Contracting for online personalisation services: An economic analysis. Issue 7 (3rd July 2019) - Lv, Hongjun; Wan, Yinghong
Abstract: Our study contributes to the literature as follows. Firstly, we are the first to develop and analyse a new contracting problem in the context of personalised services in which the vendor strategically offers two complementary personalisation services to acquire customer preference information. Secondly, given heterogeneity in the willingness to use and expected utility for complementary personalisation services, we uniquely incorporate market realities about the differentiation of customer segmentation. We investigate how the boundedly rational customer segment affects the vendor's optimal personalisation service strategies and profits. Thirdly, our study extends the privacy calculus theory in information systems through an economic model to reveal customer privacy perceptions and online behaviours. In brief, our study offers guidelines for online vendors that address online personalisation and sheds light on how to effectively carry out information acquisition strategies with boundedly rational customer issues. Market demand uncertainty and customer preference complementarity make targeted advertising and pricing decisions for online vendors particularly challenging. Advanced Internet technologies have provided vendors with the capacity to acquire customer complementary preference information. These technologies depend on the level of personalisation services and segmentation of customers. These problems get exacerbated by the fact that a vendor cannot accurately predict customer preference and thus cannot charge for online personalisation services. Therefore, designing service contracts to acquire customer preference information is vital for the vendor. This study tries to derive the optimal contracting structures (basic service, zero-utility complementary, and positive-utility complementary contracts) for vendors under information asymmetry, considering service complementarity and customer segmentation. Our study can be used in many online shopping and line-interactive systems that give the vendor a noteworthy information advantage.