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Journal ArticleDOI

Perceived helpfulness of eWOM: Emotions, fairness and rationality

TL;DR: In this paper, the authors extend existing research by examining how content of online reviews influences perceptions of helpfulness by demonstrating how different emotions can influence helpfulness of both product and service online reviews beyond a valence-based approach using cognitive appraisal theory and attribution theory.
About: This article is published in Journal of Retailing and Consumer Services.The article was published on 2020-03-01 and is currently open access. It has received 78 citations till now. The article focuses on the topics: Helpfulness & Appraisal theory.

Summary (5 min read)

1. Introduction

  • The development of Internet technologies and popularity of e-commerce has prompted electronic word-of-mouth (eWOM) communications, such as online reviews, to become a key source of information about products and services.
  • The ubiquity of online reviews means it must be an important consideration for enterprises of all sizes regardless of whether they sell directly online.
  • Next is a section detailing the research methods, after which the results of two empirical studies are presented and discussed.

2. Literature review

  • Broadly, studies have determined three antecedents of eWOM helpfulness: eWOM characteristics, information source, and information receiver (Baek et al., 2012; Kim & Gupta, 2012; Park & Kim, 2008).
  • Emotions are usually associated with two or more main appraisal dimensions, which normally include valence and any other appraisal.
  • Attribution theory studies how individuals interpret events and how it affects their thinking and behaviour (Kelley & Michela, 1980; Snead Jr et al., 2015; Swanson & Kelley, 2001).
  • Other studies also applied attribution theory in investigating factors affecting helpfulness of online reviews (Jeong & Koo, 2015; Quaschning et al.

3. Conceptual model and hypotheses development

  • This section discusses each of the constructs of the proposed research model and presents the hypotheses.
  • The research model is based on the appraisal theory of emotions and attribution theory.
  • By using cognitive appraisal it is possible to study how fairness expressed through emotions in online reviews will affect review helpfulness.
  • Applying attribution theory will help to investigate how an information source is perceived by consumers and how it will affect helpfulness of the message.
  • Using this theory, the study will be able to investigate how perceived rationality of the reviewer will have an impact on perceived helpfulness of the review and how it will mediate the relationships between emotions embedded in the message and helpfulness of eWOM communications.

3.1 Emotions embedded in online reviews

  • Individuals use emotional expression as a source of social information (van Kleef, 2010).
  • Price fairness refers to “a consumer’s overall judgment of price based on a comparison of the actual price to acceptable prices determined by both social standards (reference price) and self-interest (adaptation level)” (Namkung & Jang, 2010, p.1237).
  • It has been found that emotions can affect assessment of a product or service and that consumers who felt regret more thoroughly evaluated their experience (Buchanan et al. 2016; Inman et al., 1997).
  • Based on the previous studies on negative emotions and their relationships with perceived reviewer rationality, the following hypotheses are proposed: H3a: Frustration expressed in an online review negatively affects perceived reviewer rationality.

3.2 Expressed price fairness

  • Previous studies have found that price fairness influences a consumer’s motivation to engage in eWOM communications (Namkung & Jang, 2010).
  • When a consumer perceives an outcome as fair they will engage in eWOM communications in order to advise others on the product/service; however, when they feel that the outcome is unfair they will engage in eWOM communications to vent their negative feelings and punish the company (Wetzer et al., 2007).
  • Based on attribution theory (Folkes, 1988), the reader of the online review establishes the author’s intention to write the review which in turn affects perceived helpfulness of the review (Kim & Gupta, 2012).
  • As fairness is one of the cognitive appraisals of emotions, it can be proposed that expressed price fairness will influence helpfulness of online reviews.
  • Thus, this leads to the following research hypothesis:.

H4: Expressed price fairness positively influences perceived helpfulness of an online review.

  • The preceding discussion indicates that emotions in online reviews will not only have a direct effect on perceptions of helpfulness but also an indirect effect through expressed emotion appraisal because of the nature underlying the emotions concept (Roseman, 1991).
  • Based on the previous studies (Ahmad & Laroche, 2015; Namkung & Jang, 2010; Roseman, 1991; Yin et al. 2014) and from the developed hypotheses, it is proposed that emotions will influence price fairness, which will positively affect perceived helpfulness of the online reviews, so determining the following hypothesis:.

3.3 Perceived rationality of the reviewer

  • Reviewer rationality refers to the perception that the message source has the ability to reason (Shugan, 2006).
  • It has been found that when the information source is perceived as irrational it is seen as less informative (Folse et al., 2016; Kim & Gupta, 2012) and, as a result, less helpful for decision-making.
  • If the reader perceives the information source as rational, they will consider the online review helpful.
  • Based on the previous literature and findings, the following hypothesis is proposed:.

H6: Perceived reviewer rationality has a positive effect on the perceived helpfulness of an

  • Based on the previous discussion and results of the previous studies, only investigating the direct effect of emotions on helpfulness of online reviews will provide limited results.
  • Emotions expressed will have an effect on helpfulness of online reviews but also an indirect effect through perceived reviewer rationality (Ahmad & Laroche, 2015; Folse et al.
  • Previous studies (Folse et al., 2016; Kim & Gupta, 2012) proposed that emotions do not only have a direct effect on perceived helpfulness of online reviews but also an indirect effect through attribution about the writer.
  • Kim and Gupta (2012) found that negative emotions have an indirect effect on perceived helpfulness of online reviews through reviewer rationality for online reviews about laptops.

H7: Perceived reviewer rationality mediates the relationship between expressed emotions

  • And perceived helpfulness of an online review.
  • Welsh (2003) studied perception of fairness in negotiation and its effects on rational behaviour of individuals.
  • The results showed that when an individual perceives that the offer is unfair they reject it, even though they get nothing.
  • Based on the previous discussion, developed hypotheses and previous studies (Folse et al., 2016; Kim & Gupta, 2012; Srivastava et al., 2008; Welsh 2003), it is suggested that emotions influence review helpfulness through price fairness first and then reviewer rationality.

4. Methodology

  • During the literature review, it became apparent that differences in results between studies might be caused by variation in review focus, e.g. product reviews (Ahmad & Laroche, 2015; Kim & Gupta, 2012) or seller reviews (Yin et al., 2014).
  • Furthermore, none of these studies had considered service reviews.
  • Therefore, to examine how discrete emotions expressed in online reviews influence perceived review helpfulness, this research conducted two studies using experimental surveys.
  • Study 1 examines perceived helpfulness of product online reviews whereas study 2 investigates perceived helpfulness of service online reviews.

4.1 Stimulus Materials

  • The first step in preparation of stimuli required selection of a product and service for the reviews.
  • Product reviews from different websites such as amazon.com, ebay.com, and epinions.com were analysed.
  • As a result, emotional expression was manipulated directly by varying the sentence appearing at the end of the review.
  • I regret that I bought it” and the service review ended with “The quality of food and service was poor, but you get what you paid for, the price was low.
  • The results showed that product reviews in the frustration condition were more related to frustration than to regret (M=5.93 versus 4.03, p<0.001), and product reviews in the regret condition were more related to regret than to frustration (M=6.44 versus 3.96, p<0.001).

4.2 Instrument development

  • Participants were asked to read the product/service review and indicate perceived review helpfulness.
  • The writer of the review felt that the price was appropriate for what s/he got;.
  • To measure reviewer rationality, respondents were asked to indicate the extent to which they felt the writer of the review was: 1-irrational, 7 rational; 1-unreasonable, 7-reasonable; 1- unreliable, 7 reliable (adapted from Folse et al., 2016; Kim & Gupta, 2012).

4.3 Data collection and analysis

  • Data was collected using a convenience sample of UK consumers aged 18+ via an online survey platform as well as paper-based distribution of the survey.
  • Also, PROCESS macro uses the bootstrap method for inference about indirect effects in mediation models.
  • Bootstrapping involves repeatedly randomly sampling observations (hundreds or thousands of times) with replacement from the data set to compute the desired statistic in each resample (Hayes, 2013).
  • Considering the selected analysis methods, statistical power analyses, and sample size of similar studies (e.g. Ahmad & Laroche, 2015), it was determined that a sample size of at least 450 eligible responses was required for both studies.

5.1 Study 1: Negative emotions in the context of product reviews

  • After deletion of unusable responses - based on eligibility criteria, attention check questions, and engagement - a final test sample of 519 remained.
  • Reliability and validity of constructs were examined.
  • After including fairness in the model, frustration was still a significant predictor of helpfulness but the coefficient decreased, c’2=.51, t(514)=-4.44.

5.2 Study 2: Negative emotions in the context of service reviews

  • Following deletion of unusable responses using the same criteria as for product reviews, the total of 680 responses received was reduced to a final usable sample of 459.
  • EFA was conducted in order to assess convergent validity of the constructs, applying the principle components method with Varimax rotation.
  • So it can be concluded that price fairness and reviewer rationality does not sequentially mediate the relationship between emotions and review helpfulness.

6. Discussion

  • The results are in line with previous findings which stated that emotions expressed in the review will influence the perception of the emotion appraisal (Ahmad & Laroche, 2015, Yin et al, 2014).
  • Another explanation for the results can be the way the reader of the review perceived the reviewer.
  • So, the results of the current study showed that the influence of emotions on perceived reviewer rationality will depend on product/service type and types of discrete emotions.
  • Thus, based on the results of testing H8 it can be concluded that expressed price fairness influences perceived reviewer rationality, and both fairness and rationality sequentially mediate the relationships between expressed emotions and review helpfulness in the context of product reviews, but not in the context of service reviews.

6.1 Theoretical contributions

  • A core theoretical contribution of this study is that it went beyond a valence-based approach and used fairness appraisal theory of emotions (Ahmad & Laroshe, 2015; Ellsworth & Smith, 1988; Roseman, 1991; Smith & Ellsworth, 1985).
  • Fairness had not been tested as a mediator in previous research.
  • It has been found by previous research that it can influence the way people perceive the helpfulness of information (Folse et al., 2016; Kim & Gupta, 2012).
  • Additionally, it was found that price fairness and reviewer rationality are sequential mediators between discrete emotions and review helpfulness in the case of product reviews.
  • As a result, this study showed that people process the context of product and service reviews in different ways which adds knowledge to the theory of information processing.

6.2 Practical implications

  • The results of this study have important implications for digital marketing managers and platform administrators.
  • Marketers in companies that publish online consumer reviews must ensure that the quality of the reviews on their website is high.
  • As a result, guidelines can advise writing reviews that have more rationality and are more reasonable.
  • Also, fairness emotions should even be encouraged as they provide additional information such as price fairness and/or reviewer rationality.
  • Assuming that companies seek to identify and respond to negative reviews which are especially influential, it may often be assumed that frustration reviews deserve particular attention (Kohler et al., 2011; van Noort and Willemsen, 2011).

7. Conclusion

  • The aim of this research was to examine how discrete emotions embedded in online reviews affect perceived helpfulness.
  • Applying cognitive appraisal theory, the study investigated how different emotions of the same valence, such as frustration and regret, influence consumer judgment (Lerner & Keltner, 2000).
  • Investigating the impact of emotions on perceived helpfulness of the review is important for marketers.
  • 1 Limitations and directions for future research Despite its contributions this study is not without limitations, and these limitations provide fruitful avenues for further research.
  • Furthermore, this study did not consider any reviewer related variables, which may moderate the effect of emotions.

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TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

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TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
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TL;DR: In this paper, the authors present a discussion of whether, if, how, and when a moderate mediator can be used to moderate another variable's effect in a conditional process analysis.
Abstract: I. FUNDAMENTAL CONCEPTS 1. Introduction 1.1. A Scientist in Training 1.2. Questions of Whether, If, How, and When 1.3. Conditional Process Analysis 1.4. Correlation, Causality, and Statistical Modeling 1.5. Statistical Software 1.6. Overview of this Book 1.7. Chapter Summary 2. Simple Linear Regression 2.1. Correlation and Prediction 2.2. The Simple Linear Regression Equation 2.3. Statistical Inference 2.4. Assumptions for Interpretation and Statistical Inference 2.5. Chapter Summary 3. Multiple Linear Regression 3.1. The Multiple Linear Regression Equation 3.2. Partial Association and Statistical Control 3.3. Statistical Inference in Multiple Regression 3.4. Statistical and Conceptual Diagrams 3.5. Chapter Summary II. MEDIATION ANALYSIS 4. The Simple Mediation Model 4.1. The Simple Mediation Model 4.2. Estimation of the Direct, Indirect, and Total Effects of X 4.3. Example with Dichotomous X: The Influence of Presumed Media Influence 4.4. Statistical Inference 4.5. An Example with Continuous X: Economic Stress among Small Business Owners 4.6. Chapter Summary 5. Multiple Mediator Models 5.1. The Parallel Multiple Mediator Model 5.2. Example Using the Presumed Media Influence Study 5.3. Statistical Inference 5.4. The Serial Multiple Mediator Model 5.5. Complementarity and Competition among Mediators 5.6. OLS Regression versus Structural Equation Modeling 5.7. Chapter Summary III. MODERATION ANALYSIS 6. Miscellaneous Topics in Mediation Analysis 6.1. What About Baron and Kenny? 6.2. Confounding and Causal Order 6.3. Effect Size 6.4. Multiple Xs or Ys: Analyze Separately or Simultaneously? 6.5. Reporting a Mediation Analysis 6.6. Chapter Summary 7. Fundamentals of Moderation Analysis 7.1. Conditional and Unconditional Effects 7.2. An Example: Sex Discrimination in the Workplace 7.3. Visualizing Moderation 7.4. Probing an Interaction 7.5. Chapter Summary 8. Extending Moderation Analysis Principles 8.1. Moderation Involving a Dichotomous Moderator 8.2. Interaction between Two Quantitative Variables 8.3. Hierarchical versus Simultaneous Variable Entry 8.4. The Equivalence between Moderated Regression Analysis and a 2 x 2 Factorial Analysis of Variance 8.5. Chapter Summary 9. Miscellaneous Topics in Moderation Analysis 9.1. Truths and Myths about Mean Centering 9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis 9.3. Artificial Categorization and Subgroups Analysis 9.4. More Than One Moderator 9.5. Reporting a Moderation Analysis 9.6. Chapter Summary IV. CONDITIONAL PROCESS ANALYSIS 10. Conditional Process Analysis 10.1. Examples of Conditional Process Models in the Literature 10.2. Conditional Direct and Indirect Effects 10.3. Example: Hiding Your Feelings from Your Work Team 10.4. Statistical Inference 10.5. Conditional Process Analysis in PROCESS 10.6. Chapter Summary 11. Further Examples of Conditional Process Analysis 11.1. Revisiting the Sexual Discrimination Study 11.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model 11.3. Visualizing the Direct and Indirect Effects 11.4. Mediated Moderation 11.5. Chapter Summary 12. Miscellaneous Topics in Conditional Process Analysis 12.1. A Strategy for Approaching Your Analysis 12.2. Can a Variable Simultaneously Mediate and Moderate Another Variable's Effect? 12.3. Comparing Conditional Indirect Effects and a Formal Test of Moderated Mediation 12.4. The Pitfalls of Subgroups Analysis 12.5. Writing about Conditional Process Modeling 12.6. Chapter Summary Appendix A. Using PROCESS Appendix B. Monte Carlo Confidence Intervals in SPSS and SAS

26,144 citations


"Perceived helpfulness of eWOM: Emot..." refers methods in this paper

  • ...It was determined that OLS regression was an appropriate technique to test the proposed hypotheses by using PROCESS macro (Hayes, 2013) together with one-way ANOVA....

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  • ...Bootstrapping involves repeatedly randomly sampling observations (hundreds or thousands of times) with replacement from the data set to compute the desired statistic in each resample (Hayes, 2013)....

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Abstract: The authors examine the effect of consumer reviews on relative sales of books at Amazon.com and Barnesandnoble.com. The authors find that (1) reviews are overwhelmingly positive at both sites, but there are more reviews and longer reviews at Amazon.com; (2) an improvement in a book's reviews leads to an increase in relative sales at that site; (3) for most samples in the study, the impact of one-star reviews is greater than the impact of five-star reviews; and (4) evidence from review-length data suggests that customers read review text rather than relying only on summary statistics.

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Abstract: There has long been interest in describing emotional experience in terms of underlying dimensions, but traditionally only two dimensions, pleasantness and arousal, have been reliably found. The reasons for these findings are reviewed, and integrating this review with two recent theories of emotions (Roseman, 1984; Scherer, 1982), we propose eight cognitive appraisal dimensions to differentiate emotional experience. In an investigation of this model, subjects recalled past experiences associated with each of 15 emotions, and rated them along the proposed dimensions. Six orthogonal dimensions, pleasantness, anticipated effort, certainty, attentional activity, self-other responsibility/control, and situational control, were recovered, and the emotions varied systematically along each of these dimensions, indicating a strong relation between the appraisal of one's circumstances and one's emotional state. The patterns of appraisal for the different emotions, and the role of each of the dimensions in differentiati ng emotional experience are discussed. Most people think of emotions in categorical terms: "I was scared," or "I was sad," or "I was frustrated." In complicated situations they may say, "I felt sad and frustrated." The idea that there is a small set of fundamentally different emotions, has a long and illustrious history in science as well, dating back at least to Aristotle and reemerging in the theory of the four humors, in the works of eighteenthcentury philosophers, and in Darwin (1872/ 1965). In recent years the categorical approach to the study of emotions has become prominent in psychology, stimulated by the monumental work of Sylvan Tomkins (1962, 1963, 1982; Ekman & Friesen, 1971; hard, 1971, 1972, 1977; Izard & Buechler, 1980). This view of emotional experience admirably captures our intuition that happiness, anger, and fear are basic feeling-states, easily recognizable, and fundamentally different from each other.

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Frequently Asked Questions (2)
Q1. What are the contributions in "Perceived helpfulness of ewom: emotions, fairness and rationality" ?

This paper extends existing research by examining how content of online reviews influences perceptions of helpfulness by demonstrating how different emotions can influence helpfulness of both product and service online reviews beyond a valence-based approach using cognitive appraisal theory and attribution theory. This research contributes to existing knowledge regarding the theory of information processing, attribution theory, and cognitive appraisal theory of emotions. Using findings from this study, practitioners can make review websites more user-friendly which will help readers avoid information overload and make more informed purchase decisions. 

7. 1 Limitations and directions for future research Despite its contributions this study is not without limitations, and these limitations provide fruitful avenues for further research. As a result, future studies should empirically investigate how agency appraisal of emotions could influence helpfulness of online reviews. As this research focused on only one dimension of fairness ( price fairness ), future studies can consider other dimensions such as distributive fairness, procedural fairness, and interactional fairness, or a combination of these as suggested by Watson and Spence ( 2007 ). Hence, future research can consider the role of variables such as reviewer expertise, propensity to trust, and involvement. 

Trending Questions (1)
What is the perceived usefulness of ewom?

The paper discusses how the content of online reviews influences the perceived helpfulness of electronic word-of-mouth (eWOM) in making informed purchase decisions.