to test the hypothesised associations among the model variables. Thus, deductive research was chosen as the primary research approach for the present study as this investigation is based on quantitative data, which analyses using statistical tools and techniques to test the hypothesised relationship among the study variables.

4.5 Research Methods In a typical research study, the researchers select the most appropriate research method based on the research problem and specific research questions they want to address. In conducting research, two primary research methods are adopted: Quantitative methods and qualitative methods. Data collection and analyse differ based on the method applied in the study. Quantitative research methods mainly deal with the numbers and statistics used to test or confirm the theories and assumptions. This method can generalise the facts about the topic. The most popular quantitative data collection methods are experiments, observations recorded as numbers, and surveys with closed-ended questions. In contrast, the qualitative research method helps to understand concepts or thoughts, and include open-ended questions, in-depth interviews, focus group interviews, observations described in words, and literature reviews that explore ideas and theories. The present study mainly focuses on testing the hypotheses developed to test the relationship of the study constructs and potentially contextualise the results from the

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study sample in the broader population (or specific groups). Thus, this study adopts the quantitative research method for data collection, analysis, and data presentation. In a research study, the researcher must concern about the type of data required for the study. Two types of data are mainly considered in conducting research: secondary data and primary data (Sekaran, 2003). Some individuals or organisations may have already collected secondary data for some other purpose, which are readily available for the other researchers to use (Kothari, 2004). When the researcher utilises secondary data, the researcher must be careful about the possibilities of unsuitability and inadequacy of the data for the context of the problem, which the researcher wants to study (Coakley, 2013). Secondary data can provide useful contextual information about the research topic, which translates into a better understanding of the research problem (Saunders et al., 2009). If secondary data are obtained from a credible source, it offers high-quality information with considerable time and money-saving (Ellram & Tate, 2016). Both quantitative and qualitative data are found in secondary sources (Ruggiano & Perry, 2019; Williams & Shepherd, 2017). However, Saunders et al. (2012) reported that no commonly agreed classification of secondary data exists among business research scholars. On the other hand, primary data are collected afresh for the first time for a specific purpose (Cohen et al., 2002). In other words, primary data is not in a forum to access by the public (Kothari, 2004). Primary data is essential as they are original, relevant to the research study topic, and specifically for the study (Saunders et al., 2009). Since primary data is current, it can provide a better realistic view to the researcher (Kothari, 2004). Therefore, the researcher can obtain the most up-to-date data directly from the right population of the study (Mills et al., 2015). Furthermore, it is easier to analyse primary data as the researcher is much closer to the data with a deeper understanding of them (Saunders et al., 2009). According to the nature of this study’s objectives, primary data has to be collected using a questionnaire. Consequently, given all the factors, and consistent with the researcher’s post-positivist stance, this study adopted the quantitative approach, gathering primary data, to identify the possible relationship between social media marketing, customer-based brand equity, and the other constructs. Creswell (2014) explained that a survey is the most widely used method to conduct quantitative research. The survey method is important as it ensures anonymity and confidentiality of the respondents, low-cost, easier handling of sensitive data, quick to administer, reduces researcher’s bias involvement with the participants, and is appropriate with relative literate respondents (Saunders et al., 2009). Survey research studies the sample representing the population and provides a quantitative or numeric description of the population (Rossi et al., 2013). Saunders et al. (2009) noted that studies based on cultural differences need to adapt the survey method to gather information. Questionnaires and interviews are the two main data collection methods in survey-based research (Sekaran, 2003). Therefore, the present investigation sought to collect quantitative data via a survey questionnaire with several participants to complement the questionnaire’s findings.

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4.5.1 Survey Questionnaire The researchers widely adopt surveys to collect a considerable amount of data by investigating many research variables while generalising the research findings to the entire study population (Saunders et al., 2009; Sekaran, 2003). If the researcher uses a survey strategy, the specific population and sample size must be determined and clarify how the survey instrument will be developed and distributed (Kothari, 2004). Neuman (2007) reported that a questionnaire is the best reliable method to survey the other available methods. According to Sekaran (2003), personally administered questionnaires offer many advantages over other types of questionnaires as it facilitates gathering a large volume of data in less time. Here, the researcher’s presence facilitates to provide required clarification and motivate the respondents to provide genuine answers to all questions (Brace, 2018). In this study, quantitative data from 800 respondents (400 from each country) were the target because this number would provide the researcher with sufficient data to generalise research findings to the entire research population. The limited financial and time resources available to collect data from two countries meant that quantitative data from this number of participants could be collected more efficiently via a self-administered questionnaire. As a result, the researcher selected the survey questionnaires to collect necessary quantitative data for the study.

4.5.2 Questionnaire Design According to Brace (2018), researchers aim to gather information from a large sample and at a relatively low cost while incorporating structured, pre-tested questions to develop a questionnaire. Regarding this study’s questionnaire, the researcher reviewed the literature and found previously validated measurement scales, which was a common research practice for many investigators in the field. Bryman (2012) purports that the researcher could develop more credible research instruments by employing previous researchers’ questionnaires, providing more valid and reliable results. Consequently, all measurement scales of this study’s questionnaire were based on a combination of previously validated instruments from several relevant studies. Appendix 4A shows various sources from which the current research instrument was developed and the different types of scales used in the final version of the questionnaire. Appendix 4B presents the final layout of the questionnaire (English version). At the early stage of the design process, the researcher developed a preliminary pool of measurement items for all constructs in the theoretical framework based on the information derived from the literature review. In the next stage, an initial screening was made, with two aims; firstly, to ensure that the chosen questions were

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appropriate for the Sri Lankan and Vietnamese culture, and secondly, to confirm the undergraduates can clearly understand the problems. Furthermore, the researcher followed some general criteria recommended by previous scholars in adopting questions in questionnaires (Bryman, 2012; Easterby-Smith et al., 2012; Saunders et al., 2009; Sekaran, 2003). The general criteria adopted here was the questions should not include ambiguous, complicated, unfamiliar, or highly technical terms. 1. Questions should be clear, and simple. 2. Specific, and do not refer to generalisation. 3. Questions should be related to the research questions of the study to address all research questions in the survey using obtained data. 4. Reasonably short to avoid questions being skipped by the respondents. Appropriate items for each research constructs were incorporated in an initial questionnaire copy in the final stage of the questionnaire development process. This was then reviewed by six Ph.D. students, ten undergraduates, ten academics, and two marketing managers comprising Sri Lankans and Vietnamese to ensure the clarity of the questionnaire statements and the comprehension of the measurement scale. Modifications were made to the questions and instructions based on their feedback. For example, one expert suggested reducing the initial introduction of the questionnaire as it consumes more time for the participants; another recommended to include a brief introduction to each section of the questionnaire to emphasise the objective of the questions being asked. After that, the researcher focused on the questionnaire’s layout to make it more attractive and encouraged the respondents to answer the questions accurately, based on the recommendations from the expertise. At this stage, the main intention was to produce a clear sequence of the questions and limit the questionnaire to five pages to keep it manageable (Vietnamese Version, Appendix 4C). Saunders et al. (2012) suggested that 4–8 pages are more acceptable for the questionnaire so that the study’s questionnaire is also limited to 4 pages.

4.5.3 Questionnaire Structure The questionnaire was structured into three different parts, as mentioned below. Section one: Introduction and consent from the participants. Brief instructions depicting this survey’s purpose were included while requesting the participant’s consent in the study. Section Two: Demographic Characteristics. The required data in this section were collected through eight questions on gender, studying year to measure the brand usage experience, the field of study, the average amount of hours spend on social media per day, widely used social networking site, how often join with the social networking sites, how often post information or comments on social networking sites, and time on traditional media. This data was essential to facilitate the process of profiling the research sample and

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perform statistical comparisons with the main variables of the study. This section used multiple-choice questions. Section Three: Questions related to firm-generated content, user-generated content, subjective norms, brand credibility, and customer-based brand equity. This section aimed to measure respondents’ attitude towards the variables in the proposed theoretical framework. All questions were based on a seven-point Likert scale ranging from “strongly disagree = 1” to “strongly agree = 7”. Each of the construct’s items was grouped as follows in decreasing any confusion. 1. Firm-generated content (FGC)—This group included four items in determining the level of respondent’s agreement/disagreement with statements regarding HEIs’ generated content on different social media platforms. These items were adopted from Osei-Frimpong and McLean (2018). 2. User-generated content (UGC)—Four individual items were used to measure the extent to which the respondents level of communication with the other users in social media and the believability of the content generated by the users on social media platforms. The items were adopted from Schivinski and Dabrowski (2016). 3. Subjective norms (SN)—This group of four items was included to examine the level of acceptance and influence by information shared by social networks or acquaintances in choosing the HEI. The items were adopted from Srinivasan (2015). 4. Brand credibility (BC)—These five items were incorporated by Bougoure et al. (2016) to identify the level of trust students have in their HEIs, and to what extent the HEI provides the promised service to the undergraduates. 5. Customer-based brand equity (CBBE)—The seventeen items related to this construct aimed to detect the level of HEI brands that reside in the students’ minds. These items were adopted from Lassar et al. (1995).

4.5.4 Questionnaire Translation This questionnaire was translated into Vietnamese when surveying in Vietnam. Although many citizens throughout Vietnam can speak, or at least understand English, it is not the country’s official spoken language. Hence, it was necessary to translate the questionnaire from its English version into the Vietnamese version, which is the widely spoken language by most Vietnamese. This questionnaire was translated into Vietnamese and then back-translated to compare with the original English version. According to Maneesriwongul and Dixon (2004), the back translation is vital to ensure the questions’ validity by confirming that the instruments are almost the same in the two languages. Hence, the English version of the instrument was translated first into Vietnamese by an independent bilingual and sent to a professional translator to back-translated it to English. The back-translated English version and the original English version was

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then compared by two academic bilinguals, including one supervisor of this study, to ensure both versions were identical. The questionnaire distributed among the undergraduates in Sri Lanka was not translated into their native language since English is considered an official language in Sri Lanka, and every HEI is conducted using only the English Language.

4.6 Data Collection for the Pilot Study The questionnaire needs to be pilot tested before use (Saunders et al., 2009). The purpose of the pilot test is to ensure the respondents will not face any kind of problem when conducting the actual survey, by ensuring the validity and reliability of data, to confirm that the questionnaire is working well in full-scale data collection (Bryman, 2012; Neuman, 2007). Preliminary analysis using the pilot test can proceed to detect any weakness and deficiencies in the proposed content and make amendments, and adjustments, to avoid future problems (Neuman, 2007). The final Vietnamese version of the questionnaire was distributed among the undergraduates in Vietnam. A pilot test was conducted only in Vietnam due to time constraints, assuming that both Sri Lanka and Vietnam share the same cultural background. According to Hertzog (2008), the appropriate sample size for a pilot study is 10% of the actual sample size of the present study. Newman (2014) purports that the nature of the pilot study (rather than its size) has a more considerable impact on the accuracy of the results of the study. A larger sample is always better as the accuracy of the estimated pilot result increases with the increased sample size (Bryman, 2012). At this stage, the questionnaire was distributed among a sample of 100 undergraduates randomly selected from different private HEIs in Vietnam. The respondents were first asked to complete the questionnaire and then provide suggestions on wordings, clarity, length of the questionnaire, and the instructions. The pilot test revealed that, on average, the questionnaire took 10–15 min to be fully answered—furthermore, no complaints received on the questions and the instructions.

4.6.1 Validity and Reliability of the Piloted Questionnaire Newman (2014) suggested that a researcher should not pay attention only to the result of the study but to the rigorous of the study as well. Rigorous refers to the degree to which the research enhances its quality. According to Cohen et al. (2002), the rigorous could be achieved through the measurements of validity and reliability. Validity is the extents to which the measures are accurately representing the concept of interest (Bryman, 2012). Saunders et al. (2009, p. 157) explained that validity is about “the findings that are really about what they appear to be about”. Reliability, on the other hand, is a measure indicating the extent to which it is without bias

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(error-free), and hence, ensures consistent measurement across time and the various items in the instrument (Sekaran, 2003). The scales in the questionnaire were developed in different contexts focusing on different countries and other contexts. Therefore, there is a need to identify whether these scales can be applied to emerging countries, specifically in higher education. Based on the result obtained from the validity and reliability tests, this study develops a new structural model from the theoretical model, which will be finally used to test the relationship among the study variables. This study adopts two main types of validity measures: Content validity and construct validity (Sekaran, 2003).

4.6.2 Validity 4.6.2.1

Content Validity

Content validity reflects the extent to which a measuring instrument provides adequate coverage of the topic under study (Saunders et al., 2009). This procedure is of most relevant when the individuals are designing the questionnaires (Sekaran, 2003). The judgment of what is “adequate coverage” can be identified through the careful definition of the research through the literature reviewed and, where appropriate, prior discussion with experts in the field about the relevance of the measures used in the questionnaire (Saunders et al., 2009). Therefore, the following steps were followed to ensure content validity: 1. The development of the questionnaire was based on the continuous review of prior studies related to the study’s variables. The measurement scales in the questionnaire were taken from previous studies and modified according to the current research context and environment. All the scales included in the questionnaire have been validated by the earlier researchers. 2. The questionnaire was reviewed by a panel of six Ph.D. students, ten undergraduates, ten from academic sectors, and two marketing managers. Suggestions and recommendations were incorporated into the pilot questionnaire. Hence, the content validity of the questionnaire could be claimed.

4.6.2.2

Construct Validity

Construct validity is demonstrated by showing that the measures are related to various measures of other concepts as specified in theory (Engel & Schutt, 2014). In this study, the main constructs: Firm-generated content, user-generated content, subjective norms, brand credibility, and customer-based brand equity, have been operationalised based on validated instruments as specified in theory. Thus, construct

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validity could be claimed for this study. According to Clow and James (2013, p. 271), a construct must have convergent and discriminant validity to have construct validity.

4.6.2.3

Convergent Validity

The convergent validity of a measure ought to be gauged by comparing it to measures of the same concept developed through other methods (Newman, 2014). These measures how much-observed variable shares variance in common with different observed variables on a different latent variable (Hair et al., 2014). Convergent validity is calculated using Composite Reliability (CR) score and Average Variance Extracted (AVE). According to Hair et al. (2014), AVE estimation should be greater than or equal to 0.5, and CR estimates should be greater than or equal to 0.7 show adequate convergent validity. Since all CR and AVE values are about the acceptable level, convergent validity has confirmed (Table 4.4).

4.6.2.4

Discriminant Validity

Discriminant validity assumes that items claimed to measure the same construct correlate higher than items from different constructs that are theoretically supposed not to correlate (Newman, 2014). According to Hair et al. (2014), to test the discriminant validity of the model, (1) Maximum Shared Variance (MSV) should be less than the AVE (Table 4.4) (2) All standard regression weights value ≥0.3 (Table 4.5) As shown in Table 4.4, MSV < AVE; therefore, discriminant validity is confirmed. A measurement model is considered to have acceptable discriminant validity if the square root of the AVE of each construct is higher than any of the bivariate correlations among the constructs (Iglesias et al., 2019a, 2019b). As all the square roots of AVE were higher than the bivariate correlations among the constructs, discriminant validity was supported (Iglesias et al., 2019a, 2019b). Table 4.4 Validity results CR

AVE

MSV

FGC

Norms

BC

UGC

FGC

0.808

0.533

0.279

0.730a

Norms

0.844

0.577

0.056

−0.022b

0.760

BC

0.896

0.638

0.112

0.009

0.165

0.799

UGC

0.883

0.657

0.112

0.135

0.106

0.334

0.810

CBBE

0.974

0.881

0.279

0.528

−0.236

0.103

0.013

a b

The square root of AVE in the diagonal Pearson correlations among constructs

CBBE

0.939

4.6 Data Collection for the Pilot Study

157

Table 4.5 Standard regression weights values Estimates

Estimates Performance

←

CBBE

0.956

FGC3

←

FGC

0.779

Social_Image

←

CBBE

0.967

FGC4

←

FGC

0.347

Value

←

CBBE

0.846

CBBE1

←

Performance

0.784

Trustworthiness

←

CBBE

0.919

CBBE2

←

Performance

0.702

Attachment

←

CBBE

0.998

CBBE3

←

Performance

0.646

SN1

←

SN

0.656

CBBE4

←

Performance

0.795

SN2

←

SN

0.792

CBBE5

←

Social_Image

0.719

SN3

←

SN

0.831

CBBE6

←

Social_Image

0.782

SN4

←

SN

0.749

CBBE7

←

Social_Image

0.734

BC1

←

BC

0.830

CBBE8

←

Social_Image

0.642

BC2

←

BC

0.913

CBBE9

←

Value

0.452

BC3

←

BC

0.908

CBBE10

←

Value

0.340

BC4

←

BC

0.735

CBBE11

←

Value

0.883

BC5

←

BC

0.552

CBBE12

←

Trustworthiness

0.840

UGC1

←

UGC

0.698

CBBE13

←

Trustworthiness

0.775

UGC2

←

UGC

0.747

CBBE14

←

Trustworthiness

0.470

UGC3

←

UGC

0.948

CBBE15

←

Attachment

0.711

UGC4

←

UGC

0.826

CBBE16

←

Attachment

0.666

FGC1

←

FGC

0.820

CBBE17

←

Attachment

0.770

FGC2

←

FGC

0.855

All standard regression weights values are above 0.3, which further confirms the discriminant validity.

4.6.3 Reliability The reliability measures indicate the extent to which it is without bias (error-free) and ensures consistent measurement across time and the various items in the instrument (Saunders et al., 2009). In other words, the reliability of a measure is an indication of the stability and consistency with which the instrument measures the concept and helps to assess the “goodness” of a measure (Sekaran, 2003). In general, several reliability tests were employed to confirm the consistency of the outcome of the research instrument to check the internal consistency of the items in the questionnaire (Bryman, 2012; Kothari, 2004). The most widely and frequently used method to test the inter-item consistency is Cronbach’s alpha (Saunders et al., 2009), which calculates the average of all possible reliability coefficients (Bryman). The alpha coefficient is varied between

158 Table 4.6 Reliability results of the pilot test

4 Methodology and Methods Constructs

Number of items

Cronbach’s alpha

Comment

All constructs

34

0.867

Acceptable

FGC

4

0.786

Acceptable

BC

5

0.890

Acceptable

UGC

4

0.876

Acceptable

4

0.841

Acceptable

17

0.927

Acceptable

SN CBBE

“1” (denoting perfect internal reliability) and “0” (denoting no internal reliability) (Bryman, 2012). In general coefficient, less than 0.6 are viewed as having low reliability, and coefficient 0.7 and above are indicative of high-reliability standards. A reliability test was undertaken using Cronbach’s alpha using SPSS 23 software to assess the internal consistency of the measurement items in the questionnaire. Cronbach’s alpha values for each construct in this study should be above the 0.7 thresholds to have better internal consistency. Table 4.6 shows a summary of these results. It shows that Cronbach’s values for all the study variables are above the acceptable level (0.7), which confirms the internal consistency of the measurement items in the questionnaire. The next logical step is to determine the sample frame from which to select the sample. The current study is considered a nation-wide survey as its population is defined by all undergraduates in Sri Lanka and Vietnam. Therefore, assessment of all research population members is impossible, especially given the limited availability of finance, time, and effort to the researcher. Consequently, the study uses a sample to generalise the entire population of the study.

4.7 Sample Design According to Bryman (2012), a population is a universe of the unit, which includes a complete set of elements where the researcher wishes to derive some clarifications. In this study, the population of interest is all Sri Lankan and Vietnamese undergraduates who are currently pursuing their higher studies in the country. Undergraduates studying in the private HEIs in any stream were included. Individuals without at least one social media profile were also excluded due to their inability to provide insights about the importance of social media for the higher education sector.

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159

4.7.1 Sample Size According to the last reported data by the Department of Census and statistics in Sri Lanka, the estimated population at the end of 2017 was 164,734 undergraduates in private HEIs. In Vietnam, according to the Ministry of Education and Training, the undergraduate population of private HEIs at the end of 2017 was 243,975. Hence, based on Yamane’s formula (Yamane, 1973), the size of the current research sample of Sri Lanka and Vietnam was calculated to be 400, separately, as illustrated below. Sri Lanka n=

N 164734 = = 399.03 ≈ 400 2 1 + N (e) 1 + 164734(.05)2

Vietnam n=

243975 N = = 399.34 ≈ 400 1 + N (e) 1 + 243975(.05)

where n: Sample size N: Population e: Sampling error (usually 0.05). This study focuses only on private HEIs. The reason for not including the public HEIs in the study is discussed below. Public HEIs aim to maximise public surplus (Müller, 2017), and their costs are covered by general taxation, and access to higher education is usually determined through selective exams (Frisancho & Krishna, 2016). Therefore, a rigorous competition to attract the students and requirement to differentiate from the other HEIs do not occur among the public HEIs. By contrast, private HEIs concern more on profit (Epple et al., 2017). Private HEIs choose their educational quality level optimally and use admission requirements and tuition fees to be competitive for students (Marginson, 2016). Private HEIs’ admission policy, based on tuition fees, makes the institution attractive only to those students who are not accepted into the public HEIs and can afford to pay the private fee (Marginson, 2018a, 2018b). In private HEIs, the size of an institution’s budget largely depends on its efforts and the quality of services offered (Yusoff et al., 2015). Therefore, the competition among private HEIs is high to attract the students. Institutes must differentiate themselves from others to compete and attract students. Therefore, developing branding strategies for private HEIs are more important than public HEIs (Fay & Zavattaro, 2016). Hence, this study selects only private HEIs from both countries, based on the criteria suggested by Kandiko and Mawer (2013). According to Kandiko and Mawer (2013), the researcher must select four institutes when conducting a study on higher education: one from each category based on (a) research-intensive, (b) teaching-intensive, (c) regional-focused, and (d) special interest.

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4.7.2 Sampling Technique The process of selecting a sample is divided into two types: probability sampling and non-probability sampling (Saunders et al., 2009). The probability sampling technique has a higher level of chance of each case being selected from the population and usually equal for all cases (Sekaran, 2003). Probability samples are more deemed to represent the population (Saunders et al., 2009, p. 213). According to Bryman (2012), probability sampling techniques are adopted when the researcher is confident about the sample’s representatives drawn from the population. Hence, there is a possibility to answer the study’s research question and address research objectives statistically based on the findings of sample responses (Saunders et al., 2009; Newman, 2014). Non-probability sampling, on the other hand, provides a platform to select the sample based on subjective judgments (Lynn, 2002). Non-probability sampling techniques are adopted when the researcher has goals other than the statistical estimation of population characteristics (Kothari, 2004). Such goals may include obtaining data in an inexpensive, quick, and timely manner, piloting a survey in quantitative research and obtaining data from specific kinds of people (Sekaran, 2003). Unlike probability sampling, the chance of selecting any element in non-probability sampling is highly subjective (Lynn, 2002). As a result, non-probability sampling is sometimes referred to as purposive sampling, in which the researcher is following his/her judgment to achieve a particular reason (Saunders et al., 2009). Most social science research is adapting a purposive or judgemental sampling technique as the most appropriate and convenient way to collect data from the sample within a shorter period (Galvan & Galvan, 2017). As the current study is primarily quantitative, uses survey questionnaires to collect data, and aims to validate the applicability of the research model in the Sri Lankan and Vietnamese context, it was essential to generalise the findings to all Sri Lankan and Vietnamese undergraduates. Hence, probability sampling and purposive sampling in non-probability sampling was used to locate the required sample.

4.7.3 A Sampling of the Questionnaire Respondents Zikmund (2010) suggests that cluster sampling is advisable when the population elements are distributed over a wide geographical area. Robson (2011) stresses that multi-stage probability sampling is considered an extension of cluster sampling. Given the nationwide population for the current study, a multi-stage probability sampling process was used, as summarised in Fig. 4.1 and detailed in Appendix 4D. Via cluster sampling, 17 HEIs from Sri Lanka and 60 HEIs from Vietnam were initially grouped into four different categories based on (a) research-intensive, (b) teaching-intensive, (c) regional-focused, and (d) special interest (Kandiko & Mawer, 2013).

4.7 Sample Design

161

Fig. 4.1 Sampling process used in the current study

Research-intensive HEIs were grouped based on their involvement with pure and applied research, which has higher proportions of postgraduate research programmes with a high level of external research findings from the national and international perspectives. Teaching-intensive HEIs were categorised based on the teaching roles of academic staff that were adequately recognised and rewarded and whether there was scope to consider a role for teaching-focused appointments within the HEIs. Regional-focused HEIs were based on the regions or areas where one HEI has several campuses which provide better access to students, nationwide or worldwide. Special interest HEIs were selected based on the lower level of diversification of the study streams, focusing only on a specific area of interest. After an intensive review of the HEIs’ websites, social media profiles, and communication administration team of some HEIs, all the private HEIs in Sri Lanka and Vietnam were divided into these four categories. The HEIs that fall into more than one category out of four were grouped based on the main reason for HEI being existing. Simple random sampling was subsequently used to select one HEI from each category. Then, to ensure the minimum required sample size (400), the researcher decided to distribute 500–600 questionnaires to allow for non-completion or unusable questionnaires. A similar number of questionnaires were distributed in each HEI. In the final stage, systematic probability sampling and purposive sampling was used to approach the targeted subjects. The researcher and her data collection team selected every eighth individual who passes the main entrance of each HEI. A random number table could not be used since the list of students’ names, email addresses, student numbers, and other relevant contact details could not be obtained due to the confidential nature of the information. Purposive non-probability sampling was used

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to identify the eligibility of each individual for this study. Two criteria were applied: the first was that they must be a current undergraduate of the selected HEI because there can be prospective students, visitors, or friends of undergraduates from another HEI, and the second was they must have at least one social media profile because some questions were based on undergraduates’ experience with social media.

4.8 Statistical Analysis Techniques Used Even though many statistical tests and techniques are available to analyse data, the choice to use it is conditioned primarily by the research questions and the nature of the collected data (Saunders et al., 2009). Consequently, the statistical tests used in the present study were a function of its objective and research questions. The Statistical Package for Social Science (SPSS) version 23 and Analysis of Moment Structures (AMOS) version 23 was used to analyse the quantitative data gathered via the questionnaire. This study primarily uses SPSS to perform descriptive analysis. The Exploratory Factor Analysis (EFA) was used to derive factors from the statistical result to understand the number of factors that exist or which variable is belonging to which construct (Hair et al., 2014). EFA is designed for a situation where the relationship between the observed and the latent variables are unknown or uncertain (Byrne, 2016). Using SPSS, this study identified the loadings and communities of the factors and uncovered the underlying structure of the variables. The Confirmatory Factor Analysis (CFA) was also performed to gain knowledge about the underlying latent variable structure (Hair et al., 2014). With CFA, the researcher can identify both the number of factors that exist within a set of variables and which aspect of each variable will load highly before the result is computed (Hair et al., 2014). Based on the theory, empirical research, or both, the researcher can determine the relationship between the observed measures and the underlying factors prior, and then statistically test the hypothesised structure (Byrne, 2016). The Structural Equation Modelling (SEM) was also used to test the confirmatory (hypotheses testing) approach to test the theories (Byrne, 2016). According to Iglesias et al. (2019a, 2019b), SEM is suitable to analyse the hypothesised model in this study because (1) the model is complex (i.e., it involves 34 items for five constructs), (2) it contains complex relationship (i.e., mediators, and moderators), and (3) several hypothesised relationships that are part of the model are under-researched from an empirical standpoint. In identifying the moderating effect, the direct and indirect effects were identified through SEM. Then, the multi-group analysis was used to determine the moderating effect. In identifying the moderating impact of location, this study separately checked the relationship between the constructs among the data collected from Sri Lanka and Vietnam. The Brand usage experience was identified from students as juniors and seniors. The 1st and 2nd years are defined as junior students, and the 3rd and 4th-year students

4.9 Ethical Consideration

163

were defined as senior students, based on the literature. Next, the perception of junior students and senior students on the relationships between the constructs were tested separately. In addition, the basic index of central tendency (i.e., mean and median) and variability (i.e., standard deviation) were calculated (Sekaran, 2003). Appendix 4E summarises these statistical techniques, and Chaps. 5 and 6 provide a more detailed description of each.

4.9 Ethical Consideration It is a matter of judgment whether the strategy and data collection method(s) suggested by ethical considerations will yield valid data. The general ethical issue here is that the research design should not subject those you are researching (the research population) to embarrassment, harm, or any other material disadvantage. According to Sekaran (2003) and Saunders et al. (2009), it is essential to pay attention to ethical issues in all research since this establishes trust between researchers and research participants. It enhances the overall reliability and credibility of the findings. Before the research activity, it was necessary to obtain formal ethical approval from Vietnam Research Ethics Committee (VREC). This study was approved by RMIT University Vietnam’s Research Ethics Committee on the 25th of June 2018 (VREC 01–18) and follows the approved procedure in compliance with the Research Ethics Committee Approval. Accordingly, several key ethical issues were considered across the different data collection stages in the study as follows: 1. All questions in the questionnaire were designed to avoid causing any harm, embarrassment, stress, or discomfort to participants. 2. Each HEI were contacted before conducting the survey, and permission obtained to survey within HEI’s premises. 3. All survey questionnaire participants (pilot test and main study) were asked for their verbal informed consent to participate before their involvement. The purpose of the study was explained to the participants in detail. They were informed that their participation in this study is voluntary, and the information would be treated with strict confidentiality, anonymously, and used solely for this study. The collected data were stored in a USB with password-protected to ensure data security.

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4 Methodology and Methods

4.10 Summary This chapter provided a detailed discussion of the methodology and methods adopted within the study. It showed that after due consideration of the various alternatives, a quantitative approach was selected based on the nature of the study and the research objectives. A post-positivist philosophical paradigm was found to be the most appropriate, allowing quantitative data to be collected using questionnaires. All choices made in respect of methodology and instruments have been fully justified, and the ethical approach to the study has been carefully detailed. The next chapter presents a descriptive analysis of the quantitative data obtained from the questionnaires.

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Chapter 5

Quantitative Data Presentation and Analysis: Descriptive Analysis

5.1 Introduction This chapter provides a descriptive analysis of the quantitative data and is divided into five sections. The first section presents the preliminary consideration of data, showing the response rate and the process of data screening and cleaning. The second section deals with the demographic profiles of the respondents. The third section provides a preliminary reliability assessment of the primary constructs in the present study. The fourth section deals with findings from the descriptive analysis of the data obtained on the study’s major observed constructs, and finally, the fifth section offers a summary.

5.2 Preliminary Data Consideration 5.2.1 Response Rate The researcher personally administered the questionnaire. A survey was conducted on weekdays between 10.00 a.m. and 5.00 p.m. This ensures that students from different studying years and fields were primed to respond as lecture times vary depending on the availability of the lecture halls and the lecturers. During pilot testing, it was determined that students were more receptive after lecture time to complete the survey instrument than at the starting time. No compensation was provided to students for completing or attempting to complete the survey. The researcher spent two months collecting data, one month in each country. The researcher personally visited each HEI for the survey with the permission of their administration. After a brief explanation of the survey’s purpose, the questionnaires were distributed based on the students’ willingness to participate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_5

169

170

5 Quantitative Data Presentation and Analysis: Descriptive Analysis

Table 5.1 Detailed response rates for distributed questionnaire Country

Type of HEI

Distributes

Retrieved

Unusable

Usable

%

Sri Lanka

Research-intensive

125

113

7

106

84.8

Vietnam

Total

Teaching-intensive

127

104

5

99

78.0

Regional-focused

128

121

6

115

89.8

Special interest

120

102

10

92

76.6

Research-intensive

140

121

6

115

82.1

Teaching-intensive

204

194

4

190

93.1

Regional-focused

139

126

2

124

89.2

Special interest

167

159

7

152

91.0

1150

1040

47

993

86.3

Though the sample size is 800 (400 from each country), 1150 questionnaires were distributed, anticipating issues in collecting and analysing data. Of 500 distributed questionnaires in Sri Lanka and 650 in Vietnam, 412 (Sri Lanka) and 581 (Vietnam) were considered to be valid for subsequent quantitative analysis. Table 5.1 details the response rate, which represents the full research sample. A total of 1040 questionnaires were returned from both countries, but 47 of them were unusable for the following reasons: respondents had circled many responses for one question Likert scale items (36 cases) and many missing responses (11 cases). When incomplete and unusable responses were removed, a total of 993 valid responses were available for further analysis. All items of the questionnaire were coded before feeding data into the analysis software and carefully reviewed to identify incomplete or extreme cases. Accordingly, 993 questionnaires were considered valid for further data analysis, thereby giving a high response rate of 86.3% of the original sample size. It is worth mentioning that such a high response rate might be the outcome of the purposive sampling technique, which provided the researcher with the guarantee that the findings could be interesting to generalise. The next step was to screen and clean the collected raw data to ensure the accuracy of the statistical techniques used in the study. Bias is a type of error that systematically skews results in a certain direction. Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random (i.e., with observational studies such as cohort, case–control, and crosssectional studies). Selection bias also occurs when people volunteer for a study. Those who choose to join (i.e., who self-select into the study) may share a characteristic that makes them different from non-participants from the get-go. In order to overcome self-selection bias first, the researcher makes their study representative by including as many people as possible. Then the researcher has conducted an experimental study in which participants are randomly assigned to the present study. In designing the experimental study, the researcher has focused on the social media usage of the undergraduates, and their engagement with their

5.2 Preliminary Data Consideration

171

respective HEIs’ social networking sites. The evaluations attempt to avoid selection bias by making the control group as comparable as possible, typically by matching on observables. The more data that is available for matching, the more convincing this is. The minimum number of respondents for the study is 800 and 400 from each country. In order to gain the required number of participants, the researcher distributed 500– 600 questionnaires, and screening questions were included to match the subgroups with the population. Since, this study selects two samples from two countries, by including screening questions, this study minimised the bias and ensured that the selected sample represents the total population.

5.2.2 Data Screening and Cleaning According to Hair et al. (2014), different multivariate statistical techniques, including factor analysis and SEM, have the tremendous theoretical ability to help researchers in various fields to test their hypotheses and assess the viability of their proposed models. However, such techniques are not without restrictions. Therefore, data screening and cleaning are considered a significant concern when the intention is to use multivariate analysis. It might be time-consuming and exhaustive, as noted by Kline (2011). In data screening and cleaning, the researcher followed the methods below to check the appropriateness of numerical values of each variable under study. Table 5.2 summarises all the data screening and cleaning methods adopted in this study. The preliminary data screening was performed by checking the basic frequency and descriptive statistics distributions. Any odd or wrongly coded values were detected and then properly corrected. However, several cases were found to have missing responses. Table 5.3 shows the frequencies and the percentages of missing data. Scheffer (2002) claims that regardless of how much a researcher attempts to have a full dataset in response to any particular survey or how well s/he has designed an experiment, missing data afflict almost all research efforts. Hair et al. (2014) highlight that the problem of missing data affects the statistical analysis of the original dataset in two ways; firstly, by reducing the power of the statistical techniques in indicating any relationships in the dataset; and secondly, by generating bias in the process of parameter estimations. Although no clear rule about the acceptable percentage of missing data appears in the literature, researchers suggest that less than 1% of missing values of any variable is usually considered very slight and unimportant, 1–5% remains manageable by many statistical methods, 5–15% requires more unconventional and complicated techniques to deal with, and more than 15% missing values of a given dataset could harshly distort any kind of further data interpretation (Acuna & Rodriguez, 2004; Cohen et al., 2013). Additionally, Hair et al. (2014) and Kline (2011) claim that when the amount of missing values within a large dataset is relatively small, the researcher faces a less serious problem and could treat those missing values quickly since any treatment option could lead to similar results.

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5 Quantitative Data Presentation and Analysis: Descriptive Analysis

Table 5.2 Data screening and cleaning methods Data screening and cleaning methods The purpose and steps followed Checking data entry

To reduce the data entry errors, the researcher double-checked with the entered data set. First, the researcher created three separate SPSS files for Sri Lanka, Vietnam, and a combined file merging them. These were double-checked by randomly picking different segments to look for incorrectly entered data. Further, using descriptive analysis in SPSS, the researcher looked for numbers that are out of range

Identifying missing data

To ensure the participants have answered all questions in the questionnaire, the researcher identified the missing data values. The researcher has checked whether the missing values are less than 1%. Regression-based imputation was applied to treat the missing values

Detecting outliers

To decrease the variability of the data set, the researcher identified univariate and multivariate outliers. Univariate outliers were identified through z-score frequency distributions, and multivariate outliers were detected by calculating the Mahalanobis distance (D2 ). No standard score less than −3.29 or greater than +3.29 concerning all research variables indicated the absence of univariate outliers. Mahalanobis D2 with p < 0.001 indicated the absence of multivariate outliers

Normality

The research used Skewness-Kurtosis and normality P-P plots to ensure the sample data set has been drawn from a normally distributed population. Skewness and kurtosis values falling outside the range of −1 to +1 indicated no significant deviation from the normal distribution. The normality P-P plot depicted an acceptable level of normality as the standardised predicted value formed a line with the standardised residuals

Table 5.3 shows that the maximum percentage of missing data for the questionnaire items in the current study was 0.3%, meaning that this is extremely low and within a satisfactory level. Hence, the researcher faces a less severe problem and could treat those missing values effortlessly since any treatment option could lead to similar results. As mistakes of data entries are the possible consequences of missing values, the researcher double-checked all entries to minimise feeding wrong data into the analysis programme. However, to treat those missing values, the researcher decided to apply regression-based imputation as this method takes into consideration the relationships among different variables based on the overall responses, thereby leading to more accurate value estimation (Kline, 2011). Regression-based imputation is the best method to generate the predicted score for a missing value since other methods are mainly on pattern matching, which does not guarantee to have the most similar pattern of scores (Kline, 2011).

5.2 Preliminary Data Consideration

173

Table 5.3 Missing values statistics Item

Freq

%

Item

Freq

%

FGC1

0

0

CBBE1

0

0

FGC2

0

0

CBBE2

0

0

FGC3

0

0

CBBE3

1

0.1

FGC4

0

0

CBBE4

0

0

BC1

0

0

CBBE5

0

0

BC2

0

0

CBBE6

2

0.2

BC3

0

0

CBBE7

1

0.1

BC4

0

0

CBBE8

0

0

BC5

2

0.2

CBBE9

0

0

UGC1

2

0.2

CBBE10

0

0

UGC2

0

0

CBBE11

2

0.2

UGC3

1

0.1

CBBE12

3

0.3

UGC4

3

0.3

CBBE13

0

0

SN1

0

0

CBBE14

0

0

SN2

0

0

CBBE15

1

0.1

SN3

1

0.1

CBBE16

0

0

SN4

0

0

CBBE17

1

0.1

After treating the missing values, the next logical step was to consider outliers (univariate and multivariate), representing those cases with odd and/or extreme scores from other dataset observations. Errors in data entry, erroneous sampling techniques, missing values in the calculation, and extreme responses on multi-point scales are among the many causes of outliers. First, a check for univariate outliers was applied. On each of the variable observations, univariate outliers were identified by using z-score frequency distributions. All scores for each variable were converted to standard scores (z-scores) and then checked against the intended range. As a rule of thumb, a range of (±3 to ± 4) z-scores for samples larger than 80 was considered acceptable, with any individual observation exceeding that limits being treated as a univariate outlier (Hair et al., 2014). The z-scores of ± 3.29—the z-score that corresponds to a probability of 0.001—were used to identify any odd values within each variable’s observations. No standard score less than −3.29 or greater than +3.29 was identified from all research variables, which means the absence of univariate outliers from the dataset of the current study (Table 5.4). Next, multivariate outliers were detected by calculating the Mahalanobis distance (D2 ), which represents the distance of a case from the multidimensional mean of a distribution. Mahalanobis D2 with a probability less than or equal to 0.001, eliminated those cases from the datasheet to maintain more representative samples. However,

174 Table 5.4 Minimum and maximum values of study variables

5 Quantitative Data Presentation and Analysis: Descriptive Analysis Minimum

Maximum

FGC

−3.01666

2.01153

UGC

−2.48096

2.32610

BC

−2.98983

2.35345

SN

−2.69473

2.16349

CBBE

−3.21825

2.48143

the data set did not detect any Mahalanobis D2 with p < 0.001. Thus, requiring further consideration on outliers did not want for the current data set. The normality of the data distribution is considered as one of the most critical assumptions underlying various multivariate analysis tools such as factor analysis and SEM. The multivariate normality of a particular distribution confirms that the shape of individual variables’ distribution or the distribution of a combination of two or more variables is corresponding with the bell-shaped normal distribution (Doornik & Hansen, 2008; Hair et al., 2014). Any violation of the normality assumption could severely affect the process of data analysis and goodness-of-fit indices for the proposed SEM model (Andrews et al., 1973; Kline, 2011; Korkmaz et al., 2014). Skewness and Kurtosis are two ways of considering data that indicate the normality of a given dataset distribution (Doornik & Hansen, 2008; Thulin, 2014). Skewness demonstrates the symmetry of distribution, while Kurtosis refers to how much the distribution is peaked or flat compared with the normal distribution (Hair et al., 2010; Andrews et al., 2014). In general, a normally distributed distribution has skewness and kurtosis values of zero. However, scholars provide general guidelines about when skewness and kurtosis values might become problematic. For example, Hair et al. (2010) suggested that any skewness and kurtosis values falling outside the range of −1 to +1 represent a potential normality problem. Conversely, many researchers are less conservative, recommending that skewness less than an absolute value of 3 and a kurtosis index with an absolute value of less than 8 does not indicate a significant normality problem (Doornik & Hansen, 2008; Kline, 2011; West et al., 1995). In the current study, all individual measured items were tested for normality using skewness and kurtosis statistics as shown in Table 5.5, which reveals that for the 34 items, the skewness was in the range of −0.339 to 0.250, and kurtosis was in the range of −0.701 to 0.201. This indicates no significant deviation from the normal distribution. A further normality assessment was made from the residual analysis using the expected normality P-P plot for the regression residuals, shown in Fig. 5.1. An acceptable normality level was revealed as the standardised predicted value formed a line with the standardised residuals. P-P plots for each variable were separately depicted in Appendix 5A.

5.2 Preliminary Data Consideration

175

Table 5.5 Scale variables normality assessment Item

Skewness

Kurtosis

Item

Skewness

Kurtosis

FGC1

−0.256

−0.562

CBBE1

−0.293

0.115

FGC2

−0.171

−0.329

CBBE2

−0.203

0.036

FGC3

−0.182

−0.353

CBBE3

−0.298

0.042

FGC4

−0.183

−0.253

CBBE4

−0.202

0.024

UGC1

−0.069

−0.341

CBBE5

−0.118

−0.114

UGC2

−0.066

−0.408

CBBE6

−0.102

−0.241

UGC3

−0.098

−0.522

CBBE7

−0.334

0.201

UGC4

−0.070

−0.228

CBBE8

−0.035

−0.204

SN1

−0.282

−0.485

CBBE9

−0.016

−0.048

SN1

−0.293

−0.219

CBBE10

−0.269

−0.230

SN1

−0.339

−0.701

CBBE11

−0.149

−0.113

SN1

−0.329

−0.168

CBBE12

−0.307

−0.174

BC1

−0.186

−0.145

CBBE13

0.168

−0.255

BC2

−0.125

−0.286

CBBE14

−0.311

0.110

BC3

−0.191

−0.018

CBBE15

−0.177

−0.209

BC4

−0.154

−0.173

CBBE16

−0.178

0.057

BC5

0.250

−0.352

CBBE17

−0.261

−0.128

Fig. 5.1 Normal P-P plot of regression standardised residual

176

5 Quantitative Data Presentation and Analysis: Descriptive Analysis

5.3 Background and Demographic Profile of the Study Sample The results relating to parts one and two of the questionnaire, i.e., demographic data and social media and traditional media users, are now presented and described. Frequency distributions with respect to demographics and social media usage clarify the study sample characteristics.

5.3.1 Demographics Table 5.6 summarises the demographic data relating to gender, year, the field of study, hours on social media, widely used social networking site, how often on the most preferred social networking site, level of involvement with posting and commenting on social media posts, and hours on traditional media. It reveals that the gender breakdown was 31.10% male and 68.90% female. The sample consists of more female customers than male. This is justified when compared with the general gender distribution of Sri Lanka and Vietnam. The results agree with the latest reported gender profile in Sri Lanka and Vietnam, which is 48.4% and 51.6% for males and females, respectively, in Sri Lanka (Census and Statistics Department, 2017), and 49.3% and 50.7 for males and females, respectively, in Vietnam (OECD, 2017). Concerning the studying year, responses were received from the undergraduates from different study years starting from 1st year to 4th year; 41.0%, 12.2%, 31.4%, and 15.4% of undergraduates are studying in 1st year, 2nd year, 3rd year, and 4th year, respectively. Most undergraduates were in the 1st Year (41.0%), while other years represented a lower level of undergraduate population. The respondents were clustered into five groups as per the field of study to represent all the study streams in any HEI. According to the sample profile, 5.2% of the undergraduates are majoring in Information technology. Those studying Business Administration, Financial & accounting, and Marketing accounted for 14.8%, 34.9%, and 3.5%, respectively, while 41.5% of the undergraduates represented the other field of studies such as agriculture, tourism and hospitality management, quantity surveying, etc. The average time on social media was identified based on the active participation hours with social media. It shows a higher percentage in the last category, representing social media usage for more than 4 h (41.8%). The lowest number of respondents use social media for less than one hour (3.7%). The average social media usage per day indicates through 2–3 h. According to Vietnam Digital Landscape (2018), the average daily time spent using social media in Vietnam was 2 h and 37 min. As the researcher expected, the most widely used social networking site among the selected sample was Facebook; 49.3% of Facebook users indicate that nearly half of the sample respondents use Facebook more than other social networking sites.

5.3 Background and Demographic Profile of the Study Sample

177

Table 5.6 Demographic data of questionnaire respondents Variable

Category

Frequency

%

Cumulative %

Gender

Male

309

31.0

31.0

Female

684

69.0

100.0

Studying year

1st year

407

41.0

41.0

2nd year

121

12.2

53.2

Field of study

Hours on social media

Widely used SNS

How often do you visit the SNSs

How often do you post information or comment on SNSs

Hours on traditional media

3rd year

312

31.4

84.6

4th year

153

15.4

100.0

IT

52

5.2

5.2

Bus. Administration

147

14.8

20.0

Finance & accounting

347

34.9

55.0

Marketing

35

3.5

58.5

Other

412

41.5

100.0

Less than 1 h

37

3.7

3.7

Between 1 and 2 h

111

11.2

14.9

Between 2 and 3 h

202

20.3

35.2

Between 3 and 4 h

228

23.0

58.2

More than 4 h

415

41.8

100.0

Facebook

490

49.3

49.3

Instagram

139

14.0

63.3

YouTube

150

15.1

78.4

Pinterest

119

12.0

90.4

Google+

84

8.5

98.9

Other

11

1.1

100.0

Less than one time a month

9

0.9

0.9

Only once in a few months

12

1.2

2.1

One time per month

13

1.3

3.4

At least one time a week

101

10.2

13.6

At least one time a day

858

86.4

100.0

Less than one time per year

102

10.3

10.3

Only once in a few months

220

22.2

32.4

One time per month

114

11.5

43.9

At least one time a week

178

17.9

61.8

At least one time a day

379

38.2

100.0

Less than 1 h

642

64.7

64.7

From 1 to 3 h

272

27.4

92.0

More than 3 h

79

8.0

100.0

178

5 Quantitative Data Presentation and Analysis: Descriptive Analysis

According to Vietnam’s Digital Landscape (2019) and Digital Sri Lanka (2018), the widely used SNS in both countries was Facebook, and the second position was owned by YouTube (15.1%). To some extent, this is consistent with the study results. However, other social networking sites indicated a lower level of usage, i.e., Instagram (14.0%), Pinterest (12.0%), Google+ (8.5%), and other social networking sites such as Twitter (1.1%). The average time on the preferred SNS was identified based on five categories. It shows that the majority of the participants visit their preferred SNS at least once per day (86.4%). Similarly, a negligible number of participants (0.9%) visit SNS less than once per month, while 10.2% of the social media users share information at least one time a week, denoting a higher level of a tendency towards social media activities. Most respondents spend less than one hour on traditional media; 64.7% of respondents use traditional media for less than one hour, while 8.0% use more than 3 h. On average, Vietnamese use social media 7 h per day and traditional media 3 h per day, indicating a higher tendency towards social media than traditional media such as newspapers, television, and radio (Global web index, 2017).

5.4 Respondents’ Segmentation The sample was divided into two groups, Sri Lanka and Vietnam, to clarify social media adoption. Various associations between the two groups were then performed to discover whether respondents differed based on their demographic characteristics.

5.4.1 Sri Lankans and Vietnamese Demographic Observations on Dependence Observations of Sri Lankan and Vietnamese undergraduates in terms of the demographic characteristics of gender, studying year, the field of study, hours on social media, widely used SNSs, how often they visit the SNSs, how often they post information or comments in SNSs, and hours on social media are now presented. Chi-square tests were performed to examine the impact of these demographic variables on the two groups’ decisions to adopt social media in Sri Lanka and Vietnam. At this point, it is essential to have a good understanding of the Chi-square test and its underlying assumptions. The Chi-square test for independence is a non-parametric technique that explores any significant relationship between two categorical variables from the same sample. This test begins with the hypothesis of no association or no relationship between the two variables under consideration (the null hypothesis). The alternate hypothesis states that the two variables are associated. The decision to reject or accept the

5.4 Respondents’ Segmentation

179

null hypothesis depends on the p-value associated with the Chi-square statistic. If the p-value is less than a predetermined significance level (usually 0.05), the null hypothesis is rejected, then the assumption that the two variables are independent (no association) is rejected, and the alternate hypothesis assumption of the association between the two variables is accepted. Like any other statistical technique, the Chi-square test makes some specific assumptions about the data to ensure the statistical suitability of the test. The first assumption relates to the sampling method, which must involve a random sample chosen from the entire population. The second assumption is that the sample is large enough, with the population for each variable being at least ten times larger than the sample. The third assumption is that the two variables are both categorical. The last assumption is that the expected frequency of any sub-category of the two variables is at least five. More specifically, in any frequency table, each frequency cell of the table should present five or more expected frequency counts (Field, 2009; Pallant, 2013). A preliminary check of the data was conducted to ensure no violation of any of the above assumptions. This revealed that all assumptions were met for all involved variables except the last one (expected frequencies assumption) for two sub-categories in two variables: those who are using “other” social networking sites, and those who visit social networking sites “Less than one time a month”. The expected frequencies in those were 4.6 and 3.7. Therefore, the two sub-categories above were excluded when applying the test to keep all Chi-square test assumptions unviolated.

5.4.1.1

Gender Observations

The gender distribution of the respondents was as follows: 412 respondents were Sri Lankans (41.5% of the total respondents) of which, 150 were males (36.4% of the total Sri Lankans), and 262 were females (63.6% of the total Sri Lankans). Of 581 Vietnamese respondents (58.5% of the total respondents), 159 respondents were males (27.3% of the total Vietnamese), and 422 were females (72.7% of the total). The null hypothesis was rejected since the p-value (0.002) is less than the significance level (0.05). The Chi-square statistics (χ2 (1) = 9.193) in Appendix 5C revealed a significant association between the respondent’s gender and location. This association reflects that among male respondents, about 48.5% were Sri Lankans and 51.5% were Vietnamese; in females, about 61.7% were Vietnamese, and only 38.3% of them were Sri Lankans, as shown in Appendix 5C. This study selected respondents mainly based on social media usage. As per the above results, it can be concluded that the gender of the respondents had a significant influence since males in Sri Lanka and females in Vietnam are more willing to use social media than their counterparts. So that in implementing social media marketing strategies for branding in Sri Lanka and Vietnam, firms need to focus on the target niche based on their gender.

180

5.4.1.2

5 Quantitative Data Presentation and Analysis: Descriptive Analysis

Studying Year Observations

The studying year of Sri Lankans and Vietnamese shown in Appendix 5D reveals that most Sri Lankans were 1st year and 3rd year students (75.9% of the total Sri Lankans). Further, while almost 10.9% of the total number of Sri Lankans were 2nd year students, 35.2% of them were 4th year students. Appendix 5D indicates that most Vietnamese were also in the 1st and 3rd years, with 42.7% and 27.2%, respectively. This is obvious in the gaps between the percentages of Sri Lankan and Vietnamese social media users within their studying years. Therefore, in implementing branding strategies on social media, both Sri Lankans and Vietnamese should focus more on the 1st and 3rd year students than the ones in the 2nd and 4th years.

5.4.1.3

Field of Study Observations

In terms of field of study, Appendix 5E shows that social media users in Sri Lanka came mainly from Finance & Accounting, which accounted for 40.5%. Participants who study IT represented 5.3%, business administration represented 17.8%, marketing represented 2.4%, and the remaining 34.0% were from other subject areas such as agriculture, quantity surveying, etc. Appendix 5E also shows that the majority of the undergraduates in Vietnam from other fields (agriculture, hospitality, quantity survey, etc.) accounted for 46.8%. The Chi-square results as depicted in Appendix 5 K indicated that studying year and the location are associated (χ2 (4) = 22.329, p < 0.05), denoting that students in Finance & Accounting and other fields tend to be more willing to use the social media.

5.4.1.4

Hours of Social Media Observations

The hours spent on social media by participants, as illustrated in Appendix 5F, show that two categories dominated Sri Lankans and Vietnamese. Most Sri Lankans were using social media between 3–4 h or more than 4 h (63.8%), and the majority of Vietnamese were also from these two categories (65.4%). The frequency distributions of Sri Lankans and Vietnamese within the same hours on social media (Appendix 5F) show that 36.2% and 34.6% of the respondents from Sri Lanka and Vietnam, respectively, use social media for less than 3 h. Appendix 5K shows a slight similarity in social media usage among the Sri Lankans and Vietnamese. Therefore, it could be concluded that the hours on social media in Sri Lankans and Vietnamese do not account for a significant variation. Chi-square results (χ2 (4) = 0.851, p > 0.05) provide more support for this finding since they indicated no significant association between the hours on social media and Sri Lankans and Vietnamese’ decisions to use social media.

5.4 Respondents’ Segmentation

5.4.1.5

181

Widely Used Social Networking Sites Observations

Social networking sites (SNSs), a subdomain of social media, have been defined as a network communication platform in which participants (1) have uniquely identifiable profiles that consist of user-supplied content, content provided by other users, and/or system-provided data; (2) can publicly articulate connections that can be viewed and traversed by others; and (3) can consume, produce, and/or interact with streams of user-generated content provided by their connections on the site (Ellison & Boyd, 2013, p. 157). Appendix 5G lists the widely used social networking sites of Sri Lankans and Vietnamese. The majority of Sri Lankans use Facebook and Instagram, accounting for 26.9% and 25.2%, respectively, whereas 47.8% of total Sri Lankans used other social networking sites such as YouTube, Pinterest, and Google+. Similarly, 72.0% and 15.0% of Vietnamese were using Facebook and YouTube, respectively, reflecting the majority. Based on the above discussion, it can be concluded that an association exists between widely used social networking sites and social media usage among Sri Lankans and Vietnamese. Additionally, the Chi-square results featured in Appendix 5K (χ2 (5) = 383.968, p < 0.05) confirm a significant association between the widely used social networking site and location. However, the results showed that social media users in both countries make their presence mainly on Facebook. Apart from that, Sri Lankans are increasingly using Instagram in connecting with people. In contrast, Vietnamese are more enthusiastic about using YouTube, as it provides everything descriptively. Hence, marketing managers of higher education institutes in Sri Lanka and Vietnam should focus on Instagram and YouTube, respectively, in implementing their branding strategies in addition to Facebook.

5.4.1.6

Frequency of Social Media Observations

How often the respondents visit their preferred social media sites, illustrated in Appendix 5H, shows that two categories dominated both Sri Lankans and Vietnamese. Most Sri Lankans visit social networking sites at least one time per week (12.8%) and at least one time per day (81.6%). Likewise, the majority of Vietnamese also use these two categories (98.1%). How often Sri Lankans and Vietnamese visit social networking sites, at least one time per month or less, is accounted for 5.58% and 1.89% of Sri Lanka and Vietnam, respectively. Appendix 5H shows a slight similarity in the social media usage among the Sri Lankans and Vietnamese. Chi-square results (χ2 (4) = 21.850, p > 0.05) provide more support for this finding since they indicated no significant variation between the frequency of visiting social networking sites and Sri Lankans and Vietnamese’ decision to use social media.

182

5.4.1.7

5 Quantitative Data Presentation and Analysis: Descriptive Analysis

Frequency of Posting on Social Media Observations

The frequency of posting information and comments on social networking sites illustrated in Appendix 5I shows that two categories dominate the Sri Lankans. The majority of Sri Lankans were posting information and comments at least one time per week and at least one time per day, 83.0% indicating higher engagement with social networking sites. In contrast, most Vietnamese post on social networking sites at least one time per month (21.3%) and only once in a few months (33.6). Later, the Chi-square results as depicted in Appendix 5 K indicated that how often the respondents post information or comment on social media and location are associated (χ2 (4) = 328.590, p < 0.05), signifying that more Sri Lankans tend to be willing to share information on social networking sites.

5.4.1.8

Traditional Media Usage Observations

The traditional media usage among the participants illustrated in Appendix 5J shows that two categories dominate both Sri Lankans and Vietnamese. Most Sri Lankans use traditional media for less than 1 h and between 1 and 3 h (89.6%), and the majority of Vietnamese were also from these two categories (93.8%). The traditional media usage of Sri Lankans and Vietnamese for over 3 h shows that 10.4% and 6.2% of the respondents from Sri Lanka and Vietnam, respectively, use social media for less than 3 h. Overall, the number of female undergraduate students worldwide has exceeded the number of men since 2002 (Co-operation & Development, 2021). Data from UNESCO’s Institute of Statistics(UIS) shows that between 2000 and 2018, the Gross Enrolment Ratio (GER) in tertiary enrolment for males increased from 19 to 36%, while that for females went from 19 to 41% (UNESCO-IESALC, 2021). Women have, therefore, been the main beneficiaries of the rapid increases in higher education enrolment making up the majority of undergraduate students in all regions (Vaughn et al., 2020). Not only do females make up most of the undergraduate students, but they are also more likely to complete higher education than their male counterparts (Berg, 2019). The gender gap in higher education has virtually disappeared in most places (Barone & Assirelli, 2020). Women even outnumber men in higher education in many countries around the world, including developing countries across all regions (Neubauer, 2019). In Asia, especially, women’s enrolment has accelerated in recent years as the overall demand for higher education has accelerated (Cuthbert et al., 2019). A huge trend in higher education in Asia is increasing privatisation, which in many cases also created many educational opportunities available for women to study at private HEIs (Sanger & Gleason, 2020). Private HEIs have expanded women’s options by expanding the overall educational system in developing countries (Sanger & Gleason, 2020). In addition, the women’s experiences at HEIs are vastly different from those of men in terms of structure, nature, and facilities of

5.5 Descriptive Analysis of Respondents’ Responses

183

HEIs which motivated the women to enrol at private HEIs rather than public HEIs (Neubauer, 2019). Accordingly, Sri Lanka and Vietnam also showed similar kinds of findings. In the Sri Lankan context, 36.4% of males and 63.6% of females are pursuing their higher studies in private HEIs. Similarly, in Vietnam, 27.3% of males, and 72.7% of females are studying at private HEIs. These findings further verify the findings of the previous studies indicating that females have more positive perceptions of private HEIs than males when doing their selection process. Further, according to the findings, most of the students in Sri Lanka and Vietnam are pursuing their studies in the field of finance and accounting. 40.5% of Sri Lankans and 31.0% of Vietnamese are following fiance, and accounting courses indicate the majority. Lai et al. (2009) and Reus (2020) highlighted that numerous academic studies investigated ways to improve the design, structure, and delivery of finance courses at the undergraduate level. Some researchers focused on identifying the most important topics that can be covered in one course, and they identified that finance and accounting are the most important courses for the students who have enrolled in business management degree programmes (Alshehri, 2017; Lusardi, 2019). Accordingly, Mudzingiri et al. (2018) identified that, in recent years, many undergraduates are enrolling in finance-related subjects more than other business-related subjects due to their interest to learn about financial behaviour, risk preferences, and financial literacy. The finding also provides compelling evidence for the previous researches as the students’ enrolment rate for finance and accounting is significantly higher than the other courses. These findings highlighted that the students in developing countries are keener in understanding financial literacy, hence the majority follow the financing and accounting courses.

5.5 Descriptive Analysis of Respondents’ Responses This section presents a descriptive analysis of the data obtained from the sample. The full results appear in Appendix 5B. The following sub-sections report responses from the sample on the major constructs of the present study in central tendency and dispersion. The questionnaire consists of 5 major constructs measured by 34 different items (statements) using a seven-point Likert scale ranging from “strongly disagree” to “strongly agree”. Respondents were asked about their agreement or disagreement with each statement. Responses were coded as follows: number 1 indicated they “strongly disagreed” with the statement, number 2 “Disagree”, number 3 “somewhat disagree”, number 4 “neither disagree nor agree”, number 5 “somewhat agree”, number 6 “agree”, and number 7 “strongly agree”. Further, number 4 was chosen as the midpoint on the scale to make a distinction between the respondent’s agreement and disagreement. Therefore, the average values of each construct were considered when estimating descriptive statistics. Accordingly, the mean and standard deviation of the five main constructs of the research model is given in Table 5.7.

184 Table 5.7 Summary of descriptive statistics

5 Quantitative Data Presentation and Analysis: Descriptive Analysis Dimension

Mean

SD

FGC

4.5997

1.19327

UGC

4.0966

1.24816

BC

4.3573

1.12291

CBBE

4.5671

0.98045

SN

4.3280

1.23502

5.5.1 Firm-Generated Content Respondents were asked to indicate the extent to which the undergraduates are satisfied with how the HEIs use social networking sites to provide information about the HEIs. The results show that the mean scores of the four items used to measure FGC are between 4.48 and 4.77, with a standard deviation ranging from 1.381 to 1494. It could be concluded that most respondents (the mean score is more than the midpoint of 4) have agreed about the FGC of HEIs in terms of providing HEI-related updated information while sharing and motivating them to follow HEIs’ official social networking sites and enhancing their overall attachment with the HEIs.

5.5.2 User-Generated Content The findings reveal that the mean scores for UGC were between 4.05 and 4.19, indicating that a significant number of respondents find HEIs’ information on other social networking sites interesting and useful. They consider the information shared by others helps them gather required information on HEIs while attractively providing the information. Moreover, the descriptive statistics for UGC also revealed that the respondents were not very dispersed around their mean scores on individual items (standard deviations between 1.458 and 1.600).

5.5.3 Brand Credibility Regarding the brand credibility construct, respondents were asked to respond to five statements to measure the level of trust the undergraduates have towards their HEIs. The mean scores reveal an average of 4.3573, indicating that a relatively high level of agreement existed among respondents about this construct. To put it differently, undergraduates believe the HEIs can perform what they promise, the endowed HEIs’ quality is trustworthy, and HEI does not try to create a false image for society. Besides, the average standard deviation of 1.12291 indicates a little dispersion from that mean score.

References

185

5.5.4 Customer-Based Brand Equity Respondents were asked to provide their opinions concerning seventeen statements related to the degree to which they perceived the service outcomes of HEIs as satisfying the students’ requirements. The findings revealed that the three items had means over three (i.e., midpoint) and an average mean of 4.5671, indicating that a relatively high level of agreement existed among respondents about this construction. Respondents had a good feeling about their HEI in creating value for HEI. Again, the average standard deviation was 0.98045, indicating low dispersion among respondents’ scores around the average mean.

5.5.5 Subjective Norm Four items were used to measure the SN construct in this study. The mean scores were 4.31, 4.35, 4.19, and 4.45, all above the midpoint of four on the seven-point Likert scale. The average mean score was 4.3280, which indicated the participants’ agreement on the scale measures. Specifically, these results mean that most respondents accepted and were influenced by the information shared by social networks or acquaintances in choosing an HEI to pursue higher studies. The average standard deviation was 1.23502, indicating low dispersion among respondents’ scores around the average mean.

5.6 Summary This chapter shows that the response rate to the questionnaire was sound at 90.4%, this being accounted for by 993 usable questionnaires for statistical analysis from the original 1150 distributed among both Sri Lankans and Vietnamese. Analysis of the respondents’ demographic profile by combining information gathered from both countries reveals several similarities between the study sample and the general population structure of Sri Lanka and Vietnam. The following chapter continues the quantitative data analysis by discussing the findings of factor analysis and SEM.

References Acuna, E., & Rodriguez, C. (2004). The treatment of missing values and its effect on classifier accuracy. Classification, clustering, and data mining applications. Springer. Alshehri, A. F. (2017). Student satisfaction and commitment towards a blended learning finance course: A new evidence from using the investment model. Research in International Business and Finance, 41, 423–433.

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Andrews, D., Gnanadesikan, R., & Warner, J. (1973). Methods for assessing multivariate normality. Elsevier. Andrews, D., Gnanadesikan, R., Warner, J., & Krishnaiah, P. R. (2014). Methods for assessing multivariate normality. Multivariate Analysis, 3, 95–116. Barone, C., & Assirelli, G. (2020). Gender segregation in higher education: An empirical test of seven explanations. Higher Education, 79, 55–78. Berg, G. A. (2019). The rise of women in higher education: How, why, and what’s next. Rowman & Littlefield Publishers. Cohen, L., Manion, L., & Morrison, K. (2013). Research methods in education. Routledge. Co-operation, O. F. E., & Development (2021). Why do more young women than men go on to tertiary education? Cuthbert, D., Lee, M. N., Deng, W., & Neubauer, D. E. (2019). Framing gender issues in asia-pacific higher education. In Gender and the changing face of higher education in asia pacific. Springer. Doornik, J. A., & Hansen, H. (2008). An omnibus test for univariate and multivariate normality. Oxford Bulletin of Economics and Statistics, 70, 927–939. Ellison, N. B., & Boyd, D. (2013). Sociality through social network sites. The Oxford handbook of internet studies, 151–172. Field, A. (2009). Discovering statistics using SPSS. Sage publications. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis: Pearson new (international ed.). Pearson Education Limited. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York. Korkmaz, S., Goksuluk, D., & Zararsiz, G. (2014). MVN: An R package for assessing multivariate normality. The R Journal, 6, 151–162. Lai, M. M., Kwan, J. H., Kadir, H. A., Abdullah, M., & Yap, V. C. (2009). Effectiveness, teaching, and assessments: Survey evidence from finance courses. Journal of Education for Business, 85, 21–29. Lusardi, A. (2019). Financial literacy and the need for financial education: Evidence and implications. Swiss Journal of Economics and Statistics, 155, 1–8. Mudzingiri, C., Muteba Mwamba, J. W., & Keyser, J. N. (2018). Financial behavior, confidence, risk preferences and financial literacy of university students. Cogent Economics & Finance, 6, 1512366. Neubauer, D. E. (2019). Gender issues in asia pacific higher education: Assessing the data. Gender and the changing face of higher education in asia pacific. Springer. OECD (2017). Youth well-being policy review of Vietnam. http://www.oecd.org/countries/vietnam/ OECDYouthReportVietNam_ebook.pdf. Accessed 2 Feb 2019. Pallant, J. (2013). SPSS survival manual: A step by step guide to data analysis using SPSS for windows, 5th. McGraw-Hill International. Reus, L. (2020). English as a medium of instruction at a Chilean engineering school: Experiences in finance and industrial organization courses. Studies in Educational Evaluation, 67, 100930. Sanger, C. S., & Gleason, N. W. (2020). Diversity and inclusion in global higher education: Lessons from across Asia. Springer Nature. Scheffer, J. (2002). Dealing with missing data. Research Letters in the Information and Mathematical Sciences, 3, 153–160. Thulin, M. (2014). Tests for multivariate normality based on canonical correlations. Statistical Methods & Applications, 23, 189–208. UNESCO-IESALC (2021). Women in higher education: has the female advantage put an end to gender inequalities? UNESCO-IESALC Paris. Vaughn, A. R., Taasoobshirazi, G., & Johnson, M. L. (2020). Impostor phenomenon and motivation: Women in higher education. Studies in Higher Education, 45, 780–795. Vietnam Digital Landscape (2019). Available At: www.Slideshare.Net/Hoangdungquy/We-AreSocial-Vietnam-2019-Vietnam-Digital-Landscape-2019-Report. Accessed 16 March 2020 West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies.

Chapter 6

Quantitative Data Presentation and Analysis: Inferential Analysis

6.1 Introduction This chapter continues the process of quantitative data analysis by presenting the results of the inferential analysis, which includes Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and hypothesis testing using Structural Equation Modelling (SEM). Before analysing, the dataset was examined to determine any influential cases that may create adverse effects on the study results. Identifying the potential influence cases before analysing the data is vital as it can distort the study results. Thus, Harman’s single factor test was used to determine any effect of the standard method in the dataset. Then, to test the sample, Adequacy and Data Sphericity used the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity. Finally, the observed variables were identified, which support the study constructs through factor extraction and rotation. The second section reports the findings of confirmatory factor analysis and discussed the procedures of the measurement model validation. Structural equation modelling was used to further assess the scales utilised in this study. The structural model was tested for model fit, and then several modifications were introduced to the original model to improve the goodness-of-fit, and the second CFA revealed an acceptable model fit. The third section provides a detailed discussion of the structural model and the testing of the hypothesised causal relationships among the proposed model variables. First, it tested the relationships between UGC, FGC, SN, BC, and CBBE. Finally, the mediating effect of brand credibility and the moderating effect of location and brand usage experience was tested.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_6

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6 Quantitative Data Presentation and Analysis: Inferential Analysis

6.2 Exploratory Factor Analysis (EFA) Since the researcher has no prior evidence to identify the study constructs, EFA was used to uncover the underlying relationships between the constructs. This process also allowed the main dimensions of each construct to be examined to ensure independence among those constructs, and they all were measuring different attitudes.

6.2.1 Test for Common Method Bias Since the present study used a self-administered survey format, the bias in the responses and common method variance could inflate the relationships reported. Thus, a test was conducted for common method variance to identify the existence of such biases. Harman’s single factor test was used to determine whether the data set suffers from the common method issue (Liu & Jiang, 2020). In the extraction, all variables were loaded into one single factor and used an unrotated solution. The maximum variance explained by a single factor is 38.05%. Therefore, approximately a single factor describes 38% of the variance. Thus, we can conclude that this dataset does not suffer from a common method bias issue because the variance explained by a single factor is less than 50%.

6.2.2 Test of Sampling Adequacy and Data Sphericity The two main concerns when deciding the suitability of a particular data set for EFA are sample size and the pattern of relationships among the variables (Hair et al., 2014). The two statistical tests used were the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity. The KMO index usually ranges from zero to one, with a minimum value of 0.6 suggested for a good EFA. Still, higher values (close to one) indicate better sampling adequacy levels. The significance level for Bartlett’s test should be 0.05 or less to determine the usefulness of EFA for the data (Hair et al., 2014). Therefore, before conducting EFA, the KMO measure of sampling adequacy and Bartlett’s Test of Sphericity were performed to ensure the appropriateness of the data set for EFA. As per Table 6.1, the KMO measure of sampling adequacy exceeds the minimum acceptable value (0.951), indicating no problem with the sample size. Moreover, Bartlett’s test of Sphericity confirmed the significance value (p = 0.000), thus leading to reject the null hypothesis and conclude that an acceptable level of correlation exists among the variables in the data set, making the data appropriate for subsequent EFA. Hence, the quantitative data collected from the study sample supported the use of EFA.

6.2 Exploratory Factor Analysis (EFA) Table 6.1 KMO statistics and Bartlett’s test of sphericity

189 0.951

KMO measure of sampling adequacy Bartlett’s test of sphericity

Approx. Chi-Square

19676.235

df

561

Sig

0.000

6.2.3 Performing EFA A suitable approach to EFA was then determined. This involved establishing the factor extraction method, factor retention criteria, factor rotation method, and the interpretation of resulted factor loadings. The process was as follows: – Firstly, the precise factor extraction method was chosen to establish the minimum number of factors representing the associations among the set of variables in the best way (Pallant, 2013). – Secondly, concerning factor retention criteria, there are several approaches to determine the number of factors that best describe the underlying relationships among the study variables, including Kaiser’s criterion and Cattell’s screen test. Among other methods, Kaiser’s criterion, also known as the “eigenvalue-greater than one” rule—is found to be the most used. According to Pallant (2013), since eigenvalues refer to the amount of total variance explained by a factor, an eigenvalue of one or more denotes a significant amount of variation. On the other hand, Cattell’s screen test plots the eigenvalues and then checks where the plot curve changes to become horizontal. The suggestion is to retain all the factors above the curve’s elbow (Pallant, 2013). – Thirdly, the researcher rotated the resulting factors using the Varimax method to produce results in a simpler form.

6.2.4 EFA Results Principle component analysis was conducted to extract factors, and six factors were reported as eigenvalues greater than 01 (Fig. 6.1), and the total variance of those factors was 64.008%. That means other subsequent factors explain only a small amount of variance. Furthermore, Kaiser’s criterion and Cattell’s screen test were applied for factor extraction. The first step was to check commonalities between measured items to identify any problematic ones before proceeding to further analysis. According to Field (2009), commonalities represent the multiple correlations between variables and the factors extracted. Communality thus indicates how the extracted factors explain much variance of each original variable. Communality values usually range from zero to one. Still, higher commonalities are more desirable as variables with

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6 Quantitative Data Presentation and Analysis: Inferential Analysis

Fig. 6.1 Screen plot of factors extraction

high values are well represented in the extracted factors, whereas variables with low values are not. Moreover, in samples of more than 250, commonalities greater than or equal to 0.5 are considered good enough to ensure accurate results from Kaiser’s criterion test for the number of retained factors (Field, 2009; Jain & Raj, 2013). In this model, the commonalities are greater than 0.5, and it is a satisfactory condition for the EFA of those antecedents. Commonalities can be found in Appendix 6A. Based on the findings of the EFA, all five study constructs were retained. The initial grouping of those retained factors was also supported by these findings. Table 6.2 presents the study constructs and their measurement variables. Appendix 6B provides summaries for each construct characteristic resulting from EFA.

6.2.4.1

EFA for FGC

This study has run the EFA for FGC, using four factors, FGC1, FGC2, FGC3, and FGC4. In the rotated factor solution, each variable has a significant loading (loading is above 0.5), except for FGC4, which cross-loads on two factors (factor 1 and factor 2). Factor loadings for FGC1, FGC2, and FGC3 were 0.819, 0.870, and 0.872, respectively. Since FGC4 has cross-loading, it has loaded to 0.778 for factor 1 and 0.584 for factor 2. The correlation matrix shows that FGC4 has a high correlation with FGC1 (0.478), FGC2 (0.547), and FGC3 (0.574), indicating several high loadings of FGC4. Even though the rotated factor matrix improved upon the simplicity of the factor loadings, the cross-loading of FGC4 on factors 1 and 2 requires action. With the loading of 0.778 (factor 1) and 0.584 (factor 2), the cross-loading is so substantial as not to

6.2 Exploratory Factor Analysis (EFA) Table 6.2 Study constructs and measurement variables

191

Construct Observed variable Construct Observed variable FGC

FGC1 FGC2 FGC3 FGC4

UGC

UGC1 UGC2 UGC3 UGC4

BC

BC1 BC2 BC3 BC4 BC5

SN

SN1 SN2 SN3 SN4

CBBE

CBBE1 CBBE2 CBBE3 CBBE4 CBBE5 CBBE6 CBBE7 CBBE8 CBBE9 CBBE10 CBBE11 CBBE12 CBBE13 CBBE14 CBBE15 CBBE16 CBBE17

be ignorable. Variables with significant loading on at least two variables should be deleted from the analysis, and the loading recalculated (Hair et al., 2014). Accordingly, FGC4 was deleted to eliminate the cross-loading from the analysis by leaving FGC1, FGC2, and FGC3 in the study. Final factor loadings for FGC1, FGC2, and FGC3 were 0.860, 0.889, and 0.876. The KMO measure of sampling adequacy exceeds the minimum acceptable value (0.726), indicating no problem with the sample size. The principal component analysis was conducted for the extraction of factors, and the total variance of those factors was increased to 76.557%. Accordingly, this study has retained FGC1, FGC2, and FGC3 for the final analysis based on EFA.

6.2.4.2

EFA for UGC

This study has run the EFA for UGC, using four factors, UGC1, UGC2, UGC3, and UGC4. In the rotated factor solution, each variable has a significant loading (loading is above 0.5), except for UGC4, which cross-loads on two factors (factor 1 and factor 2). Factor loadings for UGC1, UGC2, and UGC3 were 0.860, 0.838, and 0.823, respectively. Since UGC4 has cross-loading, it has loaded to 0.780 for factor 1 and 0.607 for factor 2. Even though the rotated factor matrix improved upon the simplicity of the factor loadings, the cross-loading of FGC4 on factors 1 and 2 requires action. With the loading of 0.780 (factor 1), and 0.607 (factor 2), the cross-loading is so substantial as not to be ignorable. Variables with significant loading on at least two variables need to be deleted from the analysis, and the loading recalculated (Hair et al., 2014). Accordingly, UGC4 was deleted to eliminate the cross-loading from the analysis.

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6 Quantitative Data Presentation and Analysis: Inferential Analysis

The KMO measure of sampling adequacy exceeds the minimum acceptable value (0.715), indicating no problem with the sample size. The principal component analysis was conducted to extract factors, and the total variance of those factors was 74.373%. Accordingly, this study has retained UGC1, UGC2, and UGC3 for the final analysis based on EFA. Final factor loadings for UGC1, UGC2, and UGC3 were 0.880, 0.867, and 0.839.

6.2.4.3

EFA for SN

This study has run the EFA for SN, using four factors, SN1, SN2, SN3, and SN4. In the rotated factor solution, each variable has a significant loading (loading is above 0.5), except for UGC4, which cross-loads on two factors (factor 1 and factor 2). Factor loadings for SN1, SN2, and SN3 were 0.870, 0.842, and 0.741, respectively. Since SN4 has cross-loading, it has loaded to 0.735 for factor 1 and 0.658 for factor 2. Even though the rotated factor matrix improved upon the factor loadings’ simplicity, the cross-loading of SN4 on factors 1 and 2 requires action. With the loading of 0.735 (factor 1), and 0.658 (factor 2), the cross-loading is so substantial as not to be ignorable. Variables with significant loading on at least two variables need to be deleted from the analysis, and the loading recalculated (Hair et al., 2014). Accordingly, SN4 was deleted to eliminate the cross-loading from the analysis. The KMO measure of sampling adequacy exceeds the minimum acceptable value (0.676), indicating no problem with the sample size. The principal component analysis was conducted to extract factors, and the total variance of those factors was 67.974%. Accordingly, this study has retained SN1, SN2, and SN3 for the final analysis based on EFA. Final factor loadings for SN1, SN2, and SN3 were 0.851, 0.852, and 0.767.

6.2.4.4

EFA for BC

This study has run the EFA for BC, using five factors, BC1, BC2, BC3, BC4, and BC5. In the rotated factor solution, each variable has a significant loading (loading is above 0.5), except for BC5, which cross-loads on two factors (factor 1 and factor 2). Factor loadings for BC1, BC2, BC3, and BC4 were 0.830, 0.833, 0.863, and 0.799, respectively. Since BC5 has cross-loading, it has loaded to 0.621 for factor 1 and 0.775 for factor 2. Even though the rotated factor matrix improved upon the factor loadings’ simplicity, the cross-loading of SN4 on factors 1 and 2 requires action. With the loading of 0.621 (factor 1), and 0.775 (factor 2), the cross-loading is so substantial as not to be ignorable. Variables with significant loading on at least two variables need to be deleted from the analysis, and the loading recalculated (Hair et al., 2014). Accordingly, BC5 was deleted to eliminate the cross-loading from the analysis.

6.2 Exploratory Factor Analysis (EFA)

193

The KMO measure of sampling adequacy exceeds the minimum acceptable value (0.810), indicating no problem with the sample size. The principal component analysis was conducted to extract factors, and the total variance of those factors was 73.414%. Accordingly, this study has retained BC1, BC2, BC3, and BC4 for the final analysis based on EFA. Final factor loadings for BC1, BC2, BC3, and BC4 was 0.843, 0.895, 0.885, and 0.801.

6.2.4.5

EFA for CBBE

First, this study has run the EFA for CBBE, using 17 factors, which are CBBE1, CBBE2, CBBE3, CBBE4, CBBE5, CBBE6, CBBE7, CBBE8, CBBE9, CBBE10, CBBE11, CBBE12, CBBE13, CBBE14, CBBE15, CBBE16, and CBBE17. Only the factors with a loading above 0.5 were considered for analysis (Hair et al., 2014). Accordingly, CBBE1, CBBE7, and CBBE 14 were removed due to their poor loadings, and the rest of the factors were used to measure CBBE in the proposed model (Table 6.3). CBBE does not show any factor with cross-loading and no deletion of the factors based on cross-loading. The KMO measure of sampling adequacy exceeds the minimum acceptable value (0.957), indicating no problem with the sample size. The principal component analysis was conducted to extract factors, and five factors were reported as eigenvalues greater than 01, and the total variance of those factors was 77.502%. Table 6.3 Factor loadings of CBBE measurement items Component 1

2

3

4

5

0.712

CBBE2 CBBE3

0.783

CBBE4

0.694 0.699

CBBE5 CBBE6

0.623

CBBE8

0.760

CBBE9

0.736

CBBE10

0.625

CBBE11

0.680

CBBE12

0.612

CBBE13

0.745

CBBE15

0.736

CBBE16

0.785

CBBE17

0.643

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6 Quantitative Data Presentation and Analysis: Inferential Analysis

The basic idea of having a CBBE model is to measure the strength of the customerbrand relationship based on the customers’ feelings, attitudes, thinking, and actions towards a respective brand. CBBE in the service sector includes a series of factors to build strong service brands such as performance, social image, value, trustworthiness, and attachment. The multi-dimensional CBBE concept facilitates capturing the customers’ deep and unique association with a brand while encompassing its total utility throughout the combination of all the above factors instead of isolating specific factors. The present study uses CBBE as a multi-dimensional construct where all these factors contribute to testing the CBBE in the service context with the other study constructs.

6.2.5 EFA for the Final Data Set After removing the factors based on the EFA on each study construct, this study has conducted EFA for the final data set. The final data set consists of 27 factors. This study has identified that the final dataset does not suffer from the common method bias issue as the maximum variance explained by a single factor is 38.3% ( 0.7, CR > 0.7, and AVE > 0.5 (Hair et al., 2014). Although SRW was provided by AMOS 23, CR and AVE were not and were calculated manually. Moreover, since CR estimations were calculated in the previous section, the AVE for each construct was calculated using the following formula:  AVE =

n 

 λ2i

/n

i=1

where λ represents the standardised regression weight and n is the total number of observed variables (Fornell & Larcker, 1981).

6.5.2.2

Assessment of Discriminant Validity

Discriminant validity requires that two sets of measure items intending to measure two distinct constructs are not correlated (Hair et al., 2014). In the present study, the discriminant validity of the constructs was assessed by comparing the AVE of each construct with squared inter-construct correlations for that construct—the outcome of reliability and validity tests are presented in Table 6.9. Construct reliability was assessed through both Cronbach alpha coefficients and composite reliability (CR) values. As all the Cronbach alpha and CR values coefficients were higher than the threshold value of 0.7, construct reliability was supported (Hair et al., 2014). The AVE was assessed because it is the summary indicator of convergence. All the AVE values were higher than the threshold value of 0.5, thereby supporting the convergent validity (Hair et al., 2014). Discriminant validity was evaluated by comparing the square root of the AVE of each construct with the bivariate correlations among constructs. A measurement Table 6.9 Validity results AVE

MSV

BC

BC

0.650

0.406

0.806a

FGC

0.649

0.355

0.443b

0.806a

UGC

0.626

0.257

0.423

0.452

0.791a

Norms

0.559

0.078

0.266

0.149

0.260

0.747a

CBBE

0.841

0.406

0.637

0.596

0.507

0.279

a b

The square root of AVE in the diagonal Pearson correlations among constructs

FGC

UGC

Norms

CBBE

0.917a

6.6 The Structural Model: Structural Equation Modelling (SEM)

203

model is considered to have acceptable discriminant validity if the square root of the AVE of each construct is higher than any of the bivariate correlations among the constructs (Iglesias et al., 2019). As all the square roots of AVE were higher than the bivariate correlations among the constructs, discriminant validity was supported (Iglesias et al., 2019). Maximum Shared Variance (MSV) should be less than the AVE.

6.6 The Structural Model: Structural Equation Modelling (SEM) Having established the measurement model goodness-of-fit and confirmed the validity of all relevant constructs, the focus of the analysis then shifted towards assessing the causal relationships among these constructs.

6.6.1 The Structural Model Evaluation: Goodness-of-Fit Based on the hypothesised theoretical relationships, a structural model was constructed, as shown in Fig. 6.5, for further SEM analysis.

Fig. 6.5 The structural model

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Table 6.10 Structural model goodness-of-fit indices Obtained

Cut-off

Normed Chi-square (χ2 )

3.447

0≤

RMSEA

0.050

≤0.05

Acceptable

Associated P Close

0.561

≥0.5

Acceptable

GFI

0.926

≥0.90

Acceptable

AGFI

0.910

≥0.90

Acceptable

NFI

0.933

≥0.90

Acceptable

CFI

0.951

≥0.95

Acceptable

IFI

0.952

≥0.90

Acceptable

Fit index Absolute

Increment Tal

x df

Comment ≤ 3; 5

Acceptable

The results indicated that the structural model provides a good overall fit with the data, as displayed in Table 6.10. Appendix 6E provides the full model-fit summary for the first run of SEM. The first run of the structural model revealed acceptable model fit: χ2 = 3.447, root mean square error of approximation (RMSEA) = 0.05, Associated P Close = 0.561, goodness-of-fit index (GFI) = 0.926, Adjusted Goodness-of-fit Index (AGFI) = 0.910, normed fit index (NFI) = 0.933, comparative fit index (CFI) = 0.951, and incremental fit index (IFI) = 0.952. These fit indices that the structural model has a good fitting.

6.6.2 Testing Research Hypotheses After successfully validating the structural model’s goodness-of-fit to the data, the next step was to examine the research hypotheses using path measurement coefficients (regression weight estimates and critical ratios) from the SEM analysis performed with AMOS 23. Table 6.11 summarises these results, which indicates that hypothesised causal paths in the structural model were significant at the 0.05 level. Table 6.11 Path coefficient weights for the structural model Estimates

Hypotheses

CR

p-value

Comment

Code

Path

H1

UGC −→ BC

0.237

6.459

***

Supported

H2

FGC −→ BC

0.342

8.781

***

Supported

H3

SN −→ BC

0.161

4.811

***

Supported

H4

BC −→ CBBE

0.560

17.607

***

Supported

***

is significant at 0.05

6.6 The Structural Model: Structural Equation Modelling (SEM)

205

Hypothesis H1 Hypothesis 1 suggested that user-generated content would have a positive influence on brand credibility. Thus, this hypothesis has tested the impact of user-generated content on the HEIs’ brand credibility from the undergraduates’ perspective in Sri Lanka and Vietnam. The causal path between the two constructs revealed a significant positive influence at a level of p < 0.05 (p = 0.000). The estimated path coefficient was 0.237, and the critical ratio = 6.459, which is greater than 1.96, indicating the estimated path parameter is significant. Therefore, the null hypothesis fails to be accepted, and the alternate hypothesis is accepted (UGC positively influences BC). Any increase in UGC would positively impact the undergraduate’s intention towards the credibility of HEI brands. Hypothesis H2 Hypothesis 2 tested the influence of firm-generated content (FGC) on HEIs’ brand credibility. The causal path between the two variables disclosed a significant positive influence at a level of p < 0.05 (p = 0.000). The estimated path coefficient was 0.342, and the critical ratio = 8.781, which is greater than 1.96, indicating the estimated path parameter is significant. Accordingly, the null hypothesis fails to be accepted, and the alternate hypothesis is accepted, indicating that FGC positively influences brand credibility. In other words, any increase in FGC would positively impact the undergraduate’s perception of HEIs’ brand credibility. The results revealed that the undergraduates rely more on the content shared by the HEIs than the users in believing the information contained about HEI brands. Based on the content created by the HEIs, undergraduates perceive that a particular HEI brand has the ability and willingness to deliver what it has been promised continually. Undergraduates may perceive FGC to be more credible as it originates from HEIs than UGC as UGC is based on the online users’ personal experiences, which is not always true and correct. The previous researchers also argued that the UGC includes the contribution from a blend of amateur, semi-professional, and professional people (Stehling et al., 2018), which confronted with the tricky task of evaluating the opinions of strangers (Hernández-Ortega, 2018; Lo & Yao, 2019), and the undergraduates consider FGC is worth to rely on than UGC. Hypothesis H3 Hypothesis 3 tested the influence of subjective norms (SN) on HEIs’ brand credibility. The estimated path coefficient was 0.161, and the critical ratio = 4.811, which is greater than 1.96, indicating the estimated path parameter is significant. The pvalue was 0.000 (p < 0.05), showing a lack of support for the null hypothesis and supports the alternative hypothesis, which infers that SN has a positive direct effect and strongly influences the HEIs’ brand credibility; i.e., any increase in SN would positively influence the undergraduate’s intention towards HEI brands’ credibility. Undergraduates tend to rely on the HEIs’ credibility based on the pressure they receive from society. They seem to believe and follow an individual or group of people to develop or enhance their perception of HEIs’ brand credibility. It shows

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subjective norms’ ability to influence the undergraduates’ beliefs on how and what they think about the HEI brands. Even though the previous studies noted that the inconsistencies in the significance of the subjective norms direct the individuals in the wrong direction (Gelfand & Jackson, 2016; Kim, 2018), the undergraduates believe that the subjective norms are essential for their decisions on HEIs. The normative influence is greatly considered to maintain the undergraduates’ public dignity and social standards. Even though each individual has a different set of beliefs and behaviours, which is hard to analyse to identify the best option, the undergraduates engage in believing the HEIs’ brand credibility is based on the perceived social pressure at large. In that sense, the HEIs should be able to create a trusted position in society, as the undergraduates are mostly influenced by the actions of their surrounding society. Hypothesis H4 This hypothesis tested the influence of brand credibility (BC) on customer-based brand equity (CBBE). The causal path between the two variables disclosed a significant positive influence at a level of p < 0.05 (p = 0.000). The estimated path coefficient was 0.560, and the critical ratio = 17.607, which is greater than 1.96, indicating the estimated path parameter is significant. As shown in Table 6.11, the causal path between the two constructs revealed significant influence at a level of p < 0.05, indicating a lack of support for the null hypothesis. These results support the alternate hypothesis H4, which suggests that BC positively influences CBBE. Undergraduates believe that the HEIs’ ability and willingness to provide a superior performance creates a significant effect on the value endowed with the HEI brands. Higher brand credibility increases undergraduates’ perceptions of the utility of the HEIs’ brand, thus adding customer value to a brand.

6.6.3 Testing the Mediating Effect of Brand Credibility As Baron and Kenny (1986) and Spry et al. (2011) identified, every study needs to satisfy four conditions to consider a variable as a mediating variable: (1) the predictor variable (user-generated content, firm-generated content, and subjective norms) should significantly influence the mediating variable (brand credibility), (2) the mediating variable (brand credibility) should significantly affect the dependent variable (customer-based brand equity), (3) the predictor variable (user-generated content, firm-generated content, and subjective norms) should significantly influence the dependent variable (customer-based brand equity), and (4) the effect of the predictor variable (user-generated content, firm-generated content, and subjective norms) on the dependent variable (customer-based brand equity) should no longer be significant (full mediation) or weaken (partial mediation) after we control the mediating variable (brand credibility).

6.6 The Structural Model: Structural Equation Modelling (SEM)

207

Table 6.12 Direct and indirect effect of the mediator Direct effect

Hypotheses Code

Indirect effect

Mediation

Path

H5a

UGC −→ BC −→ CBBE

0.239***

0.177***

Partial

H5b

FGC −→ BC −→ CBBE

0.344***

0.180***

Partial

H5c

SN −→ BC −→ CBBE

0.103***

0.138***

Partial

***

Shows significance at 0.05. N/S, not significant

According to these findings, the present study meets all four conditions, which need a variable to be a mediator. The outcomes showed a significant positive effect of (1) user-generated content and brand credibility, (2) firm-generated content and brand credibility, and (3) subjective norms and brand credibility satisfying the first condition. This study found that brand credibility positively impacts customer-based brand equity (Table 6.11), satisfying the second condition. Besides, the positive relationship between (1) user-generated content and customer-based brand equity, (2) firmgenerated content and customer-based brand equity, and (3) subjective norms and customer-based brand equity was identified (Table 6.11), satisfying the third condition as well. Finally, the effect of brand credibility after controlling as a mediator was analysed (Table 6.12) using the AMOS. Therefore, brand credibility was considered as the mediating variable. Logically, the mediation variable is deemed to have the influence to increase or decrease the causal effect of independent on the dependent variable. Table 6.12 presents the effect of the mediating variable. First, it revealed that UGC was positively associated with CBBE (B = 0.239, t = 8.673, p = 0.000). Then, it also identified that UGC was positively related with BC (B = 0.391, t = 11.293, p = 0.000). Lastly, the results indicated that BC was positively associated with CBBE (B = 0.453, t = 14.205, p = 0.000). Since the paths between UGC-BC and BC-CBBE were significant, mediation analyses were tested using the bootstrapping method with bias-corrected confidence estimates (MacKinnon et al., 2004; Preacher & Hayes, 2004). In the present study, the 95% confidence interval of the indirect effects was obtained with 5000 bootstraps resamples (Preacher & Hayes, 2008). Results showed that the direct effect between UGC and CBBE was B = 0.239 (p = 0.000), and the indirect effect became significant after including BC as the mediating variable (B = 0.177, p = 0.000), signifying a partial mediating effect of BC supporting H5a. Similarly, the association between FGC and BC was identified (B = 0.440, t = 11.824, p = 0.000). It showed that FGC was positively related to CBBE (B = 0.344, t = 11.395, p = 0.000). Lastly, the results indicated that BC was positively associated with CBBE (B = 0.453, t = 14.205, p = 0.000). Since the paths between FGC-BC and BC-CBBE were significant, mediation analyses were tested using the bootstrapping method with bias-corrected confidence estimates (MacKinnon et al., 2004; Preacher & Hayes, 2004).

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In the present study, the 95% confidence interval of the indirect effects was obtained with 5000 bootstraps resamples (Preacher & Hayes, 2008). Results showed that the direct effect between FGC and CBBE was B = 0.344 (p = 0.000), and the indirect effect became significant after including BC as the mediating variable (B = 0.180, p = 0.000), signifying a partial mediating effect of BC supporting H5b. Finally, the mediation effect of BC between SN and CBBE was identified. It revealed that SN was negatively associated with CBBE (B = 0.103, t = 3.865, p = 0.000). It was also identified that SN was significantly related to BC (B = 0.258, t = 7.041, p = 0.000). Lastly, the results indicated that BC was positively associated with CBBE (B = 0.453, t = 14.205, p = 0.000). The mediation analyses were then tested using the bootstrapping method with bias-corrected confidence estimates (MacKinnon et al., 2004; Preacher & Hayes, 2004). In the present study, the 95% confidence interval of the indirect effects was obtained with 5000 bootstraps resamples (Preacher & Hayes, 2008). The results showed that the direct effect between SN and CBBE was B = 0.103 (p = 0.000), which is significant. The results further indicated that the indirect effect became significant after including BC as the mediating variable (B = 0.138, p = 0.000), which indicates a partial mediating effect of BC supporting H5c.

6.6.4 Moderating Effect of Location and Brand Usage Experience With the advancement of researchers’ disciplines, the hypothesised relationship becomes more complex, and they are more interested in assessing moderating effects rather than direct effects (Henseler & Fassott, 2010). Once a variable strengthens the interaction effect between independent and dependent variables, it is called a moderator variable (Baron & Kenny, 1986). Different factors can be used as a moderator to show system success (Olugbola, 2017). Moderator variable can be measure categories such as multi-group comparison (Jiang & Liang, 2021). The moderating effect can compare two or more different groups, especially if one of the independent variables or moderators is not continuous (Molenaar, 2020). Multigroup analysis in SEM is widely used to test the moderating effect of the variables. It allows for comparing multiple samples across multiple population groups for any identified structural equation model (Byrne, 2016). In other words, it tests whether different groups differ significantly by assuming that they are equal by examining their invariances (Molenaar, 2020). The present study also used multi-group analysis in SEM to test the moderating effect of location (Sri Lanka vs. Vietnam) and brand usage experience (Junior vs. Senior). In order to test the moderating effect of location and brand usage experience, the multi-group structural equation modelling (SEM) was adopted to investigate

6.6 The Structural Model: Structural Equation Modelling (SEM)

209

Table 6.13 Moderating effect of location Hypotheses

Estimates

Code

Path

Sri Lanka

H6a

UGC −→ BC

H6b

FGC −→ BC

H6c

SN −→ BC

H6d

BC −→ CBBE

***

CR (P)

Result

Vietnam

Sri Lanka

0.269

0.151

4.815***

2.783***

Supported

0.245

0.404

4.473***

7.228***

Supported

0.260

−0.126 (0.900)

5.616***

Not supported

0.476

6.767***

−0.03 0.677

Vietnam

16.427***

Supported

p < 0.05

whether the paths in the structural model were significantly different across the junior and senior undergraduates in Sri Lanka and Vietnam. Location is discussed as a moderator, and multi-group comparison is employed to compare the results of the Sri Lankan and Vietnamese datasets. This study created two groups in SEM, grouping the data collected from Sri Lanka and Vietnam. Using multi-group analysis in SEM, this study tested how the undergraduates’ perception of the relationship between UGC, FGC, subjective norms, brand credibility, and CBBE varies based on the location. The study further identified the moderating effect of brand usage experience through multi-group analysis. In determining its moderating effect, this study grouped the students based on their studying year. Students studying in 1st and 2nd years were grouped as junior students, and 3rd and 4th year students were grouped as senior students. Then, we identified the different perspectives of juniors and seniors on the relationship between the study constructs. These results demonstrated that location and brand usage experience moderated the direct relationships between UGC, FGC, subjective norms, brand credibility, and CBBE, but the moderating effect varied depending on the perception of the junior and senior students in Sri Lanka and Vietnam. This study identified the moderating effects of location (Sri Lanka vs. Vietnam) and brand usage experience (Junior students vs. Senior students) through multigroup analysis using AMOS. The moderating effect of the location and brand usage experience is presented in Tables 6.13 and 6.14, respectively.

6.6.4.1

Moderating Effect of Location

H6a is to test the moderating effect of location on the impact of UGC on BC. The estimated path coefficients of Sri Lanka and Vietnam are 0.269 and 0.151, respectively. The critical ratio for both countries were 4.815 and 2.783 (>1.96), respectively, which are significant at 0.05 (p = 0.000), supporting H6a. Regression coefficient divided by

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Table 6.14 Moderating effect of brand usage experience Estimates

Hypotheses

CR (P)

Result

Code

Path

Junior students

Senior students

H7a

UGC −→ BC

0.233

0.218

4.614***

4.056***

Supported

H7b

FGC −→ BC

0.404

0.287

7.181***

5.272***

Supported

H7c

SN −→ BC

0.197

0.127

4.144***

2.690***

Supported

H7d

BC −→ CBBE

0.578

0.531

13.686***

15.052***

Supported

***

Junior students

Senior students

p < 0.05

standard deviation yields the critical ratio with absolute numbers < 1.96, equating to non-significance (Gao et al., 2008; Mandrell et al., 2018). In comparing both countries, Sri Lankans are highly relying on UGC on identifying the credibility of a brand than their Vietnamese counterparts. H6b tested the moderating effect of location between FGC and BC. The estimates between FGC and BC in Sri Lanka and Vietnam were 0.245 and 0.404. The critical ratios were significant at the 0.05 level (p = 0.000), supporting H6b. H6c suggested that location moderates the relationship between SN and BC. However, the critical ratio for Sri Lanka is −0.126 ( 0.05), indicating that the path is not significant at the level of 0.05. But, in the Vietnam context, a significant positive moderating effect is noted between SN and BC [estimate path coefficient = 0.260, and critical ratio = 5.616 (p = 0.000)], indicating that Vietnamese are relying on the credibility of the brands based on the norms shared by the community than the Sri Lankan counterparts. H6d is to test the moderating effect of location on the impact of BC on CBBE. The estimated path coefficients of Sri Lanka are 0.677 and 0; 0.476 for Vietnam. The critical ratio for both countries were 6.767 and 16.427 (>1.96), which are significant at 0.05 (p = 0.000), supporting H6d. In comparing both countries, the location creates a higher moderating effect on Sri Lankans than their Vietnamese counterparts. According to the findings depicted in Table 6.13, Sri Lankans depend more on UGC than Vietnamese in developing BC for HEIs. Sri Lankans believe that the UGC could influence the performance, social image, value, trustworthiness, and attachment of an HEIs brand. Sri Lankans are possibly fostering a deeper connection with the brand community members while interacting with like-minded people to have the experience in selecting a specific HEI brand than the Vietnamese. In terms of FGC, Sri Lankans are lagging in following the information published by HEIs on the official social media pages than Vietnamese. Vietnamese seems to have relied more on information provided by the HEIs on their social media pages than the user-created contents related to HEIs.

6.6 The Structural Model: Structural Equation Modelling (SEM)

211

Interestingly, both countries depend on UGC and FGC. Even though there are differences in adopting UGC and FGC between Sri Lanka and Vietnam, the content shared by the other users on different SNS and information shared HEIs create a higher impact on both countries’ perceptions of the brand’s credibility. Both Sri Lankans and Vietnamese believe the UGC and FGC provide accurate, high-quality, and technical information to access a brand’s credibility. The results show that SN creates an impact on BC only among Vietnamese. Vietnamese rely on the social pressure they receive from others to identify the HEIs’ ability to provide a promised service continuously. But Sri Lankans do not consider others’ opinions or do not follow others’ behaviours in evaluating HEIs’ expertise and trustworthiness in providing services. Also, both Sri Lankans and Vietnamese assume that BC is vital in developing CBBE; but Sri Lankans rely more on BC than their Vietnamese counterparts in believing the perceived value of a brand. Maybe Sri Lankans rely more on the brand’s credibility as they are keen on providing the promised satisfaction than Vietnamese and increased the expected utility of a brand, thus adding value to the brand.

6.6.4.2

Moderating Effect Brand Usage Experience

This study tested the moderating effect of brand usage experience (Table 6.14), dividing students into juniors and seniors. H7a suggested that brand usage experience moderates the relationship between UGC and BC. The estimated path coefficients are 0.233 and 0.218 for junior and senior students, respectively. The critical ratio for juniors and seniors were 4.614 and 4.056 (>1.96), which are significant at 0.05 (p = 0.000), supporting H7a. H7b tested the moderating effect of brand usage experience between FGC and BC. The estimates between FGC and BC among junior and senior students were 0.404 and 0.287. The critical ratios were significant at the 0.05 level (p = 0.000), supporting H7b. H7c suggested that brand usage experience moderates the relationship between subjective norms and brand credibility. The estimated path coefficients are 0.197 and 0.127 for junior and senior students, respectively. The critical ratios for junior students and senior students were 4.144 and 2.690 (p = 0.000), respectively, supporting H7c. H7d also shows a significant moderating effect of brand usage experience between brand credibility among junior and senior students, and the estimated path coefficients were 0.578 and 0.531, respectively. The critical ratios were 13.686 and 15.052 (p = 0.000), supporting H7d. According to the results given in Table 6.14, brand usage experience has a moderating effect on both junior and senior students’ perception of UGC, FGC, BC, SN, and CBBE, supporting H7a, H7b, H7c, and H7d. When comparing the junior students (1st and 2nd year) and senior students (3rd year, and more), junior students rely on UGC and FGC more than senior students in evaluating HEIs’ brand credibility. Since junior students lack experience with HEI brands, they tend to review and obtain more information from the other users on

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social media while depending on the information shared by HEIs on their official social media pages. During their time of being in the HEI, senior students gather more experience. They become familiar with the HEI so that senior students can identify whether the HEI has the ability and willingness to provide the promised service to their stakeholders. Therefore, senior students do not require to depend more on UGC and FGC than junior students. Both junior and senior students may believe that the interaction between the social media users helps gather real experience about the HEIs, which the HEIs do not publicly publish. Since the junior students have spent a shorter period in HEIs, they are not familiar with the HEIs. Therefore, junior students depend on the information shared by other stakeholders and HEIs. Since the senior student poses higher familiarity with the HEIs, they can evaluate the information shared on the HEIs’ official Facebook pages. However, when comparing UGC and FGC from the perspective of senior students, senior students assume FGC has the most reliable and up-to-date information coming from HEIs than other users. The limited experience with HEIs leads junior students to follow others’ behaviour and perform based on the pressure they received from society. They tend to believe the HEI based on the opinions of others. Senior students assume that social behaviour could make a higher impact on the undergraduate’s perception of the HEIs’ ability to provide a better service to the stakeholders but less than junior students. Both depend on subjective norms in evaluating HEIs’ credibility, but senior students seem to follow norms less than juniors. This may be with the senior students’ willingness to diverge from the social proof of the situation and experience new opportunities. Senior students are more likely to be autonomy, disregard the ordinary instructions or the usual order of doing things because they may be confident about their decisions without following others’ opinions. But with time, when juniors become seniors, the level of maturity bestowed them to believe in themselves and do not behave according to social pressure. Both junior and senior students perceive brand credibility as an important factor in developing CBBE. But, comparatively, according to the estimates, junior students believe brand credibility is more important to CBBE than seniors. Junior students believe credible HEI brands can deliver the promised service, which, in turn, increases the students’ perception of the performance of the HEIs. So, the HEIs could add value to their brand to convince students to select the HEI later.

6.7 Covariance Among UGC, FGC, and SN Social media can not only stimulate discussion between friends, family, colleagues, and neighbours, but also with strangers, which is likely to influence people’s perceived norms and their expectations concerning the behaviour. The content shared by users and firms on social media allows users to see others’ actions, which may stimulate them to take steps independently. Furthermore, the feedback received through social media communication can nudge people to adopt certain behaviours. Ho et al.

6.8 Summary Table 6.15 Covariance among UGC, FGC, and SN

213 Covariance

Estimates

CR (P)

Comment

UGC ←→ SN

0.346

5.465***

Covariance exists

FGC ←→ SN

0.184

3.625***

Covariance exists

***

p < 0.05

(2017) purport that the relationship between the content generated in social media and subjective norms is increasingly complicated, whereas sometimes users “Like” posts that they do not actually like. Further, Han et al. (2018) noted that active communication in online platforms increases norms among online communities. This study has tested the covariance among UGC, FGC, and SN, which has not received much attention in the literature (Table 6.15). Covariance between usergenerated content and subjective norms was significant at p < 0.05 (estimated path coefficient was 0.346, and the critical ratio was 5.465 (p = 0.000)). The covariance between firm-generated content and subjective norms was also significant, and the estimate path coefficient was 0.184, while the critical ratio was 3.625 (p = 0.000). More UGC and FGC on social media will increase the norms shared by the online community, which, in turn, influence the behaviour of the online users. Although previous studies reveal that the content generated on social media can influence the scope of a person’s original intent, results in this study showed that both UGC and FGC could influence the subjective norms in the context of higher education. The findings further revealed that in higher education, user and firmgenerated content could affect the undergraduates’ behaviour. The undergraduates do not seem to be free in their decisions to follow their own constrained preferences in the performance of their roles.

6.8 Summary This chapter has presented the inferential analysis results, focusing on the EFA, CFA, and hypothesis testing results. It has discussed how, before conducting EFA, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity were performed to ensure the appropriateness of the data set to EFA. The results showed that the data collected from the study sample supported the use of EFA. After that, SEM analysis was performed via a two-step approach. In the first step, a CFA measurement model was developed and then tested for composite reliability and construct validity. After some model rectifications, the CFA results revealed acceptable goodness-of-fit indices for the measurement model. Based on the results of CFA, a structural model was developed and tested to examine the hypothesised causal relationships among the latent constructs in the proposed research model. The goodness-of-fit indices indicated the structural model

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to provide an acceptable level of overall fitness with the empirical data. Moreover, the path coefficients were examined to test the research hypotheses. The structural model also exhibited adequate fit, and the significance of the path estimate indicated the support of seven tested hypotheses across the Sri Lankan and Vietnamese samples. This partial support of the hypothesised relationships is further discussed in the next chapter, along with the meaning of the theoretical framework development results.

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Chapter 7

Discussion

7.1 Introduction This chapter provides an overview of the study findings and compares the results with prior research work discussed in Chap. 2. It concentrates on how these findings provide answers to the research questions, and in turn, meet the objectives of the study. Additionally, it gives an interpretation of the research findings presented in Chaps. 5 and 6. This chapter is divided into four sections, including this introduction (Sect. 7.1). Section 7.2 provides an overview of the present study and links them to the research objectives described in Chap. 1, providing the findings of hypotheses. Differences among the Sri Lankans and Vietnamese based on their demographic factors are detailed in Sect. 7.3, and the theoretical and practical contributions are detailed in Sect. 7.4.

7.2 Overview of the Study CBBE has become one of the most popular phenomena among researchers in the last few years. The researchers have attempted to empirically identify the factors that influence the CBBE towards products in different contexts. In addition, the SMM concept has been previously carried out, yet previous researchers have not identified the relationship between SMM and CBBE in the service sector, particularly in the higher education sector. Therefore, this study has developed research objectives and the research questions to fulfil the research gaps discussed in Chap. 1 through the empirical findings of the present study. With the background above, the present study focused on providing a better understanding of the undergraduates’ perception of SMM, subjective norms, and brand credibility and their subsequent influence on CBBE in the higher education sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_7

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Moreover, this investigation performed a comparative analysis between Sri Lanka and Vietnam and adopted a rational and comprehensive methodology recommended for this empirical study. Therefore, this study’s findings have made a considerable value addition to the existing theories and practical knowledge relating to social media marketing and brand equity related to the service sector, notably, higher education. Previous researchers have studied these concepts mainly in the context of developed countries such as the USA, Australia, and Europe (Alsaleh et al., 2019; Guritno et al., 2020). Very few studies have focused on emerging Asian countries, including Sri Lanka and Vietnam (Tarsakoo & Charoensukmongkol, 2019). Many studies have noted that higher education sectors focus on branding due to the competition among the HEIs to attract prospective students (Sataøen, 2019; Clark et al., 2020). Emerging countries such as Sri Lanka and Vietnam have experienced the same competitive pressure in their higher education sectors, witnessing critical changes such as privatisation, mergers, and acquisitions (Lomer et al., 2018). This has made the HEIs focus on building brand equity through SMM to influence the prospective students’ HEIs selection. Thus, user-generated content, firm-generated content, subjective norms, and brand credibility have been established as antecedents of CBBE related to HEIs in emerging Asian countries. Then, the differences among the undergraduates’ perceptions towards social media and branding have been identified by comparing the responses collected from Sri Lankan and Vietnamese undergraduates. The impact of undergraduates’ familiarity with HEIs in creating and enhancing CBBE was analysed using students’ brand usage experience. In order to gain a better understanding of the study constructs and the theoretical model, the focus has been shed on the research questions of the study while highlighting the findings to fill the existing gaps in the literature.

7.2.1 Discussion and Implications: Research Question 1 “What is the extent to which social media marketing, subjective norms, and brand credibility are related to customer-based brand equity, as perceived by prospective undergraduate students”? Research question 1 was examined by testing four hypotheses: H1, H2, H3, and H4. Hypothesis H1: Influence of user-generated content on brand credibility In the proposed theoretical model, H1 has proposed to test the relationship between user-generated content and brand credibility from the undergraduates’ perspective in emerging countries. The finding suggested that customers in emerging countries pursue user-generated content as an essential factor in determining the credibility of a brand. Further, this result indicated that user-generated content is a strong predictor of the brand credibility of HEIs, implying that a high level of engagement with user-generated content enables undergraduates to identify the trustworthiness and

7.2 Overview of the Study

219

expertise of an HEI brand. Higher UGC on social media would influence the credibility of HEI brands in emerging countries. This finding agrees with the earlier finding of Jiménez-Barreto et al. (2020), who found that user-generated content showed a significant positive influence on credibility in the tourism industry. This led them to suggest that travel-related user-generated content increases the credibility of the services provided by the hotels, which in turn helps hoteliers to develop marketing strategies to attract more tourists. Kitirattarkarn et al. (2019) also noted that UGC is one of the key determinants to influence the customers’ perception of branding. In addition, Viswanathan et al. (2018) argued that customers’ involvement with the brand co-creation activities on social media enhances its credibility among potential buyers. The usage of UGC, through a variety of social networking sites, informs the quality of the service of higher education sectors and triggers prospective students to believe the higher education institutes without physically visiting the institute. Thus, the ability and willingness to provide the promised service continuously by HEIs will facilitate the institutes to stay ahead among the competitors. Furthermore, prospective students mainly depend on the UGC to assess the HEI’s quality to build their trust as they are widely using social media to gather the required information. Therefore, HEIs need to pursue the stakeholders of HEIs such as students, alumni, lecturers, and parents to create influential content on social media to exhibit the credibility of the services provided by the HEIs. This could help the HEIs to create a better awareness among the existing and prospective students. Hypothesis H2: Influence of firm-generated content on brand credibility In the proposed theoretical model, H2 was to test the relationship between firmgenerated content and brand credibility from the perspective of undergraduates in emerging countries. The empirical findings of this study showed that FGC is one of the strong predictors of brand credibility relating to HEIs in emerging countries. The above findings of the present study support the conclusions of Osei-Frimpong and McLean (2018), which revealed that FGC on different social media platforms facilitates direct customer-firm interaction, which in turn changes customers’ perception and practices related to engagement with brands on social media. This kind of active interaction with brands on social media platforms would allow customers and firms to share their explicit and implicit knowledge, among others. Similarly, Goh et al. (2013) also found that FGC on social media significantly increases the customers’ brand purchase intention through embedded information and persuasion. The use of FGC would reduce the uncertainty level of selecting an HEI and positively influence the undergraduates’ perceptions of the institute. Therefore, higher education sectors in emerging countries should develop more aggressive approaches to create more FGC on various social networking sites. This suggests that the more content the HEIs make on their social media pages will positively influence the undergraduates’ perception towards the institute’s ability and willingness in providing promised service continuously than the competitors. This facilitates the HEIs to reach the prospective students to enhance their awareness, interest, and quality and shape their perception of the credibility of their preferred HEIs.

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FGC can target the individual students by creating and delivering personalised messages and services while creating a strong relationship with the current and prospective students based upon their responses. Hence, this FGC will help build a one-to-one relationship with each student while addressing their requirements. Thus, it allows the HEIs to attract prospective students while retaining current students and working towards getting their loyalty to enhance the institute’s brand credibility. According to this study’s findings, HEIs in emerging countries could use FGC to develop and maintain a one-to-one relationship with the students through all the available means of social networking sites. Therefore, HEIs must provide opportunities to motivate the current and prospective students to actively engage with the content created by the institutes without any obligation. Hence, prospective students could view the official information, reviews, and recommendations provided by the other stakeholders before they decided to enrol. Therefore, well-designed FGC on different social media platforms could enhance the prospective students’ familiarity with the HEI to get necessary knowledge about the institute to increase their favourable attitudes towards the trustworthiness and expertise of the HEI than the competitors. This could be done among the different faculties to enhance the institute’s visibility on a variety of social networking sites while providing the most updated, reliable, and relevant information. Hypothesis H3: Influence of subjective norms on brand credibility In the present study, hypothesis H3 confirmed that subjective norms have a positive impact on the undergraduates’ perception of the credibility of the HEI brand. Hence, undergraduates need to be motivated to follow the behaviour of the people who are considered to be necessary, while accepting the social pressure received to follow that behaviour, to believe the ability and expertise in providing promised service continuously by the selected HEI than the competitors. In emerging Asian countries where the people have a collectivist cultural background, subjective norms could act as one of the most influential factors in identifying the HEIs’ brand credibility. The findings of this study concur with previous studies conducted related to subjective norms in different contexts. Wong et al. (2019) found that prospective students perceive social pressure from parents, peers, teachers, and other significant others that are vital for them to perform specific behaviours to fulfil other people’s expectations. Hegner et al. (2017) concluded that subjective norms have facilitated the customers to have a high involvement with a brand when selecting a brand over competitive brands. Furthermore, Alnsour and Al Faour (2020) reported similar outcomes, mentioning that customers’ engagement with social media brands could influence by the norms shared by the brand community to have a positive perception towards the brands in the hospitality sector. Since subjective norms are not a static phenomenon both effected and affected by human behaviour (Bachrach et al., 2017; Bergquist & Nilsson, 2018), the empirical findings of this study confirmed that undergraduates in emerging countries are following subjective norms to shape their perceived behavioural to identify the credibility of the brand. Therefore, HEIs in emerging countries should consider their stakeholders’ past experiences and beliefs to enhance the other like-minded people’s

7.2 Overview of the Study

221

feelings towards the credibility of HEIs. Further, HEIs should promote their expertise and trustworthiness in providing the service than the competitors to create a favourable attitude towards the stakeholders. This study concluded that subjective norms create an influential impact on students’ perception of the trustworthiness and expertise of the services provided by the HEIs. Therefore, HEIs in emerging countries should consider their existing undergraduates’ past experiences, lifestyles, and beliefs about the institutes to motivate prospective students to follow the norms they shared. Further, HEIs should provide their service better than the competitors while exhibiting their expertise and trustworthiness to share positive norms among the community to influence the prospective students’ selection process. Hypothesis H4: Influence of brand credibility on customer-based brand equity The research model proposed in this study developed hypothesis H4 to identify the effect of brand credibility on CBBE from the perspectives of undergraduates in emerging countries. The empirical findings of this study showed that brand credibility is one of the strong predictors of CBBE for HEIs in emerging countries. This implies that a higher HEIs’ expertise and trustworthiness would influence the prospective students on a greater scale to identify the perceived value of the HEI while facilitating them to have positive judgement and confidence about the institute’s services. In other words, HEI brands’ credibility would reduce the uncertainty level to determine the perceived value of an institute to pursue higher studies. Therefore, higher education sectors in emerging countries should develop more aggressive approaches to enhance the credibility of HEIs to exert their ability and willingness in providing the promised service than other competitive higher education institutes. Thus, it helps the HEIs to attract prospective students while retaining current students to obtain their loyalty to enhance the institute’s brand credibility. This outcome showed that brand credibility creates a sense of attachment, shapes the social image, enhances the believability of performance, develops trustworthiness, and creates values for HEIs. Therefore, customising the services to regularly satisfy the students’ needs while providing a satisfying service will enhance their perception of the institute’s credibility. This will be reflected in the students’ minds and help to influence the students’ perception of the value of the higher education institutes while developing and increasing CBBE. The findings further implied that undergraduates in the emerging countries perceived the credibility of the HEIs as one of the best ways to assess the quality of the institute while providing both functional and emotional benefits of their services. Also, prospective students may gain the ability to differentiate the HEIs among the competitive institutes based on their credibility of the services. These findings support earlier research outcomes on CBBE adoption among a variety of industries. For example, Seric et al. (2014) found that in the tourism and hospitality industry, CBBE can form and maintain strong and favourable associations with the brand and enhance hotel perceived quality. These associations allow the hotel brands to convey their ability to provide services than the competitors to add value to their hotels. Therefore, customer-based brand equity leads to a significant

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increase in customer purchase behaviour embedded through informative and persuasive interactions. In contrast, firms influence it only through persuasive communication (Seric et al., 2014). Yang et al. (2015) also reported that customer experiences on brand equity help the firms to develop and maintain one-to-one-relationship with their customers by emphasising the added value of the brands related to tourism, implying the importance of brand equity for services. Further, Yang et al. (2015) noted that customer-based brand equity had a strong influence on customers’ intentions on purchasing the brands and for service performance. Similarly, an increase in customer-based brand equity can lead to greater competitiveness of the brand by influencing customer behaviour through the greater possibility of brand selection, increased brand loyalty, reduced price sensitivity, and a willingness to pay more for the brand (Wong & Teoh, 2015). Therefore, HEIs should facilitate the stakeholders to share positive information about the credibility of the services provided by the institute than the competitive institutes to create a positive value for the HEI. So, the prospective students could ensure the credibility of HEI while reducing the perceived risk in emerging Asian countries. In addition, marketers of HEIs should invest in marketing activities such as advertising and promotions to inform the institutes’ credibility among the prospective students to enhance the value of the services provided by the institute. Fulfilling the promises presented through marketing activities is a key to creating brand-oriented satisfaction and trust of the prospective students, which enhances the performance, social image, value, and trustworthiness while increasing their attachment with the institute. The findings demonstrate that, when creating and enhancing CBBE, it is important for the HEI to invest more time and resources to increase the credibility to influence the prospective students. The objective here is to make brands that are more credible in prospective students’ eyes and thus greater brand equity. Emphasising the ability and willingness of the HEIs in providing the promised service mirrors the institute’s superiority over the other competitive institutes continuously. Hence, it is crucial to have brand credibility for higher education institutes to have brand equity. Findings from numerous studies have supported the view that effective branding leads to greater competitiveness, which, in turn, leads to stronger brand credibility. However, there are very few studies that support the counterargument that an increase in brand credibility can lead to more substantial brand equity, which this study investigated and confirmed.

7.2.2 Discussion and Implications: Research Question 2 “What is the extent to which brand credibility influences the relationship between social media marketing, subjective norms, and customer-based brand equity”? As discussed in Chap. 1, the second research objective of the study aimed to test the mediating effect of brand credibility between SMM, subjective norms, and CBBE

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from the perspective of undergraduates in emerging countries and identify the causal relationships among these factors. Therefore, this study presented the mediating effect of brand credibility between (H5a) user-generated content and customer-based brand equity, (H5b) user-generated content and customer-based brand equity, and (H5c) subjective norms and customerbased brand equity. Online brand communities are aggregations of individuals in cyberspace, where social-brand relationships are highly affected by the users’ information on social media. The brand community members believe the content created by social media users is being truthful as they don’t have any commercial purpose. Advancing the value endowed with the brand through the brand-related content generated by the users would increase when the content indicates the trustworthiness and expertise of the brand’s service. Studies also seem to agree on the mediating effect of other brand-relationship constructs in linking user-generated content to customerbased brand equity, such as brand commitment (Callarisa et al., 2012), brand trust (Barreda & Bilgihan, 2013; Chahal & Rani, 2017; Ebrahim, 2020), or brand identification (Augusto & Torres, 2018). Therefore, this study hypothesised and confirmed the mediating effect of brand credibility between UGC, FGC, and CBBE. The results suggest that user-generated content and firm-generated content do not build brand equity directly; rather, it impacts a brand’s credibility, which subsequently leads to improve customer-based brand equity, confirming H5a and 5Hb, respectively. The present study revealed the mediating effect of brand credibility between subjective norms and CBBE, which received limited attention in the literature. Diddi and Niehm (2017) have suggested that based on the norms shared among the people related to a brand can create a unique value to gain a better reputation among the competitive brands while leading to a superior brand outcome such as brand positioning. According to Agustiansyah and Mardhiyah (2020), subjective norms are one of the essential attributes to attract and satisfy the perception of the brand community members regarding the brand’s credibility. Further, the subjective norms can provide a sustainable competitive advantage for a service brand’s credibility because it is difficult to evaluate a service before consuming it (Kumar et al., 2019). In addition, subjective norms help firms form a healthy relationship with their current and prospective customers to build a reputation while enhancing the value endowed with the preferred brand (Bekmeier-Feuerhahn et al., 2017). While perceived pressure the people receive because of engaging with the online brand community, we do not expect that the CBBE would be primarily driven only by subjective norms. This study suggested that brand credibility toward the perceived value of a brand significantly results in successful outcomes, and customers are relying on the value of a brand based on the ability and expertise of the brand. In order to implement effective CBBE policies, it is crucial to build credibility on the subjective norms shared by the members of the online brand communities. Accordingly, it has been confirmed that brand credibility mediates the relationship between subjective norms and CBBE.

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The Mediating Role of Brand Credibility

The previous discussion revealed that brand credibility mediates the influence of UGC, FGC, and subjective norms on CBBE. These findings offered two important contributions to higher education and marketing literature. First, it may be possible to develop and enhance the brand equity from the customers’ perspective by strengthening customers’ believability on the brand’s credibility in the service sector, particularly in the higher education sector. Second, prospective customers’ level of credibility towards the brand can be strengthened by the content developed by the online users and firms on social media while pressurising them to follow the norms shared by the community while creating a sense of belonging among the online brand communities. Overall, this study’s findings have major implications that show the need for brands to sustain high levels of intra-community engagement to ensure appropriate levels of brand-relationship quality and subsequent purchase behaviour. Furthermore, it advances the relevance of a multidimensional view of the user and the firm’s engagement in creating the content on social media in building the brand relationship and enhancing brand equity. Therefore, the effect of mediating effect in creating brand equity through SMM in the higher education sector supported the existing gaps in the literature.

7.2.3 Discussion and Implications: Research Question 3 “Are there any difference in adopting social media marketing, subjective norms, brand credibility, and customer-based brand equity based on location, and undergraduates’ brand usage experience”? As discussed in Chap. 1, the third research objective of the study aimed to test the moderating effect of location (Sri Lanka versus Vietnam) and brand usage experience (junior students vs senior students) between SMM, subjective norms, and CBBE from the perspective of undergraduates in emerging countries and to identify the causal relationships among these factors. Research question 3 was examined by testing four hypotheses: H6 (a, b, c, d) and H7 (a, b, c, d).

7.2.3.1

The Moderating Effect of Location and Brand Usage Experience

The empirical finding of the present study revealed that location creates a significant moderating effect between UGC, FGC, subjective norms, brand credibility, and CBBE supporting H6 (a, b, c, d). Although many countries are classified as emerging countries, apparent differences are depicted among them based on people’s habits and attitudes in terms of social media usage and branding activities (Kowalczyk & Kucharska, 2020; Mulvey et al., 2020). This study showed the different perceptions

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among Sri Lankans and Vietnamese towards social media marketing, subjective norms, and branding. The findings revealed that Sri Lankans have higher involvement with the UGC than the Vietnamese counterparts in believing the credibility of a brand. This has further been justified by the demographic factors of the Sri Lankans and Vietnamese discussed in Chap. 3. The demographic factors have confirmed that Sri Lankans are more enthusiastic in posting the content on social networking sites, at least one time per day (76.0%) than Vietnamese (24.0%), depicting that Sri Lankans are creating more UGC than Vietnamese. The findings further confirmed that the Vietnamese had been mainly depending on the firms’ advertising in facilitating firm-customer interaction. Vietnamese like to actively engage with the brands based on the information shared by the firms on social media than the Sri Lankans, so that FGC has become an increasingly essential factor for Vietnamese to determine the credibility of a particular HEI brand. Accordingly, this study confirmed that Sri Lankans highly depend on UGC and Vietnamese depend on FGC in assessing the credibility of HEIs’ brands. Hence, the marketing managers in Sri Lanka need to motivate the online users to create a more influential and interesting content about HEIs on social media, while HEIs in Vietnam need to create more content on their official social media pages. In addition, the undergraduates’ attitudes on subjective norms revealed that the countries with the same social-cultural background exert different perceptions in following norms. Izuagbe et al. (2019) argued that subjective norms are essential to influence the customers’ perceptions of purchasing the brands. But, according to the findings of the present study, subjective norms will not be able to influence people’s perception of brands and their credibility always because Sri Lankans do not follow norms in evaluating a brand’s credibility, although Vietnamese have a higher involvement with subjective norms to believe the brand’s credibility. However, both countries rely on the credibility of a brand in identifying the value endowed with the brand. Since the present study aimed to investigate the extent to which UGC, FGC, subjective norms, and brand credibility develop CBBE across Sri Lanka and Vietnam, two important contributions have been made to the higher education and marketing literature. Firstly, even though Sri Lanka and Vietnam have been classified as emerging countries in Asia, findings from one emerging country could not be applied to another emerging country directly without the required modifications. Secondly, although previous studies have highlighted the importance of social media for HEIs, the findings of this study showed that the content generated on social media is not equally important for undergraduates in the same manner for every country. Therefore, the strategies developed by the HEIs need to be revised according to the undergraduates’ behaviours according to the country context. This study further identified the moderating effect of students’ brand usage experience (Junior vs Senior) to reveal their perception towards the branding on social media. Since the undergraduates are considered as the customers of the higher education sector, their perception towards the HEI’s brand equity has become important than ever, and it expects to vary based on their level of brand user experience. The

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brand usage experience of students was analysed by categorising undergraduates as juniors and seniors. Their current study year was used for the categorisation. Then the moderating impact of brand usage experience was identified using multi-group analysis through SEM. The limited experience and the shorter period in the HEI have made the junior students gather information shared on social media by different online users and HEIs. Since senior students have already spent most of their time in the HEI, at least three years of their higher education life, they have good experience with the HEI. Therefore, senior students do not exert much enthusiasm in engaging with social media to gather information related to the HEIs in selecting an institute to pursue their higher studies. In addition, junior students’ perception of subjective norms is significantly higher than the senior students. Since junior students are considered to be the freshmen not familiar with the HEI has induced to believe and follow the behaviour of the other people in determining the credibility of the institute. In contrast, senior students do not behave much in a specific manner to comply with other people’s views in identifying the HEI’s credibility since they are wellexperienced with the services provided by the institute. The empirical findings further reveal that junior students rely more on brand credibility in creating and enhancing the CBBE than the senior students as they view the perceived value of the HEI’s brand based on their credibility. In contrast, Senior students enjoy lower information gathering and processing costs as they exert a high level of knowledge about HEI brands. Therefore, it is to be expected that the greater the knowledge and experience of the senior students with the HEI, the lesser processing they need to perform on the information they receive about the credibility of the institute. These findings provide a compelling contribution to the higher education literature. If the HEIs want to develop social media strategies for branding, they need to focus more on junior students rather than seniors to retain them with the HEIs. Because, with the development of the higher education sector, undergraduates have found the opportunities to transfer to other HEIs after completing their diploma level or first two years in one HEI. Therefore, marketing and branding need to shed light on the juniors than the seniors.

7.3 Comparative Analysis Between Sri Lanka and Vietnam Based on Their Demographic Observations This study has used the descriptive analysis first and then conducted Chi-square tests to identify the differences between Sri Lankans and Vietnamese based on their demographic factors. This was to identify the role of each demographic variable to decide whether to implement social media and branding strategies for Sri Lankans and Vietnamese, particularly for the higher education sector. According to the findings of this study, both Sri Lankan and Vietnamese females are more oriented toward using social media than their male counterparts. But,

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when comparing Sri Lanka and Vietnam based on gender, it showed that Vietnamese females engage with social media more than Sri Lankans. This finding further provided evidence about the effect of gender in using social media in the emerging country context by highlighting the differences in decision-making processes between males and females concerning the acceptance of new technologies such as social media. Previous researchers have confirmed these findings by reporting that females’ estimates of social media benefits were significantly higher than males, and their satisfaction with social media was high; hence, they were more willing to adopt social media than males (Khan, 2017; Yarchi & Samuel-Azran, 2018). According to the findings of the present study, females in emerging countries are using social media on a large scale, which reported a predominance of females among social media users, mainly in other emerging countries. Even though Vietnam is ahead in adopting social media than Sri Lankans, both countries need to focus more on the females, and the content should be created to satisfy the requirements of the females mainly than the male counterpart. In addition, empirical findings revealed that junior students who are studying in 1st year in Sri Lanka and Vietnam are more willing to use social media services compared to students who are studying in 2nd year, 3rd year, and 4th year. Therefore, in the emerging country context, the undergraduates’ studying year has become one of the influential factors in determining social media usage. Therefore, HEIs in emerging countries need to focus on addressing the requirements of junior students through social media since they are widely engaging with social networking sites. Moreover, junior students in Vietnam are using social media more than Sri Lankans, so that HEIs in Vietnam undergraduates are ahead in adopting new technologies compared to Sri Lankans. This study confirmed that Sri Lankans studying finance and accounting are using social media more than the other undergraduates enrolled in other degree courses such as engineering and IT. This finding supports the study of Wong (2015), who found that social media adoption among the students who have enrolled in business-related courses is higher than the students in other majors. In contrast, a higher proportion of Vietnamese belonged to the OTHER study category (e.g., agriculture, hospitality, and quantity survey). Nevertheless, when comparing the field of study between Sri Lankans and Vietnamese, the adoption percentage gap within each particular category was found to be slightly different, suggesting that the differences among major of studying the field of Sri Lankans and Vietnamese have some influence upon their adoption of social media. Even though the undergraduates in both countries are using social media for at least 3 h per day, the Vietnamese engage with social media more than Sri Lankans. Nguyen (2018) and Krishen et al. (2019) also noted that Vietnamese’ perception of the benefits received through social media was significantly higher, and their satisfaction level with social media was also greater; hence, they were more willing to spend their time on social media. Moreover, the results regarding hours on social media per day in the current study confirmed the findings from the previously conducted studies,

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which reported a predominance of Vietnamese among social media users, mainly in other emerging Asian countries (Chau et al., 2020). The social networking site usage of the study’s respondents showed that Facebook was dominated by the samples from both Sri Lanka and Vietnam. Moreover, 79.0% of the total respondents who were using Facebook were Vietnamese, whereas Sri Lankan Facebook users were only 21% of the total respondents. Even though both countries are increasingly using Facebook, Vietnam is still ahead of Sri Lanka. Moreover, Sri Lankans were widely using Instagram (25.2%) next to Facebook, and next to Facebook, the Vietnamese’ second choice is YouTube (15.0%). Alhabash and Ma (2017) identified that social media users are engaged with a variety of social media networking sites based on different motivations such as convenience, entertainment, information sharing, self-expression, social interaction, and self-documentation motivations. Further, Dutton and Reisdorf (2019) investigated the college students’ usage of social media and found that Twitter was widely popular a few years ago, and newer social networking sites such as Instagram and YouTube are becoming famous. However, Facebook remains popular among young adults to create and maintain online relationships, both personal and professional (Frost & Rickwood, 2017; Coco et al., 2018; Houghton et al., 2020). In addition, Alhabash and Ma (2017) explored the motivations to use Facebook, Twitter, Instagram, and Snapchat, and time spent on the site predicts the intensity of using them. They identified that college students are spending at least 106.35 (minutes/day) on Facebook and 108.73 (minutes/day) on Instagram, indicating a higher engagement level on Facebook and Instagram. Studies related to emerging countries have reported that Facebook is the most widely used social networking site, specifically among the young generation (Raza et al., 2017; El-Tah & Jaradat, 2018; Choudhury, 2018). They network much more through Facebook than the other available means of social networking sites. Manca (2020) noted that Facebook is a highly popular social networking site in the higher education sector. Wickramaratne (2019) and de Silva (2019) found that Sri Lankans are widely using Facebook in communicating with other online members, supporting the findings of the present study. Several scholars have also reported that Vietnamese were using Facebook (Krishen et al., 2019), confirming the findings of the present study. The frequency of social media usage of the study’s respondents showed that Sri Lankans and Vietnamese were visiting social networking sites at least once per day. Still, Vietnamese are highly engaged with social media than Sri Lankans. He et al. (2017) noted that the frequency of visiting social networking sites to be significantly associated with the adoption of social media services. Based on the prevailing telecommunication facilities, Vietnamese are using social media entertainment, study, business platform, communication, etc., more than Sri Lankans (Nguyen et al., 2020). Erkan and Evans (2016) noted that marketers frequently post information on their social media platforms to connect with their customers to enhance their purchase intention. Sri Lankans are widely using social media platforms in posting their photo or video snaps to their own stories, public stories, or privately sending them to other

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users (Karunanayake & Madubashini, 2019; Sandunima et al., 2019). Since the Sri Lankans are widely using Facebook and Instagram while posting contents at least one time per day, they rely more on user-generated content than the Vietnamese. In contrast, Vietnamese are widely using YouTube, followed by Facebook (Bucatariu & George, 2020). Since YouTube contains various videos uploaded by firms, the Vietnamese may be willing to visit those content on YouTube than post their content on it. This was further confirmed by the previously discussed hypotheses H6a and H6b relating to the location. Ajanthan (2017) identified that social media adoption in Sri Lanka is much higher in comparison with traditional media usage. The users’ reachability with social media and the users’ engagement with a variety of social networking sites overtake the traditional media such as newspapers and televisions (Baccarella et al., 2018). Moreover, Odoom et al. (2017) found that emerging countries are shifting towards globalisation while moving out of traditional media, which has transformed their way of communication. In addition, Mills and Plangger (2015) found that the majority of Young adults in emerging Asian countries are increasingly adopting social media more than traditional media as it facilitates them to communicate with their friends easily, quickly, and at a lower cost. Moreover, young adults are always engaging with social media to gather information than traditional media (Berryman et al., 2018). The results regarding the traditional media usage in the current study confirm the findings from previous studies, which reported that emerging Asian countries are increasingly moving out of traditional media and adopting social media. Accordingly, the present study findings provide several breakthroughs in terms of theoretical and managerial knowledge and help make some modifications to the existing theories as well.

7.4 Theoretical and Managerial Contribution of the Study SMM and CBBE have become the most popular phenomenon among marketing and branding studies conducted mainly on consumer marketing during the last few years. Most previous researchers have attempted to conduct empirical studies to identify the impact of social media on branding in different cultural and social contexts (Lin et al., 2017; Garanti & Kissi, 2019). However, a limited focus has been placed on identifying the relationship between SMM and CBBE in the higher education context. Based on the gaps identified in Chap. 2, this study has filled the existing gaps found in the higher education and marketing literature. Therefore, the impact of UGC, FGC, subjective norms, and brand credibility in developing CBBE for HEIs was studied, conducting a comparative analysis between Sri Lanka and Vietnam, generalising the emerging countries. Therefore, the findings of the present study could provide considerable value addition to the existing knowledge. The contribution of the study is separately discussed while highlighting the contribution to the existing theories and practical knowledge.

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7.4.1 Theoretical Contribution Firstly, this study provides a critical evaluation of the determinants of service brand equity in the context of a relatively high-credence service—higher education. Developing branding strategies for service marketing has become vital due to their intangible nature, which resulted in higher perceived risk in its purchasing. Although a growing body of literature is available about the brand equity related to products from the customers’ perspectives, relatively limited studies have explored brand equity in the service context (Šeri´c et al., 2018; Iglesias et al., 2019a, 2019b). Many researchers highlight the differences between goods and services, and they noted the main difference as the intangible nature of the services (San Lam et al., 2020). Goods are tangible items that can be seen, felt, or touched before the purchase and could be returned if it does not fulfil the customers’ expectation (Persaud et al., 2019). In contrast, intangibility, perishability, heterogeneity, and inseparability of the services made the difference between the goods and the customers’ inability to return the service once it was provided (Çifci et al., 2016; San Lam et al., 2020). Therefore, the researchers argued that the studies conducted on brand equity based on the products such as luxury brands, motor vehicles, and electronic items could not be applied to test the service brand equity (Ahmed & Latif, 2019; Godey et al., 2016; Seo & Park, 2018). Hence, there is a requisite to examine the importance of brand equity for service context from the customer’s perspectives. An extensive review of the literature revealed that the contemporary models used to measure product-based brand equity were not quite suitable for service branding. Accordingly, adopting the scale proposed by Lassar et al. (1995), this study identified the importance of brand equity for the service sector, contributing to the service marketing literature. This study verified five main dimensions important to develop brand equity for the service sector: performance, social image, value, trustworthiness, and attachment with these service-providing firms. A comprehensive explanation of the antecedents of brand equity for service marketing is expected to enhance customer’s perception of the perceived value of the service brands. Therefore, the distinctive contribution of this research is in its empirical contribution through selecting higher education as an example of service and investigating the determinants of brand equity from a customer’s viewpoint to enrich the existing literature on customer-based brand equity. It is hoped that our conceptualisation will shed light on service branding by indicating the role and importance of social media marketing and its effect on other customer-based brand equity components. Secondly, although various scholars have argued that branding strategies are important for the service contexts (Endo et al., 2019; Flikkema et al., 2019), there has been little academic attention to branding in higher education marketing. Further, Mourad et al. (2020) argue that empirical research examining the CBBE in the higher education domain is still very thin. More specific research is needed in this domain because of the distinct nature of higher education services and the greater number of student-university brand interactions that the higher education sector encompasses

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(Eldegwy et al., 2018). Thus, this paper contributes to the literature on higher education branding to a greater extent while aiming to contribute to an underdeveloped area in the literature related to brand equity and its importance in the context of the higher education sector. Therefore, the present study reviewed the relevant literature to identify the factors important for higher education branding, develop and test the discriminant and convergent validity of measurement scales for brand equity for HEIs, and investigate the relative importance of these brand equity dimensions in creating a strong HEI brand as perceived by undergraduates in Sri Lanka and Vietnam. Therefore, this study has tested the conceptual model of this study in two different country contexts to highlight the contribution to the higher education literature. The two chosen contexts reflect one mature with the number of local and foreign HEIs, and higher enrolment rate (Vietnam), and one emerging higher education market that indicated the lowest enrolment rate among the emerging countries (Sri Lanka) to enhance current understanding of the determinants of CBBE in higher education services sectors in emerging countries and portrays a constructed structure that is robust across contexts. The results have shown that the determinants of CBBE reported in the literature may vary depending on the higher education industry maturity and the country and cultural contexts. This has clarified the requirement to identify the factors that influence the CBBE related to the higher education sector. Previous researchers argued that brand equity for higher education institutes is crucial as it is experiencing fierce competition to attract prospective students among many competitive offerings provided by the other competitive institutes (Dennis et al., 2017; Mourad et al., 2020). In the general consumer market, brand equity is vital to create a unique position in the minds of the brand’s target market (Foroudi et al., 2018). It is not only getting a target market to choose a specific brand over the competition, but also getting the prospects to see a particular brand as the only one which provides a solution for their problem (Wang et al., 2018). Similarly, creating and enhancing brand equity for HEIs is essential as it delivers the value proposition clearly to connect the target prospects emotionally while confirming the institute’s credibility. Since the intangibility aspect of higher education incurred high perceived risk, developing brand equity in the higher education market could be the best solution to minimise the risk. Accordingly, this study has identified the determinants of brand equity from the perspectives of students (customers) to encompass broader insights into the higher education sector. Further, this study empirically tested the conceptual model in two higher education markets in emerging countries to enhance the generalisability of the research results. The findings support the work of Mourad et al. (2020) and also imply that the determinants of brand equity in higher education sectors portray a constructed structure that is robust across service contexts. Hence, the current study contributes to the extant literature by offering a comprehensive model that includes both determinants and dimensions of CBBE while examining the robustness of this conceptualisation through testing in two diverse contexts (Sri Lanka and Vietnam).

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Thirdly, this study provided insights into social media as a marketing communication tool in the higher education sector. SMM is a new paradigm that is increasingly receiving the attention of researchers and marketers (Shareef et al., 2019; AlAwadhi & Al-Daihani, 2019). The importance of SMM is already experimenting in various contexts such as tourism, hospitality, and luxury brands (Huang et al., 2018; Canovi & Pucciarelli, 2019). Hariguna et al. (2019) noted that official websites of higher education institutions provide the basis to engage with the students, but social media is the ideal extension for relational marketing activities to collaborate with the students. However, the number of studies on social media and their effectiveness is still limited. Very little is known about social media as a marketing communication tool for higher education marketing. As Kinsky et al. (2016) noted, the “penetration of social media into seemingly all facets of life” has contributed to “growing scholarly interest in exploring how college students use social media” and to the “perceptions of students who use social media in higher education” (p. 3). Thus, the previous studies have focused on SMM for students’ recruitment and brand awareness related to higher education (Wisk et al., 2019). Therefore, in this study, we took another approach to study the importance of social media marketing for higher education institutes from the undergraduates’ perspectives. The prior research already pointed out that higher education institutes seem to have effective and successful ways of engaging and reaching their customers through social media (Peruta & Shields, 2018; Stathopoulou et al., 2019). Therefore, this study investigated social media as a potential information source and a marketing communication tool as perceived by undergraduates in the higher education sector. Regarding the behaviour of undergraduates in the social media environment, this study found that they are highly engaging with social networking sites. Although prior literature recognised the importance of social media marketing (Satpathy & Patnaik, 2019), this study is focusing on UGC and FGC to measure SMM. The literature indicated that previous researchers’ focus was on customers’ evaluation of both UGC and FGC but a limited emphasis on the higher education sector. While social media marketing is increasingly popular among the students, it is becoming more pertinent to understand the impacts of the content generated on social media related to higher education institutes. Perrault et al. (2019) argued that the content contribution of the user-generated information and the firm-generation information was found to be important when contemplating connecting the students. Therefore, this study focused on identifying the importance of UGC and FGC from the perspectives of students. The results of this study situate the brand-related contents on social media in the realm of marketing communications. Hence, the results supported the literature on social media marketing. Therefore, the current study contributes to the extant literature by offering a comprehensive model that includes both UGC and FGC while supporting the existing literature on SMM and higher education marketing. Fourth, an increasing number of studies provide social media implications for brand building (Thompson et al., 2018; Mudambi et al., 2019) and the many management opportunities and challenges this entails (Carlson et al., 2019). Yet, researchers

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have so far struggled to find empirical evidence of how SMM influences brand equity successfully. This research addresses this critical gap in the literature by offering a study on higher education institute brands in social media. This present study contributes to prior literature by providing a holistic framework that demonstrates how SMM influences CBBE. Concerning the findings on the role of social media communications in the development of CBBE, the results presented in this research corroborate the teeming number of previous studies on social media communications by revealing that the advent of social media has brought about significant changes in the dissemination and reception of marketing communications. The branding of a higher educational institution and web-based marketing communication to accelerate the efforts is not a novel idea. However, the review of the literature, as stated earlier, finds that there is a lack of integration in approaching SMM communication in building brand equity. Therefore, this study clarifies how SMM can play its role in creating the CBBE. The findings of this study provide several insights that contribute to the growing body of literature in social media marketing by addressing the role of UGC and FGC in enhancing important branding goals, including brand equity in the service sector, particularly in the higher education sector. Godey et al. (2016) noted that the measurement and conceptualisation of social media marketing are still challenging. Hence, the impact of UGC and FGC on CBBE has been identified from the perspectives of the undergraduates. Therefore, another contribution of the study is that it finds that social media marketing has a significant positive effect on brand equity and the two main dimensions of SMM: UGC and FGC. This result means that social media marketing should not only be thought of as a means of raising brand awareness and reaching new customers but also as an increasingly important and genuine brand equity tool. Even when the two countries (Sri Lanka and Vietnam) with distinctive customers are compared, the effect of SMM on CBBE was considered consistently more in the higher education sector. Fifth, this study is focusing on subjective norms related to the higher education sector. Although the previous studies have highlighted the importance of subjective norms, only limited studies were available in the higher education sector (Scholl et al., 2019). In addition, subjective norms related to service context also received inadequate attention (Gong et al., 2019). Thus, this study extended the present literature on subjective norms to the service sector through the higher education sector. The findings revealed the students’ behavioural intention to follow subjective norms. This finding is important because previous studies suggested that norms have a strong influence on both usefulness and usability perceptions of the customers’ behaviour (Kashif et al., 2018a, 2018b). Although the importance of subjective norms is well evidenced in developed countries, the relationship is inconsistent in emerging countries (Petrescu et al., 2018). Thus, this study contributes to the literature by identifying the importance of subjective norms for emerging countries. It makes another contribution to the current higher education literature by investigating the impact of subjective norms on brand equity, previously unexplored in the marketing literature. Our findings reveal that subjective norms are an even more potent predictor of brand credibility. Our explanation for

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the finding is that the social pressure on the students led them to follow the others’ behaviour to receive a kind of identification. Further, in drawing upon the subjective norms, our study fills an existing gap in the marketing literature by identifying the impact of subjective norms on brand credibility in the higher education context. Sixth, we further found that brand credibility plays a critical mediating role in driving brand equity of the higher education institutes, answering the call for empirical research into the drivers and outcomes of customer-based brand equity (Šeri´c et al., 2018). The direct effect of brand credibility on UGC, FGC, subjective norms, and CBBE shed new light on the importance of ascribed brands as symbolic resources for customers to construct their social identity. Prior researchers have primarily studied brand credibility to signal a brand’s quality (An et al., 2019). In this sense, brand credibility serves as a driver of brand loyalty, brand perceived quality, purchase intention, and price sensitivity. We extend this body of knowledge, combining the signalling and social information processing theory to identify the signalling effect of brand credibility on the study constructs. Also, limited studies have investigated the mediating role of brand credibility between SMM, subjective norms, and CBBE. Supporting the social information processing perspective on the value ascribed to brands by customers, our findings signify the important mediating of brand credibility in imperfect and asymmetrical markets, such as higher education with credence services, where prospective students have difficulties in gathering service information and cannot make confident evaluations even after experiencing or enrolling to a particular HEI. This study showed that the effect of UGC, FGC, and subjective norms on CBBE is partially mediated by brand credibility. The content generated by users and firms on social networking sites would enhance the prospective customers’ perception of the brand’s ability and willingness in providing the promised service continuously and subsequently promote customer-based brand equity that encourages extra roles beyond their formal brand duties. Thus, it is confirmed that the brand credibility of the higher education institutions strengthens the students’ perception of brand equity on social media pages. This paper adds to the theoretical body of knowledge of how the content generated on social media and subjective norms in higher education can create the credibility of the institute brand. The findings highlight the role of UGC, FGC, and subjective norms as drivers for brand credibility. Our results also add to the body of knowledge of how having a strong presence on social media can lead to a strong identification with the brand credibility of higher education institutes, which leads to higher brand equity. Seventh, more significantly, this study has explored the moderating effects of location taking two samples from Sri Lanka and Vietnam, which are still unexplored in the literature. This study sampled the customers from two countries (Sri Lanka and Vietnam) to examine whether SMM, branding, and subjective norms play a role in explaining the differences between social media and branding adoption from the following perspectives. Both countries are currently using social media with a significantly higher number of social media users; the adoption of UGC and FGC in determining the credibility of brands differs between the two countries. Though Vietnam customers are a wide range of social media users than Sri Lankan customers,

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Sri Lankans highly rely on social media in identifying brand credibility than the Vietnamese. The Vietnam higher education sector is one of the most advanced and matured among the emerging Asian countries, whereas the Sri Lankan higher education sector is still in the development stage. The differences in higher education service adoption behaviour between the two countries can be explained by the national cultural differences between the two countries. For example, Sri Lankans are highly influenced by subjective norms than Vietnams, so that Sri Lankans are highly engaging with subjective norms in determining the credibility of institutes brands which leads to higher brand equity from the students’ perspective. Thus, identifying how location plays a moderating role in explaining the differences of social media, subjective norms, and branding adoption between customers in the two countries contributes to the existing literature by highlighting the underlying differences in students’ perceptions towards the higher education sectors. Previous research had identified the location of a higher education institute to be of high importance to student choice (Briggs, 2006; Vrontis et al., 2007; Rutter et al., 2017). The findings of this research show that even for higher education institutes located in the Asian country context, the branding activities with social media and students’ perception towards subjective norms is quite different and distinct. These differences are likely to reflect the diverse nature of their backgrounds and the different marketing activities in higher education sectors in each country. This extends previous research, which found that higher education institutes’ brand personalities were being formed through references to their location (Dholakia & Acciardo, 2014) by identifying the effect of location on CBBE. More significantly, this study has explored the moderating effects of students’ brand usage experience in participating in social media branding. Students’ seniorlevel moderates the relationships between UGC, FGC, subjective norms, brand credibility, and CBBE, which did not receive attention in the literature. The findings indicate that juniors and seniors cause different effects on their perception of the content generated on social media, subjective norms, and branding. Our results suggest that junior students are involved with UGC, FGC, and subjective norms in creating brand credibility, which leads to higher CBBE, more than the seniors in our sample. This study highlights that those students with less time in higher education institutes need a higher level of marketing on social media and other related people’s opinions in influencing their perception towards institutes more than their longer-established students. Eighth, the present study builds on and extends a recently published, processfocused customer-based brand equity model across emerging countries. Even though previous researchers have made considerable attempts to examine those issues in diverse other different contexts, the facts above are constructive evidence that still there is a crucial requirement to study the above-mentioned research issues in the emerging country context. Therefore, this study makes another contribution to the marketing literature in emerging countries by developing a new theoretical model to identify the impact of UGC, FGC, subjective norms, and brand credibility on CBBE. Sobaih et al. (2016) purport that higher education institutes in emerging countries

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suffer from a lack of communication technology to connect with them. Therefore, this study took another attempt to enrich the literature by identifying the importance of social media as a marketing communication tool for HEIs. Besides, previous studies have mainly focused on developed countries, and limited studies were conducted in emerging country context (Jamali & Karam, 2018). The findings of this study contribute to the socio-cultural perspective of student brand engagement in emerging countries, shedding light on why students depend on social media marketing and norms in selecting an institute to pursue their higher studies. Finally, given the emergent nature of this research area, this work is one of the few studies to empirically examine students’ participation in SMM and subjective norms in creating brand credibility and CBBE from the social information processing perspective. A social information processing lens was used to advance our understanding of the social media marketing phenomenon. The importance of social information processing theory has in relationship marketing had been well documented (Fujita et al., 2018). We empirically demonstrated and extended the usefulness of this theory to social media and branding literature. Further, by taking the social information processing theory and applying it to the higher education context, a significant contribution has been made to understand the students’ involvement with the content generated on social media and their perception about the brand credibility in creating brand equity for HEIs.

7.4.2 Practical Implications In the competitive environment where firms are struggling to differentiate their products and services, developing and managing the CBBE has become one of the main strategic goals for any kind of firm. “In an abstract sense, brand equity provides marketers with a strategic bridge from their past to their future” (Keller, 2003, p. 154). The findings of this study provide significant implications for the marketing managers in developing and managing CBBE for HEIs. First, the marketing managers of HEIs should be aware of the interrelations between the dimensions of CBBE. Specifically, students will not be able to identify the added value endowed with the institute if they are unable to perceive the performance of the HEI, the social image, the value of having a degree from a particular HEI, HEI’s attachment with the students, and trustworthiness of their services. Therefore, marketing managers should place special attention to foster performance, social image, value, attachment, and trustworthiness in improving brand equity to recognise and renown the institute and invest more to create and maintain brand equity determinants rather than merely expanding their promotional campaigns. That is to say that creating positive value in terms of the services and symbolic attributes will result in developing positive brand equity for HEIs. Second, this study’s results provide valuable insight for marketing marketers of HEIs on how to conduct marketing activities on social media using content generated on social media to enhance HEI-student relationships. Understanding what drives

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students’ engagement with institute brands and how the students and HEIs communicate with each other’s is crucial for marketing managers to develop a healthy relationship with the students. Based on our study findings, it is recommended that HEIs need to dedicate resources to creating more content on social media to strengthen their relationship with prospective students. In particular, marketing managers need to implement various social media platforms that are available to most students. But, if an HEI creates multiple social media pages, it will confuse the students as it slows in providing information that is relevant, interesting, and accurate. This could create a detrimental quality of students’ relationship with the HEI. It would always be helpful for HEIs to motivate students to generate content on social media and solicit feedback and interact with their prospective students. More UGC will lead to higher satisfaction which will enhance students’ positive perceptions towards the HEI brands. This could encourage making new students to become “followers” of their social media pages in developing their relationships with the HEI. Besides, HEIs should consider the students’ most preferable social networking sites (e.g., Facebook) to create more content to communicate with the students. In addition, marketing managers must create coherent and customised content to attract prospective students and engage with current ones. Third, the findings provide insight for marketing managers to identify the usefulness of SMM and students’ behaviour with the content generated on social media in creating and managing the brand equity for HEIs. Therefore, marketing managers should improve their communication on social media to add value to the HEIs as a proper and well-thought-out marketing communication on social media that influence the prospective students’ behaviour towards the HEI’s brand equity. Since the research defined that students are more relying on the content generated by the HEIs, HEIs need to create more content to enhance brand equity to attract prospective students. Fourth, since our framework suggests the central importance of brand credibility on CBBE, the marketing managers of HEIs should ensure the clarity of the brand message to direct prospect students’ focus on what the institute stands for. Further, marketing managers should be consistent in their marketing mix decisions, including communication with the students. To succeed, they should create and communicate the credibility of higher education institutes’ services to be ahead among the competitors. The managers need to have a clear vision for the brand from the planning stage and develop values than the other competitive institutes. Besides, marketing managers of higher education institutions can utilise students’ brand engagement from a cross-cultural perspective and provide valuable insights to assist communication professionals in managing cross-cultural branding campaigns. Fifth, this study identifies the differences between junior and senior students in adopting social media and branding strategies. Based on this study’s findings, marketing managers of HEI need to focus on building branding strategies on social media based on their time being in HEI. Since junior students are more enthusiastic about the contents generated on social media in believing the credibility of the institute’s services, HEIs can focus more on junior students in implementing branding strategies to enhance brand equity.

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In addition, based on the country context, marketing managers in Sri Lanka need to encourage the stakeholders (e.g., students, alumni, community, etc.) to create more positive content about HEIs to increase their credibility among the competitors. In contrast, Vietnamese marketing managers should implement their marketing strategies mainly through the content generated by HEIs to enhance the institute’s credibility to increase their brand equity. Also, marketing managers of HEIs in Vietnam should develop successful norms to influence the students’ perceptions towards the credibility of the institute through their past experiences and best practices. Finally, this research has provided insights into the development of brand elements in service brands. This outcome implied that in the service sector, marketing managers could create brand equity through the content generated on social media. In the case of promoting the service brands to the target market, combining cultural values and norms could enhance the service brand value. In addition, this study’s findings could be applied to other service sectors, in Sri Lanka or Vietnam, and possibly other countries with a similar cultural heritage. Accordingly, a summary of the key findings of each specific objective of the study and their contribution to the existing theories is presented in Table 7.1. Table 7.1 Summary of key findings and theoretical contribution Objectives

Summary of contribution to theory based Result on key findings

• Social media marketing Objective 01 (user-generated and firm-generated To evaluate the undergraduates’ content) has a significantly positive perception of social media marketing, effect on brand credibility subjective norms, and brand credibility • Subjective norms were discovered to in the higher education sector and its positively associate with brand causal relationship with customer-based credibility, and that influence was brand equity considered significant • There is a strong significant impact of brand credibility on customer-based brand equity as perceived by undergraduates in higher education institutions • In general, the subsequent influence of social media marketing, brand credibility, and subjective norms is vitally important in creating brand equity for the service sector, particularly for the higher education sector Objective 02 To examine the mediating effect of brand credibility among social media marketing, subjective norms, and customer-based brand equity

Table 6.11

• The direct effect of social media Table 6.12 marketing (user-generated and firm-generated content) and subjective norms on customer-based brand equity have been partially mediated by brand credibility (continued)

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Table 7.1 (continued) Objectives

Summary of contribution to theory based Result on key findings

Objective 03 • The location has a strong moderating Table 6.13 impact on the relationship between Table 6.14 To evaluate the moderating effect of social media marketing (user-generated location (Sri Lanka vs Vietnam) and of content and firm-generated content) undergraduates’ brand usage experience and brand credibility. However, it (junior student vs senior student) from makes a higher impact on Sri Lankans the undergraduates’ perspectives in Sri than on the Vietnamese Lanka and Vietnam • Subjective norms on brand credibility are relatively higher among the Vietnamese rather than Sri Lankans • Undergraduate’s perception of social media usage and branding varies based on the background of the country • Undergraduates’ brand usage experience moderates the relationship between social media marketing (user-generated content and firm-generated content), subjective norms, brand credibility and customer-based brand equity • Junior students were found to have a strong influence on brand credibility and customer-based brand equity than senior students • Brand usage experience plays a significant moderating role in customer-based brand equity. However, it has no significant impact on Sri Lankans

7.5 Summary This chapter has discussed the present study findings in detail and combined the results gathered via the questionnaire. This chapter further discussed how this study filled the literature gaps to provide a compelling contribution to the existing knowledge while achieving the objectives of the study. Overall, UGC, FGC, subjective norms, and brand credibility tested in the preliminary research model were found to have a significant and direct influence on the developing CBBE in the higher education context. Therefore, these variables have been incorporated into the final model. Brand credibility was found to be a mediator between the study constructs, and it partially mediates the relationship between UGC-CBBE, FGC-CBBE, and subjective norms-CBBE. The location (Sri Lanka vs. Vietnam) and brand usage experience (junior students vs senior students) moderate the relationship between study constructs. The results

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of comparisons between Sri Lankans and Vietnamese revealed that these groups significantly differ in their demographic characteristics and attitudes towards all variables investigated in the present study. The social media and branding adoption proposed in the study was validated, confirmed, and proved to be effective in explaining undergraduates’ intention to adopt social media marketing while following subjective norms to rely on the credibility of the brand to enhance the customer-based brand equity in the higher education sectors in emerging countries. In addition, the researcher attempted to elaborate on the theoretical and practical contributions to the present study. Mainly theoretical contributions have been discussed in light of the main findings. The key findings of the study have also been critically discussed in comparison to the previous results of the respective research areas. Finally, this chapter presents practical contributions as directions to leverage the brand equity phenomenon as a marketing communication tool for service firms. In the following chapter, the thesis is drawn to a conclusion, and limitations and recommendations based on the findings are presented. Some directions for future research are also offered.

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Chapter 8

Conclusion

8.1 Introduction This chapter presents some concluding remarks of the study, paying special attention to the limitations and some areas to be researched in future studies. In the first section, a brief overview of the study will be discussed. Then, some conclusions will be drawn with special reference to the research objectives. Limitations of the study are discussed in the third section, followed by the areas for further research to enhance the knowledge of social media marketing and customer-based brand equity in the final section of this chapter.

8.2 Brief Overview of the Study The primary purpose of the present study was to test the relationship between social media marketing (user-generated content and firm-generated content), subjective norms, and brand credibility towards customer-based brand equity from the perspectives of undergraduates in the higher education sector. The conceptual model was also developed based on the critical review of previous literature and the main research objectives and research questions. Signalling theory was used in creating a conceptual framework with the hope of extending the social information processing theory to social media and branding environments. In the second section, this study identified the moderating effect of brand credibility between the study constructs. In addition, as of the details presented in chapter two, this study was conducted in two stages. First, the impact of social media marketing, subjective norms, and brand credibility on customer-based brand equity was investigated in relation to the moderating role of location (Sri Lanka vs Vietnam). Second, this study tested the moderating role of brand usage experience (junior students vs senior students) among the study constructs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 C. H. Perera et al., Social Media Marketing and Customer-Based Brand Equity for Higher Educational Institutions, https://doi.org/10.1007/978-981-19-5017-9_8

247

248

8 Conclusion

Therefore, the proposed research model consisted of three phases. In the first phase, the impact of user-generated content, firm-generated content, subjective norms, and brand credibility on customer-based brand equity was tested. In the second section, the moderating impact of brand credibility on the study constructs was investigated. In the third phase, the moderating role of location and brand usage experience was investigated among the study constructs. Therefore, customer-based brand equity becomes the outcome of the proposed model. Furthermore, seven hypotheses were developed to demonstrate the relationship between the primary constructs of the proposed research model. A pilot survey was performed with 100 undergraduates, and the findings were used to finalise the survey questionnaire. Based on the Yamane (1973) sampling formula, 1150 respondents were selected for the main survey. After an extensive data cleaning process, 993 questionnaires were entered into the data analysis process. As per the empirical nature of the study, SPSS 23.0 and AMOS 23.0 for SEM were employed as principal data analysis techniques to test the mediating effect and moderating effect through the multi-group analysis. Further, AMOS 23.0 was adopted as a supportive statistical technique for testing hypotheses in addition to descriptive statistics tools.

8.3 Limitation of the Study Notwithstanding its theoretical contributions and managerial implications, this research also has some limitations. However, the findings should be generalised while paying attention to several limitations associated with the study. First, the present study was limited to 993 respondents representing private higher education institutions in Sri Lanka and Vietnam. However, if the same study is carried out with a larger sample representing public higher education institutions, one would achieve a better understanding of the present research issues. However, the samples were represented by undergraduates in private higher education institutes that are relatively homogeneous. Therefore, the sample can be justified to generalise the findings. Second, the present study focuses only on the higher education sector in Sri Lanka and Vietnam. However, customers’ perceptions of the branding on social media in other industries can be reasonably different from the present study findings. Further, this study is limited to the higher education sectors, and therefore, the external validity of the findings is an issue. Third, as data were only collected through questionnaires, mono-method bias is a concern. This study was limited only to quantitative analysis methods so that this research could triangulate surveys with qualitative data sources, such as direct observations or in-depth interviews. Fourth, this study was limited to higher education institutes in emerging countries. Therefore, the students’ perceptions of developed countries could be varied.

8.5 Summary

249

8.4 Suggestions for Future Research Future studies considering these limitations would provide useful information not only to higher education sectors but also to marketers in other industries seeking solutions for social media marketing strategies and brand decision-making. First, future research can address the impact of social media marketing on brand equity in other industries while considering a wide range of moderating and mediating variables. Furthermore, it will be worthy of conducting some comparative studies among different nations on the same research topic. Second, consumer behaviour is highly influenced by the marketing communication strategies adopted by firms. Therefore, the degree of social media adoption by customers may be directly affected by the creativity and the intensity of such communication strategies. Since this study has focused only on the higher education sector, the role of marketing communication for other service sectors in developing brand equity can be investigated in further research. Future researchers could widen the diversity of service settings in the sample and replicate this investigation to discover if the results are consistent across the whole services sector. Third, this research used private higher education intuitions for our sample so that the data about social media marketing, subjective norms, brand credibility, and customer-based brand equity was only available for private institutes. Further, researchers could extend this study to the public higher education sector to generalise the findings. Fourth, future researchers can test this model in the field of goods and compare the results across services and goods; that would also be interesting. Finally, in line with recent suggestions, future research could extend the model of this article by examining other brand-related factors to identify the antecedences and consequences of brand equity.

8.5 Summary The summary of the findings was discussed in Chap. 7 as contributions to theoretical and practical knowledge. Some conclusions can be drawn in general, considering the results of the present study. First, it was noted that social media marketing, subjective norms, and brand credibility are generally accepted tools to develop customer-based brand equity in the higher education sector. The findings of the present study were also concluded with the acceptance of the previous researchers’ views. Even though the degree of social media adoption of Sri Lankan and Vietnamese customers is at a medium level, usergenerated content and firm-generate content have a strong impact on customer-based brand equity. Moreover, it revealed that the impact of user-generated content, firm-generated content, subjective norms, and brand credibility on customer-based brand equity

250

8 Conclusion

varies in different cultural and social contexts and the maturity of the higher education sector. Based on the findings, it can be further concluded that as social media continues to grow and serve as a marketing information resource, the prospect of identifying these relationships for the sake of broadening the impact of university marketing campaigns is of great importance. As our results suggest, these relationships could lead to beneficial outcomes for higher education institutions and their stakeholders in the future.

Appendix 4A: Sources of the Measurement Scales for the Questionnaire

Construct

Study scale

FGC

– I follow the information posted by this Osei-Frimpong and McLean (2018) university on their official social networking sites to get the updated information about the university. – I follow up by communicating with the university’s official social networking sites to get more information about the university – I will follow the information on the university’s official social networking sites if the university-related information is interesting – I follow the university’s social networking sites to learn more about the upcoming activities of the university

References

UGC

– Within these social networking sites, I feel Schivinski and Dabrowski (2016) satisfied with the content shared by other users about my university – Within these social networking sites, the content shared by other users about my university meets my expectation – Within these social networking sites, the content shared by other users about my university is very attractive – Within these social networking sites, the content shared by other users about my university is better than other universities (continued)

Appendix 4A: Sources of the Measurement Scales for the Questionnaire

251

(continued) Construct

Study scale

References

SN

– My relatives think that I should choose to study at this university – People I know think that choosing this university is a good idea – The people I know influence my decision to choose this university – I came across reports saying that this university is a good choice

Srinivasan (2015)

BC

– This university does exactly what they Bougoure et al. (2016) promise – The quality promoted by this university is trustworthy – I can count on the brand of this university – This university reminds me of a graduate who is equipped with enough skills and knowledge and knows what they are doing – This university does not try to promote quality images that are untrue (continued)

252

8 Conclusion

(continued) Construct

Study scale

CBBE

– I can expect an outstanding teaching Lassar et al. (1995) quality from this university – During the study period, I found that the quality of this university was less prone to errors – This university seems to be a learning place without quality problems – I feel this university works very well – I feel this university suits my personality – I feel proud of the decision to enrol in this university – This university will be well appreciated by my friends – Based on information on social networks, this university is suitable for my personality – This university degree is valuable – Considering the level of tuition that I pay for this university; I believe I will receive more than that tuition that I spend – I chose this university degree because of the benefits I received – If I look at the faculty and facilities of the university, I find this university very reliable. – For students’ interests, the university seems very attentive. – I believe this university does not take advantage of its students – After experiencing this university, I was very pleased with my growth and knowledge – I have a positive feeling for this university – The time at the university helped me feel friendlier towards this university

References

Appendix 4B: The Questionnaire Sampling Process Clustering all Sri Lankan HEIs (17) and Vietnamese HEIs (65) into researchintensive, teaching-intensive regional-focused, and special interest categories using the cluster-sampling technique.

Appendix 4C: Data Analysis Techniques Used

253

Sri Lanka

Vietnam

Research-intensive

6

14

Teaching-intensive

7

33

Regional-focused

1

6

Special-interest

3

12

Selecting the required number of districts from each governorate by using random number tables. ● Approaching target subjects by using systematic random sampling. ● Every eighth student comes into the HEI through the main gate asked to participate ● Eligibility criteria: – Currently pursuing higher studies in selected HEI – Have at least one social media profile.

Appendix 4C: Data Analysis Techniques Used

Techniques and software pckaged used

Purpose(s) of the analysis

Descriptive statistics (SPSS 23)

– Create a profile data of the surveyed respondents’ characteristic – Summarises the results in a form of easy-to-understand tables, and charts

– Kurtosis and skewness – Screen plot test SPSS 23

– To check the normality of the quantitative data in the current research (the extent to which data distribution is closer to the line)

Cronbach’s alpha (SPSS 23)

– To assess construct internal consistency of the current study questionnaire (inter-item consistency reliability)

Exploratory Factor Analysis (EFA) (SPSS 23) – To identify the underlying structure of the research model constructs and the observed variables for these constructs – To summarise and reduce the number of the study variables to a smaller and more manageable set of variables – To explain the variance in the observed variables in terms of underlying latent factors Confirmatory Factor Analysis (CFA) (AMOS 23)

– To assess the goodness-of-fit for the measurement model in the present study – To validate relationships between the observed and latent variables – To confirm the validity and reliability of the scale and derived from EFA

254

Appendix 5A: P-P Plot of Each Variable

8 Conclusion

Appendix 5B: Descriptive Statistics for the Questionnaire Measurement …

255

Appendix 5B: Descriptive Statistics for the Questionnaire Measurement Items Items

Mean Std. deviation

FGC1: I follow the information posted by this university on their official 4.77 social networking sites to get updated information about the university

1.494

FGC2: I follow up by communicating with the university’s official social 4.56 networking sites to get more information about the university

1.381

FGC3: I will follow the information on the university’s official social networking sites if the university-related information is interesting

4.60

1.396

FGC4: I follow the university’s social networking sites to learn more about the upcoming activities of the university

4.48

1.452

UGC1: I follow the university’s social networking sites to learn more about the upcoming activities of the university

4.05

1.416

UGC2: Within these social networking sites, the content shared by other 4.19 users about my university meets my expectation

1.528

UGC3: Within these social networking sites, the content shared by other 4.07 users about my university is very attractive

1.600

UGC4: Within these social networking sites, the content shared by other 4.07 users about my university is better than other universities

1.458

4.24

1.435

BC1: This university does exactly what they promise BC2: The quality promoted by this university is trustworthy

4.25

1.405

BC3: I can count on the brand of this university

4.61

1.320

BC4: This university reminds me of a graduate who is equipped with enough skills and knowledge and knows what they are doing

4.48

1.353

BC5: This university does not try to promote quality images that are untrue

4.21

1.525

SN1: My relatives think that I should choose to study at this university

4.31

1.602

SN2: People I know think that choosing this university is a good idea

4.35

1.474

SN3: The people I know influence my decision to choose this university

4.19

1.753

SN4: I came across reports saying that this university is a good choice

4.45

1.497

CBBE1: I can expect outstanding teaching quality from this university

4.58

1.396

CBBE2: During the study period, I found that the quality of this university was less prone to errors

4.56

1.275

CBBE3: This university seems to be a learning place without quality problems

4.57

1.304

CBBE4: I feel this university works very well

4.73

1.226

CBBE5: I feel this university suits my personality

4.58

1.304

CBBE6: I feel proud of the decision to enrol in this university

4.63

1.312

CBBE7: This university will be well appreciated by my friends

4.42

1.268

CBBE8: Based on information on social networks, this university is suitable for my personality

4.39

1.341 (continued)

256

8 Conclusion

(continued) Items

Mean Std. deviation

CBBE9: This university degree is valuable

4.65

1.238

CBBE 10: Considering the level of tuition that I pay for this university; I 4.55 believe I will receive more than the tuition that I spend

1.368

CBBE11: I chose this university degree because of the benefits I received 4.47

1.364

CBBE12: If I look at the faculty and facilities of the university, I find this 4.61 university very reliable

1.375

CBBE13: For students’ interests, the university seems very attentive

1.384

4.53

CBBE14: I believe this university does not take advantage of its students 4.61

1.381

CBBE15: After experiencing this university, I was very pleased with my 4.61 growth and knowledge

1.311

CBBE16: I have a positive feeling about this university

4.57

1.315

CBBE17: The time at the university helped me feel friendlier towards this university

4.58

1.429

Appendix 5C: Gender Distribution Among Sri Lankans and Vietnamese Social media users in Sri Lanka

Social media users in Vietnam

Frequency

% Sri Lankans

% Gender

Frequency

% Vietnamese

% Gender

Freq.

Total %

Male

150

36.4

48.5

159

27.3

51.5

309

31.1

Female

262

63.6

38.3

422

72.7

61.7

684

68.9

Total

412

100

581

100

993

100

Appendix 5D: Studying Year of Sri Lankans and Vietnamese

257

Gender Observations for Sri Lankans and Vietnamese

Appendix 5D: Studying Year of Sri Lankans and Vietnamese

Social media users in Sri Lanka

Social media users in Vietnam

Frequency

% Sri Lankans

% Studying year

Frequency

% Vietnamese

% Studying year

Freq.

Total %

1st year

159

38.6

39.0

248

42.7

60.9

407

41.0

2nd year

45

10.9

37.2

76

13.1

62.8

121

12.2

3rd year

154

37.4

49.3

158

27.2

50.6

312

31.4

4th year

54

13.1

35.2

99

17.0

64.7

153

15.4

Total

412

100

581

100

993

100

258

8 Conclusion

Studying Year of Sri Lankans and Vietnamese

Appendix 5E: Field of Study of Sri Lankans and Vietnamese

Social media users in Sri Lanka Social media users in Vietnam Frequency % Sri % Frequency % % Total Lankans Studying Vietnamese Studying Freq. % area area 5.3

42.3

30

5.2

57.7

52

5.2

Bus. 73 administration

17.8

49.7

74

12.7

50.3

147

14.8

Finance & accounting

40.5

48.1

180

31.0

51.9

347

35

IT

22

167

Marketing

10

2.4

28.6

25

4.3

71.4

35

3.5

Other

140

34

33.9

272

46.8

66

412

41.5

Total

412

100

581

100

993

Appendix 5F: Hours on Social Media of Sri Lankans and Vietnamese

259

Field of Study of Sri Lankans and Vietnamese

Appendix 5F: Hours on Social Media of Sri Lankans and Vietnamese Social media users in Sri Lanka Social media users in Vietnam Frequency

% Sri Lankans

% Hours on social media

Frequency

% Vietnamese

% Hours on social media

Total Freq.

%

Less than 1 hour

15

3.6

40.5

22

3.8

59.5

37

3.7

Between 1-2 hours

48

11.7

43.2

63

10.8

56.8

111

11.2

Between 2-3 hours

86

20.9

42.6

116

20

57.4

202

20.3

Between 3-4 hours

89

21.6

39.0

139

24

61.0

228

23

More than 174 4 hours

42.2

41.9

241

41.4

58.0

415

41.8

412

100

581

100

993

100

Total

260

8 Conclusion

Hours on Social Media of Sri Lankans and Vietnamese

Appendix 5G: Widely Used Social Networking Site of Sri Lankans and Vietnamese Sri Lanka Frequency

Vietnam % Sri Lankans

% SNSs

Frequency

% Vietnamese

% SNSs

Freq.

Total %

Facebook

111

26.9

21

418

72.0

79.0

529

53.2

Instagram

104

25.2

74.8

35

6.0

25.2

139

14.0

YouTube

63

15.4

42

87

15.0

58.0

150

15.1

Pinterest

72

17.5

90.0

8

1.4

10.0

80

8.1

Google+

62

15.0

73.8

22

3.8

26.2

84

8.5

Other

0

0

0

11

1.8

100

11

1.1

Total

412

100

581

100

993

100

Appendix 5H: Frequency of Visits to Social Networking Sites of Sri …

261

Widely Used Social Networking Site of Sri Lankans and Vietnamese

Appendix 5H: Frequency of Visits to Social Networking Sites of Sri Lankans and Vietnamese Social media users in Sri Lanka

Social media users in Vietnam

Frequency % Sri % Frequency % % Total Lankans Frequency Vietnamese Frequency Freq. % on SNSs on SNSs Less than 1 time a month

9

2.2

100

0

0

0

9

0.9

7 Only once in a few months

1.7

58.3

5

0.9

41.6

12

1.2

1 time per month

1.7

53.8

6

1.0

46.2

13

1.3

At least 53 one time a week

12.8

52.5

48

8.3

47.5

101

10.2

At least 336 1 time a day

81.6

39.2

522

89.8

61.0

858

86.4

7

(continued)

262

8 Conclusion

(continued) Social media users in Sri Lanka

Social media users in Vietnam

% Frequency % % Total Frequency % Sri Lankans Frequency Vietnamese Frequency Freq. % on SNSs on SNSs Total

412

100

581

100

993

100

Frequency of Visits to Social Networking Sites of Sri Lankans and Vietnamese

Appendix 5I: Frequency of Posting on Social Networking Sites by Sri Lankans and Vietnamese

Social media users in Sri Lanka Social media users in Vietnam Frequency

% Sri Lankans

% Posts on SNSs

Frequency

% Vietnamese

% posts on SNSs

Freq.

Less than 8 1 time per year

1.9

7.8

Only once 25 in a few months

6.0

1 time per 37 month

9.0

Total %

94

16.2

92.2

102

10.3

11.3

195

33.6

88.6

220

22.1

32.4

77

13.2

67.5

114

11.5

(continued)

Appendix 5I: Frequency of Posting on Social Networking Sites by Sri …

263

(continued) Social media users in Sri Lanka Social media users in Vietnam % Sri Lankans

% Posts on SNSs

Frequency

13.1

30.3

At least 1 288 time a day

70

76.0

Total

100

Frequency

At least one time a week

54

412

% Vietnamese

% posts on SNSs

Freq.

Total %

124

21.3

70.0

178

18.0

91

15.7

24.0

379

38.1

581

100

993

100

Frequency of Posting on Social Networking Sites by Sri Lankans and Vietnamese

264

8 Conclusion

Appendix 5J: Traditional Media Usage Among Sri Lankans and Vietnamese Social media users in Sri Lanka

Social media users in Vietnam

Frequency % Sri % Frequency % % Total Lankans Traditional Vietnamese Traditional Freq. % media media usage usage Less 210 than 1 hour

51

32.7

432

74.4

67.3

642

64.7

38.6

58.5

113

19.4

41.5

272

27.3

More 43 than 3 hours

10.4

54.4

36

6.2

45.6

79

8.0

Total

100

581

100

993

100

From 1-3 hours

159

412

Traditional Media Usage Among Sri Lankans and Vietnamese

Appendix 5K: Expected Frequencies for Sri Lankans, and Vietnamese …

265

Appendix 5K: Expected Frequencies for Sri Lankans, and Vietnamese and Results of Chi-square Tests

Sri Lankan

Vietnamese

Exp. Freq.

Exp. Freq.

Male

128.2

180.8

Female

283.8

400.2

1st year

168.9

238.1

2nd year

50.2

70.8

3rd year

129.5

182.5

4th year

63.5

89.5

IT

21.6

30.4

Bus. administration 61.0

86.0

Finance & accounting

144.0

203.0

Marketing

14.5

20.5

Other

170.9

241.1

Less than 1 hour

15.4

21.6

Between 1-2 hours

46.1

64.9

Between 2-3 hours

83.8

118.2

Between 3-4 hours

94.6

133.4

More than 4 hours

172.2

242.8

Facebook

219.5

309.5

Instagram

57.7

81.3

YouTube

62.2

87.8

Pinterest

33.2

46.8

Google+

34.9

49.1

Other

4.6

6.4

Less than 1 time a month

3.7

5.3

Only once in a few months

5.0

7.0

1 time per month

5.4

7.6

At least one time a week

41.9

59.1

At least 1 time a day

356.0

502.0

Less than 1 time per year

42.3

59.7

Demographic Variables

Gender Studying year

Field of study

Hours on social media

Widely used SNS

How often do you visit the SNSs

How often do you post information or comment on SNSs

Chi-square

df

Sig.

9.193

1

0.002

12.284

3

0.006

22.329

4

0.000

0.851

4

0.931

383.968

5

0.000

21.850

4

0.000

328.590

4

0.000

(continued)

266

8 Conclusion

(continued) Sri Lankan

Vietnamese

Exp. Freq.

Exp. Freq.

Only once in a few months

91.3

128.7

1 time per month

47.3

66.7

At least one time a week

73.9

104.1

At least 1 time a day

157.2

221.8

Hours on traditional Less than 1 hour media From 1-3 hours

266.4

375.6

112.9

159.1

32.8

46.2

Demographic Variables

More than 3 hours

Chi-square

df

Sig.

58.086

2

0.000

Appendix 6A: Communality Statistics for the Observable Variables (EFA)

Variable

I1nitial

Extraction

Variable

Initial

Extraction

FGC1

1

0.701

CBBE1

1

0.559

FGC2

1

0.767

CBBE2

1

0.641

FGC3

1

0.756

CBBE3

1

0.578

FGC4

1

0.584

CBBE4

1

0.626

BC1

1

0.704

CBBE5

1

0.602

BC2

1

0.777

CBBE6

1

0.66

BC3

1

0.74

CBBE7

1

0.574

BC4

1

0.635

CBBE8

1

0.558

BC5

1

0.399

CBBE9

1

0.565

UGC1

1

0.744

CBBE10

1

0.555

UGC2

1

0.704

CBBE11

1

0.549

UGC3

1

0.695

CBBE12

1

0.63

UGC4

1

0.602

CBBE13

1

0.641

SN1

1

0.663

CBBE14

1

0.618

SN2

1

0.711

CBBE15

1

0.752

SN3

1

0.574

CBBE16

1

0.699

SN4

1

0.539

CBBE17

1

0.661

Appendix 6B: The EFA Summary for the Study’s Constructs

267

Extraction Method: Principle Component Analysis

Appendix 6B: The EFA Summary for the Study’s Constructs

FGC observed variable

Cronbach’s alpha

% of explained variance

FGC1

I follow the information posted by this university on their official social networking sites to get the updated information about the university

0.854

69.80

FGC2

I follow up by communicating with the university’s official social networking sites to get more information about the university

FGC3

I will follow the information on the university’s official social networking sites if the university-related information is interesting

FGC4

I follow the university’s social networking sites to learn more about the upcoming activities of the university

BC observed variable

Cronbach’s alpha

% of explained variance

BC1

This university does exactly what they promise

0.856

64.78

BC2

The quality promoted by this university is trustworthy

BC3

can count on the brand of this university

BC4

This university reminds me of a graduate who is equipped with enough skills and knowledge and knows what they are doing

BC5

This university does not try to promote quality images that are untrue

268

8 Conclusion

UGC observed variable

Cronbach’s alpha

UGC1

I follow the university’s social 0.843 networking sites to learn more about the upcoming activities of the university

UGC2

Within these social networking sites, the content shared by other users about my university on meets my expectation

UGC3

Within these social networking sites, the content shared by other users about my university are very attractive

UGC4

Within these social networking sites, the content shared by other users about my university is better than other universities

% of explained variance 68.19

SN observed variable

Cronbach’s alpha

% of explained variance

SN1

My relatives think that I should choose to study at this university

0.784

61.25

SN2

People I know think that choosing this university is a good idea

SN3

The people I know influence my decision to choose this university

SN4

I came across reports saying that this university is a good choice

CBBE observed variable

Cronbach’s alpha

CBBE1

I can expect outstanding teaching 0.947 quality from this university

CBBE2

During the study period, I found that the quality of this university was less prone to errors

CBBE3

This university seems to be a learning place without quality problems

CBBE4

I feel this university works very well

CBBE5

I feel this university suits my personality

CBBE6

I feel proud of the decision to enrol in this university

% of explained cariance 61.01

(continued)

Appendix 6C: Model-Fit Summary for CFA (first-run)

269

(continued) Cronbach’s alpha

CBBE observed variable CBBE7

This university will be well appreciated by my friends

CBBE8

Based on information on social networks, this university is suitable for my personality

CBBE9

This university degree is valuable

CBBE10

Considering the level of tuition that I pay for this university; I believe I will receive more than the tuition that I spend

CBBE11

I chose this university degree because of the benefits I received

CBBE12

If I look at the faculty and facilities of the university, I find this university very reliable

CBBE13

For students’ interests, the university seems very attentive

CBBE14

I believe this university does not take advantage of its students

CBBE15

After experiencing this university, I was very pleased with my growth and knowledge

CBBE16

I have a positive feeling for this university

CBBE17

The time at the university helped me feel friendlier towards this university

% of explained cariance

Appendix 6C: Model-Fit Summary for CFA (first-run) Model Fit Summary CMIN Model Default model Saturated model Independence model

RMR, GFI

NPAR 86 595 34

CMIN

DF

P

CMIN/DF

1613.989

509

0.000

3.171

0.000

35.509

0.000

0

19920.556

561

270

8 Conclusion

Model

RMR

GFI

AGFI

PGFI

0.896

0.779

0.129

0.169

Default model

0.080

0.911

Saturated model

0.000

1.000

Independence model

0.683

0.179

Baseline Comparisons Model

NFI Delta1

RFI rho1

IFI Delta2

TLI rho2

CFI

Default model

0.919

0.911

0.943

0.937

0.943

0.000

0.000

0.000

0.000

Saturated model

1.000

Independence model

0.000

1.000

1.000

Parsimony-Adjusted Measures Model

PRATIO

PNFI

PCFI

Default model

0.907

0.834

0.856

Saturated model

0.000

0.000

0.000

Independence model

1.000

0.000

0.000

NCP Model

NCP

LO 90

HI 90

Default model

1104.989

987.446

1230.114

Saturated model

0.000

0.000

0.000

Independence model

19359.556

18901.702

19823.748

FMIN Model

FMIN

F0

LO 90

HI 90

Default model

1.627

1.114

0.995

1.240

Saturated model

0.000

0.000

0.000

0.000

Independence model

20.081

19.516

19.054

19.984

RMSEA Model

RMSEA

LO 90

HI 90

PCLOSE

Default model

0.047

0.044

0.049

0.980

Independence model

0.187

0.184

0.189

0.000

Appendix 6D: Model-Fit Summary for CFA (Second-Run)

271

AIC Model

AIC

BCC

BIC

CAIC

Default model

1785.989

1792.280

2207.452

2293.452

Saturated model

1190.000

1233.521

4105.935

4700.935

Independence model

19988.556

19991.043

20155.181

20189.181

Model

ECVI

LO 90

HI 90

MECVI

Default model

1.800

1.682

1.927

1.807

Saturated model

1.200

1.200

1.200

1.243

Independence model

20.150

19.688

20.618

20.152

ECVI

HOELTER Model

HOELTER 0.05

HOELTER 0.01

Default model

346

361

Independence model

31

32

Appendix 6D: Model-Fit Summary for CFA (Second-Run) Model Fit Summary CMIN Model

NPAR

CMIN

DF

P

CMIN/DF

Default model

73

866.179

305

0.000

2.840

0.000

45.357

Saturated model

378

0.000

0

Independence model

27

15920.274

351

RMR, GFI Model

RMR

GFI

AGFI

PGFI

Default model

0.059

0.939

0.924

0.758

Saturated model

0.000

1.000

Independence model

0.705

0.200

0.138

0.185

272

8 Conclusion

Baseline Comparisons Model

NFI Delta1

RFI rho1

IFI Delta2

TLI rho2

CFI

Default model

0.946

0.937

0.964

0.959

0.964

Saturated model

1.000

Independence model

0.000

1.000 0.000

1.000

0.000

0.000

0.000

Parsimony-Adjusted Measures Model

PRATIO

PNFI

PCFI

Default model

0.869

0.822

0.838

Saturated model

0.000

0.000

0.000

Independence model

1.000

0.000

0.000

NCP NCP

Model

LO 90

HI 90

Default model

561.179

477.078

652.915

Saturated model

0.000

0.000

0.000

Independence model

15569.274

15159.657

15985.216

FMIN Model

FMIN

F0

LO 90

HI 90

Default model

0.873

0.566

0.481

0.658

Saturated model

0.000

0.000

0.000

0.000

Independence model

16.049

15.695

15.282

16.114

RMSEA

LO 90

HI 90

RMSEA Model

PCLOSE

Default model

0.043

0.040

0.046

1.000

Independence model

0.211

0.209

0.214

0.000

AIC Model

AIC

BCC

BIC

CAIC

Default model

1012.179

1016.420

1369.932

1442.932

Saturated model

756.000

777.959

2608.476

2986.476

Independence model

15974.274

15975.843

16106.594

16133.594

Appendix 6E: Model-Fit Summary for CFA (Structural Model)

273

ECVI Model

ECVI

LO 90

HI 90

MECVI

Default model

1.020

0.936

1.113

1.025

Saturated model

0.762

0.762

0.762

0.784

Independence model

16.103

15.690

16.522

16.105

HOELTER Model

HOELTER 0.05

HOELTER 0.01

Default model

398

419

Independence model

25

26

Appendix 6E: Model-Fit Summary for CFA (Structural Model) Model Fit Summary CMIN Model

NPAR

CMIN

DF

P

CMIN/DF

0.000

3.447

0.000

45.357

Default model

69

1065.065

309

Saturated model

378

0.000

0

Independence model

27

15920.274

351

RMR, GFI Model

RMR

GFI

AGFI

PGFI

Default model

0.148

0.926

0.910

0.757

0.138

0.185

Saturated model

0.000

1.000

Independence model

0.705

0.200

274

8 Conclusion

Baseline Comparisons Model

NFI Delta1

RFI rho1

IFI Delta2

TLI rho2

CFI

Default model

0.933

0.924

0.952

0.945

0.951

Saturated model

1.000

Independence model

0.000

1.000 0.000

1.000

0.000

0.000

0.000

Parsimony-Adjusted Measures Model

PRATIO

PNFI

PCFI

Default model

0.880

0.821

0.838

Saturated model

0.000

0.000

0.000

Independence model

1.000

0.000

0.000

NCP NCP

Model

LO 90

HI 90

Default model

756.065

660.550

859.158

Saturated model

0.000

0.000

0.000

Independence model

15569.274

15159.657

15985.216

FMIN Model

FMIN

F0

LO 90

HI 90

Default model

1.074

0.762

0.666

0.866

Saturated model

0.000

0.000

0.000

0.000

Independence model

16.049

15.695

15.282

16.114

RMSEA

LO 90

HI 90

RMSEA Model

PCLOSE

Default model

0.050

0.046

0.053

0.561

Independence model

0.211

0.209

0.214

0.000

AIC Model

AIC

BCC

BIC

CAIC

Default model

1203.065

1207.073

1541.215

1610.215

Saturated model

756.000

777.959

2608.476

2986.476

Independence model

15974.274

15975.843

16106.594

16133.594

References

275

ECVI Model

ECVI

LO 90

HI 90

MECVI

Default model

1.213

1.116

1.317

1.217

Saturated model

0.762

0.762

0.762

0.784

Independence model

16.103

15.690

16.522

16.105

HOELTER Model

HOELTER 0.05

HOELTER 0.01

Default model

327

345

Independence model

25

26

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