| Abstract: |
Setting appropriate service promises is a challenge for on-demand platforms. Speedy promises are widely believed to boost sales, but should all service providers adopt aggressive promises? In this study, we leverage a quasi-experimental setting where expedited service promises (ESPs) were introduced for a subset of food items on a platform in a staggered manner. Using a stacked difference-in-differences framework, we find that ESPs increased overall item sales by 22.9%, driven by a 32.4% rise in on-demand orders, while advance-scheduled orders remained unaffected. This resulted in an 11.9 percentage point increase in the share of on-demand orders. However, not all items benefit equally from speedy promises. Dishes that were frequently delivered earlier than requested prior to ESPs saw larger sales boosts compared to those that were consistently late. This highlights how customers evaluate service promises against actual on-time performance. Interestingly, the biggest beneficiaries of ESPs were hot dishes that were delivered early—i.e., those hot dishes with “slack” in their promises. This provides empirical evidence of “earliness costs” and the risks of quality deterioration when promises are overly conservative. Contrary to the common wisdom of “under-promise and over-deliver,” our findings suggest otherwise for hot dishes. After all, no one wants their fries to go soggy before pickup. Our results provide actionable insights for restaurants when deciding how to quote promises for their food items based on serving temperature and current on-time performance.
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Biography:
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Simin Li is an Assistant Professor of Management Science in the Freeman School of Business at Tulane University. Her research focuses on studying the operational challenges and innovations in digital platforms and healthcare settings. She applies causal inference and economic modeling to examine issues in service operations including demand management, labor supply, and information sharing. She received her Ph.D. in Operations Management from the Kellogg School, Northwestern University. Prior to graduate school, she worked as a data scientist at Microsoft.
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