Online persuasion of review emotional intensity: A text mining analysis of restaurant reviews

https://doi.org/10.1016/j.ijhm.2020.102558Get rights and content

Highlights

  • This study explores the impact of review emotional valence and discrete emotional contents on review usefulness.

  • Text mining approach and econometric analysis are used.

  • Positive emotion has a negative impact on review usefulness, whereas negative emotion has a positive impact.

  • Reviews expressing anger are more useful than those expressing anxiety.

  • Review length and reviewer expertise can moderate the effect of emotional intensity on review usefulness.

Abstract

Consumer-generated restaurant reviews are important sources in consumers’ purchase decisions. The purpose of this study is to explore the impact of emotional intensity on perceived review usefulness as well as the moderating effects of review length and reviewer expertise. Data from 600,686 reviews of 300 popular restaurants in the US were obtained from Yelp. Using a text mining approach and econometric analysis, empirical results show that (1) positive emotional intensity has a negative impact on perceived review usefulness, whereas negative emotional intensity has a positive impact on perceived review usefulness; (2) among the two most prevalent discrete negative emotions in online reviews (i.e., anger and anxiety), reviews expressing anger are more useful than those expressing anxiety; and (3) review length and reviewer expertise can moderate the effect of emotional intensity on perceived review usefulness.

Introduction

To reduce uncertainty and risk, customers often read product reviews online to make informed purchase decisions (Basuroy et al., 2003; Zhang et al., 2019). Helpful reviews exert stronger influences on subsequent customers’ purchase decisions and product sales compared to reviews perceived as unhelpful (Luca, 2011); therefore, the determining factors behind perceptions of review helpfulness warrant exploration. Recent studies have revealed that various aspects of review text can affect the perceived usefulness of online reviews, including review length (Filieri et al., 2019; Hlee et al., 2019; Kwok and Xie, 2016; Liang et al., 2019; Yang et al., 2017a), review readability (Fang et al., 2016; Liu and Park, 2015; Liang et al., 2019; Yang et al., 2017a), review explanatory cues (Li et al., 2019; Moore, 2015), and review content concreteness (Shin et al., forthcoming), etc.

Emotional expressions are pervasive in online reviews and other forms of computer-mediated communication (Lee et al., 2017). In fact, the anonymous environment of online review websites enables consumers to share their emotions more easily. Sevaral pioneer studies have focused on the impact of emotional content on the helpfulness of online reviews (Ahmad and Laroche, 2015; Lee et al., 2017; Peng et al., 2014; Ullah et al., 2015; Yin et al., 2014a); however, results have been inconsistent. Ullah et al. (2015) indicated that movie reviews with more emotional content were perceived as more helpful than reviews with less emotional content. Based on reviews of seven products (including search goods and experiential goods) from Epinions.com, Peng et al. (2014) found emotional intensity to exert a negative impact on review helpfulness, although this effect only applied to positive emotions. These contradictory findings can be partially explained by the valence of emotions, the discrete emotions contained in online reviews, as well as potential moderators like review length and reviewer expertise.

First, emotions can be divided into positive and negative emotions by valence (Ullah et al., 2015), both of which can influence consumers’ judgement in distinct ways (Malik and Hussain, 2017). According to attribution theory, consumers could make different causal attributions in response to positive and negative information in online reviews (Chen and Lurie, 2013; Morewedge, 2009). Moreover, the negativity bias effect suggests that negative information plays a greater role than positive ones in consumer judgement and decision-making (Rozin and Royzman, 2001). Second, consumers encounter various emotions during consumption (Richins, 1997), each with unique appraisal dimensions that can influence consumer reactions beyond the simple valence of the emotion (i.e., either positive or negative), such as certainty, arousal and control (Ahmad and Laroche, 2015; Nabi, 2003; Smith and Ellsworth, 1985; Yin et al., 2014a). For instance, both anger and anxiety are negative emotions but may have different influences due to their different levels of arousal. As such, it is intriguing to investigate the impacts of emotional valence and discrete emotional contents on consumers’ responses. In addition, the elaboration likelihood model (ELM, Petty and Cacioppo, 1986) suggests that information load could influence individuals’ information processing, such that people tend to rely on peripheral cues for judgements and decision-makings, like affective cues in review content, and reviewers’ expertise shown in their profiles. Moreover, reviewer expertise is an important factor influencing consumers’ causal attributions of online reviews (Folse et al., 2016; Quaschning et al., 2015). Therefore, this study seeks to reconcile the inconsistent findings by exploring the moderating effects of review length (representing information load) and reviewer expertise on the role of emotional content in consumers’ responses.

Given the intangible and hedonic nature of service and experiential products, the consumption process is primarily emotional and sensory; consumers purchase such products for intrinsic enjoyment rather than functionality (Moore, 2015; Pan and Zhang, 2011). As such, customers are more likely to evaluate these products based on subjective emotional experiences like excitement and fun. Therefore, it is meaningful to study the role of emotional content in online reviews of experiential-oriented hospitality products. Although prior studies have examined the roles of emotions in product reviews, scant efforts have sought to investigate the function of emotions in the service and hospitality industries (Lu and Stepchenkova, 2015). Lee et al. (2017) conducted the only hospitality study to date that has examined the role of negative emotional expressions embedded in online consumer reviews on perceived helpfulness. The authors found that negative emotional intensity had a positive impact on perceived helpfulness. However, the authors did not take positive emotional intensity, discrete emotions and the possible moderating effects of review length and reviewer expertise into account.

To address the aforementioned gaps in the research literature, a research framework was proposed based on attribution theory and ELM. Specifically, this study investigates the impact of emotional valence (either positive or negative) and discrete emotional contents (e.g., anger and anxiety) of reviews on perceived review helpfulness in the context of the restaurant industry. This study also attempts to examine the moderating effects of review length and reviewer expertise on the relationships between the emotional intensity of reviews and perceived review helpfulness. Specifically, we attempt to answer the following research question: Do customers find the emotional intensity of reviews more or less helpful depending on perceived reviewer expertise and review length?

Section snippets

Emotional intensity

Emotion can be defined as “a mental state of readiness that arises from cognitive appraisals of events or thoughts” (Bagozzi et al., 1999, p. 184). An array of emotions can be felt in a consumption situation (Ahmad and Laroche, 2015). For example, customers may feel pleasant when receiving unexpected high-quality service; in contrast, customers may experience anger in response to core service failures, and dealing with unresponsive or impolite employees. Intensity is an important characteristic

Hypotheses development

The present study proposes that perceived review helpfulness is different depending on emotional intensity of review content. Literature suggests that the role of emotional intensity might be different depending on emotional valence and different discrete emotional content (Ullah et al., 2015; Peng et al., 2014; Yin et al., 2014a). Hence this study investigates the role of emotional intensity by its valence (positive or negative) and discrete emotional content (specific emotions like anger and

Data

Data in this study were obtained from one of the largest online review platforms in hospitality industry, Yelp.com, which specializes in restaurant reviews. We collected the online reviews of the 300 popular restaurants (in terms of number of reviews posted) in the US, which boasts a well-developed hospitality and tourism industry. These restaurants cover an array of categories ranging from fine dining to causal and from full service to limited service. In summary, there were three different

Estimation results

Results of the negative binomial regression 2 model are presented in Table 5, Table 6, Table 7. The number of usefulness votes a review received was treated as the dependent variable. The model specification was tested by using a likelihood-ratio test, indicating that the Poisson regression model was an appropriate choice for data analysis.

Table 5 shows the direct influences of emotional content in a review on perceived review usefulness. Model 1.1 included only the control variables as

Findings and discussion

This study examined the impact of emotional content, including positive emotions, negative emotions, and two discrete negative emotions, on perceived review helpfulness along with the moderating effects of review length and reviewer expertise on the relationship between emotional content and review helpfulness. Research hypotheses were proposed based on ELM and attribution theory and then tested based on restaurant online reviews posted on Yelp.com. A negative binomial regression model was used

Acknowledgments

The authors acknowledge the support of research funds from the National Natural Science Foundation of China (71902169 and 71772053).

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