Artificial intelligence and its implications for data privacy.

Contemporary, multidisciplinary research sheds light on data privacy implications of artificial intelligence (AI). This review adopts an AI ecosystem perspective and proposes a process-outcome continuum to classify AI technologies; this perspective helps to understand the nuances of AI relative to psychological aspects of privacy decision-making. Specifically, different types of AI affect traditionally studied privacy decision-making frameworks including the privacy calculus, psychological ownership, and social influence in varied ways. By understanding how the process- or outcome-orientation of an AI technology affects privacy decision-making, we explain how AI creates privacy benefits but also poses challenges. Future research is needed across privacy decision-making, but also more generally at the intersection of privacy and AI, to help foster an ethical, sustainable society.

Digital technologies: tensions in privacy and data.

Driven by data proliferation, digital technologies have transformed the marketing landscape. In parallel, significant privacy concerns have shaken consumer-firm relationships, prompting changes in both regulatory interventions and people's own privacy-protective behaviors. With a comprehensive analysis of digital technologies and data strategy informed by structuration theory and privacy literature, the authors consider privacy tensions as the product of firm-consumer interactions, facilitated by digital technologies. This perspective in turn implies distinct consumer, regulatory, and firm responses related to data protection. By consolidating various perspectives, the authors propose three tenets and seven propositions, supported by interview insights from senior managers and consumer informants, that create a foundation for understanding the digital technology implications for firm performance in contexts marked by growing privacy worries and legal ramifications. On the basis of this conceptual framework, they also propose a data strategy typology across two main strategic functions of digital technologies: data monetization and data sharing. The result is four distinct types of firms, which engage in disparate behaviors in the broader ecosystem pertaining to privacy issues. This article also provides directions for research, according to a synthesis of findings from both academic and practical perspectives.

Critical roles of knowledge and motivation in privacy research.

Much research has focused on privacy concern, which describes individuals' motivation to protect personal information from unauthorized access, collection, storage, and use. Variation in privacy concern has been attributed to differences in three key factors: 1) chronic privacy attitudes, 2) information sensitivity, and 3) context. While each factor affects individuals' motivation to protect personal information, none of them explicitly accounts for differences in individuals' privacy knowledge (or privacy literacy), which consists of factual, procedural, or experiential familiarity with privacy-related issues. Calling attention to how little research has investigated both privacy concern and privacy literacy, we argue that understanding how knowledge and motivation interact is critical to accurately predicting how people will respond to privacy threats.