Professor Markus Blut markus.blut@durham.ac.uk
Professor
Testing the performance of online recommendation agents: A meta-analysis
Blut, Markus; Ghiassaleh, Arezou; Wang, Cheng
Authors
Dr Arezou Ghiassaleh arezou.ghiassaleh@durham.ac.uk
Assistant Professor
Cheng Wang
Abstract
Many retailers (e.g., Amazon, Walmart) use various types of online recommendation agents (RAs) on their websites to suggest goods and services to consumers. These RAs screen millions of options to ease consumers’ information search and evaluation. To determine which RA types best support consumers’ efforts, the present research reports a meta-analysis of perceived recommendation quality research, a key performance metric that gauges RAs from consumers’ perspectives. To test the framework derived from this meta-analysis, the authors rely on data gathered from 32,172 consumers, reported in 122 samples. The results affirm that some RAs perform better than others in leveraging the effects of perceived recommendation quality on consumers’ decision-making satisfaction, RA satisfaction, and intention to use the RA in the future. The best performing RAs feature specific algorithms (i.e., collaborative filtering, interactive RAs, and self-serving recommendations), recommendation presentations (i.e., solicited recommendation), and data sources (i.e., location-based and social network–based RAs). Moreover, the results suggest that some RAs perform better than others in leveraging the effects of decision-making and RA satisfaction on future use intentions. These insights advance RA theory and provide guidance for managers, with regard to choosing the optimal RA.
Citation
Blut, M., Ghiassaleh, A., & Wang, C. (2023). Testing the performance of online recommendation agents: A meta-analysis. Journal of Retailing, 99(3), 440-459. https://doi.org/10.1016/j.jretai.2023.08.001
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 7, 2023 |
Online Publication Date | Aug 19, 2023 |
Publication Date | 2023-09 |
Deposit Date | Aug 21, 2023 |
Publicly Available Date | Aug 21, 2023 |
Journal | Journal of Retailing |
Print ISSN | 0022-4359 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 99 |
Issue | 3 |
Pages | 440-459 |
DOI | https://doi.org/10.1016/j.jretai.2023.08.001 |
Public URL | https://durham-repository.worktribe.com/output/1721890 |
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/© 2023 The Author(s). Published by Elsevier Inc. on behalf of New York University. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)
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Publisher Licence URL
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Copyright Statement
(c) The Author(s). Published by Elsevier Inc. on behalf of New York University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/
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