The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
More than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates
Open accessAbstract
Customers increasingly rely on other consumers' reviews to make purchase decisions online. New insights into the customer review phenomenon can be derived from studying the semantic content and style properties of verbatim customer reviews to examine their influence on online retail sites' conversion rates. The authors employ text mining to extract changes in affective content and linguistic style properties of customer book reviews on Amazon.com . A dynamic panel data model reveals that the influence of positive affective content on conversion rates is asymmetrical, such that greater increases in positive affective content in customer reviews have a smaller effect on subsequent increases in conversion rate. No such tapering-off effect occurs for changes in negative affective content in reviews. Furthermore, positive changes in affective cues and increasing congruence with the product interest group's typical linguistic style directly and conjointly increase conversion rates. These findings suggest that managers should identify and promote the most influential reviews in a given product category, provide instructions to stimulate reviewers to write powerful reviews, and adapt the style of their own editorial reviews to the relevant product category.
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“Affect in and of itself is not a linguistic property but refers to an “internal feeling state” (Cohen et al. 2008, p. 297) that is “consciously accessible as the sim-More Than Words / 89 plest raw (nonreflective) feelings evident in moods and emotions” (Russell 2003, p. 148).”
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“In addition, reviews have long been related to sales (or its proxies), and yet there is growing consensus that online conversion rate offers a better metric for gauging online retail performance (Gurley 2000; Moe and Fader 2004).”
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Uniting the Tribes: Using Text for Marketing Insight
Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., & Schweidel, D. A. (2020) · Journal of Marketing · doi:10.1177/0022242919873106 · Cited by 742
Self-citationAutomated Text Analysis for Consumer Research
Humphreys, A., & Wang, R. J.-H. (2018) · Journal of Consumer Research · doi:10.1093/jcr/ucx104 · Cited by 636
A study of factors that contribute to online review helpfulness
Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.01.010 · Cited by 364
Unstructured data in marketing
Balducci, B., & Marinova, D. (2018) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0581-x · Cited by 300
A critical review of algorithms in HRM: Definition, theory, and practice
Cheng, M. M., & Hackett, R. D. (2021) · Human Resource Management Review · doi:10.1016/j.hrmr.2019.100698 · Cited by 283
Metadata
- Authors
- Ludwig, S., de Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., & Pfann, G.
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2013-01
- Volume
- 77
- Issue
- 1
- Pages
- 87-103
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W2096472197
- Cited by
- 736
- References
- 11
- FWCI
- 67.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ludwig, S., de Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., & Pfann, G. (2013). More than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates. Journal of Marketing. https://doi.org/10.1509/jm.11.0560
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