Temporal Effects of Repeated Recognition and Lack of Recognition on Online Community Contributions

• 成果类型: Article
• 署名作者: Bhattacharyya, Samadrita; Banerjee, Shankhadeep; Bose, Indranil; Kankanhalli, Atreyi
• 署名单位: Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; National University of Singapore
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2020.1759341
• 发表日期: 2020
• 页码: 536-562
• 关键词: word-of-mouth; knowledge repositories; VIRTUAL COMMUNITIES; QUALITY; satiation; performance; incentives; reputation; awards; PARTICIPATION

摘要

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.