Abstract:

Purpose: This study examines how AI-driven personalisation on e-commerce platforms shapes consumer purchase intention and customer satisfaction, and tests three mechanisms—perceived usefulness, trust, and customer satisfaction—through which the effect operates, together with perceived privacy risk as a boundary condition.

Design/methodology/approach: Integrating the Technology Acceptance Model and Expectation-Confirmation Theory, a model with nine hypotheses is proposed and tested on survey data from active online shoppers in India using partial least squares structural equation modelling (PLS-SEM).

Findings: AI-driven personalisation positively influences perceived usefulness, trust, and satisfaction, and these constructs jointly mediate its effect on purchase intention; the direct path remains significant but modest. Perceived privacy risk significantly weakens the personalisation-to-outcome relationships.

Originality/value: The study offers an integrated, mechanism-level account of personalisation effects in the contemporary AI context and provides retailers with evidence on balancing relevance against privacy concerns.

Keywords:

AI-Driven Personalisation; Recommender Systems; Trust; Purchase Intention; Customer Satisfaction; Perceived Privacy Risk; PLS-SEM.

Aim/Objectives

Methods