Game for Brainstorm: The Impact of a Badge System on Knowledge Sharing

Abstract

Practice- and Policy-oriented AbstractGamification systems such as badge rewards are widely used to encourage user engagement, yet their effectiveness depends heavily on design. This study investigates how badge volume, variety, and valence—the 3Vs—influence knowledge sharing on platforms such as Stack Overflow. Using a structural hidden Markov model with a copula correction for endogeneity, we uncover that badge volume and valence significantly increase both the quantity and quality of user contributions, particularly among inactive and experienced users. However, excessive variety in badge types reduces long-term engagement, suggesting that diversification may dilute motivational focus. Counterfactual simulations reveal that reducing the difficulty of earning answer badges (“volumizing”) enhances contributions, whereas altering the rarity of high-valence badges (e.g., gold) often backfires. These insights highlight the importance of aligning gamification mechanics with user psychology and engagement trajectories. Practically, platforms can personalize badge offerings, offering easy wins for newcomers and prestige rewards for experienced users. Aligning badge incentives with content goals (e.g., Python questions) and visually showcasing rare badge collections can further deepen engagement. Our findings offer actionable guidance for platform designers to refine gamification systems that balance challenge, recognition, and motivation.Gamification has been widely adopted to engage users in various domains. To improve the effectiveness of gamification systems, we propose a generic framework to design and fine-tune a gamification system. Our study focuses on a badge system and considers three major design elements, namely, volume (the number of badges), variety (the number of badge categories), and valence (high-tier badges). We characterize the dynamics of user-system interactions on Stack Overflow and conduct policy experiments using a structural hidden Markov model (HMM). We sharpen our empirical analyses by incorporating Gaussian copulas into our HMM to address potential endogeneity from badge elements. Our HMM-copula model quantifies both the short-term and long-term impacts of the three design elements on the corresponding user knowledge contributions and engagement-state transitions. Our results demonstrate that the badge system encourages the quantity and quality of user contributions. These positive impacts vary across the four engagement states: Inactive, Gentle, Active, and Vigorous. Specifically, high badge volume and valence keep users staying or moving up to higher engagement states, whereas high badge variety discourages them from sharing more. More importantly, we use the individual structural parameter estimates to run counterfactual experiments to understand the impacts of badging strategies. Overall, this study provides a novel design aspect to contribute to the gamification literature, proposes a generic copula approach to address endogeneity in HMM, and delivers actionable implications for platform managers and gamification system designers.History: Param Vir Singh, Senior Editor; Tianshu Sun, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.0091 .