The Effect of Electronic Word of Mouth on Sales: A Meta-Analytic Review of Platform, Product, and Metric Factors
Open accessAbstract
The increasing amount of electronic word of mouth (eWOM) has significantly affected the way consumers make purchase decisions. Empirical studies have established an effect of eWOM on sales but disagree on which online platforms, products, and eWOM metrics moderate this effect. The authors conduct a meta-analysis of 1,532 effect sizes across 96 studies covering 40 platforms and 26 product categories. On average, eWOM is positively correlated with sales (.091), but its effectiveness differs across platform, product, and metric factors. For example, the effectiveness of eWOM on social media platforms is stronger when eWOM receivers can assess their own similarity to eWOM senders, whereas these homophily details do not influence the effectiveness of eWOM for e-commerce platforms. In addition, whereas eWOM has a stronger effect on sales for tangible goods new to the market, the product life cycle does not moderate the eWOM effectiveness for services. With respect to the eWOM metrics, eWOM volume has a stronger impact on sales than eWOM valence. In addition, negative eWOM does not always jeopardize sales, but high variability does.
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Online word of mouth has been and will continue to be important in marketing.
“Indeed, online WOM has been—and, we contend, will continue to be—important in marketing (e.g., in the meta-analysis by Babić Rosario et al. 2016 the authors found, on average, a positive correlation between online WOM and sales).”
From The future of social media in marketing (2020) · cites this paper as Babić Rosario et al. 2016
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Attracting Comments: Digital Engagement Metrics on Facebook and Financial Performance
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Personal or Product Reputation? Optimizing Revenues in the Sharing Economy
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App Popularity: Where in the World are Consumers Most Sensitive to Price and User Ratings?
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Negative word of mouth can be a positive for consumers connected to the brand
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Influencing Social Media Influencers Through Affiliation
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Anger in Consumer Reviews: Unhelpful but Persuasive?
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- Authors
- Babić Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. A.
- Affiliations
- HEC Paris
- Vrije Universiteit Amsterdam (NL)
- Faculty of Economics and Business, University of Groningen
- Paris
- Corresponding author
- Sotgiu, Francesca
- Bijmolt, Tammo H.A.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2016-06
- Volume
- 53
- Issue
- 3
- Pages
- 297-318
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2239794783
- Cited by
- 1,073
- References
- 81
- Citations by year
- 2026: 99 · 2025: 85 · 2024: 108 · 2023: 141 · 2022: 143 · 2021: 124 · 2020: 117 · 2019: 110 · 2018: 83 · 2017: 39 · 2016: 23 · 2015: 1
- FWCI
- 161.09
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Journal of Marketing Research (publishedVersion)
- Digital Commons - DU (University of Denver) (submittedVersion, cc-by-nc-nd)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- VU Research Portal (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Agence Nationale de la Recherche (LABX-0047, ANR-11-IDEX-0003, ANR-11-LABX-0047, ANR-11-IDEX)
- Fondation HEC (ANR‐11‐IDEX‐0003/Labex Ecodec/ANR‐11‐LABX‐0047, Ecodec/ANR-11-LABX-0047, ANR-11-IDEX-0003/Labex, F1307)
- Labex Ecodec (ANR-11-IDEX-0003/Labex Ecodec/ANR-11-LABX-0047, ANR - 11-LABX-0047, ANR-11-IDEX-0003/Labex, Ecodec/ANR-11-LABX-0047, ANR-11-IDEX-0003)
- Topics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- electronic word of mouth, sales, social networking sites, e-commerce platforms, homophily, product life cycle, tangible goods, service, purchase decision
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 ›
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