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

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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.

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Notes

  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. 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.

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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

Authors and Affiliations

  1. Robins School of Business, University of Richmond, 1 Gateway Road, Richmond, VA, 23173, USA

    Sara Hanson

  2. Leavey School of Business, Santa Clara University, 500 El Camino Real, Santa Clara, CA, 95053, USA

    Lan Jiang

  3. Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC, V6T IZ2, Canada

    Darren Dahl

Authors
  1. Sara Hanson
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  2. Lan Jiang
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  3. Darren Dahl
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Corresponding author

Correspondence to Sara Hanson.

Additional information

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

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Appendices

Appendix 1

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

figure a

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.

figure b

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

figure c

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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

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