The advent of e-commerce puts traditional retail companies under a lot of pressure. A way retailers try to attract more customers to their physical stores is by offering online services on the retail sales floor. Such services are enabled through pervasive retail systems. These systems, however, do not only offer new opportunities but also bear risks for retailers because they heavily depend on privacy-related data, which customers could perceive as a potential privacy threat. In the present paper, we thus investigate the antecedents of customers’ usage intention towards such systems and the trade-off between the perceived benefits and the perceived privacy costs that are associated with their use. To this end, we propose a model based on the most recent version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) and the Extended Privacy Calculus Theory. We validate our model considering a smart fitting room application and show that the model is able to explain 67.1% of the variance in the behavioral intention to use the system and 43.1% of the variance in a person’s willingness to disclose private information. Our results can be leveraged to design pervasive systems that are perceived as valuable instead of privacy threatening.
Weinhard, Alexander; Hauser, Matthias; and Thiesse, Frédéric, "Explaining Adoption of Pervasive Retail Systems with a Model based on UTAUT2 and the Extended Privacy Calculus" (2017). PACIS 2017 Proceedings. 217.