The effects of prior reviews on perceived review helpfulness: A configuration perspective

Zhu, Yongmin; Liu, Miaomiao; Zeng, Xiaohua; Pei, Huang

doi:10.1016/j.jbusres.2020.01.027

Cited by 38 publications

(16 citation statements)

References 54 publications

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“…Moreover, the significance level of a few features varies from model to model by using a different combination of features, i.e., R_Polarity, B_Review_Count, U_Business_Choice, U_Fan_Count, to highlight a few. The results showed that our findings on R_Word_Count are consistent with the findings reported in recent studies [69], [71], [73], [82]. However, a study also reported a negative relationship of review length with the helpfulness of online reviews [83].…”

Section: Important Features Of Review Helpfulnesssupporting

confidence: 92%

“…A study proposed a convolutional neural network model, using textual features based on bag-of-words, to automatically predict the helpfulness of online reviews [81]. Zhu et al [82] examined the impact of the previous reviews on the helpfulness of the subsequent reviews. It was found that if the reviews come in very quickly, the descriptive reviews are more helpful.…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

“…Moreover, very limited business features have been studied in the literature. These business features include review volume, number of checkins, and business star rating [51], [52], [80], [82]. In addition, previous literature has reported the importance of different review content features and their influence on the helpfulness of online reviews.…”

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

“…The regression model based on all features is given by Equation 20 Keeping the dependent variable in its original form, Poisson or Negative Binomial regression models can be used in this study. Since the variance and mean of the dependent variable is not equal, we used Negative Binomial regression as used in previous studies [29], [78], [82], [104]. Moreover, for both models, we perform the logarithmic transformation of independent variables that are right-skewed.…”

Section: Data Analysis and Modelingmentioning

confidence: 99%

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Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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The increasing volume of online reviews and the use of review platforms leave tracks that can be used to explore interesting patterns. It is in the primary interest of businesses to retain and improve their reputation. Reviewers, on the other hand, tend to write reviews that can influence and attract people's attention, which often leads to deliberate deviations from past rating behavior. Until now, very limited studies have attempted to explore the impact of user rating behavior on review helpfulness. However, there are more perspectives of user behavior in selecting and rating businesses that still need to be investigated. Moreover, previous studies gave more attention to the review features and reported inconsistent findings on the importance of the features. To fill this gap, we introduce new and modify existing business and reviewer features and propose a user-focused mechanism for review selection. This study aims to investigate and report changes in business reputation, user choice, and rating behavior through descriptive and comparative analysis. Furthermore, the relevance of various features for review helpfulness is identified by correlation, linear regression, and negative binomial regression. The analysis performed on the Yelp dataset shows that the reputation of the businesses has changed slightly over time. Moreover, 46% of the users chose a business with a minimum of 4 stars. The majority of users give 4-star ratings, and 60% of reviewers adopt irregular rating behavior. Our results show a slight improvement by using user rating behavior and choice features. Whereas, the significant increase in R 2 indicates the importance of reviewer popularity and experience features. The overall results show that the most significant features of review helpfulness are average user helpfulness, number of user reviews, average business helpfulness, and review length. The outcomes of this study provide important theoretical and practical implications for researchers, businesses, and reviewers. INDEX TERMS Business reputation, rating behavior, review helpfulness, user profiling, crowd-sourced reviews.

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Section: Important Features Of Review Helpfulnesssupporting

confidence: 92%

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Section: B Features For Predicting Review Helpfulnessmentioning

confidence: 99%

Section: Data Analysis and Modelingmentioning

confidence: 99%

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Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

et al. 2020

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“…Moreover, prevalent and available online word of mouth makes it possible that consumers can encounter WOM dispersion about the same product [2][3][4][5]. Retailers actually believe that WOM dispersion leads to much more risks and uncertainties for prospective consumers [4][5][6], and consumers normally prefer products with consistent WOM [7]. Therefore, they usually tend to pursue products with ratings of five stars (highly praised by all reviewers) after offering reviewers coupon codes or other incentives (i.e., gifts such as Mickey Mouse, pen containers, and sometimes even cell-phone refill cards).…”

Section: Introductionmentioning

confidence: 99%

Promoting Reviewer-Related Attribution: Moderately Complex Presentation of Mixed Opinions Activates the Analytic Process

Xie

Yuan

et al. 2021

Sustainability

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Using metacognition and dual process theories, this paper studied the role of types of presentation of mixed opinions in mitigating negative impacts of online word of mouth (WOM) dispersion on consumer’s purchasing decisions. Two studies were implemented, respectively. By employing an eye-tracking approach, study 1 recorded consumer’s attention to WOM dispersion. The results show that the activation of the analytic system can improve reviewer-related attribution options. In study 2, three kinds of presentation of mixed opinions originating from China’s leading online platform were compared. The results demonstrated that mixed opinions expressed in moderately complex form, integrating average ratings and reviewers’ impressions of products, was effective in promoting reviewer-related attribution choices. However, too-complicated presentation types of WOM dispersion can impose excessively on consumers’ cognitive load and eventually fail to activate the analytic system for promoting reviewer-related attribution choices. The main contribution of this paper lies in that consumer attribution-related choices are supplemented, which provides new insights into information consistency in consumer research. The managerial and theoretical significance of this paper are discussed in order to better understand the purchasing decisions of consumers.

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Looking for negativity! How review sentiment and review subjectivity form review helpfulness across cultures

Shah,

Gohary,

Lie

2023

J of Consumer Behaviour

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Nowadays, many consumers rely heavily on product reviews before making decisions; however, not all reviews are similar in terms of sentiment and helpfulness. Moreover, reviewers' linguistic styles vary according to their culture. Although various multinational companies such as eBay and Amazon operate worldwide, little research has been devoted to understanding how review sentiment influences the subjectivity and helpfulness of product reviews in a multicultural marketplace. Therefore, the present research identifies how review subjectivity explains the relationship between review sentiment and review helpfulness in individualistic (i.e., the U.S.) and collectivistic (i.e., India) cultures. By means of two studies, the first using secondary data collected from Amazon.com and the second a controlled experiment setting, we find that review subjectivity mediates the influence of review sentiments only in individualistic cultures such as the U.S., but not in collectivistic cultures like India. Furthermore, we find that the moderated mediation is evident only when comparing positive and negative sentiments. This research has implications for academic and industry practitioners in terms of incorporating culture in decisions on review helpfulness.

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The effects of prior reviews on perceived review helpfulness: A configuration perspective

Cited by 38 publications

References 54 publications

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”

Promoting Reviewer-Related Attribution: Moderately Complex Presentation of Mixed Opinions Activates the Analytic Process

Looking for negativity! How review sentiment and review subjectivity form review helpfulness across cultures

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