Penalty or Reward? The Effect of Social Disincentives on Online Users’ Contributions

Existing research on online communities has primarily demonstrated that users are motivated by social rewards or social incentives such as positive social feedback and enhanced reputation. In contrast, this study examines how social penalties, or social disincentives, including negative social feedback and loss in reputation, influence online users’ voluntary contributions in the short and long term. We develop empirical models to investigate reviewers’ decisions on whether to contribute (i.e., review incidence decision) and how much to contribute (i.e., quality-adjusted review effort decision) over time. Using a state-space model, we capture the dynamics in reviewers’ latent review motivation that stems from both social incentives and social disincentives. Based on a unique data set from the Amazon review system, our analysis shows that, surprisingly, social disincentives increase reviewers’ motivations to contribute more frequently and devote more effort to contributions. Such effects prevail in both the short and long term. In addition, a reviewer’s real name identity and experience moderate the impact of social disincentives on a reviewer’s contribution decisions. We find that loss in reputation has more impact on less experienced reviewers and anonymous reviewers. Our policy simulation exercise suggests that not providing negative social feedback could diminish reviewers’ review propensity by 7.85% (2.39%) and review effort by 14.82% (4.50%) in the short term (long term). Not allowing reputation loss on a review site decreases reviewers’ review propensity by 2.82% (1.40%) and review effort by 7.52% (3.45%) in the short term (long term). This paper was accepted by Chris Forman, information systems. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.01115 .

• 저널Management Science · 71(8) · 6770–6792
• 토픽Digital Marketing and Social Media · Sociology and Political Science
• DOI10.1287/mnsc.2021.01115
• 저자Wenqi Shen, Yan Liu, Yun Wang