Perceived Algorithmic Personalisation and Platform Trust Among Generation Z in India: The Role of Cultural Congruence

This study examines how algorithmic personalisation relates to platform trust among Generation Z users in India. It proposes cultural congruence, the perceived fit between recommendations and a user’s cultural identity, as a construct that may link personalisation to trust, tests whether it also conditions that link, and models a parallel privacy pathway. Survey data from 302 platform users were analysed with covariance-based structural equation modelling and moderated regression. Perceived personalisation was positively associated with cultural congruence (β = 0.459) and privacy concern (β = 0.238). Cultural congruence was positively associated with trust in platforms’ data practices (β = 0.280), and the indirect effect of personalisation on trust through cultural congruence was significant (β = 0.129), whereas the bootstrapped direct effect was not significant. The test of cultural congruence as a moderator of the personalisation–trust association was inconclusive (B = 0.169, p = 0.249 with HC3 standard errors). Privacy concern was positively associated with algorithmic resistance (β = 0.427) but not with trust; unexpectedly, resistance was positively associated with trust (β = 0.239). The findings are consistent with the possibility that, for this cohort, personalisation is associated with trust largely to the extent that recommendations are experienced as culturally fitting. As a suggestion for policy discussion, limited to young users who are in or have completed higher education, we note that India’s Digital Personal Data Protection Act 2023 regulates consent but not the cultural representativeness of recommendation outputs.