Vote or not? How various information cues affect helpfulness voting of online reviews

Understanding Users' Group Behavioral Decisions About Sharing Articles in Social Media: An Elaboration Likelihood Model Perspective

The decision to share information is a common phenomenon in individuals' daily social media use (e.g., Twitter, micro-blogs). However, research on the information to be shared mainly focuses on short texts, and the research on long texts/article sharing is relatively limited. Based on the elaboration likelihood model (ELM), this study established a conceptual model to reveal the determinants of users' behavior in sharing articles. Data on 1311 articles were collected on WeChat, China's most popular social media, and were processed using multiple linear regression. We found that both the central path and the peripheral path of the ELM affect users' decision-making about article-sharing behavior, and that amount of reading and perceived usefulness have the greatest impact. The rhetorical title, the number of pictures, and the number of fans have a negative impact on users' decision-making about article-sharing behavior. Further, the factors that affect users' online-community sharing and sharing with friends are also different. This study is one of the first to apply ELM to examine the influencing factors of users' decisions about sharing general articles on social media, contributing to the research on the decision-making behavior of users sharing long texts on social media.

Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?

With a surging number of online courses on MOOC (Massive Open Online Course) platforms, online learners face increasing difficulties in choosing which courses to take. Online course reviews posted by previous learners provide valuable information for prospective learners to make informed course selections. This research investigates the effects of reviewer experience and expertise on reviewer competence in contributing high-quality and helpful reviews for online courses. The empirical study of 39,114 online reviews from 3276 online courses on a leading MOOC platform in China reveals that both reviewer experience and expertise positively affect reviewer competence in contributing helpful reviews. In particular, the effect of reviewer expertise on reviewer competence in contributing helpful reviews is much more prominent than that of reviewer experience. Reviewer experience and expertise do not interact in enhancing reviewer competence. The analysis also reveals distinct groups of reviewers. Specifically, reviewers with low expertise and low experience contribute the majority of the reviews; reviewers with high expertise and high experience are rare, accounting for a small portion of the reviews; the rest of the reviews are from reviewers with high expertise, but low experience, or those with low expertise, but high experience. Our work offers a new analytical approach to online learning and online review literature by considering reviewer experience and expertise as reviewer competence dimensions. The results suggest the necessity of focusing on reviewer expertise, instead of reviewer experience, in choosing and recommending reviewers for online courses.

Consumers with specialised and diverse experience produce more helpful reviews

Purpose

Experienced reviewers in general can produce high-quality product reviews, and thereby get more helpful votes. This paper explores the question that whether the depth and width of the reviewers' experience distribution have effects on the helpfulness of their reviews.

Design/methodology/approach

Adopting the restaurant review data from Yelp, the present paper classifies the restaurants in to different categories applying the Word2Vec technique, such as Asian or fast food. By evaluating the number of a user's historical reviews in a specific category, and the evenness of such distribution in different categories, the experience specialty and experience diversity are defined respectively.

Findings

The analysis shows that users specialised in a given category can produce more helpful reviews in that category. The users with diverse historical experience, i.e. have posted reviews for many categories, also can produce helpful reviews. In addition, the experience diversity shows a positive moderation effect on the influence of experience specialty. Thus, users with diverse experience while specialized in a particular category are the source of most helpful reviews.

Originality/value

While previous studies mostly consider the raw number of historical reviews as a reviewer's experience, we distinguish such experience by product category and focus on the width and depth of its distribution. The results not only shed lights on the mining of high-quality reviews and reviewers but also provide insights on the management of online review platforms and electronic marketing.

What makes a helpful online review? Empirical evidence on the effects of review and reviewer characteristics

Purpose

The reviews submitted by users are the foundation of user-generated content (UGC) platforms. However, the rapid growth of users brings the problems of information overload and spotty content, which makes it necessary for UGC platforms to screen out reviews that are really helpful to users. The authors put forward in this paper the factors influencing review helpfulness voting from the perspective of review characteristics and reviewer characteristics.

Design/methodology/approach

This study uses 8,953 reviews from 20 movies listed on Douban.com with variables focusing on review characteristics and reviewer characteristics that affect review helpfulness. To verify the six hypotheses proposed in the study, Stata 14 was used to perform tobit regression.

Findings

Findings show that review helpfulness is significantly influenced by the length, valence, timeliness and deviation rating of the reviews. The results also underlie that a review submitted by a reviewer who has more followers and experience is more affected by review characteristics.

Originality/value

Previous literature has discussed the factors that affect the helpfulness of reviews; however, the authors have established a new model that explores more comprehensive review characteristics and the moderating effect reviewer characteristics have on helpfulness. In this empirical research, the authors selected a UGC community in China as the research object. The UGC community may encourage users to write more helpful reviews by highlighting the characteristics of users. Users in return can use this to establish his/her image in the community. Future research can explore more variables related to users.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0186.