The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Dyadic Study of Interpersonal Information Search
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
The Persuasion Knowledge Model: How People Cope with Persuasion Attempts
Friestad, M., & Wright, P. (1994) · Journal of Consumer Research · doi:10.1086/209380 · Cited by 3,811
Customer Switching Behavior in Service Industries: An Exploratory Study
Keaveney, S. M. (1995) · Journal of Marketing · doi:10.2307/1252074 · Cited by 2,905
The role of involvement in attention and comprehension processes
Celsi, R. L., & Olson, J. C. (1988) · Journal of Consumer Research · doi:10.1086/209158 · Cited by 2,070
Cited by
The influence of online product recommendations on consumers' online choices
Senecal, S., & Nantel, J. (2004) · Journal of Retailing · doi:10.1016/j.jretai.2004.04.001 · Cited by 1,585
Word-of-Mouth Processes within a Services Purchase Decision Context
Bansal, H. S., & Voyer, P. A. (2000) · Journal of Service Research · doi:10.1177/109467050032005 · Cited by 1,334
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
A multi-stage model of word-of-mouth influence through viral marketing
De Bruyn, A., & Lilien, G. L. (2008) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2008.03.004 · Cited by 906
Park, D.-H., & Kim, S. (2008) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2007.12.001 · Cited by 837
Trust me, trust me not: A nuanced view of influencer marketing on social media
Kim, D. Y., & Kim, H.-Y. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.05.024 · Cited by 804
Social media engagement behaviour: a uses and gratifications perspective
Dolan, R., Conduit, J., Fahy, J., & Goodman, S. (2016) · Journal of Strategic Marketing · doi:10.1080/0965254x.2015.1095222 · Cited by 720
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 effect of characteristics of source credibility on consumer behaviour: A meta-analysis
Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2019.01.005 · Cited by 504
Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018) · Information & Management · doi:10.1016/j.im.2018.04.010 · Cited by 477
Money, R. B., & Crotts, J. C. (2003) · Tourism Management · doi:10.1016/s0261-5177(02)00057-2 · Cited by 448
Word of Mouse: The Role of Cognitive Personalization in Online Consumer Reviews
Xia, L., & Bechwati, N. N. (2008) · Journal of Interactive Advertising · doi:10.1080/15252019.2008.10722143 · Cited by 365
Reichelt, J., Sievert, J., & Jacob, F. (2014) · Journal of Marketing Communications · doi:10.1080/13527266.2013.797758 · Cited by 262
de Vries, L., Gensler, S., & Leeflang, P. S. H. (2017) · Journal of Marketing · doi:10.1509/jm.15.0178 · Cited by 260
In Search of Negativity Bias: An Empirical Study of Perceived Helpfulness of Online Reviews
Wu, P. F. (2013) · Psychology & Marketing · doi:10.1002/mar.20660 · Cited by 242
Muda, M., & Hamzah, M. I. (2021) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-04-2020-0072 · Cited by 225
The consumerist turn in higher education: Policy aspirations and outcomes
Naidoo, R., Shankar, A., & Veer, E. (2011) · Journal of Marketing Management · doi:10.1080/0267257x.2011.609135 · Cited by 221
Receiver responses to rewarded referrals: the motive inferences framework
Verlegh, P. W. J., Ryu, G., Tuk, M. A., & Feick, L. (2013) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-013-0327-8 · Cited by 161
Understanding Virtual Reality in Marketing: Nature, Implications and Potential
Barnes, S. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2909100 · Cited by 149
Susceptibility to and impact of interpersonal influence in an investment context
Hoffmann, A. O. I., & Broekhuizen, T. L. J. (2009) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-008-0128-7 · Cited by 122
The Role of Metaperception on the Effectiveness of Referral Reward Programs
Wirtz, J., Orsingher, C., Chew, P., & Tambyah, S. K. (2013) · Journal of Service Research · doi:10.1177/1094670512462138 · Cited by 119
Exploring the WOW in online-auction feedback
Weinberg, B. D., & Davis, L. (2005) · Journal of Business Research · doi:10.1016/j.jbusres.2004.06.004 · Cited by 108
Tuzovic, S. (2010) · Journal of Services Marketing · doi:10.1108/08876041011072564 · Cited by 81
(Monroe) Meng, L., Kou, S., Duan, S., & Bie, Y. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114874 · Cited by 80
The impact of uncertain rewards on customers’ recommendation intention in social networks
Wang, Q., Sun, Y., Zhu, J., & Zhang, X. (2018) · Internet Research · doi:10.1108/intr-03-2017-0116 · Cited by 21
Metadata
- Authors
- Gilly, M. C., Graham, J. L., Wolfinbarger, M. F., & Yale, L. J.
- Venue
- Journal of the Academy of Marketing Science
- Published
- 1998-04-01
- Volume
- 26
- Issue
- 2
- Pages
- 83-100
- ISSN
- 0092-0703 (ISSN-L)
- OpenAlex
- W2003119551
- Cited by
- 735
- References
- 3
- FWCI
- 7.18
- 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.