The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products
Abstract
Consumers assess the credibility of online product reviews to guide their purchase decisions. However, little is known about how consumers determine the credibility of online product reviews. This article examines the effect of the level of detail in a product review and the level of reviewer agreement with it on the credibility of a review and consumers’ purchase intentions for search and experience products. Overall, the results indicate that more credible reviews lead to higher purchase intentions. Interestingly, the findings also demonstrate that consumers determine the credibility of a review differently for search and experience products. For search products, consumers deem online reviews to be more credible when the reviews contain detailed information about the product. However, for experience products, consumers determine the credibility of a review by assessing the level of reviewer agreement with a review. The lack of diagnosticity of detailed information in online reviews of experience products is attributed to the idiosyncratic nature of experiences. Implications for research and practice are discussed.
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SPSS and SAS procedures for estimating indirect effects in simple mediation models
Preacher, K. J., & Hayes, A. F. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03206553 · Cited by 17,383
Reconsidering Baron and Kenny: myths and truths about mediation analysis
Zhao, X., Lynch, J. G., & Chen, Q. (2010) · Journal of Consumer Research · doi:10.1086/651257 · Cited by 12,583
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
The Persuasiveness of Source Credibility: A Critical Review of Five Decades' Evidence
Pornpitakpan, C. (2004) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.2004.tb02547.x · Cited by 2,205
Error bars in experimental biology
Cumming, G., Fidler, F., & Vaux, D. L. (2007) · The Journal of Cell Biology · doi:10.1083/jcb.200611141 · Cited by 940
Mitra, K., Reiss, M. C., & Capella, L. M. (1999) · Journal of Services Marketing · doi:10.1108/08876049910273763 · Cited by 507
Andrews, J. C., Burton, S., & Netemeyer, R. G. (2000) · Journal of Advertising · doi:10.1080/00913367.2000.10673615 · Cited by 232
Cited by
Xiao, L., Li, X., & Zhang, Y. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103170 · Cited by 214
Online reviews and product sales: the role of review visibility
Alzate, M., Arce-Urriza, M., & Cebollada, J. (2021) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer16040038 · Cited by 45
Online shopping: How can algorithm performance expectancy enhance impulse buying?
Gallin, S., & Portes, A. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103988 · Cited by 41
Ketron, S. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2017.08.008 · Cited by 37
Sudaryanto, S., Hanim, A., Rosediana Dewi, I., Kartikasari, A. D., & Rusdiyanto, R. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2503093 · Cited by 21
Metadata
- Authors
- Jiménez, F. R., & Mendoza, N. A.
- Venue
- Journal of Interactive Marketing
- Publisher
- SAGE Publications
- Published
- 2013-08
- Volume
- 27
- Issue
- 3
- Pages
- 226-235
- ISSN
- 1094-9968 (ISSN-L)
- 1520-6653
- OpenAlex
- W2049801154
- Cited by
- 383
- References
- 7
- FWCI
- 54.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jiménez, F. R., & Mendoza, N. A. (2013). Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products. Journal of Interactive Marketing. https://doi.org/10.1016/j.intmar.2013.04.004
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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