ABSTRACT
Personalized recommendation is used in e-commerce websites to reduce users’ cognitive load by offering highly relevant products and services addressing their implicit needs. But little is known about the effect of personalized recommendation techniques and their attribute effects on users’ privacy concerns and trust towards purchase behavior. This research addresses gaps and develops a predictive model exploring the effect of personalized recommendation on users’ purchase decision mediated by users’ trust, and privacy concerns towards personalization e-commerce sites. The proposed work provides a comprehensive review of recommendation technique collaborative filtering used in the e-commerce website Amazon.in. The structural equation modeling technique is used to identify the fit of the proposed model of users’ decision making and recommendation effect. The results show the best model fit with root mean squared error of approximation (RMSEA) value 0.039 stating the effects of personalized recommendation quality, relevance, and timing on users’ privacy concerns, and trust as a major factor affecting purchase intention with e-commerce websites. Highly relevant personalized recommendations induce a positive effect on users’ purchases from e-commerce websites. The proposed model shows that users’ purchase behavior is highly dependent on quality and relevance of personalized recommendations and are also affected by trust and privacy concerns. Users having high privacy concerns and trust issues lose purchase interest if their privacy concerns are not addressed with personalized recommendation.
