Search results

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

The paper attempts to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a two-part tariff coordination mechanism.

To deal with this pricing conflict problems of dual-channel supply chain consisting of dominant manufacturer and a retailer, considering the fact that online reviews and in-sale service are important factors on consumers’ purchase decisions, the authors establish some basic models and exploit them to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a profit-sharing coordination mechanism.

The results show that the optimal online direct selling price is positively correlated with product perceived quality obtained from online reviews and negatively correlated with the in-sale service. The traditional retail price is positively correlated with the in-sale service and weakly correlated with online reviews. For the manufacturer and retailer, whether decentralized decision or coordination contract, their profits increase with the increase of the in-sale service in a certain range and quality perceived from spontaneous online reviews. Online reviews and in-sale service are important factors on consumers’ purchase decisions. Positive in-sale services and online reviews can provide consumers with a better shopping experience, thereby promoting their enthusiasm for shopping and improving their quality of life. The two-part tariff coordination mechanism improves the profits of the manufacturer and the traditional retailer, respectively, through the transfer fee.

The proposed approach can well analyze the channel conflicts and pricing problems between retailers and manufacturers with respect to product offline price and online price. The analysis and results can inform decision-making for manufacturers and retailers.

This paper aims to investigate textual characteristics of customer reviews that motivate companies to respond (sentiment negativity and sentiment deviation) and how aspects of these company responses (response intensity, length and tailoring) affect subsequent customer review quality (comprehensiveness and readability) over time.

Leveraging a large data set from a leading app website (Shopify), the authors combine text mining, natural language processing (NLP) and big data analysis to examine the antecedents and outcomes of online company responses to reviews.

This study finds that companies are more likely to respond to reviews with more negative sentiment and higher sentiment deviation scores. Furthermore, while longer company responses improve review comprehensiveness over time, they do not have a significant influence on review readability; meanwhile, more tailored company responses improve readability but not comprehensiveness over time. In addition, the intensity (volume) of company responses does not affect subsequent review quality in either comprehensiveness or readability.

This paper expands on the understanding of online company responses within the digital marketplace – specifically, apps – and provides a new and broader perspective on the motivations and effects of online company responses to customer reviews. The study also extends beyond the short-term focus of prior works and adds to literature on long-term effects of online company responses to subsequent reviews. The findings provide valuable insights for companies (especially those with apps) to enhance their online communication strategies and customer engagement.

This paper aims to evaluate and structure the basic heuristics consumers use in evaluating word-of-mouth (WOM) about luxury hotel brands while analyzing the impact of deception in online consumer reviews.

The research used a two-study mixed-methods approach, using interpersonal deception theory and social proof theory as lenses to conduct our analysis. For the first study, a qualitative conceptual mapping analysis was conducted, examining online consumer reviews to identify key concepts and their relationships in the context of luxury hotel brands. In the second study, the themes were further examined using a fuzzy-set qualitative comparative analysis to analyze their causal complexity and association between variables to determine how they influence the perceived helpfulness of online reviews for luxury hotel brands.

The results underline the importance of functional, objective variables, such as the number of reviews and stars, as social proof heuristics and other factors, including clout, authenticity and analytic tone, as interpersonal communication heuristics. Therefore, consumers use a combination of social and interpersonal communication heuristics to extract information from reviews and manage deception risk.

The paper contributes to the consumer–brand relationship literature by assessing the heuristics consumers use in evaluating online reviews and provides additional information for research in online reputation management.

This study’s results can help marketing practitioners and brand managers manage their online reputations better. It can also aid managers in improving their messaging on hotel websites to entice consumers to complete bookings. Heuristics play an essential role in such messaging and understanding them can help marketers appeal directly to their target market.

This study contributes to the literature on consumer–brand relationships by providing a framework of heuristics that consumers use when evaluating luxury service brands and contributes to WOM and online reputation research by highlighting factors that may make online reviews more helpful.

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.

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.

Drawing on the elaboration likelihood model (ELM), the authors examine the influence of perceived professor teaching qualities, as central cues, on online professor ratings. Also, our study investigates how the volume and period of reviews, as peripheral cues, affect online professor ratings.

Leveraging stratified random sampling, the authors collect reviews of 892 Information Systems professors from 250 American universities. The authors employ regression models while conducting robustness tests through multi-level logistic regression and causal inference methods.

Our results suggest that the central route from perceived professor qualities to online professor ratings is significant, including most qualitative pedagogical factors except positive assessment. In addition to course difficulty, the effect of the peripheral route is limited due to deficient diagnosticity.

Our primary concern about the data validity is a lack of a competing and complementary dataset. However, an institutional evaluation survey or an experimental study can corroborate our findings in future research.

Online professor review sites can enhance their perceived diagnosticity and credibility by increasing review vividness and promoting site interactivity. In addition to traditional institutional evaluations, professors can obtain insightful feedback from review sites to improve their teaching effectiveness.

To our best knowledge, this study is the first attempt to employ the ELM and accessibility-diagnosticity theory in explicating the information processing of online professor reviews. It also sheds light on various determinants and routes to persuasion, thus providing a novel theoretical perspective on online professor reviews.

Wine producers and marketing professionals increasingly recognize the significance of online wine reviews. Emotions have long been acknowledged as influential in online review behaviors. However, considering the multisensory nature of the wine experience, consumers’ wine expertise also plays a substantial role. Hence, this study aims to examine the online review behaviors exhibited by wine consumers through the dual lens of wine expertise and emotionality.

Two studies were conducted to address the research question. Study 1 explored the relationship among expertise, emotionality and review behaviors using a panel data model, with a data set consisting of 4,600,922 reviews from Vivino.com. Study 2 used a multigroup structural equation modeling (SEM) analysis using data obtained from an online survey. Study 2 aimed to investigate the interactive impact of emotionality and expertise on online review intention mediated by customer engagement.

The findings from Study 1 demonstrated a positive correlation between emotionality and online wine reviews. In addition, expertise displayed a bell-shaped relationship with both emotionality and online wine reviews. Study 2, in turn, uncovered that novices and experts experienced a direct influence of emotionality on their review intentions. In contrast, for those classified as ordinary, the influence of emotionality on review intention occurred indirectly through the mediation of customer engagement.

This paper extends the current literature on online wine review by integrating the effect of emotion and expertise on online wine review behaviors, expanding the examination of Dunning–Kruger effect in the wine literature. It also adds value by introducing emotionality and the Evaluative Lexicon into the hospitality literature, extending the measurement of emotion from valence and extremity to a third dimension, emotionality, in hospitality and wine domains.

The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.

The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.

Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.

The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.

First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.

The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.