Designing technology for on-demand delivery: The effect of customer tipping on crowdsourced driver behavior and last mile performance

Abstract

Crowdsourcing technology platforms specializing in on-demand last mile delivery face a novel problem—heightened agent independence increases uncertainty in last mile delivery and has the potential to undermine operational performance. Uncertainty emanates from drivers' competing interests, or opportunity costs. Rather than increase remuneration, some platforms have designed the technology to allow customers to tip, but the subsequent effects on driver behavior and last mile performance have not yet been studied. We explore this feature of on-demand delivery technology design—customer tipping—and its implications for crowdsourced delivery performance. Using netnography and secondary data sources, we empirically ground an adaptive, multi-agent hybrid simulation to propose how customer tipping impacts service performance through context-contingent driver behavior. Our results generally indicate that tipping as a technology design feature mitigates uncertainty in the crowdsourced delivery fleet and reduces fulfillment times and unit delivery costs. We also find evidence suggesting that the impact of tipping on driver behavior is geography-contingent, with tipping having unexpectedly detrimental effects in high population density areas relative to low-density areas. We conclude by making a series of theoretical propositions and managerial implications about designing technology to facilitate crowdsourced delivery.