scholarly journals Online Review Helpfulness: Role of Qualitative Factors

2016 ◽  
Vol 33 (11) ◽  
pp. 1006-1017 ◽  
Author(s):  
Arpita Agnihotri ◽  
Saurabh Bhattacharya
Keyword(s):  
Online Review ◽  
Review Helpfulness ◽  
Qualitative Factors

When profile photos matter: the roles of reviewer profile photos in the online review generation and consumption processes

2020 ◽  
Vol 14 (4) ◽  
pp. 391-412
Author(s):  
Jong Min Kim ◽  
Miyea Kim ◽  
Sookyoung Key
Keyword(s):  
Sample Size ◽  
Critical Role ◽  
Experimental Studies ◽  
Online Reviews ◽  
Online Review ◽  
Generation Process ◽  
Content Type ◽  
Managerial Implications ◽  
Review Helpfulness

Purpose Many online review sites, such as TripAdivisor.com, encourage review posters to upload a profile photo to improve the perceived reliability of online reviews. This study aims to examine the roles of reviewer profile photos in the online review generation and consumption processes. Design/methodology/approach Data were collected via Amazon MTurk. Two experimental studies were conducted. Study 1 had a sample size of 106 respondents. In Study 1, this paper examined the role of a reviewer profile photo in the online review generation process. Study 2 had a sample size of 482 respondents. In Study 2, this paper examined the role of a reviewer profile photo in the online review consumption process under two different circumstances, namely, comprehensive and incomprehensive review text. Findings The findings show that reviewer profile photos play different roles when consumers generate online reviews versus when they consume reviews. In the review generation process, reviewers are more likely to upload a profile photo to improve the credibility of their reviews. On the other hand, in the review consumption process, reviewer profile photos do not contribute to an increase in the perceived review helpfulness. Originality/value If the readers have difficulty processing the review content, review profile photos play a critical role in determining perceived review helpfulness. This study provides both theoretical and managerial implications by indicating how reviewer profile photos play different roles in online review posting and consuming behavior.

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

2018 ◽  
Vol 108 ◽  
pp. 1-12 ◽  
Author(s):  
Michael Siering ◽  
Jan Muntermann ◽  
Balaji Rajagopalan
Keyword(s):  
Online Review ◽  
Review Helpfulness

Assessing the Unacquainted: Inferred Reviewer Personality and Review Helpfulness

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1113-1148
Author(s):  
Angela Xia Liu ◽  
◽  
Yilin Li ◽  
Sean Xu ◽  
◽  
...  
Keyword(s):  
Personality Traits ◽  
Supervised Classification ◽  
Large Population ◽  
Superior Performance ◽  
Product Reviews ◽  
Review Helpfulness ◽  
Personality Theories ◽  
Deep Learning Model ◽  
Practical Implications

This work examines the question of who is more likely to provide future helpful reviews in the context of online product reviews by synergistically using personality theories and data analytics. It trains a deep learning model to infer a reviewer’s personality traits. This enables analyses to reveal the role of personality traits in review helpfulness among a large population of reviewers. We develop hypotheses on how personality traits are associated with review helpfulness, followed by hypotheses testing that confirms that higher review helpfulness is related to higher openness, conscientiousness, extraversion, and agreeableness and to lower emotional stability. These results suggest the appropriateness of using these five personality traits as inputs for developing a model for predicting future review helpfulness. Based on an ensemble model using supervised classification algorithms, we develop a predictive model and demonstrate its superior performance. Theoretical and practical implications are discussed.

scholarly journals Explaining and Predicting Helpfulness & Funniness of Online Reviews on the Steam Platform

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Applied Research ◽  
Online Shopping ◽  
Product Information ◽  
Online Reviews ◽  
Online Review ◽  
Gradient Boosting ◽  
Related Data ◽  
Review Helpfulness ◽  
Display Content ◽  
The Impact

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

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