The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Why pass on viral messages? Because they connect emotionally
Overview
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Citations
Cites
Should we delight the customer?
Rust, R. T., & Oliver, R. L. (2000) · Journal of the Academy of Marketing Science · doi:10.1177/0092070300281008 · Cited by 712
Cited by
Social media: The new hybrid element of the promotion mix
Mangold, W. G., & Faulds, D. J. (2009) · Business Horizons · doi:10.1016/j.bushor.2009.03.002 · Cited by 4,111
Hughes, C., Swaminathan, V., & Brooks, G. (2019) · Journal of Marketing · doi:10.1177/0022242919854374 · Cited by 789
Social media and the formation of organizational reputation
Etter, M., Ravasi, D., & Colleoni, E. (2019) · Academy of Management Review · doi:10.5465/amr.2014.0280 · Cited by 428
Good Friends, Bad News - Affect and Virality in Twitter
Hansen, L. K., Arvidsson, A., Nielsen, F. A., Colleoni, E., & Etter, M. (2011) · Communications in Computer and Information Science · doi:10.1007/978-3-642-22309-9_5 · Cited by 361
A comparison of social media marketing between B2B, B2C and mixed business models
Iankova, S., Davies, I., Archer-Brown, C., Marder, B., & Yau, A. (2019) · Industrial Marketing Management · doi:10.1016/j.indmarman.2018.01.001 · Cited by 338
Stand out of our Light: Freedom and Resistance in the Attention Economy
Williams, J. (2018) · doi:10.1017/9781108453004 · Cited by 303
Cheng, Z., & Li, Y. (2024) · Social Science Computer Review · doi:10.1177/08944393231178603 · Cited by 131
“Another direction is to explore how messages containing discrete emotions like anger, disgust, surprise, sadness, and fear predict message virality (e.g., de León & Trilling, 2021; Dobele et al., 2007; Lin & Utz, 2015).”
BackgroundSo, J., Prestin, A., Lee, L., Wang, Y., Yen, J., & Chou, W.-Y. S. (2016) · Health Communication · doi:10.1080/10410236.2014.940675 · Cited by 123
Alhabash, S., Almutairi, N., Lou, C., & Kim, W. (2019) · Media Psychology · doi:10.1080/15213269.2017.1416296 · Cited by 80
Giving electronic word of mouth (eWOM) as a prepurchase behavior: The case of online group buying
Lim, W. M., Ahmed, P. K., & Ali, M. Y. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.093 · Cited by 79
Roy, A., Huh, J., Pfeuffer, A., & Srivastava, J. (2017) · Journal of Advertising · doi:10.1080/00913367.2017.1297272 · Cited by 41
Metadata
- Authors
- Dobele, A., Lindgreen, A., Beverland, M., Vanhamme, J., & van Wijk, R.
- Venue
- Business Horizons
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 50
- Issue
- 4
- Pages
- 291-304
- ISSN
- 0007-6813 (ISSN-L)
- OpenAlex
- W2095507885
- Cited by
- 502
- References
- 1
- FWCI
- 29.35
- 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.
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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.
