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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.

This study aims to explore the use of consumer-generated online product reviews as a source of brand associations in a sport setting.

A total of 800 reviews were collected and categorized into 13 brand association dimensions derived from previous literature. Reviews were further categorized into three valence types (i.e. positive, negative and neutral) via a correspondence analysis.

A correspondence analysis revealed that positive product reviews were highly linked to performance and product-related attributes, while negative reviews were related to conformance associations. Additionally, the results showed that product-related (90.8%) attributes, experiential benefits (89.1%) and functional benefits (86.6%) were the most frequently communicated brand associations. The findings of this study underline the credibility of assessing brand associations from the consumers’ experience, through online consumer reviews.

The findings of the current investigation contribute to existing knowledge by examining consumer-based brand equity (CBBE) in an online setting. Previous, CBBE literature suggests that brand associations are held in the minds of the brand's consumers. To date, however, few studies have examined brand associations generated by consumers and instead have relied upon brand association dimensions developed by the researcher as opposed to the consumer (Ross et al., 2006). This, study however, utilized online sport product reviews as a source of consumer derived brand associations, and, therefore, will further the knowledge as to how brand associations are identified and measured.

Webrooming (i.e. searching information online and making the final purchase in a physical store) has become a popular shopping practice, but remains insufficiently studied. To address this, a research framework encompassing online and offline channel attributes (i.e. online review diagnosticity, online search convenience, expected price loss, offline purchase effort and offline after-sales service convenience), consumer traits (i.e. anticipated regret) and shopping experience (i.e. smart-shopping perception) as determinants of webrooming continuance intention is proposed.

The proposed model was validated by conducting a questionnaire-based survey that yielded 354 useable responses. The data was subjected to partial least squares structural equation modelling and importance-performance map analysis.

According to the obtained results, online review diagnosticity, offline after-sales service convenience and anticipated regret are the vital antecedents of webrooming continuance intention, while smart-shopping perception acts as the mediator.

The current study adds significantly to the body of knowledge about webrooming by validating the inter-relationships between online review diagnosticity, after-sales service convenience, anticipated regret, smart-shopping perception and webrooming continuance intention.

This study aims to investigate the impact of a salesperson’s babyface in his/her profile picture on the number of online reviews the salesperson receives. In addition to testing the direct relationship, this study explores the moderating roles of salesperson gender and consumer involvement.

Responding to the call for field-based consumer research, the authors test their theory using an experimental design and a field study. Study 1 employs an experimental design in high and low involvement service settings to test the effect of a babyface on consumers’ intention to write online reviews. Study 2 uses field data, utilising real estate salespeople’s online profile pictures to test the effect of salespeople’s babyface on the number of online reviews they receive. It does so by using an artificial intelligence facial recognition application interface.

A salesperson’s babyface results in fewer online reviews in situations in which consumers are highly involved in the purchase process. By contrast, a salesperson’s babyface engenders more online reviews when consumers purchase low involvement services. The adverse effect of a babyface on the number of online reviews, however, attenuates when a salesperson is female.

Limited information about salespeople, a skewed number of online reviews and blurry online profile pictures from a real-world data set constitute the study’s limitations.

When consumers are highly involved in the purchase process, salespeople should appear mature in their online profile photos to engender more online reviews. However, salespeople providing low involvement services should opt for online profile pictures reflecting babyish facial features to generate more online reviews.

Research has shown that salespeople’s physical appearance plays an important role in consumers’ perceptions of salespeople and their performance. Although abundant research and practice have shown the importance of online reviews, less is known about how online profile pictures affect online reviews. Thus, building on well-studied cases of an overgeneralization effect, this work examines the extent to which salespeople’s babyface features in their online profile picture affects the number of online reviews received in a real-world setting.

In this paper the authors aim to review the state-of-the-art literature on online review systems and their impacts on consumer behavior and retailers' performance with the aim of identifying research gaps related to different design features of review systems and developing future research agenda.

The authors conducted a systematic review based on PRISMA 2020 protocol, focusing on studies published in the domains of retailing and marketing. This procedure resulted in 48 selected papers investigating the design features of retailer online review systems.

The authors identify eight design features that are controllable by retailers in an online review system. The design features have been researched independently in previous literature, with some features receiving more attention. Most selected studies focus on the design features adapted metrics and review presentations, while other features are generally neglected (e.g. rating dimensions). Previous literature argues that design features affect consumer behaviors and retailers' performance. However, the interactions among the features are still neglected in the literature, creating a relevant gap for future research.

This paper distinguishes between different types of retailer online review systems based on how they are implemented. The authors summarize the state-of-the-art of relevant literature on design features of online review systems and their effects on consumer- and retailer-related outcome variables. This systematic literature review distinguishes between online reviews provided on websites controlled by retailers (internal systems) and third-party websites (external systems).

The purpose of this paper is to explore the main effect of brand perception (brand warmth vs brand competence) on purchase intention, the mediating effect of brand love and the moderating effects of the emotional polarity of online reviews.

This paper utilizes experimental design and machine learning to collect and clean data. The ANOVA, t-test and bootstrap analysis methods are used to verify the assumed hypotheses.

Findings demonstrate that brand perception influences purchase intention with the mediating effect of brand love and the moderating effect of the emotional polarity of online reviews. In particular, brand perception can promote brand love and further enhance purchase intention. When consumers browse positive online reviews, brand warmth (vs brand competence) will lead to higher purchase intention. However, when consumers browse negative online reviews, brand competence (vs brand warmth) will weaken purchase intention more.

The findings of the current research contribute to purchase intention in the context of online reviews by highlighting the importance of brand love and the key role of brand perception, to which prior studies have paid little attention. The authors' research also provides some suggestions for enterprises about how to strengthen brand love by investigating consumers' perceptions of brand warmth and brand competence and further increasing purchase intention while consumers face positive or negative online reviews.

Consumer-perceived review attitude determines consumer overall information adoption and is a core part of consumer’s online-shopping. This study aims to focus on factors that could influence consumer review attitude and can be used by marketers to shape individual information perception.

The study used the questionnaire method to collect data from online shoppers and the modelling of structural equations as an empirical approach to analyse the data.

The findings demonstrate that both systematic and heuristic cues impact the reviewer’s credibility and perceived website attitude differently, which, in turn, influence review attitude. Review characteristics, such as factuality, consistency and relevancy, have a positive relationship with reviewer credibility, while only review consistency and relevancy appears to have a relationship with review attitude. Website characteristics such as reputation, familiarity and social interactivity positively influence the website attitude, which positively influences review attitude. Apart from this, review skepticism has a significant negative relationship with review attitude.

This study could help to foster a positive attitude towards online reviews. Digital marketers need to motivate trusted reviewers to post consistent, fact-based reviews. Further improving the overall website reputation and interactivity could bring a positive attitude towards the reviews. Also, digital marketers must filter and avoid contradictory reviews or reviews that have a bipolar message and reviews expressing numerous emotions to enhance review relevance and consistency.

The current study addresses the need to understand the formation of consumer review attitude through both review and website characteristics using heuristic – systematic model. The paper captures the complex process undergone by the consumer to decipher review attitude and thereby extend the understanding of consumer information processing.

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.

This research investigates consumer intention to follow online community advice. Applying the technology acceptance model (TAM) to the context of online restaurant communities, the study empirically examines the effects of perceived usefulness, perceived ease of use, attitude and trust on the intention to follow online advice.

The data were collected from 360 members of online restaurant communities on Facebook and analyzed using structural equation modeling (SEM).

The findings revealed that trust, perceived usefulness and attitude are key predictors of the intention to follow online restaurant community advice.

Extant research on the influence of online reviews on consumer behavior in the restaurant industry has largely focused on the characteristics of the review, reviewers or readers. Moreover, other studies have investigated consumers' motivations to write online restaurant reviews. This study, however, takes a different approach and examines what drives consumers to follow the advice from online restaurant communities.

The current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information consistency (rating inconsistency, review consistency and text similarity) influences perceived helpfulness. Moreover, the role of product type is examined in online consumer reviews of perceived helpfulness.

The current study collected 61,900 online reviews, including 600 products in six categories, from Amazon.com. Additionally, 51,927 reviews were filtered that received helpfulness votes, and then text mining and negative binomial regression were applied.

The current study found that rating inconsistency and text similarity negatively affect perceived helpfulness and that review consistency positively affects perceived helpfulness. Moreover, peripheral cues (rating inconsistency) positively affect perceived helpfulness in reviews of experience goods rather than search goods. However, there is a lack of evidence to demonstrate the hypothesis that product types moderate the effectiveness of central cues (review consistency and text similarity) on perceived helpfulness.

Previous studies have mainly focused on numerical and textual factors to investigate the effect on perceived helpfulness. Additionally, previous studies have independently confirmed the factors that affect perceived helpfulness. The current study investigated how information consistency affects perceived helpfulness and found that various combinations of cues significantly affect perceived helpfulness. This result contributes to the review helpfulness and ELM literature by identifying the impact on perceived helpfulness from a comprehensive perspective of consumer review and information consistency.