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Online Review Consistency Matters: An Elaboration Likelihood Model Perspective

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Abstract

To date, online review usefulness studies have explored the independent influence of central and peripheral cues on online review usefulness. Employing the Elaboration Likelihood Model (ELM), however, we argue that central and peripheral cues are jointly, not independently, processed by online users. For this exploration, we develop and measure “review consistency” variable (i.e., level of consistency between a review text and its attendant review rating), and rating inconsistency (i.e., level of inconsistency between a review rating and the average rating). We find a positive effect of review consistency on the review usefulness. Contrary to our hypothesis, however, we find a positive effect of rating inconsistency on the review usefulness. Our results also indicate that the contingency effect of rating inconsistency on the relationship between review consistency and review usefulness. Particularly, we find that rating inconsistency negatively moderates the effect of review consistency on the review usefulness. The theoretical and practical implications of the findings are discussed.

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Notes

  1. Review usefulness in Yelp and review helpfulness in Amazon essentially serve the same function of assessing online review quality. Therefore, we just use the term usefulness henceforth.

  2. Our decision to code review consistency using a binary variable is theoretically motivated by the ELM. According to R. E. Petty et al. (2009), while consumers’ decision-making process is made through a thoughtful information processing, sometimes their decision-making process is instant. We believe that, in the context of online review systems, consumers’ decision to diagnose the consistency of a review text with its corresponding numerical rating is almost immediate. This is because consumers are limited with time and cognitive efforts to process a huge volume of online reviews (Mudambi et al. 2014). Therefore, a slight inconsistency between a review text and its review rating may trigger an inner impulse for consumers to instantaneously discount such reviews in their decision-making process.

  3. Kappa value 0.21 ~ 0.40 is considered as fair, 0.41 ~ 0.60 moderate, 0.61 ~ 0.80 substantial, and 0.81 ~ 1.00 almost perfect agreement between independent coders (Landis and Koch 1977).

  4. For the negativity bias test, we substitute our independent variables with the review sentiment, and estimated the below regression model. The review sentiment was operationalized as a continuous variable ranging from −1 (negative sentiment) to 1 (positive sentiment).

    $$ Review\ Usefulness={\beta}_0+{\beta}_1\ln \left( review\ sentiment\right)++{\beta}_2\left(\mathrm{Elite}\ \mathrm{Badge}\ \mathrm{Member}\right)+{\beta}_3\ln \left( Review e{r}^{\prime }s\ Number\ of\ Friends\right)+{\beta}_4\left( Review e{r}^{\prime }s\ Number\ of\ Followers\right)+{\beta}_5\ln\ \left(\mathrm{Reviewer}'\mathrm{s}\ \mathrm{Number}\ \mathrm{of}\ \mathrm{Reviews}\right)+{\beta}_6\ln \left( Review\ Longevity\right)+{\beta}_7\ln \left( Review\ Length\right)+\varepsilon $$

    Ordered logit regression model yielded the result that the review sentiment has a significantly negative effect on review usefulness (β1= −0.725, p < 0.001), validating the existence of negativity bias in our data.

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Authors and Affiliations

  1. Department of Management, Gary W. Rollins College of Business, University of Tennessee Chattanooga, 615 McCallie Ave, Chattanooga, TN, 37403, USA

    Navid Aghakhani

  2. Information System Group, The Business School, University of Colorado Denver, 1475 Lawrence St, Denver, CO, 80202, USA

    Onook Oh, Dawn G. Gregg & Jahangir Karimi

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  1. Navid Aghakhani
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Correspondence to Onook Oh.

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Aghakhani, N., Oh, O., Gregg, D.G. et al. Online Review Consistency Matters: An Elaboration Likelihood Model Perspective. Inf Syst Front 23, 1287–1301 (2021). https://doi.org/10.1007/s10796-020-10030-7

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