Generative AI adoption and solvers’ popularity on supply-driven crowdsourcing platforms: the dual role of price signals

Purpose We investigate the effect of solvers’ adoption of Generative AI (GenAI) on their popularity in a supply-driven crowdsourcing platform. We also examine the impact of price signals as well as their heterogeneous impact based on the solvers’ membership duration on the platform. Design/methodology/approach Our analysis focuses on solvers who adopt GenAI for design-related gigs on the supply-driven crowdsourcing platform. By combining propensity score matching (PSM) with multi-period difference-in-differences (DID), we examine how GenAI adoption impacts solvers’ popularity and how price signals affect this main effect. Findings Our findings reveal that solvers who adopt GenAI tend to experience a surge in popularity on the platform. However, the price difference between GenAI-assisted and non-GenAI-assisted gigs results in divergent impacts of price signals manifested as boomerang or boosting effects. Practical implications To optimize GenAI integration, platforms should democratize access through training and embedded tools to empower a broader solver base. Solvers should proactively disclose GenAI use and align pricing with value perception to maximize market attractiveness. Finally, policymakers should establish regulations and incentives to ensure ethical GenAI deployment in digital labor markets. Originality/value We examine how GenAI affects creative service solvers’ popularity on a supply-driven crowdsourcing platform, making our work one of the first to establish a connection between GenAI adoption and solver-related outcomes. More importantly, we extend signaling theory by demonstrating the dual role of signaling.