Three Papers on the Role of Information in Online Consumer Reviews

This dissertation is comprised of three papers. The first two papers investigate how the various informational elements of online reviews, including their textual portion, impact the perceived helpfulness of those reviews. The third paper proposes a methodological refinement to improve the process by which reviews and reviewers are ranked with respect to their helpfulness and has potential applicability in fake review detection. These three papers utilize natural language processing methods, including Latent Semantic Analysis (LSA), which allows for the automatic analysis of large amounts of text with minimal human intervention.Ph.D., Business Administration -- Drexel University, 201

A Knowledge Adoption Model Based Framework for Finding Helpful User-Generated Contents in Online Communities

Many online communities allow their members to provide information helpfulness judgments that can be used to guide other users to useful contents quickly. However, it is a serious challenge to solicit enough user participation in providing feedbacks in online communities. Existing studies on assessing the helpfulness of user-generated contents are mainly based on heuristics and lack of a unifying theoretical framework. In this article we propose a text classification framework for finding helpful user-generated contents in online knowledge-sharing communities. The objective of our framework is to help a knowledge seeker find helpful information that can be potentially adopted. The framework is built on the Knowledge Adoption Model that considers both content-based argument quality and information source credibility. We identify 6 argument quality dimensions and 3 source credibility dimensions based on information quality and psychological theories. Using data extracted from a popular online community, our empirical evaluations show that all the dimensions improve the performance over a traditional text classification technique that considers word-based lexical features only

Effect of Website Design Characteristics on the Concreteness of Online Reviews: A Construal Level Perspective

Online reviews are a crucial aspect of businesses’ success. Thus, businesses encourage reviewers to write reviews offering concrete helpful information but such reviews are scarce. This study attempts to move beyond the traditional approaches to understanding reviewers’ behavior and uses a novel lens of ‘human construal’. By drawing on Construal Level Theory (CLT) we tend to understand reviewers\u27 and readers’ perceptions and utilize that for the maximization of review concreteness. Review concreteness is a semantic characteristic of a review and is considered an important aspect of reviews’ perceived quality. We examine if reviewers’ and readers’ construal can be utilized to manipulate some factors of website design in a way that it drives reviewers to write and readers to perceive more concrete reviews and consequently enhance the overall concreteness of reviews. The study amalgamates literature from psychology, marketing, and information systems and has significant potential to benefit both businesses and consumer

Revisiting Review Depth in Search for Helpful Online Reviews

This study investigates online review features that constitute review depth and assess their impacts on review helpfulness. It develops a model capturing the moderating effects of heuristic and systematic cues of an online review on the relationship between review length and its helpfulness. In particular, this study examines the moderating effects of price, product type, review readability and the presence of two-sided arguments. For testing the model, a dataset of 568,454 reviews from 256,059 different reviewers on Amazon.com were analyzed. The variables were operationalized using test processing techniques and relationships were empirically tested using regression and machine learning models. The results highlight significant moderating effects of review readability and the presence of two-sided arguments on the relationship between review length and its helpfulness. However, the results did not confirm the moderating effects of price and product type. This article discusses the significant implications for a better understanding of review depth and helpfulness in e-commerce platforms

Predicting the “helpfulness” of online consumer reviews

YesOnline shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites

Understanding the Association between Star Ratings and Review Helpfulness: The Perspectives of Expectation Confirmation Theory and Negativity Bias

Consisting of textual, multimedia, and numerical information elements, online consumer reviews (OCR) have been considered an essential information source of products for prospective consumers. Researchers have made significant efforts to comprehend how these information elements are associated with OCRs’ information value or helpfulness. However, there is a paucity of theoretical evidence on consumers’ perception and evaluation of star ratings and their information, even though star ratings as numerical information cues can imply multiple meanings. In this study, we leverage (1) expectation-confirmation theory to delineate star ratings as the extent of consumer satisfaction and (2) negativity bias to explain the relationship between star ratings and helpfulness. Using 45,621 reviews of 20 products across three categories, we empirically find that our theoretical approaches improve our understanding of the effect of star ratings on helpfulness. Therefore, this study contributes to the extant literature on OCRs by providing the theory-based evaluation of star ratings in relation to helpfulness

Profiling users' behavior, and identifying important features of review 'helpfulness'

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 R2 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

A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews

© 2020 Edith Cowan University With the proliferation of user generated online reviews, uncovering helpful restaurant reviews is increasingly challenging for potential consumers. Heuristics (such as “Likes”) not only facilitate this process but also enhance the social impact of a review on an Online Opinion Platform. Based on Dual Process Theory and Social Impact Theory, this study explores which contextual and descriptive attributes of restaurant reviews influence the reviewee to accept a review as helpful and thus, “Like” the review. Utilising both qualitative and quantitative methodologies, a big data sample of 58,468 restaurant reviews on Zomato were analysed. Results revealed the informational factor of positive recommendation framing and the normative factors of strong argument quality and moderate recommendation ratings, influence the generation of a reviewee “Like”. This study highlights the important filtering function a heuristic can offer prospective customers which can also result in greater social impact for the Online Opinion Platform