More than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates

By Stephan Ludwig, Ko de Ruyter, Mike Friedman, E. Brüggen, Martin Wetzels, G. Pfann. 2013

709 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/5ea4b26c602b389d0089d9fdce8298d030560d47

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Marketing

In Sociology and Political Science

Citations in the corpus

Detecting, Preventing, and Mitigating Online Firestorms in Brand Communities (2019, background): “Recent research has suggested that different drivers of virality may reinforce one another (Ludwig et al. 2013).”
Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages (2019, background): “Joint Implications of Speech and Rhetoric for Consumer Sharing The inseparability of content and style in communication (Ludwig et al. 2013) stems from the inherent association between speech acts and rhetoric (i.e., alliteration and repetition) (Austin 1962).”
What We Know and Don't Know about Online Word-of-Mouth: A Review and Synthesis of the Literature (2014, background): “For instance, Ludwig et al. (2013) find that linguistic styles can positively affect source perceptions and lead to higher conversion rates.”
Sentence-Based Text Analysis for Customer Reviews (2016, background): “• Analyzing the change in conversion rates as a result of changes in a↵ective content and linguistic style of online reviews (Ludwig et al., 2013).”
Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews (2019, methodology): “Customer ratings supported by lengthy textual reviews containing rich information are favored by customers, and thus hotels should identify and promote the most influential reviews and provide instructions to motivate customers to write powerful reviews (Ludwig et al., 2013).”
Keep Your Cool or Let it Out: Nonlinear Effects of Expressed Arousal on Perceptions of Consumer Reviews (2017, background): “Ludwig et al. (2013) show that the positive or negative tone of online retailer reviews influences ultimate conversion rates.”
Online Reviews (2016, background): “Previous research suggests that peripheral factors influence consumers’ product/service evaluations when they are exposed to a large amount of information in an online environment (e.g., tone of certainty in reviews, Karmarkar and Tormala 2010; linguistic style, Ludwig et al. 2013).”