Unintended Consequences of Advances in Matching Technologies: Information Revelation and Strategic Participation on Gig-Economy Platforms

Abstract

Recent years have witnessed significant advancements in matching technologies used to improve the matching between workers and employers requesting job tasks on a gig-economy platform. While conventional wisdom suggests that technologies with higher matching quality benefit the platform by assigning better-matched jobs to workers, we discover a possible unintended revenue-decreasing effect. Our stylized game-theoretic model suggests that while a technology’s matching enhancement effect can increase a platform’s revenue, the jobs assigned by the better matching technology can also unintentionally reveal more information about uncertain labor demand to workers, especially when demand is low, and thus unfavorably change workers’ participation decisions, resulting in a revenue loss for the platform. We extend our model to cases in which (1) the share of revenue between workers and platform is endogenous, (2) the matching quality can be improved continuously, (3) the opportunity cost of workers is affected by competition between platforms, and (4) workers compete for job tasks. We find consistent results with additional insights, including the optimal matching quality that a platform should pursue. Furthermore, we examine two approaches to mitigate the potential negative effect of employing an advanced matching technology for the platform, and find that under certain conditions, the platform can benefit from revealing labor demand or competition information directly to workers. Our results shed light on both the intended positive and unintended negative effects of improvements in matching quality, and also highlight the importance of thoughtful development, management, and application of matching technologies in the gig economy.

Keywords: matching technologies, gig worker, game theory, platform strategy