Designing gamification for complex crowdsourcing based on motivational affordance theory: A controlled field study of task familiarity and topical interest

Abstract

Crowdsourcing depends on engaging qualified participants, referred to as solvers, and gamification can facilitate their engagement. As task complexity increases, solver motivation decreases, making it critical to design gamification systems that sustain motivation in complex tasks requiring skilled solvers. Existing gamification designs typically select game elements based on popularity rather than motivational theory, limiting their effectiveness in complex tasks where solvers face cognitive demands and evolving goals. Motivational affordance theory (MAT) provides a systematic framework mapping system features to user motivations through affordances, yet MAT remains underexplored in complex crowdsourcing gamification. To address this gap, this study integrates MAT principles into gamification design to systematically support solver motivation in complex crowdsourcing tasks. We employ a two-stage research design. In Study 1, we compared four crowdsourcing task types (processing, rating, solving, creating) to identify which tasks solvers perceive as complex and how they expect to benefit from gamification. Based on these insights, we developed a Motivational Affordance Perspective (MAP) design incorporating motivational affordances to guide gamification system design. In Study 2, we implemented a mobile application featuring three gamification designs: baseline, popular, and MAP, and evaluated their effects over a three-week field deployment with 68 registered participants. The analysis sample comprised 44 participants who met the predefined minimum participation criterion across all three systems. The study revealed the influence of task familiarity and topical interest on psychological and behavioral outcomes, indicating that gamification systems should consider both task complexity and individual solver factors. Based on our findings, we provide implications for designing gamification systems for complex crowdsourcing tasks. © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.