The impact of financial incentives in instant retail: An empirical investigation

Problem definition: Financial incentives, which commonly charge consumers higher prices to subsidize service providers, are widely used on crowdsourced delivery platforms to address supply-demand imbalances. 

However, the instant retail context differs from most crowdsourced delivery platforms in that the platforms themselves bear the incentives, and the dense store networks may generate complex network effects. Considering the unclear and complicated effects of such platform-borne financial incentives in instant retail, this study empirically examines their direct-effect and spillover-effect on order acceptance speed from a network perspective, using large transactional datasets from a leading instant retail firm in China. 

Methodology and results:  First, we employ a regression discontinuity design to identify the influence of financial incentives on focal stores' order acceptance speed. Our results show that financial incentives significantly reduce the order acceptance time by 23.66%. Second, from a network perspective, we employ propensity score matching to examine the spillover-effect of the focal stores' financial incentives on the nearest stores. Interestingly, our findings reveal opposing impacts that depend on the nearest store's status. Specifically, the focal store's financial incentives cause a longer order acceptance time at the nearest store without financial incentives; however, the opposite spillover-effect is observed at the nearest store with financial incentives. To better understand the underlying mechanisms, we identify the siphon effect and clustering effect as the key drivers of this phenomenon. Our counterfactual analysis suggests that compared to the practice, optimizing financial incentives considering network effects could reduce the incentive cost by an average of 22.69%, while also decreasing total order acceptance duration by an average of 1.55%. 

Managerial implications: Our study provides essential insights for crowdsourced delivery platforms in instant retail. To enhance order acceptance speed, it's critical for platforms to extend incentives beyond focal stores and capitalize on surrounding ones to promote a clustering effect, while being mindful of the potential siphon effect. In addition, platforms should consider the interplay between nearby stores, particularly store type consistency and distance, when formulating incentive strategies to boost operational efficiency and cost-effectiveness. 

Biography 

Dr Kejia Hu is an Associate Professor in Management Science at Saïd Business School, University of Oxford and a Governing Body Fellow of Exeter College, Oxford. Previously, she was an Assistant Professor at Vanderbilt University. Kejia earned her PhD from Kellogg School of Management in 2017, an MS from UC Davis in 2013 and a BS from Fudan University in 2011. Her research focuses on unlocking business value from data through human-AI collaboration, emphasising human insight.