The Personalization–Privacy Paradox in AI-Enabled Airline Services: A Systematic Critical Review of Perceived Value, Algorithmic Trust, Privacy Risk and Passenger Loyalty

Abstract

 Artificial Intelligence (AI) is changing the services of airlines through a highly personalized customer journey, i.e. travel planning booking pricing, airport operations, managing disruption, customer support, loyalty programs and post-flight communication. Even though personalization may bring about the benefits of higher relevance convenience efficiency, perceived usefulness and better passenger experiece, its reliance on personal and behavioural data creates a personalization-privacy paradox. This systematic critical review aims to integrate various research results on the subjects of AI-powered personalization, perceived value, algorithmic trust, privacy risk satisfaction repeats purchase intention and passenger loyalty. The analysis of the review suggests that personalization by itself is not a loyalty driver, its impacts mainly flow through perceived value, satisfaction and engagement on one side and algorithmic or digital trust on the other, whereas privacy risk may limit these positive effects. The study conducted from 128 sample of passengers of Pakistan International Airlines not only supports the theoretical argument with the empirical data at the level specific to the airline industry, but also reveals the relationship between customers satisfaction and AI perceived usefulness (r = 0.610; p <0.001), partial mediation role of digital trust and a negative moderating effect of privacy concerns on the (AI benefits, customer loyalty relationship) = -0.717, p = 0.033). Synthesized evidence from these reviews highlights that trustworthy personalization depends heavily on the extent to which one is informed about personal data usage (transparency), the extent to which one can easily make sense of how personal data are processed (explainability), correctness of data processing (accuracy), equitable distribution of the processing outcomes among the stakeholders (fairness), protection of the data against misuse (security), the amount of control one feels over own data (perceived control) and data governance at individual level that reflects one's responsibilities and accountabilities (responsible data governance). The review proposes that when the value created by AI is balanced with the passengers' trust in the AI-generated decisions and their perception of the privacy risks of their data having been used in AI to deliver personalized services. It also highlights research areas that need to be explored like limited industry-specific evidence, use of the same set of survey questions over time designs (cross-sectional), different versions of trust scales, less cross-cultural as well as longitudinal investigations and the exploration of the nonlinear effects of personalization is yet to be a research priority. The review offers an integrated model showing how airlines could be able to combine technology-driven personalization with passenger autonomy privacy trust and relationship outcomes.