Explaining and predicting online review helpfulness: The role of content and reviewer-related signals

Siering, Michael; Muntermann, Jan; Rajagopalan, Balaji

doi:10.1016/j.dss.2018.01.004

Cited by 220 publications

(207 citation statements)

References 59 publications

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“…Receivers assessing WOM information are sensitive to whether a particular message is consistent or inconsistent with prior WOM, in terms of its star rating and content. Messages with star ratings that deviate less from the average are perceived as more helpful (Siering, Muntermann, & Rajagopalan, ; Yin et al, ) because consistency in valence across reviews suggests consensus among senders (Quaschning, Pandelaere, & Vermeir, ). Consensus increases receivers’ confidence in the majority opinion (Koriat, Adiv, & Schwarz, ) and implies that the product outcome is stable and caused by the product (Quaschning et al, ).…”

Section: Sendermentioning

confidence: 99%

How online word‐of‐mouth impacts receivers

Moore

Lafreniere

2019

Consumer Psychology Review

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Online word‐of‐mouth (WOM) can impact consumers’ product evaluations, purchase intentions, and choices—but when does it do so? How do those receiving WOM know whether to rely on a particular message? This article suggests that the multiple players involved in online WOM (receivers, senders, sellers, platforms, and other consumers) each have their own interests, which are often in conflict. Thus, receivers of WOM are faced with a judgment task in deciding what information to rely on: They must make inferences about the product in question and about the players who provide or present WOM. To do so, they use signals embedded in various components of WOM, such as average star ratings, message content, or sender characteristics. The product and player information provided by these signals shapes the impact of WOM by allowing receivers to make inferences about (a) their likelihood of product satisfaction, and (b) the trustworthiness of WOM players, and therefore the trustworthiness of their content. This article summarizes how each player changes the impact of online WOM, providing a lens for understanding the current literature in online WOM, offering insights for theory in this context, and opening up pathways for future research.

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Section: Sendermentioning

confidence: 99%

How online word‐of‐mouth impacts receivers

Moore

Lafreniere

2019

Consumer Psychology Review

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“…The most common reviewer's attributes that affect review helpfulness will be reviewed in the following. First, Siering Michael and Muntermann Jan in [22] investigated the impact of reviewer-related attributes such as reviewer expertise and reviewer non-anonymity on review helpfulness. Furthermore, they consider other control variables include Review depth, review readability, and review extremity as content-related attributes.…”

Section: Reviewers' Attributesmentioning

confidence: 99%

“…They crawled data for nine months every week by using R Week-by-Week. Then, they obtained important variables by using XML package in R. Finally, in [22] the dataset collected from Amazon's website for two product types. They collected data from different product kinds and picked 100 best-selling products.…”

Section: Attributes Fieldmentioning

confidence: 99%

Review helpfulness prediction: Survey

Almutairi¹,

Abdullah²,

Alahmadi³

2019

PEN

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Online reviews have become the major driving factor influencing purchasing behavior and patterns of social customers. However, it is difficult for customer to cover good reviews about any product or service according to massive amount of reviews latest years. Many previous researches provide innovative models about predicting review helpfulness in E-commerce websites. Some of these studies exploring the direct effect of review attributes on review helpfulness while others focused on reviewer's attributes only. The main objective of this research is to review the most important attributes that have an affect on review helpfulness from many perspectives such as datasets, techniques, frameworks and evaluation methods of the experiments. The paper ends up with important findings about most attributes effect the review helpfulness such as Review Valence.

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“…eWOM influences consumer purchase intentions by changing the preferences for alternatives and in turn influences product sales based on information theory [13,14]. We introduce multiattribute attitude theory in this research domain.…”

Section: Literature Reviewmentioning

confidence: 99%

Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

et al. 2019

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Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.

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Explaining and predicting online review helpfulness: The role of content and reviewer-related signals

Cited by 220 publications

References 59 publications

How online word‐of‐mouth impacts receivers

How online word‐of‐mouth impacts receivers

Review helpfulness prediction: Survey

Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

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