The effect of perceived usefulness of recommender systems and information sources on purchase intention

Abstract

Purpose

The current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.

Design/methodology/approach

The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.

Findings

This study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.

Practical implications

Developers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.

Originality/value

This research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

Citation

Mican, D. and Sitar-Taut, D.-A. (2023), "The effect of perceived usefulness of recommender systems and information sources on purchase intention", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-08-2022-1145