Search Results (23)

The growth of electronic word-of-mouth (eWOM) on digital platforms has heightened the need to distinguish authentic user-generated content from covert promotional material. This study proposes an integrated framework combining Natural Language Processing (NLP), machine learning, and Latent Dirichlet Allocation (LDA) to classify sentiment and detect advertising features in online game reviews. Reviews from the Steam platform were analyzed using Support Vector Machine (SVM), Decision Tree, and Naïve Bayes classifiers, with class imbalance addressed through SMOTE and SMOTE–Tomek techniques. The SMOTE-augmented SVM achieved the highest performance, with 98.18% overall accuracy and 97.52% negative sentiment detection. LDA and Quality Function Deployment (QFD) further uncovered latent promotional themes, providing insights into how advertising elements manifest in positive reviews and how negative feedback reflects genuine user concerns. The framework assists platform managers in enhancing eWOM credibility and supports marketers in designing data-driven advertising strategies. By bridging sentiment analysis with covert marketing detection, this research contributes a novel methodological approach for assessing review trustworthiness, improving transparency, and fostering consumer trust in digital information environments. Full article

As a part of sustainable transportation, robotaxis have been rapidly developing around the world because of their advantages in energy saving, improving road safety, and enhancing environmental sustainability, thereby providing consumers with sustainable transportation services. In China, as the number of pilot cities increases, more people are using robotaxi services. This study investigates the factors that affect consumer satisfaction and behavioral intentions after using a robotaxi, aiming to provide data to guide market strategy decisions. To do this, the value-based adoption model was extended and modified by including the technology readiness variable to examine satisfaction, intention to reuse, and electronic word-of-mouth (e-WOM) intentions. Using 425 valid responses, structural equation modeling (SEM) and multi-group analysis were carried out with AMOS 26.0. The results indicate that perceived usefulness, enjoyment, optimism, and innovativeness positively influence service satisfaction, whereas perceived risk and discomfort have negative effects. Consumer satisfaction positively affects both intention to reuse and e-WOM intention. Additionally, uncertainty avoidance shows a moderating effect between satisfaction and intention to reuse. Full article

Internet platforms and self-media have become vital online communities for promoting positive reputations for hotels. Previous studies have primarily focused on enhancing positive electronic word-of-mouth (eWOM) through improvements in hotel infrastructure and staff services. As hotels deepen their digital transformation, the application of various artificial intelligence technologies in hotel service encounters significantly impacts the service experience. This study explores the effects of voice assistant (VA) attributes on the online reputation of hotels. Specifically, it examines how the attributes of VAs (anytime connectivity, information association, and interactivity) influence positive customer evaluations in hotels. Utilizing a questionnaire survey method, we collected 529 valid questionnaires offline and employed structural equation modeling along with the PROCESS plugin in SPSS to conduct path analysis, as well as mediation and moderation effect analyses. The results indicate that perceived value and the existence of human–AI rapport mediate the impact of VA attributes on positive eWOM, although the direct effect of some attributes (information association) was not supported. Furthermore, anytime connectivity enhances the influence on human–AI rapport through social presence, while privacy concerns negatively affect the relationship between perceived value and intentions to engage in eWOM. These insights are critical for hotels seeking to maximize the benefits of digital transformation. Full article

During the COVID-19 pandemic, the tourism sector encountered multiple challenges. Numerous governments chose to lock down their cities and countries. Despite this, many companies found their online businesses making the greatest leaps in their portfolios, and social media platforms became one of the most valuable sources of information for purchase decisions. There have been numerous studies on the effects of social media reviews—a form of electronic word-of-mouth (eWOM)—on consumer behavior. Few were found to be related to their impact on group package tours (GPTs) while considering mixed eWOM, that is, both the positive and negative forms present in word-of-mouth communication. As the tourism sector gradually revives, the need to further explore how tourism and hospitality service providers can adapt to changes in post-pandemic consumer behavior has become imperative. The influence of social media reviews on consumers’ value perceptions of a GPT to Japan, allowing for the influence of the marketing mix element of advertised price, was examined through online experiments in this study. Positive, negative, and mixed eWOM were examined. It was found that eWOM was more influential on consumers’ value perceptions than the advertised price for all price acceptability levels. Mixed eWOM was found to negatively affect consumers’ final price perceptions which override the impact of quality perceptions in value formations. The value perceptions of the GPT became less acceptable when eWOM was mixed compared to when eWOM was absent or was positive. Mixed eWOM had a negative effect on value perceptions but not as great as when negative eWOM was present, and this was consistently found to apply for all price acceptability levels of the GPT. This study’s contribution to eWOM research and implications for the post-pandemic recovery of tourism and hospitality service providers are made, together with suggested strategies using innovative technologies and communications to enhance their adaptive resilience in the new normal. Full article

Given the increasing competition and the impact of digital media in the automobile industry, dealerships need to understand the antecedents of customer happiness and brand love. The goals of the study are to analyse the combined influence of the cognitive and affective drivers of brand love for high-involvement products and its effects on behavioural intentions, paying special attention to the moderating role of susceptibility to information posted on social media. Using a sample of 317 Jordanian car buyers, a structural model is tested that confirms that the sales consultant’s empathy is a strong predictor of customer happiness during a car purchase and a stronger predictor of his/her trust in the car dealership. Happiness and trust translate into greater brand love, which in turn can generate resistance towards negative information posted on social media; positive electronic word-of-mouth; and willingness to pay more. Happiness fully mediated the relationship between empathy and car brand love. The effect of the impact of the perceived empathy of salespeople on customer happiness was stronger for consumers with low susceptibility to information posted on social media. This work expands the academic knowledge of the direct mediating and moderating effects of brand love. Full article

As climate change continues, environmental sustainability has become a popular topic among brands and consumer groups. The fashion industry has detrimental impacts on the natural environment; however, little is known about how brand benefits can help sustainable fashion brands develop relationships with consumers and promote consumer behavior. This study focuses on Instagram to investigate how consumers’ perceived brand benefits predict relationship commitment, electronic word-of-mouth (eWOM), and purchase intention. Prior studies have overlooked the possible effects of various benefits. This study outlines five benefits of sustainable fashion brands: inner self-expression, social self-expression, warm glow, green, and economic benefits. Results from a survey of sustainable fashion brand followers on Instagram showed that eWOM positively related with economic benefits and negatively with warm glow and green benefits. Findings further indicated a mediating effect of relationship commitment between benefits and consumers’ behavior. Lastly, the level of environmental attitude influenced the mediating impact of relationship commitment. The implications of these findings are discussed, and suggestions for future research are provided. Full article

Confirmation biases make consumers feel comfortable because consistent beliefs simplify the processing of electronic word-of-mouth (eWOM). Whether the helpfulness of eWOM is a belief of information underlying biased information, i.e., positive–negative asymmetry, or an illusion of overconfidence underlying biased judgment, i.e., belief consistency, is crucial to the foundation of theory and the advance of practice in user-generated persuasion. The questions challenge the literature that the helpfulness of product reviews relies on unbiased information and/or unbiased judgment. Drawing on the cognitive-information lens, we developed a research model to explain how belief consistency affects the helpfulness beliefs of eWOM, and examined the effects of positive–negative asymmetry. Using a scenario-based questionnaire survey, we collected 334 consumer samples to test the research model. According to the empirical results, the conflicts of influence between positive and negative confirmation indicated that perceived review helpfulness was a belief of information and constrained by the positive–negative review frame. Without using personal expertise, respondents’ consistent beliefs were significant to confirm positive reviews as useful and thereby perceive the review content as helpful, which is an illusion of overconfidence and constrained by belief consistency. Whether personal expertise reinforces the effect of belief consistency depends on the positive–negative asymmetry. Full article

The purpose of this study is to develop a framework to examine the intrinsic driving paths of negative electronic word-of-word. In this paper, the “contextual” factor—user involvement, and the “individual” factor—user self-construal were selected to consider their influence on the model path. Data were collected using online questionnaires; then, the model and hypotheses were tested using structured equation model (SEM) software. The research results showed that, firstly, negative online shopping experiences positively influence eWOM motivations, and user involvement partially mediated the relationship between negative product quality, negative online shopping platform environments and negative eWOM motivations; secondly, there was a significant correlation between negative eWOM motivations and eWOM behavior, and the personality traits of the communicators themselves influenced the path of “negative eWOM motivation-negative eWOM behavior”. In addition, the user’s self-construal has a significant moderating effect on the “eWOM motivation–immediate eWOM behavior” path under immediate eWOM behavior. Full article

Understanding the antecedents and consequences of electronic word-of-mouth (eWOM) is of significant theoretical value and great interest to both consumers and firms. To reconcile the disagreement in the relationship between eWOM and product sales, we investigate the joint effect of different dimensions of eWOM on the sales of durable goods (i.e., cars). Our study is based on eWOM collected from Autohome, a leading online review platform of automobiles in China. Our empirical results show that the volume and valence of eWOM positively affect car sales, and variance negatively affects car sales when the volume of eWOM is low. Further analyses of the joint effect show that the positive impact of eWOM volume on sales is stronger when the valence is lower and the variance is higher. This research contributes to the literature on eWOM and provides helpful suggestions for firms by shedding light on the joint effect of different dimensions of eWOM in the context of the Chinese automobile industry. Full article

The purpose of this study is to investigate the role of negative electronic word-of-mouth (N-eWOM) messages on attitudes, subjective norms, perceived behavior control (PBC), and the intention to purchase sustainable dairy products. This study also investigates the moderating role of product sustainability claims to reduce the effect of N-eWOM on customers. It comprises two experiments on college students (n = 120; 90) who have at least two accounts on different social media platforms. We use both qualitative and quantitative techniques. The model was developed and tested on data collected from questionnaires. The results of Study 1 suggest that N-eWOM reduces purchase intentions, attitudes, subjective norms, and PBC. High N-eWOM reduces purchase intention more than the low N-eWOM. Study 2 found that with high N-eWOM, product sustainability claims (congruent or incongruent) moderate the effect of N-eWOM on attitudes, subjective norms, PBC, and purchase intention. Purchase intention is higher when a product sustainability claim is congruent. These novel findings contribute to our understanding of ways to mitigate the impact of N-eWOM by taking preventive actions, such as making product sustainability claims. Full article

The nexus of global tourism, disasters and sustainability have always been triggered by numerous crises, e.g., political unrest, wars, and pandemics. However, there is still fragmented research on destination crisis marketing, and its impact on willingness to travel remains largely unknown. To address this critical research gap, the present study used an experimental research design by framing destination crisis marketing campaigns (2 × 2 frames including DCM implemented versus DCM not implemented) and electronic word of mouth (2 × 2 frames including positive e-Wom versus negative e-Wom) to examine their impact on destination trust and global expat’s willingness to travel. Based on the experimental settings of global expats (N = 232; representing over 10 nationalities) with four framing groups (Group 1 to Group 4, configured in frames as ± DCM and ± e-Wom), the new evidence suggests that global expats who are provoked by positive crisis marketing campaigns and positive e-Wom (Group 4) have higher levels of destination trust than those who are exposed to either negative crisis marketing scenario and/or negative e-Wom (i.e., Group 1 to Group 3). The findings also revealed that global expat’s willingness to travel is significantly influenced by destination crisis marketing campaigns and e-Wom. Interestingly, for all framing groups (Group 1 to Group 4), the effects of destination crisis marketing and e-WOM on expat’s willingness to travel, was significantly mediated by destination trust. Utilizing prominent theories (i.e., signal theory, image repair theory and trust transfer theory), the study implications highlighted that crisis marketing and positive e-Wom could serve as the cornerstones for destinations to stay relevant, regenerate sustainable practices, as well as create new opportunities out of a crisis. Full article

(1) Background: During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions and its high population density. This study investigated how limited intervention policies have affected public health and economic conditions in the COVID-19 context and aimed to gain insight into the effective and sustainable measures against new infectious diseases in densely inhabited areas. (2) Methods: A system dynamics approach was employed. Qualitative causal loop analysis and stock and quantitative flow model analysis were performed, using a Tokyo Metropolitan area dataset. (3) Results: A causal loop analysis suggested that there were risks in prematurely terminating such interventions. Based on this result and the subsequent quantitative modeling, we found that the short-term effectiveness of a short-term pre-emptive stay-at-home request caused a resurgence in the number of positive cases, whereas an additional request provided a limited negative add-on effect for economic measures (e.g., number of electronic word-of-mouth communications and restaurant visits). (4) Conclusions: These findings suggest the superiority of a mild and continuous intervention as a long-term countermeasure under epidemic pressures when compared with strong intermittent interventions. Full article

Retailers have little control over what their customers say about their products and services online. Review platforms (e.g., Yelp and Travelocity) are rife with negativity, from both real customers with bad experiences and from fake reviews created by competitors. These negative reviews have been shown to influence the purchasing behavior of future consumers. Many platforms do afford companies some control by including them in the online conversation about their products or services. Crafting a response to a poor review which appeals to future consumers may mitigate some of the negative outcomes associated with that review. This study advances our knowledge of responding to negative reviews by adding to the growing body of research, using a simulation-based experiment to test the influence of three elements of a review response on purchase intention (i.e., an apology, an explanation and a pledge to correct the problem identified in the review). In doing so, the data show that purchase intention increases only when a response contains all three elements. Implications for e-commerce researchers and review platform developers are discussed. Full article

Web 2.0 technology enables customers to share electronic word of mouth (eWOM) about their experiences. eWOM offers great market insights to the organization, and important for organization’s success. eWOM monitoring and management is one of the major contemporary challenges for the organization, because of high volume and frequency of the content. It is nearly impossible for an organization to manually monitor content generated by each user. In this paper, we propose sentiment analysis as an alternative method for analysis of emotions and behavioral intentions in real-time data. Sentiment analysis is performed on women’s e-clothing reviews collected from the Kaggle data repository. The dataset consists of 23,486 reviews, comprising ten feature variables. This study applied artificial neural network techniques to determine polarity of the data in terms of positive or negative. Sentiment analysis was performed by using two artificial neural networks, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), to classify the review as recommended (positive) or not recommended (negative). The proposed models have been evaluated on these performance measuring parameters: accuracy, recall, specificity, F1-score and roc-curve. The LSTM method outperformed CNN and achieved classification accuracy of 91.69%, specificity 92.81%, sensitivity 76.95%, and 56.67% F1-score. Based on results of this study, LSTM technique is highly recommended for the sentiment analysis of unstructured text-based user-generated content. Full article

This study is focused on communications that come from consumer-to-consumer (C2C) ecommerce relationships. This topic is directly associated with the electronic word-of-mouth (eWOM) phenomenon. eWOM is related to the set of positive or negative opinions made by potential, actual, or former customers about a seller. The present study proposes a structural equation modeling with partial least squares (PLS-SEM) research model to analyze consumers’ opinions impact on attitude toward purchasing. This model is based on the Information Adoption Model (IAM) in combination with an ecommerce satisfaction perspective, comprising five constructs: (1) service quality, (2) ecommerce satisfaction, (3) argument quality, (4) source credibility and (5) purchase intention. The model was tested by applying the Smart Partial Least Squares (SmartPLS) software for which 116 effective data from customers of the Taobao C2C platform were used. The findings reveal that all of the defined relationships were supported, confirming the positive impact of all the proposed constructs on the purchase intention. In this respect, the findings suggest that C2C platforms should strengthen the analyzed connections to grow the business and to promote transactions. Finally, implications and limitations related to the explanatory capacity and the sample are identified. Full article