Omnichannel services in the presence of customers' valuation uncertainty

全渠道 估价(财务) 社会福利 在线和离线 业务 服务(商务) 服务提供商 网络效应 福利 计算机科学 微观经济学 经济 营销 产业组织 财务 操作系统 市场经济 法学 政治学
作者
Huan Liu,Ping Cao,Yaolei Wang
出处
期刊:Naval Research Logistics [Wiley]
卷期号:72 (1): 148-165 被引量:1
标识
DOI:10.1002/nav.22213
摘要

Abstract With the advancement of online‐ordering technology, an increasing number of service providers are transforming to sell services through online channels alongside traditional offline stores. Our paper studies this emerging business model (also known as omnichannel services) in which customers can choose between online and offline ordering. We develop a queueing‐game‐theoretic model to evaluate the performance of omnichannel systems in terms of throughput and social welfare when customers strategically choose their ordering channels in the presence of service valuation uncertainty. We also contrast omnichannel services with single‐channel services, that is, online‐only and offline‐only services. Our analysis yields the following main insights. First, although customers run the risk of making suboptimal decisions and suffering from unexpected losses when they are uncertain about their service valuations, we find that all customers would be better off in expectation with an increasing level of service valuation uncertainty. Second, while social welfare improves as ordering online becomes more convenient in some cases, it (even online customer welfare) can also be worse off in others, especially when the system operates under heavy congestion, because customers' self‐interested channel choice behavior would impose significant negative congestion externalities among customers. Third, despite the fact that omnichannel services provide customers with an additional, more convenient ordering channel option in comparison with conventional offline‐only services, we find that, somewhat surprisingly, adopting omnichannel services does not necessarily guarantee improvement in social welfare. Finally, we discuss two alternative modeling assumptions to demonstrate the robustness of our main insights.

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