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Valence and volume of online reviews are generally considered to influence sales positively. However, existing findings regarding the relative influence of these two components have been inconclusive. This paper aims to explain some of these inconsistencies by examining the moderating role of regulatory focus (both as a chronic disposition and as a situational focus induced by the product category) in the relationship between online review volume/valence and consumers purchase decisions.

Two studies were conducted. Study 1 used a 2 (Volume: high/ low) * 3 (Valence: high/medium/low) within-subject experimental design. Study 2 analyzed real-world data from Amazon.com. Logistic and panel regression analyses were used to test the research hypotheses.

The studies confirmed the hypothesized effect of regulatory focus on online review valence and volume effects. Specifically, Study 1 showed that online review valence was more impactful for consumers with a promotion focus than for consumers with a prevention focus. The opposite was true for online review volume effects, where consumers with a prevention focus were influenced more by volume in their decision-making compared to consumers with a promotion focus. Study 2 showed that the pattern of results we found in Study 1 also applied to situational regulatory focus induced by the product category. The effect of review volume on sales rank was stronger for prevention-oriented products, whereas the effect of valence was stronger for promotion-oriented products.

In Study 1, one product category was involved in the study (Digital camera). Involving more different product categories will add reliability to the results of current research. Also, it can offer external validity to current research results. In Study 2, there was no exact measurement for sales, as Amazon.com does not share that kind of information. Instead, Sales Rank was used as a proxy variable. Future research could look into the websites that offer access to the exact sales information.

The current research findings suggest the need for companies to adapt their consumer review management strategy to the regulatory orientation of their target market and products. When a promotion-focused mindset is targeted, strategies for increasing the favorability of product reviews should be used, in contrast, tactics for increasing the quantity of reviews may be more suitable when a prevention-focused mindset is involved.

To the best of the authors' knowledge, this research is the first to investigate the interaction between regulatory focus of consumers and products and online review components.

While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.

The research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.

The study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.

Although only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.

This research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.

To explore the psychological mechanism through which consumer reviews affect people’s purchasing decisions and behavior, this study aims to examine the impact of statistical evidence embedded in product reviews on consumers’ perceptions and purchasing intentions.

The effects review valence and review volume are tested using a 3 (valence: positive vs neutral vs negative) × 2 (volume: high vs low) quasi-experimental design and online questionnaires.

The study finds that review valence has a stronger impact on consumers’ perceptions than review volume does. Negative reviews induce higher risk perception and a less favorable attitude toward purchases compared to positive reviews. In addition, although both attitude toward purchase and subjective norm are good antecedents of purchase intention, the attitude statistically has a stronger impact than the subjective norm.

This study contributes to extant literature from three perspectives. The authors have reexamined the findings of econometric models and advanced their implications by explaining the related psychological changes in people’s perceptions. Second, the authors have extended the application of the theory of reasoned action and found it to be a good fit in explaining consumers’ behavior related to consumer reviews. And finally, the authors have provided a clear guideline on the magnitude of the effects of review valence and volume on consumers’ perceptions.

This study provides a good complement to econometric studies from both theoretical and practical perspectives. It bridges the gap between exploratory studies and behavioral studies in the field of consumer reviews.

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.

The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.

This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.

This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

Online reviews have been indicated to play an important role in consumers’ decision-making process, as supported by numerous studies. However, none of them has considered the neighborhood effect of online reviews. The purpose of this paper is to analyze the impact of neighbor store’s reviews on central store’s, along with the moderating effects of store density and product similarity.

Using data from dianping.com, this study conducts economic analysis accounting for endogeneity.

The results show that the neighbor store’s reviews exert a negative impact on that of central stores. Nevertheless, the relationship is moderated by store density and product similarity, such that the negative effect is stronger if there are a lot of stores around the central store, or if the neighbor store and central store provide similar products.

This study is the first to investigate the neighborhood effect of online reviews.

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the RSR review column, “Recent Reference Books,” by Frances Neel Cheney. “Reference Books in Print” includes all additional books received prior to the inclusion deadline established for this issue. Appearance in this column does not preclude a later review in RSR. Publishers are urged to send a copy of all new reference books directly to RSR as soon as published, for immediate listing in “Reference Books in Print.” Reference books with imprints older than two years will not be included (with the exception of current reprints or older books newly acquired for distribution by another publisher). The column shall also occasionally include library science or other library related publications of other than a reference character.

This paper aims to examine whether a film’s search volume causes its ticket sales in different stages of its lifecycle.

This study tests the causality between searches and sales by using an instrumental variable approach. This study exploits the ideas that consumers’ perception of a product’s consumption risk is correlated with their search efforts and consumers use multiple information sources to infer a product’s consumption risk. As an instrument for a focal film’s search volume, this paper uses review disagreement for past movies related to the focal film. This paper incorporates the ideas in a model of weekly online search volume and revenues and apply it to a movie data set.

Films’ search volume influences their revenues only until the opening week. A 10% increase in opening-week search volume generates a 7.4% increase in opening-week revenue, while the same increase in pre-launch search volume generates a 4.1% increase. Although searches are not an influencer of sales from the second week on, the random forest models and cross-validation studies find that weekly search volume is a strong predictor of weekly revenues in this period.

Testing the findings in other product categories is important for generalizing the findings.

This study suggests different usage values for online searches, depending on a film’s lifecycle stages. Furthermore, given that review disagreement has a positive influence on opening-week revenue through searches, managers should encourage diverse opinions about their films until the opening week to increase sales through searches.

Regarding the role of online search, previous studies have maintained the perspective that online search is a predictor of sales. This is the first study that finds causality between searches and sales for films.

This paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.

In Study 1, influencing factors reflected as service features were identified by applying a feature extraction method to physician reviews, and the importance of each feature was determined based on word frequencies and the PageRank algorithm. Sentiment analysis was used to analyze patient satisfaction with each service feature. In Study 2, regression models were used to analyze the relationships between the service features obtained from Study 1 and the doctor's consultation volume.

The study identified 14 service features of patients' concerns and found that patients mostly care about features such as trust, phraseology, overall service experience, word of mouth and personality traits, all of which describe a doctor's soft skills. These service features affect patients' trust in doctors, which, in turn, affects doctors' consultation volumes.

This research is important as it informs doctors about the features they should improve, to increase their consultation volume on OMC platforms. Furthermore, it not only enriches current trust-related research in the field of OMC, which has a certain reference significance for subsequent research on establishing trust in online doctor–patient relationships, but it also provides a reference for research concerning the antecedents of trust in general.

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.