The Privacy Paradox in AI-Driven Marketing: A Theoretical Synthesis of Consumer Trust and Hyper-Personalization

Abstract

Consumers have a high level of privacy concerns and share personal information despite this. This mismatch, the privacy paradox, has been explained by rational tradeoff, bounded rationality, resignation and measurement artefact and continues to be the organizing puzzle in privacy research in marketing. This paper suggests that the puzzle is now mis-specified. All of the most dominant explanations place consumer agency at disclosure, as this was the time when disclosure was the means by which a firm learned about the consumer. Artificial intelligence has broken that connection: modern marketing systems can deduce attributes that have never been revealed, including behavioural data which contains no apparent information. Inference is when a consumer's disclosure behaviour is not contradictory to his/her privacy concern, but the disclosure is merely irrelevant to the outcome that he/she is interested in. This paradox is at least a relic of a disclosure theory in an inference technology. In this paper, the authors introduce the Inferential Trust Model, combining elements of privacy calculus, contextual integrity, and personalization research through three constructs: inferential distance, contextual congruence, and inferential awareness and a mechanism to differentiate between latent and felt privacy violations. The model helps to answer why the paradox is stable (violations are not revealed when the awareness is low), why trust breaks rather than slowly, and why transparency is double-edged. It also suggests an accuracy paradox: The more accurate the model, the harder it is to violate, and the more it is violated, the less trusting the personalization will be. There are then seven propositions and an operationalisation protocol. The report does not include any empirical data, but is, instead, conceptual.