The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
On Brands and Word of Mouth
Abstract
Brands and word of mouth (WOM) are cornerstones of the marketing field, and yet their relationship has received relatively little attention. This study aims to enhance understanding of brand characteristics as antecedents of WOM by executing a comprehensive empirical analysis. For this purpose, the authors constructed a unique data set on online and offline WOM and characteristics for more than 600 of the most talked-about U.S. brands. To guide this empirical analysis, they present a theoretical framework arguing that consumers spread WOM on brands as a result of social, emotional, and functional drivers. Using these drivers, the authors identify a set of 13 brand characteristics that stimulate WOM, including three (level of differentiation, excitement, and complexity) that have not been studied to date as WOM antecedents. The authors find that whereas the social and functional drivers are the most important for online WOM, the emotional driver is the most important for offline WOM. These results provide an insightful perspective on WOM and have meaningful managerial implications for brand management and investment in WOM campaigns.
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Cites
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
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
Computer-Mediated Communication: Impersonal, Interpersonal, and Hyperpersonal Interaction
WALTHER, J. B. (1996) · Communication Research · doi:10.1177/009365096023001001 · Cited by 4,798
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178 · cited 1×
“Consuming or thinking about a brand can evoke emotions that people might like to share with others (Heath, Bell, and Sternberg 2001; Nardi et al. 2004; Peters and Kashima 2007) to express or ease emotional arousal (Berger and Milkman 2012).”
BackgroundOpinion Leadership and Social Contagion in New Product Diffusion
Iyengar, R., Van den Bulte, C., & Valente, T. W. (2011) · Marketing Science · doi:10.1287/mksc.1100.0566 · Cited by 996
Firm-Created Word-of-Mouth Communication: Evidence from a Field Test
Godes, D., & Mayzlin, D. (2009) · Marketing Science · doi:10.1287/mksc.1080.0444 · Cited by 837
What Drives Immediate and Ongoing Word of Mouth?
Berger, J., & Schwartz, E. M. (2011) · Journal of Marketing Research · doi:10.1509/jmkr.48.5.869 · Cited by 663
Arousal Increases Social Transmission of Information
Berger, J. (2011) · Psychological Science · doi:10.1177/0956797611413294 · Cited by 600
Cited by
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178
Word of mouth and interpersonal communication: A review and directions for future research
Berger, J. (2014) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2014.05.002 · Cited by 1,469
Lamberton, C., & Stephen, A. T. (2016) · Journal of Marketing · doi:10.1509/jm.15.0415 · Cited by 1,127
Mobile advertising: A framework and research agenda
Grewal, D., Bart, Y., Spann, M., & Zubcsek, P. P. (2016) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2016.03.003 · Cited by 316
Akpinar, E., & Berger, J. (2017) · Journal of Marketing Research · doi:10.1509/jmr.13.0350 · Cited by 267
Metadata
- Authors
- Lovett, M. J., Peres, R., & Shachar, R.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2013-08
- Volume
- 50
- Issue
- 4
- Pages
- 427-444
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W3123768472
- Cited by
- 446
- References
- 9
- FWCI
- 111.28
- Type
- journal article
- Language
- en
- Open access
- closed
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