Exploring Cross-National Differences in Online Review Topics between China and the United States

The fast growing cross-border e-commerce makes it imperative for online merchants to deeply understand the cross-national differences in consumers’ preferences and online shopping behaviors. Using a data-driven topic model, this study plans to investigate the semantic differences in online product reviews posted by consumers from China and the United Sates. The preliminary results from a pilot study of online reviews of books show that Chinese reviewers focus more on a product’s concrete attributes while American reviewers prefer to express their general evaluations of the product

A Meta-Analysis on the Determinants of Online Review Helpfulness

Online consumer reviews can help customers decrease uncertainty and risk faced in online shopping. However, information overload and conflicting comments in online reviews can get consumers confused. Therefore, it is important for both researchers and practitioners to understand the characteristics of helpful reviews. But studies examining the determinants of perceived review helpfulness produce mixed findings. We review extant research about the determinant factors of perceived helpfulness. Conflicting findings exist for six review related factors, namely review extremity, review readability, review total votes, linear review rating, quadratic review rating, and review sentiment. We conduct a meta-analysis to reconcile the contradictory findings on the influence of review related factors over perceived review helpfulness. The meta-analysis results confirm that review extremity, readability, total votes, and positive sentiment have a negative influence on helpfulness, but review rating is positively related to helpfulness. We also examine those studies whose findings are contradictive with the meta-analysis results. Measure discrepancy and reviewed product type are the two main reasons why mixed findings exist in extant research

How Does the Authenticity in an Online Review Affect Its Helpfulness? A Decision Tree Induction Theory Development Approach

Drawing on multi-dimensionality of authenticity, this study focuses on the role of two distinct authenticities: nominal and expressive. We propose that the type of authenticity in a review will vary based on the reviews’ lexical density (word level) and breadth (sentence level). Using the decision tree induction approach, the main and interaction effects of the dimensions and forms of authenticity are examined for their effect on review helpfulness. The preliminary analysis of 470 reviews demonstrate that the lexical density form of expressive authenticity is a predominant predictor of online review helpfulness. Additionally, the effects of expressive authenticity depth, nominal authenticity breadth and depth on online review helpfulness, vary based on the expressive breadth. The decision tree induction approach provides new theoretical insights that extends the frontiers of authenticity and practical implications on online review helpfulness

Investigating Usefulness Configurations of Online Consumer Reviews: A Fuzzy-Set Qualitative Comparative Analysis

Online reviews have a significant impact on consumers’ purchasing decisions. Many researchers have studied the relationship between review usefulness based on various factors related to online review, but existing studies have focused only on the linear relationship between variables methodologically. Therefore, this study examines the usefulness of online reviews from a configurational perspective derived from the complex interactions between elements, and aims to identify how these configurations differ according to product types. This study developed a conceptual model by combining HSM and ELM based on the theoretical discussion on the information processing and analyzed 7,316 cases collected from Amazon.com using fsQCA. As a result, three configurations affecting online usefulness were derived from search goods and four from experience goods. In short, consumers consume reviews through the complex interaction of various factors related to reviews, and the factors affecting the usefulness of search goods and experience goods are different

Do same-level review ratings have the same level of review helpfulness? The role of information diagnosticity in online reviews

This research examines whether the written contents of online reviews can generate systematic differences in the review’s perceived helpfulness even with identical ratings. In addition, this research explores which underlying psychological mechanism creates the systemic differences related to helpfulness. Specifically, the results from our two experiments demonstrate that, when an online hotel review has a positive rating, written contents containing both positive and negative information is perceived as more helpful than reviews with only positive written content. In contrast, when an online hotel review has a negative rating, written contents that contain only negative information is perceived as more helpful than reviews with written content containing both positive and negative information. Importantly, our study shows that the degree of information diagnosticity in online reviews behaves as an underlying psychological mechanism in the process. Our findings not only contribute to the extant literature but also provide useful insights and practical implications for travel websites

Impact of the length of stay at hotels on online reviews

Purpose The length of stay (LoS) is of major importance from the perspective of the management of tourist destinations. As tourists heavily rely on the online reviews of other travelers as a primary information source, this study aims to empirically examine how the LoS can influence the online reviews for hotels, with special emphasis on the textual review content. Design/methodology/approach This study analyzes online review data collected from Booking.com by using the Linguistic Inquiry and Word Count program to operationalize review depth, analytical thinking and the authenticity reflected in customer reviews. Based on the analyzed data, this study used a series of regression analyses to understand the impacts of the LoS on online reviews. Findings The author’s analysis found that a longer stay at a hotel causes consumers to be more likely to post online reviews that not only include a numerical rating as well as written content but also lengthier and more detailed descriptions of their hotel experiences. Further analysis found that the LoS at hotels causes systematic differences in the linguistic attributes of the review content. Specifically, consumers who stay longer tend to write reviews with more analytical information, resulting in consumers perceiving the online reviews as more authentic. Research limitations/implications Although the LoS has been considered a significant issue in tourism, studies examining the impact of different lengths of stay on consumers’ post-purchase behaviors are limited. In this light, the author’s findings demonstrate how the LoS can change the linguistic attributes of online reviews. It expands the body of knowledge of the LoS in tourism. Originality/value This study represents the first attempt to empirically examine and reveal how the different length of stay at a hotel systemically influences consumer review-posting behaviors

The Force Awakens: Artificial intelligence for consumer law

Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision

The Force Awakens: Artificial Intelligence for Consumer Law

Recent years have been tainted by market practices that continuously expose us, as consumers, to new risks and threats. We have become accustomed, and sometimes even resigned, to businesses monitoring our activities, examining our data, and even meddling with our choices. Artificial Intelligence (AI) is often depicted as a weapon in the hands of businesses and blamed for allowing this to happen. In this paper, we envision a paradigm shift, where AI technologies are brought to the side of consumers and their organizations, with the aim of building an efficient and effective counter-power. AI-powered tools can support a massive-scale automated analysis of textual and audiovisual data, as well as code, for the benefit of consumers and their organizations. This in turn can lead to a better oversight of business activities, help consumers exercise their rights, and enable the civil society to mitigate information overload. We discuss the societal, political, and technological challenges that stand before that vision