More Than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates
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Citations
What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature
Automated Text Analysis for Consumer Research
Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse
Uniting the Tribes: Using Text for Marketing Insight
Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews
References
Biases in dynamic models with fixed effects
Social psychology: Handbook of basic principles.
Using Online Conversations to Study Word-of-Mouth Communication
Toward a mechanistic psychology of dialogue
What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com
Related Papers (5)
Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics
Research Note: What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com
Frequently Asked Questions (15)
Q2. How did the authors calculate the interaction term?
The exogenous variables indicated frequency (percentages of affect-laden positive and negative content words in the review text; match percentage in function words), so the authors mean-centered the variables and calculated the interaction term by multiplying mean-centered variable scores.
Q3. Why did the sample size for the models fall to 4763 observations?
Because the authors used first differencing and the lagged values for conversion rate, the sample size for the models fell to 4763 observations (591 books).
Q4. What is the effect of changes in helpfulness on conversion rates?
Changes in helpfulness were weakly significant, positive predictors of subsequent conversion rate changes (helpfulness = .006, p < .10).
Q5. What is the role of linguistic styles in the evaluation of a customer review?
In an online customer review context, readers often have little but the review text to use to form their perceptions of the review’s diagnosticity, so linguistic styles may serve as identity-descriptive information that, as a heuristic cue, shapes consumers’ evaluations of the review and thus of the product.
Q6. What is the effect of affective content on the evaluation of a product?
Evidence from previous studies that use experimental manipulations to prime affective states suggests that exposure to affective cues influences evaluations and/or judgments of attitude objects, such as brands and products: Positive (negative) affective cues lead to more positive (negative) evaluations and judgments (e.g., Lau-Gesk and Meyers-Levy 2009).
Q7. What are the main aspects of the diagnosticity of affective content words?
Two facets of diagnosticity documented in prior literature seem relevant to (conversion) behavior: (1) perceived representativeness, which is related to the extent to which consumers believe that affective content reflects the target and whether the representation of the sender indicates qualifications to express his or her opinions, and (2) perceived validity, or whether affective cues appear consistent with other cues and across multiple sources (Gasper and Clore 1998).
Q8. Why did the authors conduct separate calculations for title and text intensities?
Because review titles are particularly prominent, the authors mined and conducted separate calculations for title and text intensities similar to Cao, Duan, and Gan (2011).
Q9. What is the effect of the anonymous nature of online review settings?
The anonymous nature of online review settings makes it difficult to establish sender qualifications, but extreme deviations in affective cues lower the value of feelings as information and elicit counterproductive effects by reducing diagnosticity (Andrade 2005).
Q10. What are the main concerns about the validity of reviews?
Various concerns persist about the validity of reviews (Mudambi and Schuff 2010), and the authors confirm that in the case of sharp increases in positive affective content, the conversion rate increases are smaller than if the positive affective content increase were more moderate.
Q11. What does the research show about the effect of online review settings?
Online review settings remove the faceto-face contacts that traditionally have informed word-ofmouth recommendations, but their research reveals that the contents of reviews have significant effects when their linguistic style elicits source similarity perceptions.
Q12. What is the linear relationship between affect and the linguistic style of the review?
The linear relationship tests the notion from the affect transfer and priming literature that predominantly negative (positive) reviews over time increase the negative (positive) affect conveyed through reviews, leading to reduced (increased) product conversion rates.
Q13. What is the effect of change in affective content on conversion rate?
The figure illustrates how a change in the reviews’ content toward more positive affect leads to higher predicted changes in conversion rate and yet tapers off at extreme degrees of change.
Q14. What is the effect of perceived rapport on consumer judgments and behaviors?
Such perceived rapport provides readily accessible diagnostic information, which directly influences consumer judgments and behaviors if they process information heuristically, as is the case for online information searches (Chaiken and Maheswaran 1994; Jones, Ravid, and Rafaeli 2004).
Q15. What is the importance of function words in determining conversational outcomes?
Humans are highly attentive to the conveyance of messages (Giles and Smith 1979), and prior work in several scientific disciplines has demonstrated the importance of function words for determining conversational outcomes (Huffaker, Swaab, and Diermeier 2011).