The Impact of Trust and Recommendation Quality on Adopting Interactive and Non-Interactive Recommendation Agents: A Meta-Analysis

ABSTRACT

Research on recommendation agents (RAs) originally focused on interactive RAs, which rely on explicit methods, i.e., eliciting user-provided inputs to learn about consumers’ needs and preferences. Recently, due to the availability of large amounts of data about individuals, the focus shifted toward non-interactive RAs that use implicit methods rather than explicit ones to understand users’ needs. This paper examined the differences between interactive and non-interactive RA types in terms of how they influence the impacts of two important antecedents of RA adoption, namely recommendation quality and trust on users’ cognitive and affective attitudes and behavioral intention. To that end, we developed a set of hypotheses and tested them empirically using a meta-analytic structural equation modeling approach. Our findings provide strong support for the influence of interactivity on RA users’ attitudes and cognitions. While we found that recommendation quality exerts a strong influence on consumers’ cognitive attitudes toward interactive RAs, this influence is statistically non-significant in the context of non-interactive RAs, in which recommendation quality mainly drives consumers’ affective attitudes toward the agent. Furthermore, while we found that cognitive attitudes exert a stronger influence than affective ones on consumers’ adoption of non-interactive RAs, our results indicate that the reverse is true with interactive RAs. Given the recent rise in the popularity of non-interactive RA tools, our results carry important implications for researchers and practitioners. Specifically, this study contributes to the extensive literature on consumers’ use of RAs by providing a better understanding of the differences between interactive and non-interactive RAs. For practitioners, the findings provide guidance for designers and providers of RAs on developing and improving RAs that are more likely to be adopted by consumers.

KEYWORDS:

• Recommendation agents
• electronic commerce
• system adoption
• online trust
• recommendation quality
• meta-analysis
• TSSEM
• heuristic-systematic model
• affective response model
• interactivity
• recommenders

Acknowledgements

We thank the Editor-in-Chief, Vladimir Zwass, and the anonymous reviewers for their valuable and constructive comments and suggestions throughout the review process. We are also grateful to Christian Matt and Weiquan Wang for their helpful feedback on an earlier version of this manuscript.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1. It is noteworthy that in the long history of support for the notion of multi-component view of attitudes, evaluative judgements are reflected in cognitive, affective, and conative (behavioral) responses [e.g., 13, 101]. However, recent research has focused mainly on the affective and cognitive components of attitudes [2, 29, 119, 120] considering the conative component of attitude as behavioral intention [35, 45]. Similarly, in this article, we focus on consumers’ cognitive and affective responses to RAs.

2. It should be noted that HSM and the elaboration likelihood model (ELM) are the two main variants of dual-process theories. While HSM and ELM share fundamental similarities, our decision to employ HSM in this study is because of its emphasis on a more complex interplay between systematic and heuristic cues to attitude change. More specifically, unlike ELM, which suggests the existence of a trade-off between the two paths to attitudes change (i.e., as systematic processing goes up, peripheral processing goes down), HSM posits that these two paths can and do take place simultaneously [141]. Indeed, according to HSM, heuristic and systematic processing could occur concurrently, and the results reinforce each other [141].

3. Some papers comprised more than one study.

4. During this stage, we had to drop some studies included in our data set used to test the main research model (i.e., Figure 1) because they either included both RA types (e.g., RA type was manipulated), or it was not possible to identify the RA type.

Additional information

Funding

This project was partially funded by the Faculty of Liberal Arts and Professional Studies, York University. In addition, Professor Benbasat thanks the Natural Sciences and Engineering Research Council of Canada (NSERC) for its support.

Notes on contributors

Sepideh Ebrahimi

Sepideh Ebrahimi ([email protected], corresponding author) is an Assistant Professor of Management Information Systems at the School of Administrative Studies, York University, Canada. Her research focuses on effective use of decision aids by individuals and organizations; ethical decision-making using information technology; and information technology adoption and use. Dr. Ebrahimi’s work has been published in Journal of Strategic Information Systems, Information & Management, and Electronic Markets has appeared in the conference proceedings such as those of International Conference on Information Systems and Hawaii International Conference on System Sciences.

Maryam Ghasemaghaei

Maryam Ghasemaghaei ([email protected]) is an Associate Professor and the Area Chair of Information Systems at DeGroote School of Business at McMaster University, Canada. Her research interests relate to the use of data analytics in organizations. Dr. Ghasemaghaei has published over 35 peer-reviewed articles in such journals as MIS Quarterly, Journal of Management Information Systems, Journal of Strategic Information Systems, Information Systems Journal, European Journal of Information Systems, Information & Management, Decision Support Systems, and many others.

Izak Benbasat

Izak Benbasat ([email protected]) is a member of the Order of Canada, a fellow of the Royal Society of Canada, and professor emeritus at Sauder School of Business, University of British Columbia. He holds a Ph.D. from the University of Minnesota and a Doctorat Honoris Causa, Université de Montréal. He was editor in chief of Information Systems Research, editor of the Information Systems and Decision Support Systems Department of Management Science, and a senior editor of MIS Quarterly. Dr. Benbasat received the LEO Award for Lifetime Exceptional Achievements in Information Systems from the Association for Information Systems, and is a Distinguished Fellow of the Institute for Operations Research and Management Sciences (INFORMS) Information Systems Society.