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ABSTRACT
A reason for online communities to confer recognition (e.g., badges) on members is to acknowledge and encourage contributions. Yet, it is unclear whether such recognition or lack of it changes members’ contribution behaviors over time. While anticipated recognition has been found to motivate members’ contributions, past findings are limited regarding members’ post-recognition behaviors. Especially, the impact of multiple recognitions over time remains unexplored. Also, the contribution behavior of deserving, yet unrecognized members lacks investigation, which can help uncover the negative side effects of recognition systems. Motivated by these gaps in understanding, we build on reinforcement theory to propose a positive role of first-time recognition as a social reinforcer of contribution behavior, while repeated recognition is hypothesized to suffer from reinforcer satiation. However, for deserving, yet unrecognized members we propose a decrease in contributions due to recognition inequity. Using quasi-experiments on 81,393 reviewers of one of the largest online business review sites, Yelp.com, we find empirical support for our hypotheses, with contribution effort and quantity as outcomes. Additional analysis with contribution quality as outcome shows differing relationships for repeatedly recognized versus deserving, unrecognized members. Other than its research contributions, this study provides practical insights for designing effective recognition systems for online communities.
Acknowledgement
The research was supported in part by a Category I research grant from the Indian Institute of Management Calcutta with the work order number RP:ITRRLROCC/3809/2019-20.
Notes
3. Anecdotal evidence from postings on sites such as Yelp suggest that a number of members perceive they should have received Elite status based on their contributions, but did not do so. e.g., https://www.yelp.com/topic/san-jose-how-to-become-an-elite-yelper
5. Tips are usually single-topic nuggets of information regarding local businesses shared by Yelpers. In Yelp, reviews are expected to be more detailed, with the aim of sharing one’s opinion or feedback, whereas tips are one/two-liners and focus on some specific information about the business that could be of help to potential customers.
6. Anecdotal evidence for this issue can be found in various comments from Yelpers on different community sites e.g., https://www.yelp.com/topic/san-jose-how-to-become-an-elite-yelper
8. We did a baseline comparison to ensure that the decline in contribution effort and quantity for the repeatedly recognized group is significant as compared to a natural decline in contribution behaviour over time for members in online communities in general. We found that the declining slope from 2015-2017 for the repeatedly recognized group (difference in effort: -51.55; difference in quantity: -31.53) did not change significantly (became difference in effort: -50.05; difference in quantity: -30.70) even after accounting for the baseline decline in contribution behavior for the full sample in the same period. Thus, our results for H2a and H2b are robust, even with the baseline comparison.
9. Please note that the coefficients a0, b0, b1, b2 are just provided for representation purpose. We do not intend to imply that the coefficients would be the same for each equation.
10. Prior to conducting the Durbin-Wu-Hausman test, we ran a logistic regression to check whether all 5 matching parameters/variables in year 2014 significantly contributed to predicting Elite status in 2015. The results showed that the review-related variables were significant predictors of Elite status, while the tips-related variables were not.
11. There are two other possible scenarios that we did not report here. First, for the R, R, NR condition, the sample size was too small (N=2) and hence not included. Second, for the NR, NR, R condition, the results were materially the same as for any of the first-time recognition conditions. Finally, the R, R, R condition is already included in our main hypotheses, H2a and H2b.
12. To validate our proposed theoretical explanations, we carried out a small-scale survey of Yelp reviewers (12 respondents) with a few open-ended questions about their reaction to multiple recognitions and to lack of recognition. The responses largely agreed with our explanations.
Additional information
Notes on contributors
Samadrita Bhattacharyya
Samadrita Bhattacharyya ([email protected]) is a doctoral student of Management Information Systems at the Indian Institute of Management Calcutta. She holds an M.Tech in VLSI Design from Indian Institute of Engineering Science and Technology, Shibpur. Her research interests include social commerce, online reviews, social networks, business analytics, optimization, and algorithms. Her research articles have appeared in Decision Support Systems, Information & Management, and the proceedings of Australasian Conference on Information Systems, Hawaii International Conference on System Sciences, and IEEE.
Shankhadeep Banerjee
Shankhadeep Banerjee ([email protected]) is a doctoral student of Management Information Systems at the Indian Institute of Management Calcutta. He holds an MBA from that school. His research focus on the human related to crowdfunding, online reviews, virtual communities, e-tailing, and other contemporary technologies. He has extensive practitioner experience working at top technology firms, including Microsoft, Amazon, and eBay. His publications have appeared in Decision Support Systems, Information & Management, and the proceedings of International Conference on Information Systems and Australasian Conference on Information Systems.
Indranil Bose
Indranil Bose ([email protected]; corresponding author) is Professor of Management Information Systems at the Indian Institute of Management Calcutta. He holds a Ph.D. from Purdue University. His research interests focus on business analytics, digital transformation, information security, and management of innovation. His publications have appeared in MIS Quarterly, Communications of the ACM, Decision Support Systems, Information & Management, European Journal of Operational Research, Communications of the AIS, Journal of Organizational Computing and Electronic Commerce, among others. He serves as Senior Editor of Decision Support Systems and as Associate Editor of Communications of the AIS, Journal of the AIS, and Information & Management.
Atreyi Kankanhalli
Atreyi Kankanhalli ([email protected]) is Provost's Chair Professor and Deputy Head in the Department of Information Systems and Analytics at the National University of Singapore (NUS). She is the Coordinator of the Service Systems Innovation Research Laboratory at NUS. She has been a visiting scholar at UC Berkeley, London School of Economics and Political Science, and ESSEC Business School. Her research interests are in the areas of online communities and digital collaboration, and digital innovation and transformation (particularly in public and healthcare sectors). Her publications have appeared in premium journals such as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, and Research Policy, among others. She has received several awards and is serving or has served on the board of MIS Quarterly and Information Systems Research.