Estimating impacts of logistics processes on online customer ratings: Consequences of providing technology‐enabled order tracking data to customers

Abstract

AbstractRecent advances in logistics tracking technologies have enabled e‐commerce firms to both accurately track the shipments and obtain accurate estimates of delivery times. With customers constantly tracking the status of their orders, the effects of logistics processes on customers' evaluation of their online shopping experience are relatively unknown as academic literature provides little guidance. Drawing upon the expectation‐disconfirmation theory and attribution theory, and using a unique dataset assembled in collaboration with an e‐commerce firm, we empirically investigate the effects of order processing and delivery times on online customer ratings. We also estimate the impact of positive deviation (i.e., early delivery) and negative deviation (i.e., late delivery) from the promised delivery date on online customer ratings and whether the impact of delivery status (early or late) depends on order cost and freight cost. Our empirical findings demonstrate that longer order processing and delivery times are associated with lower ratings. Our results also highlight that the late delivery of an order is negatively associated with ratings and that the order cost amplifies this effect. Furthermore, freight cost reduces both the negative effect of late delivery and the positive effect of early delivery on online ratings. Our results also indicate that there exist curvilinear relationships between online ratings and the number of days an online order is late or early. Specifically, the negative effect of a late delivery follows a convex‐shape curve such that its impact decreases as the number of days an online order is late increases. Likewise, the positive effect of an early delivery increases at a decreasing rate as the number of days an online order is early increases. Our supplementary analyses further account for potential endogeneity issues and corroborate our main results with additional empirical evidence obtained through alternative model specifications and estimation strategies. We present important theoretical implications and managerial takeaways from our findings.