An empirical study of brand microblog users' unfollowing motivations: The perspective of push-pull-mooring model

Abstract Brand microblogs (BMs) have been increasingly utilized by companies to facilitate communication and foster deeper relationships with their customers. In addition to attracting new followers, retaining existing followers is equally, if not more, important to the success of BM operators. Drawing upon the migration theory, this study develops a push-pull-mooring (PPM) model of BM unfollowing motivations to enhance our understanding of the significant antecedents that promote BM users’ unfollowing intention. The study empirically investigates the three categories of antecedents of the BM unfollowing intention: push (dissatisfaction with information quality, dissatisfaction with service quality, and person brand unfit), pull (alternative attractiveness), and mooring (perceived unfollowing costs) effects. The results suggest that the three groups of unfollowing motivations display varying degrees of influence on BM users’ unfollowing intention. Theoretical and managerial implications of the findings of this study are also discussed.

[1]  Ya-Peng Zhang,et al.  Content or context: Which matters more in information processing on microblogging sites , 2014, Comput. Hum. Behav..

[2]  E. Kwon,et al.  Follow Me! Global Marketers’ Twitter Use , 2011 .

[3]  Kem Z. K. Zhang,et al.  Zhang Et Al.: Online Service Switching Behavior Online Service Switching Behavior: the Case of Blog Service Providers , 2022 .

[4]  Ke-Wei Huang,et al.  The Monetary Value of Twitter Followers: Evidences from NBA Players , 2014, ICIS.

[5]  Andrew B. Whinston,et al.  Content Sharing in a Social Broadcasting Environment: Evidence from Twitter , 2014, MIS Q..

[6]  Benedikt Jahn,et al.  How to Transform Consumers into Fans of Your Brand , 2012 .

[7]  Chien-Lung Hsu,et al.  Effect of Commitment and Trust towards Micro-blogs on Consumer Behavioral Intention: A Relationship Marketing Perspective , 2010, Int. J. Electron. Bus. Manag..

[8]  A. Enders,et al.  The long tail of social networking.: Revenue models of social networking sites , 2008 .

[9]  Viswanath Venkatesh,et al.  Model of Acceptance with Peer Support: A Social Network Perspective to Understand Employees' System Use , 2009, MIS Q..

[10]  Detmar W. Straub,et al.  Specifying Formative Constructs in Information Systems Research , 2007, MIS Q..

[11]  Moon-Koo Kim,et al.  The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services , 2004 .

[12]  Lei-da Chen,et al.  Understanding Information Systems Continuance for Information-Oriented Mobile Applications , 2012, Commun. Assoc. Inf. Syst..

[13]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[14]  Morad Benyoucef,et al.  Building brand loyalty in social commerce: The case of brand microblogs , 2016, Electron. Commer. Res. Appl..

[15]  David L. Mothersbaugh,et al.  Switching barriers and repurchase intentions in services , 2000 .

[16]  Zhenya Tang,et al.  How to Keep Brand Fan Page Followers? The Lens of Person Environment Fit Theory , 2018, AMCIS.

[17]  Weiguo Fan,et al.  Determinants of users' continuance of social networking sites: A self-regulation perspective , 2014, Inf. Manag..

[18]  Lixuan Zhang,et al.  Drivers and Outcomes of Brand Relationship Quality in the Context of Online Social Networks , 2013, Int. J. Electron. Commer..

[19]  Haewoon Kwak,et al.  Fragile online relationship: a first look at unfollow dynamics in twitter , 2011, CHI.

[20]  Bo Xu,et al.  Structures of broken ties: exploring unfollow behavior on twitter , 2013, CSCW.

[21]  Gordon L. Fullerton How commitment both enables and undermines marketing relationships , 2005 .

[22]  Jason Bennett Thatcher,et al.  Moving Beyond Intentions and Toward the Theory of Trying: Effects of Work Environment and Gender on Post-Adoption Information Technology Use , 2005, MIS Q..

[23]  Jun Yang,et al.  Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services , 2016, Inf. Manag..

[24]  Barbara H Wixom,et al.  A Theoretical Integration of User Satisfaction and Technology Acceptance , 2005, Inf. Syst. Res..

[25]  Thomas J. Johnson,et al.  Social networking site as a Political Filtering Machine: Predicting the Act of Political Unfriending and Hiding on Social Networking Sites , 2018 .

[26]  Olivier Toubia,et al.  Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter? , 2013, Mark. Sci..

[27]  Naresh K. Malhotra,et al.  Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research , 2006, Manag. Sci..

[28]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[29]  Siu Man Lui,et al.  Impacts of information technology commoditization : selected studies from ubiquitous information services , 2005 .

[30]  Moez Limayem,et al.  How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance , 2007, MIS Q..

[31]  Joseph F. Hair,et al.  When to use and how to report the results of PLS-SEM , 2019, European Business Review.

[32]  Morad Benyoucef,et al.  Consumer participation and gender differences on companies' microblogs: A brand attachment process perspective , 2015, Comput. Hum. Behav..

[33]  Zhongsheng Hua,et al.  Investigating continuance intention to follow a brand micro-blog , 2016 .

[34]  Stefan Stieglitz,et al.  Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior , 2013, J. Manag. Inf. Syst..

[35]  Hai Liang,et al.  Information Overload, Similarity, and Redundancy: Unsubscribing Information Sources on Twitter , 2017, J. Comput. Mediat. Commun..

[36]  Chen Ye Post-adoption switching of personal information technologies: A push-pull-mooring-habit model. , 2009 .

[37]  A. Parasuraman,et al.  The Behavioral Consequences of Service Quality , 1996 .

[38]  Bernard J. Jansen,et al.  Questioner or question: Predicting the response rate in social question and answering on Sina Weibo , 2018, Inf. Process. Manag..

[39]  Essi Pöyry,et al.  Can we get from liking to buying? Behavioral differences in hedonic and utilitarian Facebook usage , 2013, Electron. Commer. Res. Appl..

[40]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[41]  Zhao Huang,et al.  From e-commerce to social commerce: A close look at design features , 2013, Electron. Commer. Res. Appl..

[42]  Ofir Turel,et al.  Quitting the use of a habituated hedonic information system: a theoretical model and empirical examination of Facebook users , 2015, Eur. J. Inf. Syst..

[43]  Jamie Carlson,et al.  Examining the drivers and brand performance implications of customer engagement with brands in the social media environment , 2014 .

[44]  E. Ravenstein The Laws of Migration , 1885, Encyclopedia of Gerontology and Population Aging.

[45]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[46]  I-Cheng Chang,et al.  The push, pull and mooring effects in virtual migration for social networking sites , 2014, Inf. Syst. J..

[47]  D. S. Derue,et al.  The convergent and discriminant validity of subjective fit perceptions. , 2002, The Journal of applied psychology.

[48]  David Godes,et al.  Introduction to the Special Issue - Social Media and Business Transformation: A Framework for Research , 2013, Inf. Syst. Res..

[49]  Yang Chen,et al.  Key values driving continued interaction on brand pages in social media: An examination across genders , 2016, Comput. Hum. Behav..

[50]  Stuart J. Barnes,et al.  Can consumers be persuaded on brand microblogs? An empirical study , 2018, Inf. Manag..

[51]  Tao Hu,et al.  Why People Continue to Use Social Networking Services: Developing a Comprehensive Model , 2008, ICIS.

[52]  Paul Benjamin Lowry,et al.  Partial Least Squares (PLS) Structural Equation Modeling (SEM) for Building and Testing Behavioral Causal Theory: When to Choose It and How to Use It , 2014, IEEE Transactions on Professional Communication.

[53]  Susan Y. McGorry Measurement in a cross‐cultural environment: survey translation issues , 2000 .

[54]  H. Bansal,et al.  “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors , 2005 .

[55]  Jen-Ruei Fu,et al.  Understanding career commitment of IT professionals: Perspectives of push-pull-mooring framework and investment model , 2011, Int. J. Inf. Manag..

[56]  J. Paul,et al.  Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future , 2018 .

[57]  Yongjun Sung,et al.  Brand followers' retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth , 2014, Comput. Hum. Behav..

[58]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[59]  E. Lee A theory of migration , 1966, Demography.

[60]  Yongqiang Sun,et al.  Understanding users' switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework , 2017, Comput. Hum. Behav..

[61]  Izak Benbasat,et al.  Integrating Service Quality with System and Information Quality: An Empirical Test in the E-Service Context , 2013, MIS Q..

[62]  Matthew K. O. Lee,et al.  Understanding the role of gender in bloggers' switching behavior , 2009, Decis. Support Syst..

[63]  Bruce E. Moon,et al.  Paradigms in migration research: exploring 'moorings' as a schema , 1995, Progress in human geography.

[64]  Sung S. Kim,et al.  Out of Dedication or Constraint? A Dual Model of Post-Adoption Phenomena and its Empirical Test in the Context of Online Services , 2009, MIS Q..

[65]  Johann Füller,et al.  Personality, person–brand fit, and brand community: An investigation of individuals, brands, and brand communities , 2011 .

[66]  Judy K. Frels,et al.  Consumer switching costs: A typology, antecedents, and consequences , 2003 .

[67]  Viswanath Venkatesh,et al.  Person-organization and Person-job Fit Perceptions of New Employees: Work Outcomes and Gender Differences , 2017, MIS Q..

[68]  Zhongyun Zhou,et al.  Influence of traits and emotions on boosting status sharing through microblogging , 2017, Behav. Inf. Technol..

[69]  Caroline Wiertz,et al.  Advertising to Early Trend Propagators: Evidence from Twitter , 2017, Mark. Sci..

[70]  Yue Jin,et al.  Why do consumers participate in brand microblogs? , 2017, Electron. Commer. Res. Appl..

[71]  Si Shi,et al.  Investigating Customers' Satisfaction with Brand Pages in Social Networking Sites , 2015, J. Comput. Inf. Syst..

[72]  John Lim,et al.  Retaining and attracting users in social networking services: An empirical investigation of cyber migration , 2014, J. Strateg. Inf. Syst..

[73]  P. Patterson,et al.  A cross-cultural study of switching barriers and propensity to stay with service providers , 2003 .

[74]  Tapani Rinta-Kahila,et al.  Toward a refined conceptualization of IS discontinuance: Reflection on the past and a way forward , 2020, Inf. Manag..

[75]  Xuping Jiang,et al.  Tweeting as a Marketing Tool: A Field Experiment in the TV Industry , 2017 .

[76]  Francisco Javier Lloréns Montes,et al.  How do small firms learn to develop a social media competence? , 2015, Int. J. Inf. Manag..

[77]  Detmar W. Straub,et al.  A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example , 2005, Commun. Assoc. Inf. Syst..

[78]  Bang Nguyen,et al.  Constructing online switching barriers: examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers , 2016, Electronic Markets.

[79]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..