Investigating Value Propositions in Social Media: Studies of Brand and Customer Exchanges on Twitter

Investigating Value Propositions in Social Media: Studies of brand and customer exchanges on Twitter Mostafa Alwash A thesis submitted for the degree of Doctor of Philosophy At the University of Otago, Dunedin, New Zealand June 2019 Abstract Social media presents one of the richest forums to investigate publicly explicit brand value propositions and its corresponding customer engagement. Seldom have researchers investigated the nature of value propositions available on social media and the insights that can be unearthed from available data. This work bridges this gap by studying the value propositions available on the Twitter platform. This thesis presents six different studies conducted to examine the nature of value propositions. The first study presents a value taxonomy comprising 15 value propositions that are identified in brand tweets. This taxonomy is tested for construct validity using a Delphi panel of 10 experts – 5 from information science and 5 from marketing. The second study demonstrates the utility of the taxonomy developed by identifying the 15 value propositions from brand tweets (nb=658) of the top-10 coffee brands using content analysis. The third study investigates the feedback provided by customers (nc=12077) for values propositioned by the top-10 coffee brands (for the 658 brand tweets). Also, it investigates which value propositions embedded in brand tweets attract ‗shallow‘ vs. ‗deep‘ engagement from customers. The fourth study is a replication of studies 2 and 3 for a different time-period. The data considered for studies 2 and 3 was for a 3-month period in 2015. In the fourth study, Twitter data for the same brands was analysed for a different (nb=290, nc= 8811) 3-month period in 2018. This study thus examines the nature of change in value propositions across brands over time. The fifth study was on generalizability and replicates the investigation of brand and customer tweets (nb=635, nc=7035) in the market domain of the top-10 car brands in 2018. Lastly, study six conducted an evaluation of a software system called Value Analysis Toolkit (VAT) that was constructed based on the research findings in studies 1 - 5. This tool is targeted at researchers and practitioners who can use the tool to obtain value proposition-based insights from social media data (brand value propositions and the corresponding feedback from customers). The developed tool is evaluated for external validity using 35 students and 5 industry participants in three dimensions (tool‘s analytics features, usability and usefulness). Overall, the contributions of this thesis are: a) a taxonomy to identify value propositions in Twitter (study 1) b) an approach to extract value proposition-based insights in brand tweets and the corresponding feedback from customers in the process of value co-creation (studies 2 - 5) for the top-10 coffee and car brands, and c) an operational tool (study 6) that can be used to analyse value propositions of various brands (e.g., compare value propositions of different brands), and identify which value propositions attract positive electronic word of mouth (eWOM). These value proposition-based insights can be used by social media managers to devise social-media strategies that are likely to stimulate positive discussions about a brand in social media. 2 Acknowledgements The author with deep gratitude would like to thank the reviewers of this thesis, who contributed considerably to the strengthening of this research work. I wish to recognize and acknowledge all the academic peers who I have been fortunate to have had a chance to meet and learn from during my PhD journey and who were not only my friends but also my mentors. To the administrative staff at the Department of Information Science, Gail Mercer and Heather Cooper who shared with me their support, wisdom and confidence, thank you from the bottom of my heart. To the academic students who I had been fortunate to have taught along with faculty at the Otago Business School, it has been a true honour to teach and through it I have been taught the lesson of servitude in my community and so forever I will be grateful. To the business practitioners who freely participated in this research and contributed their industry knowledge and professional insights. Significant thanks goes to you, for willing to help bridge understanding between academia and industry. To my PhD supervisors who always saw the best in me and trained me to be a constructive software engineer as well as a sound marketer. My deepest acknowledgements go to my primary supervisor Tony Savarimuthu who was imperative in my transformation and growth into an independent researcher and is a devoted educator in the discipline, thank you. Lastly, to my beloved family who have always supported me without question or hesitation, their understanding, compassion and love has made me to who I am today, without their help and support, this work would not have been possible. 3 Publications Alwash, M., Savarimuthu, B. T. R., & Parackal, M. (2016). Identifying and Classifying Value Propositions in Brand Tweets – A Study of Top-10 Coffee Brands, Proceedings of the 20th Pacific Asia Conference on Information Systems (PACIS 2016). http://aisel.aisnet.org/pacis2016/168 Alwash, M., Savarimuthu, B. T. R., & Parackal, M. (2019). Shallow Vs. Deep Customer Engagement – A Study of Brand Value Propositions in Twitter. Proceedings of the 27th European Conference on Information Systems (ECIS 2019). https://aisel.aisnet.org/ecis2019_rp/96 4 List of Tables Table 1: Different lens in G-D and S-D paradigms .................................................................................... 46 Table 2: Description, Purpose and ROI of eWOM target variables............................................................ 51 Table 3: Top Coffee brands, Twitter handles and Market revenues ........................................................... 57 Table 4: Value Taxonomy literature source ................................................................................................ 65 Table 5: Customer sentiment categories and examples .............................................................................. 68 Table 6: Brand Tweets, Customer Tweets and eWOM outcomes for top coffee brands in 2015 ............... 72 Table 7: Brand Tweets, Customer Tweets and eWOM outcomes for top coffee brands in 2018 ............... 75 Table 8: Top Car brands, Twitter handles and Market revenues ................................................................ 77 Table 9: Digital Marketing outcomes for top-10 coffee and car brands in 2018 ........................................ 78 Table 10: Coding dimensions, Tweet examples and Corpus tokens ........................................................... 81 Table 11: Value Analytics Toolkit Database Schema................................................................................. 85 Table 12: VAT 21-item Questionnaire ....................................................................................................... 89 Table 13: Results of Round one from the Delphi Panel .............................................................................. 94 Table 14: Round one scoring for the Delphi panel ..................................................................................... 95 Table 15: Results of Round two from the Delphi Panel ............................................................................. 97 Table 16: Round two scoring for the Delphi panel ..................................................................................... 98 Table 17: Cohen's kappa (κ) coefficient for independent raters ............................................................... 102 Table 18: Value Taxonomy Levene‘s Homogeneity test and ANOVA results ........................................ 106 Table 19: CVT Levene‘s Homogeneity test and ANOVA results ............................................................ 106 Table 20: Paired sample t-test on Consumption value-pairs ..................................................................... 107 Table 21: Multiple Regression results from CVT on Like‘s and Retweet‘s ............................................. 109 Table 22: Significant regression models with Likes as the dependent variable ........................................ 117 Table 23: Significant regression models with Shares as the dependent variable ...................................... 117 Table 24: Significant regression models with Comments as the dependent variable ............................... 118 Table 25: Significant regression models with Positive valence as the dependent variable ...................... 118 Table 26: Significant regression models with Negative valence as the dependent variable ..................... 119 Table 27: Brand-wise Multiple Regression results for Like across Marketing periods ............................ 122 Table 28: Brand-wise Multiple Regression results for Share across Marketing periods .......................... 122 Table 29: Brand-wise Multiple Regression results for Comment across Marketing periods ................... 123 Table 30: Brand-wise Multiple Regression results for Positive valence across Marketing periods ......... 123 Table 31: Brand-wise Multiple Regression results for Negative valence across Marketing periods ........ 124 Table 32: Value proposition means and standard deviation measures by market domain ........................ 128 5 Table 33: Shallow regression models for top-8 coffee brands .................................................................

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