Rest assured: the influence of chatbots’ assurance statements and service outcome personalization on user data management

Data-driven services are becoming an ever more vital part of the business landscape, with chatbots an integral component of this change. Functioning as a longstanding barrier to the efficacy of personalized services, however, are user concerns surrounding data management that culminate in managerial challenges such as the Personalization-Privacy paradox (i.e., where informing users about data management practices in personalized services heightens privacy concerns and reduces willingness to disclose). In the current paper, across two experiments, we tackle this paradox by manipulating privacy assurance statements made by the chatbot (i.e., short statements at the start of the service interaction) inspired by Communication Privacy Management theory and service personalization (i.e., depth of medical diagnosis at the services conclusion). Using the Elaboration Likelihood Model as our core theoretical anchoring, we examine direct effects on user data management outcomes (privacy concerns, willingness to disclose) and collaboration with the chatbot (working alliance), as well as indirect effects via the mediator perceived information control to discern the activation of peripheral or central route to persuasion. Results show that when service personalization is low, privacy assurance statements activate the peripheral route as users make a surface assessment of their perceived degree of information control. When service personalization is high, however, they additionally activate the central route as users weigh the information delivered at the start of the service interaction with that delivered at the services conclusion. We support this theoretical rationale with calculations of direct, indirect, and conditional indirect effects alongside tests of equivalency. • Chatbot privacy assurances can improve privacy outcomes and collaboration. • However, their effect depends on the degree of service personalization. • When services are more personalized, privacy assurances are deeply processed. • When services are less personalized, privacy assurances are superficially processed. • In this case, users make a surface assessment as to their level of control.