Search results

In an online shopping environment, individual reviews and aggregated ratings are important anchors for consumers’ purchasing decisions. However, few studies have considered the influence of aggregated ratings on consumer decision-making, especially at the neural level. This study aims to bridge this gap by investigating the consumer decision-making mechanism based on aggregated ratings to uncover the underlying neural basis and psychological processing.

An event-related potential experiment was designed to acquire consumers’ electrophysiological records and behavioral data during their decision-making processes based on aggregated ratings. The authors speculate that during this process, review valence categorization (RVC) processing occurs, which is indicated by late positive potential (LPP) components.

Results show that LPP components were elicited successfully, and perceptual review valence can modulate its amplitudes (one-star [negative] and five-star [positive] ratings evoke larger LPP amplitudes than three-star [neutral] ratings). The electroencephalogram data indicate that consumer decision-making processes based on aggregated ratings include an RVC process, and behavioral data show that easier review valence perception makes the purchase decision-making easier.

This study enriches the extant literature on the impact of aggregated ratings on consumer decision-making. It helps understand how aggregated ratings affect consumers’ online shopping decisions, having significant management implications. Moreover, it shows that LPP components can be potentially used by researchers and companies to evaluate and analyze consumer emotion and categorization processing, serving as an important objective physiological indicator of consumer behavior.

This study aims to explore the objectivity in third-party ratings. Third-party ratings are often based on some form of aggregation of various experts' opinions with the assumption that the potential judgment biases of the experts cancel each other out. While psychology research has suggested that experts can be unintentionally biased, management literature has not considered the effect of expert bias on the objectivity of third-party ratings. Thus, this study seeks to address this issue.

Ranking data from the US News and World Report between 1993 and 2008, institution-related variables and, to represent sports prominence, NCAA football and basketball performance variables are leveraged in testing our hypotheses. A mediating-model is tested using regression with panel-corrected standard errors.

This study finds that the judgments of academicians and recruiters, concerning the quality of universities, have been biased by the prominence of a university's sports teams and that the bias introduced to these experts mediates the aggregated bias in the resultant rankings of MBA programs. Moreover, it finds that experts may inflate rankings by up to two positions.

This study is particularly relevant for university officials as it uncovers how universities can tangibly manipulate the relative perception of quality through sports team prominence. For third-party rating systems, the reliability of ratings based on aggregated expert judgments is called into question.

This study addresses a significant gap in the literature by examining how a rating system may be unintentionally biased through the aggregation of experts' judgments. Given the heavy reliance on third-party rating systems by both academics and the general population, addressing the objectivity of such ratings is crucial.

This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems.

This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating.

Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment.

This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

Prior literature on online customer reviews (OCRs) suggests that individuals are socially influenced by information shared by others. Given that the online environment brings together users from different cultures, understanding how users differ in their processing and generation of OCRs across cultures is imperative. Specifically, this paper explores how cross-cultural differences influence OCR generation when there are inconsistencies between recent and overall review ratings.

The authors employ an empirical study and an experimental approach to test the predictions. For the empirical study (Study 1), the authors collected and analyzed actual review data from an online hotel review platform, Booking.com. This was followed by an experimental study (Study 2) in which the authors manipulated the thinking style represented by each cultural orientation to further explain how and why cross-cultural differences exist.

The results show that compared with the review ratings of users from collectivist cultures, those of users from individualistic cultures are more likely to follow recent review ratings. Based on the experimental study, the authors further find that such cross-cultural differences in OCR generation are driven by differences in thinking style.

This research extends the literature by demonstrating the cross-cultural differences in individuals' herding tendencies in OCR generation. The authors also add to the literature by showing in which direction OCR herding occurs when there is a discrepancy between overall and recent review ratings. From a managerial perspective, the findings provide guidelines for online platforms serving the global market on predicting customers' OCR generation and constructing appropriate response strategies.

Product ratings and reviews are popular tools to support buying decisions of consumers. Many e-commerce platforms now offer product ratings and reviews as ratings and reviews are valuable for online retailers. However, luxury goods industry is somewhat slow to adapt to the digital terrain. The purpose of this paper is to answer “how luxury consumers see user-generated product ratings and reviews for their online shopping experience and what important factors or values are perceived by the luxury consumers when they shop online?”

To understand how luxury consumers use product ratings and reviews before buying online, a survey with a situational set up of variations of rating, review and price options in association with a number of hypothetical luxury goods was conducted among 421 global luxury consumers out of over 6,000 people. The study was carried out from September to October 2018 for six weeks in the form of online and mobile survey. User population is high net-worth individuals or luxury consumers derived from the author’s various professional and social networks and communities. Their geographical coverage would be global, but concentrated around the major cities.

The survey shows that ratings and reviews can be important source of information for luxury consumers. Online ratings and reviews are rated as helpful by 76.01% of the participants. People who chose the highly rated one (4.8/5) over the poorly rated (3.7/5) was 86.94%, while all else such as product category, star rating and price range are about the same. Feedback from the open question survey indicates that the perceived helpfulness of rating and review systems could vary. Comparing user reviews is time-consuming because of unstructured nature of contextual reviews and the relative nature of human perception on the rating scale.

There are two aspects of ratings and reviews playing an important role for luxury consumers’ buying decision. First, it is about helpfulness of collective rating score. Luxury consumers see a user-generated rating score and use the score when they make a choice even if the rating is not an absolute, but relative figure, not exactly like the star rating system in the hotel industry. Second, there is discrepancy between the status of the brand in association with its price position and perceived value as the industry does not cope with classifying their brands in any official star rating system.

Consumers need compact and concise information about the products they need. When there are only a few potential products left in their short wish-list, full user reviews can be helpful to get more details and general opinions about the products on the short list before making a final decision. In that regard, a primary indicator that will guide through the decision-making process of the luxury consumers would be the trustworthiness of user rating of each product in an aggregated score along with a potential use of sub-ratings, which has to be visible from the product landing page.

Even if there is a wide use and ubiquitous nature of product ratings and reviews in other consumer products, the author is curious about how luxury consumers use ratings and reviews for their buying decision because there are not that many researches done previously in spite of the importance of this issue. Luxury goods industry has hit €320bn in 2017 according to Bain and Co., and 25% of the trading volume will be replaced by the digital commerce by 2025.

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

This study seeks to identify the main determinants of the optimal capital structure by reexamining the interpretation of the conventional set of explanatory variables used as proxies for the costs and benefits of debt in the context of the dynamic tradeoff theory.

The authors isolate the variation in leverage due to different targets from that caused by deviations by aggregating the data across a dimension identifying firms with similar targets – credit rating category.

Contrary to theoretical priors, large and profitable rated firms have lower targets. The authors show that size and profitability proxy for non-financial risk and that, for rated firms, non-financial risk is positively correlated to the optimal leverage. The benefits of a better rating outweigh the costs of foregone tax shields for firms with relatively low non-financial risk. The authors find support for that theory in institutional trading – institutional investors do not punish highly rated firms when credit downgrades occur.

This paper contributes to the capital structure literature by developing a new approach based on data aggregation. This study is the first, to the authors’ knowledge, to find a positive effect of the firm's non-financial risk on target leverage among rated firms. The authors argue that the benefit of a better credit rating is an increasing function of the rating itself. The authors also contribute to the literature on the impact of credit ratings on the capital structure choices of the firm.

This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap.

FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine.

The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating.

This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base.

This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system.

This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

The purpose of this paper is to analyze the effect of signaling a review as the most helpful review according to other users’ votes on product attitude. Thus, the first study focuses on the influence of signaling a review as the most helpful on consumer attitude and analyzes whether the interaction between that signaled review and incongruent aggregate information in valence clarify the main effect. Additionally, the authors further investigate whether the level of fit between the consumer’s goals and the content of the signaled review moderates the initial effect.

The authors conducted two experiments: a 3 (presence of most helpful review) × 2 (overall valence) between-subjects design and a 2 (presence of a most helpful review) × 3 (level of fit between the consumers’ goals and the most helpful review content) × 2 (overall valence) design.

The results confirm that the presence of a “most helpful” review whose valence is incongruent with the overall valence of the reviews significantly impacts attitude towards the product. Specifically, the authors found that the impact of a review which has been voted as the most helpful on consumers’ attitudes depends on: the congruity between the valence of the most helpful review and the overall average valence of all the reviews received by the product; and the congruity between the consumer’s goals and the most helpful review content.

This paper contributes to the electronic WOM literature by examining how signaling a review as the most helpful affects attitude, being that effect moderated by the congruency between that signaled review and the aggregated overall valence of the reviews and the level of fit with the consumer’s goals.

This paper aims to provide insight into the use of a standard for condition assessment.

The paper is based on a literature review, an analysis of the development, content and practical use of the Dutch Standard for Condition Assessment of Buildings, and the findings of several research projects about condition assessment and maintenance planning by Dutch housing associations.

By using the standard for condition assessment, building inspectors can provide property managers with objective data about the condition status of building components. Aggregated condition data could be used for setting condition targets for built assets and for benchmarking. It is anticipated that as a result of the standardisation, condition surveys will become more reliable and as a consequence more popular among large‐scale property owners.

The standard has been introduced recently. At present there is little experience of the use of (aggregated) condition data for maintenance planning and benchmarking built assets.

The standard is a tool to assess the technical status of the properties to underpin the long‐term maintenance expectations. Condition assessment is not meant for preparing the annual maintenance budget and planning of the work. Supplementary information is needed in the phase of preparing for the execution of remedial work.

This paper provides practical tools for condition assessment and maintenance planning.