Enhancing consumer engagement in an online brand community via user reputation signals: a multi-method analysis

Abstract

Generating and maintaining consumers’ engagement in online brand communities is critical for marketing managers to enhance relationships and gain customer loyalty. In this research, we investigate how the type of signal used to indicate user reputation can enhance (or diminish) consumers’ community engagement. Specifically, we explore differences in perceptions of points (i.e., point accrual systems), labels (i.e., descriptive, hierarchical identification systems), and badges (i.e., descriptive, horizontally-ordered identification systems). We argue that reputation signals vary in the degree to which they can provide role clarity—the presence of user roles that deliver information about expected behaviors within a group. Across several studies, including a natural experiment using panel data, a survey of community members, and two controlled experiments, we show that signals that evoke a positive social role have the ability to drive greater engagement (i.e., creating discussions, posting comments, and future engagement intentions) than signals that do not provide role clarity. The effect is moderated by user tenure, such that new consumers’ engagement is particularly influenced by signal type. These findings have important implications for marketers as they use reputation signals as a strategic tool when managing online communities.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    In a small percentage of cases, the highest performing community members were also provided with a “Pillar” icon that indicated their high status in the community.

  2. 2.

    We compared the dataset with incompletes to the dataset of completes to ensure that the distribution of members along the user tenure variable was similar and that there was no apparent selection bias. The distribution proved to be very similar with frequencies within 1–2%. We also compared early responders to late responders and found no significant difference in responses.

References

  1. Adjei, M. T., Noble, S. M., & Noble, C. H. (2010). The influence of C2C communications in online brand communities on customer purchase behavior. Journal of the Academy of Marketing Science, 38(5), 634–653.

    Article  Google Scholar 

  2. Allen, D. G. (2006). Do organizational socialization tactics influence newcomer embeddedness and turnover? Journal of Management, 32(2), 237–256.

    Article  Google Scholar 

  3. Ashforth, B. E., & Mael, F. (1989). Social identity theory and the organization. Academy of Management Review, 14(1), 20–39.

    Article  Google Scholar 

  4. Ba, S. (2001). Establishing online trust through a community responsibility system. Decision Support Systems, 31(3), 323–336.

    Article  Google Scholar 

  5. Bauer, T. N., Bodner, T., Erdogan, B., Truxillo, D. M., & Tucker, J. S. (2007). Newcomer adjustment during organizational socialization: A meta-analytic review of antecedents, outcomes, and methods. Journal of Applied Psychology, 92(3), 707–721.

    Article  Google Scholar 

  6. Bhattacharya, C. B., & Sen, S. (2003). Consumer–company identification: A framework for understanding consumers’ relationships with companies. Journal of Marketing, 67(2), 76–88.

    Article  Google Scholar 

  7. Bishop, J. (2007). Increasing participation in online communities: A framework for human-computer interaction. Computers in Human Behavior, 23(4), 1881–1893.

    Article  Google Scholar 

  8. Bolton, C. D. (1981). Some consequences of the meadian self. Symbolic Interaction, 4(2), 245–259.

    Article  Google Scholar 

  9. Bolton, G. E., Katok, E., & Ockenfels, A. (2004). How effective are electronic reputation mechanisms? An experimental investigation. Management Science, 50(11), 1587–1602.

    Article  Google Scholar 

  10. Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66(1), 105–114.

    Article  Google Scholar 

  11. Burke, P. J., & Stets, J. E. (1999). Trust and commitment through self-verification. Social Psychology Quarterly, 62(4), 347–366.

    Article  Google Scholar 

  12. Callero, P. L. (1985). Role-identity salience. Social Psychology Quarterly, 48(3), 203–215.

    Article  Google Scholar 

  13. Carron, A. V., Brawley, L. R., & Widmeyer, W. N. (1998). The measurement of cohesiveness in sport groups. Advances in Sport and Exercise Psychology Measurement, 213–226.

  14. Chen, Y., Harper, F. M., Konstan, J., & Li, S. X. (2010). Social comparisons and contributions to online communities: A field experiment on Movielens. American Economic Review, 100(4), 1358–1398.

    Article  Google Scholar 

  15. Chung, J. E., Namkee, P., Hua, W., Fulk, J., & McLaughlin, M. (2010). Age differences in perceptions of online community participation among non-users: An extension of the technology acceptance model. Computers in Human Behavior, 26(6), 1674–1684.

    Article  Google Scholar 

  16. Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87.

    Article  Google Scholar 

  17. Cornwell, B., Schumm, L. P., Laumann, E. O., & Graber, J. (2009). Social networks in the NSHAP study: Rationale, measurement, and preliminary findings. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 64(1), 47–55.

    Article  Google Scholar 

  18. Cova, B., & Pace, S. (2006). Brand community of convenience products: New forms of customer empowerment - the case “my Nutella the community”. European Journal of Marketing, 40(9/10), 1087–1105.

    Article  Google Scholar 

  19. Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.

    Article  Google Scholar 

  20. Dellarocas, C. (2010). Online reputation systems: How to design one that does what you need. MIT Sloan Management Review. http://sloanreview.mit.edu/article/online-reputation-systems-how-to-design-one-that-does-what-you-need/. Accessed 15 Jan 2016.

  21. Dellarocas, C., Fan, M., & Wood, C. A. (2003). Self-interest, reciprocity, and participation in online reputation systems. 2003 Workshop in Information Systems and Economics (WISE), Seattle, WA.

  22. Dholakia, U. M., Bagozzi, R. P., & Pearo, L. K. (2004). A social influence model of consumer participation in network-and small-group-based virtual communities. International Journal of Research in Marketing, 21(3), 241–263.

    Article  Google Scholar 

  23. Donath, J. (2007). Signals in social supernets. Journal of Computer-Mediated Communication, 13(1), 231–251.

    Article  Google Scholar 

  24. Drèze, X., & Nunes, J. C. (2009). Feeling superior: The impact of loyalty program structure on consumers’ perceptions of status. Journal of Consumer Research, 35(6), 890–905.

    Article  Google Scholar 

  25. Farzan, R., DiMicco, J. M., Millen, D. R., Dugan, C., Geyer, W., & Brownholtz, E. A. (2008). Results from deploying a participation incentive mechanism within the enterprise. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 563–572.

  26. Fombelle, P. W., Bone, S. A., & Lemon, K. N. (2016). Responding to the 98%: Face-enhancing strategies for dealing with rejected customer ideas. Journal of the Academy of Marketing Science, 44(6), 685–706.

    Article  Google Scholar 

  27. Grand, R., & Carron, A.V. (1982). Development of a team climate questionnaire. Proceedings of the Annual Conference of the Canadian Society for Psychomotor Learning and Sport Psychology, 217–222.

  28. Goes, P. B., Guo, C., & Lin, M. (2016). Do incentive hierarchies induce user effort? Evidence from an online knowledge exchange. Information Systems Research, 27(3), 497–516.

  29. Gruner, R. L., Homburg, C., & Lukas, B. A. (2014). Firm-hosted online brand communities and new product success. Journal of the Academy of Marketing Science, 42(1), 29–48.

  30. Hagel, J., & Armstrong, A. G. (1997). Net gain: Expanding markets through virtual communities. Boston: Harvard Business School.

    Google Scholar 

  31. Harmeling, C. M., Moffett, J. W., Arnold, M. J., & Carlson, B. D. (2017). Toward a theory of customer engagement marketing. Journal of the Academy of Marketing Science, 45(3), 312–335.

    Article  Google Scholar 

  32. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press.

    Google Scholar 

  33. Heehyoung, J., Olfman, L., Islang, K., Joon, K., & Kyungtae, K. (2008). The influence of online brand community characteristics on community commitment and brand loyalty. International Journal of Electronic Commerce, 12(3), 57–80.

    Article  Google Scholar 

  34. Hertel, G., Niedner, S., & Herrmann, S. (2003). Motivation of software developers in open source projects: An internet-based survey of contributors to the Linux kernel. Research Policy, 32(7), 1159–1177.

    Article  Google Scholar 

  35. Hewstone, M. (1996). Contact and categorization: Social psychological interventions to change intergroup relation. Stereotypes and Stereotyping. New York: Guilford, 323–368.

  36. Hogg, M. A., & Abrams, D. (1993). Towards a single-process uncertainty-reduction model of social motivation in groups. Group Motivation: Social Psychological Perspectives. London: Harvester-Wheatsheaf, 173–190.

  37. Hornsey, M. J., & Hogg, M. A. (2000). Assimilation and diversity: An integrative model of subgroup relations. Personality and Social Psychology Review, 4(2), 143–156.

    Article  Google Scholar 

  38. Kahn, R. L., Wolfe, D. M., Quinn, R. P., Snoek, D., & Rosenthal, R. A. (1964). Organizational stress: Studies in role conflict and ambiguity. Oxford: Wiley.

    Google Scholar 

  39. Kinch, J. W. (1967). A formalized theory of self-concept. Symbolic Interaction: A Reader in Social Psychology, eds. J. G. Manis and G. N. Meltzer. Boston: Allyn & Bacon.

  40. Kozinets, R. V. (1999). E-tribalized marketing? The strategic implications of virtual communities of consumption. European Management Journal, 17(3), 252–264.

    Article  Google Scholar 

  41. Kumar, V., & Shah, D. (2004). Building and sustaining profitable customer loyalty for the 21st century. Journal of Retailing, 80(4), 317–329.

    Article  Google Scholar 

  42. Kwon, K. H., Halavais, A., & Havener, S. (2015). Tweeting badges: User motivations for displaying achievement in publicly networked environments. Cyberpsychology, Behavior, and Social Networking, 18(2), 93–100.

    Article  Google Scholar 

  43. Lakhani, K. R., & Wolf, R. G. (2005). Why hackers do what they do: Understanding motivation and effort in free/open source software projects. Perspectives on Free and Open Source Software, 1, 3–22.

    Google Scholar 

  44. Lampel, J., & Bhalla, A. (2007). The role of status seeking in online communities: Giving the gift of experience. Journal of Computer-Mediated Communication, 12(2), 434–455.

    Article  Google Scholar 

  45. Leary, M. R., & Tangney, J. P. (2003). Handbook of self and identity. New York: Guilford Press.

    Google Scholar 

  46. Lee, M. T., & Ofshe, R. (1981). The impact of behavioral style and status characteristics on social influence: A test of two competing theories. Social Psychology Quarterly, 44(2), 73–82.

  47. Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technology design, identity verification, and knowledge contribution in online communities. Information Systems Research, 18(1), 42–67.

    Article  Google Scholar 

  48. McAlexander, J. H., & Schouten, J. W. (1998). Brandfests: Servicescapes for the cultivation of brand equity. Servicescapes: The Concept of Place in Contemporary Markets, 377–402.

  49. McWilliam, G. (2000). Building stronger brands through online communities. Sloan Management Review, 41(3), 43–54.

    Google Scholar 

  50. Mead, G. H. (1934). Mind, self, and society from the standpoint of a social behaviorist. Chicago: University of Chicago Press.

    Google Scholar 

  51. Nambisan, S., & Baron, R. A. (2007). Interactions in virtual customer environments: Implications for product support and customer relationship management. Journal of Interactive Marketing, 21(2), 42–62.

    Article  Google Scholar 

  52. Oakes, P. J., Turner, J. C., & Haslam, S. A. (1991). Perceiving people as group members: The role of fit in the salience of social categorizations. British Journal of Social Psychology, 30(2), 125–144.

    Article  Google Scholar 

  53. Owens, D. A., & Sutton, R. I. (1999). Status contests in meetings: Negotiating the informal order. Groups at Work: Advances in Theory and Research. Mahwah, NJ: Lawrence Erlbaum and Associates.

  54. Rashid, A. M., Ling, K., Tassone, R. D., Resnick, P., Kraut, R., & Riedl, J. (2006). Motivating participation by displaying the value of contribution. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 995–958.

  55. Ren, Y., Harper, F. M., Drenner, S., Terveen, L. G., Kiesler, S. B., Riedl, J., & Kraut, R. E. (2012). Building member attachment in online communities: Applying theories of group identity and interpersonal bonds. MIS Quarterly, 36(3), 841–864.

    Article  Google Scholar 

  56. Resnick, P., & Zeckhauser, R. (2002). Trust among strangers in internet transactions: Empirical analysis of eBay’s reputation system. The Economics of the Internet and E-commerce, 11(2), 23–25.

    Google Scholar 

  57. Resnick, P., Kuwabara, K., Zeckhauser, R., & Friedman, E. (2000). Reputation systems. Communications of the ACM, 43(12), 45–48.

    Article  Google Scholar 

  58. Shen, W., Hu, Y. J., & Ulmer, J. R. (2015). Competing for attention: An empirical study of online reviewers’ strategic behavior. MIS Quarterly, 39(3), 683–696.

    Article  Google Scholar 

  59. Sluckin, A. M., & Smith, P. K. (1977). Two approaches to the concept of dominance in preschool children. Child Development, 48(3), 917–923.

    Article  Google Scholar 

  60. Solomon, M. R. (1983). The role of products as social stimuli: A symbolic interactionism perspective. Journal of Consumer Research, 10(3), 319–329.

    Article  Google Scholar 

  61. Spiller, S. A., Fitzsimons, G. V., Lynch, J. G., & McClelland, G. H. (2013). Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. Journal of Marketing Research, 50(2), 277–288.

    Article  Google Scholar 

  62. Stokburger-Sauer, N. (2010). Brand community: Drivers and outcomes. Psychology and Marketing, 27(4), 347–368.

    Article  Google Scholar 

  63. Sun, Y., Fang, Y., & Lim, K. H. (2012). Understanding sustained participation in transactional virtual communities. Decision Support Systems, 53(1), 12–22.

  64. Tajfel, H. (1981). Human groups and social categories: Studies in social psychology. Cambridge: Cambridge University Press.

    Google Scholar 

  65. Thompson, S. A., & Sinha, R. K. (2008). Brand communities and new product adoption: The influence and limits of oppositional loyalty. Journal of Marketing, 72(6), 65–80.

    Google Scholar 

  66. Tsai, H. T., & Bagozzi, R. P. (2014). Contribution behavior in virtual communities: Cognitive, emotional, and social influences. MIS Quarterly, 38(1), 143–163.

    Article  Google Scholar 

  67. Turner, J. C. (1982). Towards a cognitive redefinition of the social group. Social Identity and Intergroup Relations. Cambridge, England: Cambridge University Press, 15–40.

  68. van Knippenberg, A. D. (1984). Intergroup differences in group perceptions. The social dimension: European developments in social psychology, 2, 560–578.

  69. Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57.

    Article  Google Scholar 

  70. Zhang, W., & Storck, J. (2001). Peripheral members in online communities. Proceedings of AMCIS 2001 the Americas Conference on Information Systems, 7, 586–593.

    Google Scholar 

  71. Zhou, T. (2011). Understanding online community user participation: A social influence perspective. Internet Research, 21(1), 67–81.

Download references

Funding

This research was partially supported by the National Science Foundation of China (No. 71502156) via City University of Hong Kong (the second author’s former institution).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sara Hanson.

Additional information

Rebecca Hamilton served as Special Issue Guest Editor for this article.

Electronic supplementary material

ESM 1

(DOCX 4136 kb)

Appendices

Appendix 1

T-Mobile Support Community: Before and After Reputation Signal Type Change

figurea

Appendix 2

Factor Analysis

We conducted a pilot study to verify the survey instrument validity. The sample included 100 U.S. participants on Amazon Mechanical Turk (MAge = 34.06, age range = 21–60, 54% male). A factor analysis using Principle Axis Factoring and Varimax rotation indicated that the 12 total items loaded onto three factors, explaining 85.29% of the variance. Within each construct, the primary loadings were all above .7 and no secondary loadings were above .48, providing support for discriminant validity of the constructs. The factor loading matrix for the final solution is presented below.

  Engagement Intentions Role Clarity Connectedness to the Community
I would participate in this community. .74 .38 .40
I would communicate with other users in this community. .80 .36 .33
I would visit this community. .84 .31 .28
I would contribute to this community. .72 .36 .46
How likely are you to participate in this community? .85 .30 .28
People have roles in this community. .27 .77 .24
I feel like I have a role in this community. .36 .84 .32
My role in this community is clear. .29 .88 .30
I would play a part in this community. .44 .74 .31
I feel attached to this community. .34 .34 .79
I feel welcomed by this community. .48 .30 .74
How close do you feel is your relationship with this community? .33 .37 .70

Additionally, the Cronbach’s alpha scores from the pretest indicated that the construct scales are internally consistent and reliable.

Construct α
Engagement Intentions .96
Role Clarity .88
Connectedness to the Community .91

Appendix 3

Study 4 Figures

Role Clarity as a Function of Reputation Signal Type and User Tenure.

figureb

Connectedness to the Community as a Function of Reputation Signal Type and User Tenure.

figurec

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hanson, S., Jiang, L. & Dahl, D. Enhancing consumer engagement in an online brand community via user reputation signals: a multi-method analysis. J. of the Acad. Mark. Sci. 47, 349–367 (2019). https://doi.org/10.1007/s11747-018-0617-2

Download citation

Keywords

  • Online brand community
  • Reputation
  • Engagement
  • Marketing strategy
  • Role clarity