On-demand delivery through sharing platforms represents a rapidly rising segment of the global workforce. The emergence of sharing platforms enables gig workers to choose when and where to work, allowing them to do so flexibly. However, such flexibility brings notorious challenges to platforms in managing the gig workforce. Thus, understanding the behavioral and incentive issues of gig workers in this new business model is inherently meaningful. This paper investigates how the incentive mechanisms of sharing platforms, namely earnings, ratings, and penalties, affect the working decisions of gig workers and their nuanced relationships. To achieve this goal, we utilize data from one leading on-demand delivery platform with more than 50 million active consumers in China and implement a two-stage Heckman model with instrumental variables to estimate the impact of earnings, ratings, and penalties. We first show that a higher percentage of five-star ratings motivates gig workers to work more. However, interestingly, when ratings are employed together with earnings, workers with a higher percentage of five-star ratings tend to be less sensitive toward an earning increase (i.e., negative rating moderating effect). Second, we uncover that higher penalties discourage workers from working more, whereas, interestingly, workers with higher penalties tend to be more sensitive toward an earning increase (i.e., positive penalty moderating effect). Moreover, we observe nonlinear effects for both moderating effects; that is, the marginal effects diminish when the magnitude of ratings or penalties increases. Finally, we conduct follow-up surveys to understand the underlying mechanisms of the observed moderating effects from both psychological (i.e., intrinsic versus extrinsic motivation and self-esteem) and economic (i.e., risk-aversion) perspectives, and run additional regression analyses on worker behavioral patterns to investigate when gig workers achieve better performance feedback. The ultimate goal is to provide guidelines for sharing platforms on how to design better incentive mechanisms by understanding the interplay of earnings, ratings, and penalties
