The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites
Abstract
Recommendations and consumer reviews are universally acknowledged as significant features of a business-to-consumer website. However, because of the well-documented obstacles to measuring the causal impact of these artifacts, there is still a lack of empirical evidence demonstrating their influence on two important outcome variables in the shopping context: perceived usefulness and social presence. To test the existence of a causal link between information technology (IT)-enabled support for the provision of recommendations and consumer reviews on the usefulness and social presence of the website, this study employs a novel approach to generate the experimental conditions by filtering the content of Amazon.com in real time. The results show that the provision of recommendations and consumer reviews increases both the usefulness and social presence of the website.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Trust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635
The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Komiak, S. Y. X., & Benbasat, I. (2006) · MIS Quarterly · doi:10.2307/25148760 · Cited by 1,374
Self-citationManaging User Trust in B2C e-Services
Gefen, & Straub (2003) · e-Service Journal · doi:10.2979/esj.2003.2.2.7 · Cited by 1,074
Online peer and editorial recommendations, trust and choice in virtual markets
Smith, D., Menon, S., & Sivakumar, K. (2005) · Journal of Interactive Marketing · doi:10.1002/dir.20041 · Cited by 683
The ethics of persuasive technology
Fogg, B. J. (2003) · Persuasive Technology · doi:10.1016/b978-155860643-2/50011-1 · Cited by 473
Kim, D., & Benbasat, I. (2006) · Information Systems Research · doi:10.1287/isre.1060.0093 · Cited by 239
Self-citation
Cited by
The impact of electronic word-of-mouth communication: A literature analysis and integrative model
Cheung, C. M. K., & Thadani, D. R. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.06.008 · Cited by 2,005
Erkan, I., & Evans, C. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.03.003 · Cited by 1,188
What makes a useful online review? Implication for travel product websites
Liu, Z., & Park, S. (2015) · Tourism Management · doi:10.1016/j.tourman.2014.09.020 · Cited by 963
Qiu, L., & Benbasat, I. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222250405 · Cited by 737
Self-citationSomeone out there? A study on the social presence of anthropomorphized chatbots
Konya-Baumbach, E., Biller, M., & von Janda, S. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107513 · Cited by 283
Zhang, T. (., Agarwal, R., & Lucas, H. C. (2011) · MIS Quarterly · doi:10.2307/41409964 · Cited by 233
Trust transfer in the continued usage of public e-services
Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2014) · Information & Management · doi:10.1016/j.im.2014.05.016 · Cited by 204
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00704-3 · Cited by 204
“Social presence resulting from anthropomor-phization has been associated with trust, engagement, and satisfaction (Kumar & Benbasat, 2006; Picard, 1997; Qiu & Benbasat, 2009; Turkle, 2017; Bleier, Harmeling, & Palmatier 2019).”
BackgroundTravel web-site design: Information task-fit, service quality and purchase intention
Dedeke, A. (. (2016) · Tourism Management · doi:10.1016/j.tourman.2016.01.001 · Cited by 171
Exploring the mechanism of social media addiction: an empirical study from WeChat users
Cao, X., Gong, M., Yu, L., & Dai, B. (2020) · Internet Research · doi:10.1108/intr-08-2019-0347 · Cited by 158
Who to trust? Applying trust to social commerce.
Sharma, S., Menard, P., & Mutchler, L. A. (2019) · Journal of Computer Information Systems · doi:10.1080/08874417.2017.1289356 · Cited by 156
The Effects of Culture on Consumers’ Consumption and Generation of Online Reviews
Kim, J. M., Jun, M., & Kim, C. K. (2018) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2018.05.002 · Cited by 118
New Information Technology and Implicit Bias
Elsbach, K. D., & Stigliani, I. (2019) · Academy of Management Perspectives · doi:10.5465/amp.2017.0079 · Cited by 74
An Examination of Factors Associated with User Acceptance of Social Shopping Websites
Shen, J., & Eder, L. B. (2011) · International Journal of Technology and Human Interaction · doi:10.4018/jthi.2011010102 · Cited by 65
Wang, Y.-Y., Luse, A., Townsend, A. M., & Mennecke, B. E. (2015) · Information Systems and e-Business Management · doi:10.1007/s10257-014-0269-9 · Cited by 54
Haque, M. R., & Rubya, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555146 · Cited by 48
Positivity Bias in Customer Satisfaction Ratings
Park, K., Cha, M., & Rhim, E. (2018) · Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 · doi:10.1145/3184558.3186579 · Cited by 19
Li, Z., & Shi, Z. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2584725 · Cited by 9
Laghari, A. H., han, P., Khoso, W. M., & Memon, S. (2026) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2026.104885 · Cited by 2
Metadata
- Authors
- Kumar, N., & Benbasat, I.
- Venue
- Information Systems Research
- Published
- 2006-12
- Volume
- 17
- Issue
- 4
- Pages
- 425-439
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2163528872
- Cited by
- 504
- References
- 7
- FWCI
- 25.36
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
