Achieving a Balance Between Privacy Protection and Data Collection: : A Field Experimental Examination of a Theory-Driven Information Technology Solution

Companies face a trade-off between creating stronger privacy protection policies for consumers and employing more sophisticated data collection methods. Justice-driven privacy protection outlines a method to manage this trade-off. We built on the theoretical lens of justice theory to integrate justice provision with two key privacy protection features, negotiation and active-recommendation, and proposed an information technology (IT) solution to balance the trade-off between privacy protection and consumer data collection. In the context of mobile banking applications, we prototyped a theory-driven IT solution, referred to as negotiation, active-recommendation privacy policy application, which enables customer service agents to interact with and actively recommend personalized privacy policies to consumers. We benchmarked our solution through a field experiment relative to two conventional applications: an online privacy statement and a privacy policy with only a simple negotiation feature. The results showed that the proposed IT solution improved consumers’ perceived procedural justice, interactive justice, and distributive justice and increased their psychological comfort in using our application design and in turn reduced their privacy concerns, enhanced their privacy awareness, and increased their information disclosure intentions and actual disclosure behavior in practice. Our proposed design can provide consumers better privacy protection while ensuring that consumers voluntarily disclose personal information desirable for companies.

Companies face a trade-off between creating stronger privacy protection policies for consumers and employing more sophisticated data collection methods. Justice-driven privacy protection outlines a method to manage this trade-off. We built on the theoretical lens of justice theory to integrate justice provision with key privacy protection features and conceptualized the extent to which these features affect privacy concerns and information disclosure behavior. Notably, we proposed an information technology (IT) solution to balance the trade-off between privacy protection and consumer data collection. In the context of mobile banking applications, we prototyped a theory-driven IT solution, referred to as negotiation, active-recommendation privacy policy application, which enables customer service agents to interact with and actively recommend personalized privacy policies to consumers. We benchmarked our solution through a field experiment relative to two conventional applications: a non-negotiation privacy policy application (only a nonnegotiable privacy statement is posted) as a base method and a negotiation, non-active-recommendation privacy policy application (only a negotiation feature is integrated with the privacy policy). The results showed that the proposed negotiation, active-recommendation privacy policy application decreased privacy concerns and increased consumers’ information disclosure intentions and actual disclosure behavior. A post hoc analysis corroborated these findings, indicating that our design enhanced perceived procedural justice, interactional justice, and distributive justice among consumers and made them feel comfortable to disclose their personal information. Likewise, companies would be able to collect additional personal information from consumers, thereby contributing to a privacy-friendly environment. We discuss contributions and the implications of our proposed IT solution for consumers, companies, developers, and public policy officials.