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Firms are beginning to use chatbots to collect information from users (e.g., online reviews), a task traditionally done through forms. We used two experiments to investigate how collecting reviews using chatbots in place of forms might impact user experience and, in turn, alter the nature of the reviews submitted by users. Study 1 compares chatbots to web forms, finding that chatbots may improve perceived efficiency but otherwise have no discernible effect on satisfaction and usage intention. At the same time, chatbot usage results in shorter, lower-quality reviews. While structured templates enhance form-based reviews, introducing structure to chatbot interactions does not positively impact satisfaction and usage intent, potentially making the process less efficient. Nonetheless, a structured chatbot approach yields longer reviews and mitigates declines in quality. Adding structure to a chatbot takes the chat out of the chatbot, turning the interaction from casual conversation to a formal process, as demonstrated in Study 2. Hence, while this structured approach for chatbots improves review quality without harming satisfaction and usage intent, it may not be the most effective method for enhancing the reviewer experience. Our research shows that the chatbot and structure pulled users towards contradicting genre rules (the social structures that guide technology use) and triggered users to subconsciously enact distinctly different thought patterns as they composed reviews. [ABSTRACT FROM AUTHOR]

This study focuses on the cultural distances between gaming tourists and service providers based on Hofstede’s culture model and aims to identify the correlation between cultural distances and various experience components. Online reviews of casino resorts in Macau are collected from TripAdvisor. Seven key experience categories in casino resorts and their correlations with cultural distances of six national cultural dimensions are revealed by combining the topic modeling method and correlation analysis. The significance of correlations is found to vary with distinct types of experience categories, which provides new insight into the convergence-divergence debate of culture in tourism studies. [ABSTRACT FROM AUTHOR]

Hiking tourism is an essential means of destination development. A holistic understanding of the hiking experience could benefit destination management. This study adopted topic modelling and an online review-based approach to determine the key attributes of hiking tourism in the context of seasonality. Data were collected from TripAdvisor and a comprehensive list of the attributes, discussed by hikers when visiting Dragon's Back Trail in Hong Kong, is constructed and categorized into five aspects. Insights into the hikers' interests and concerns about the experience attributes across seasons are revealed. The results show that the concerns for some attributes are more sensitive in specific seasons compared with other attributes. Precisely, this study found that seasonality has heterogeneous effects on hikers' experience. The results are beneficial to support local hiking tourism marketing and hedging strategies for different seasons. [ABSTRACT FROM AUTHOR]

A significant advancement in marketing studies has been the use of big data analytics to explore and derive customer experience (CX) insights. With fitness businesses' pivoting towards managing CX along the entire customer journey, this study aimed to understand CX in the commercial fitness industry. A textual corpus of three million words was extracted from a total of 24,231 Yelp reviews on 1,045 fitness clubs in the United States. Structural Topic Modeling (STM), content analysis, and topic network analysis were performed to identify CX themes and their inter-relationships. We then inductively identified topics and themes germane to CX in fitness clubs and derived a general CX model. Sixty-eight relevant topics were identified through an iterative STM and content analysis process and were subsequently interpreted using the CX paradigm and experience design framework (Funk, 2017; Voss et al., 2008). Our final model integrates experience design elements (i.e. stageware, orgware, customerware), customer journey (i.e. pre-usage, usage, post-usage) and CX (i.e. subjective and internal responses) and shows the interrelationships among them. Our model offers an integrative and coherent theoretical framework to examine CX in commercial fitness clubs. Our model and methodology allow researchers and organizations not only to capture CX and the relative salience of CX topics, but also to track CX trends over time, compare the experience design elements of an organization, measure CX performance against competitors, and link CX topics to other criterion variables (e.g. retention rate, financial performance, business survivability). [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To bridge the knowledge gap relating to consumers' efforts to distinguish fake from authentic online reviews, this study explores the impact of different forms of address for P2P accommodation hosts (formal address vs. nickname) on consumers' perceptions of review authenticity. Through three experiments, the results revealed that using a host's nickname acted as a noteworthy social cue, notably enhancing consumers' perceptions of authenticity, confirming the mediating role of inferred relationship closeness. This study offers a fresh perspective on online review authenticity literature and reveals a new research direction for tourism and hospitality, i.e. the novel concept of "inferred relationship closeness." [ABSTRACT FROM AUTHOR]

Examination of the relationships between Electronic Word of Mouth (eWOM), Purchase Decision (PDC), and Customer Satisfaction (CSF) within the context of Shopee, an Indonesian top-commerce platform. The study aims to analyze the influence of eWOM on PDC, the impact of PDC on CSF, and the mediating role of PDC in the relationship between eWOM and CSF. The research employs nonprobability-purposive sampling to collect data from 182 Shopee users in Indonesia exposed to eWOM and actively making PDC. The five Likert-scale questionnaires were distributed online for measurement. Partial Least Squares (PLS) analysis confirms significant positive relationships between eWOM toward PDC, PDC toward CSF, and eWOM toward CSF. Notably, PDC emerges as a crucial mediator, bridging the relationship between eWOM and CSF. The results highlight the significance of eWOM in influencing PDC and perceptions of e-commerce customers. The recommendation is that marketers and online sellers can foster positive eWOM through clear, detailed, and favourable reviews. They are enhancing PDC by streamlining the purchasing process and ensuring satisfactory experiences. For future research, exploring the moderating effects of demographic variables and investigating the impact of website design and social influence on eWOM, PDC, and CSF could enrich understanding in this domain.

One of the common strategies in the content business is the book-to-movie adaptation, where the textual content in the book medium is derived as visual and sound content in the film medium. While books include a variety of genres, the novel is most widely used as the basis for a movie. A unique feature of the novel-based movie is the presence of two types of movie consumers: “original novel fans,” who read the original novel and watch the movie because they like it, and “no-novel readers,” who watch the movie without having read the original novel. Given this unique consumer feature and the fact that electronic word-of-mouth (e-WOM) is the key factor in film success, it is essential to address the following questions: “Are original novel fans and non-novel readers are more active in e-WOM generation?” and “What is their mechanism of e-WOM generation?”. We aim to address these research questions through two studies. In Study 1, we examine which of the two types of movie consumers generate e-WOM and show that original novel fans are more likely to generate e-WOM. In Study 2, we focus on original novel fans, which are key consumers of e-WOM generation, and examine their e-WOM generation mechanisms; the results show that original novel fans generate e-WOM through social needs. Our study contributes to a better understanding of consumer behaviors regarding e-WOM generation. Moreover, we provide useful insights for business managers involved in the novel-to-movie adaptation.

Trust is an important component of any purchasing situation. In an online setting, trust becomes even more important as buyers and sellers are not able to interact directly with one another. Compounding the situation is the fact that the consumer cannot directly experience the product under consideration. Organizations have attempted to address this shortcoming by allowing third parties to offer reviews of the product in question. This paper addresses how consumers process these product reviews. By rooting this paper in trust and uncertainty reduction theory, a testable model was developed. The model was tested, and in general, more helpful reviews, lower intra-review variability, and an onymous review leads to a greater probability of trusting a reviewer. However, these general findings need to be interpreted through the lens of several significant interactions. The paper concludes with prescriptions for practitioners and several avenues for future research. [ABSTRACT FROM AUTHOR]

This study aimed to empirically verify the impact of tourism destination image on perceived usefulness from the perspective of construal‐level theory. It also attempted to compare the results of the survey and data mining techniques regarding methodology. An online survey was initially conducted, and statistical analysis was performed using the R program. Concurrently, 25,952 reviews from TripAdvisor were gathered using a web crawler, Octoparse, and analyzed using the Tidyverse approach. The results indicated that, in the probabilistically distant group, affective destination image had a significant effect on perceived usefulness, whereas cognitive image did not affect perceived usefulness. Contrastingly, in the probabilistically close group, cognitive images had a stronger effect on perceived usefulness than affective images. Both the influential relationship between affective and cognitive images and the comparison of the two research methods were partially supported. These findings have theoretical and practical implications. [ABSTRACT FROM AUTHOR]

It is crucial for enterprises to clearly identify user needs during the process of formulating product design improvement plans. Therefore, it is essential to comprehensively and accurately identify user needs, explore the reasons behind the emergence of these needs, and incorporate user opinions into the process of product design improvement. A method is proposed to comprehensively and accurately capture user requirements and address the challenge of identifying the underlying causes of user requirements. This method utilizes online comments and operational data to identify user requirements and their influencing factors. First, text sentiment analysis techniques are employed to quantify the importance and performance values of product feature topics. Second, we construct a quadrant model to identify product features requiring improvement, and the original negative comments related to these features are traced. However, the quadrant model alone is insufficient to reflect specific product issues that users are concerned about. Therefore, a functional structure model based on product issues is designed to filter and identify factors that influence user requirements using operational data. Finally, a Bayesian network inference approach is utilized to identify the key influencing factors on user requirements, enabling analysis of the causes behind user requirements and the proposal of product design improvement strategies. The feasibility and effectiveness of the proposed method are validated through experiments conducted on heavy-duty truck data. By analyzing the original negative comments related to the power characteristics, specific user demands regarding the insufficient power of the product were identified, such as “obviously insufficient power when climbing slopes” and other issues. Based on the vehicle power system functional structure model, combined with expert knowledge and operational data, factors related to the state of parts and user behavior that may affect “insufficient vehicle power” were identified. Based on the analysis results, suggestions were made to improve the engine intake air temperature control strategy and to enhance vehicle performance by promoting correct user behavior through informational campaigns. [ABSTRACT FROM AUTHOR]

Purpose: Online reviewers' identity information is an essential cue by which consumers judge reviews on ecommerce platforms. However, few studies have explored how prior anonymous reviews and focal reviews affect reviewers' preference for anonymity. The purpose of this paper is to investigate why reviewers seek anonymity in terms of prior anonymous reviews and focal reviews. Design/methodology/approach: Based on restaurant reviews collected from meituan.com, one of the largest group-buying ecommerce platforms in China, this study employed logistic regression to examine how prior anonymous reviews and focal reviews are associated with reviewers' preference for anonymity. Findings: Results show that the volume and sequence of prior anonymous review are positively associated with the likelihood of reviewers' preference for anonymity, whereas focal review valence is negatively correlated with this preference. Focal review length is positively correlated with reviewers' preference for anonymity but negatively moderates the roles of review valence and prior anonymous reviews on this preference. Originality/value: This study expands the information disclosure literature by exploring determinants of user identity disclosure from a reviewer perspective. This research also offers a methodological contribution by employing a more accurate measure to calculate reviewers' preference for anonymity, enhancing the empirical results. Lastly, this work supplements the online review literature on how prior anonymous reviews and focal reviews are associated with reviewers' identity disclosure. [ABSTRACT FROM AUTHOR]

It's a major challenge for marketers to combine various sensory information to create a synergistic effect of '1 + 1 > 2'. However, there is a lack of research on sensory synergy. This study attempts to quantitatively confirm the existence of sensory interaction and sensory conflict and to investigate their effects on consumer behavior. To measure the effect of sensory synergy, we use text classification and sentiment analysis methods to analyze online restaurant reviews. The results show both have significant effects on consumer rating behavior. Specifically, in sensory interaction, the more negative sensory information there is, the lower the consumer rating. However, it is not the case that the more positive sensory information, the higher the consumer rating, but rather it should be kept at an appropriate level. In addition, positive sensory sentiment can compensate for the negative in sensory conflict, both within and across senses. These findings can help marketers implement sensory marketing. [ABSTRACT FROM AUTHOR]

The study aimed to explore the relationship and interaction between humans and nature in specific areas. Latent Dirichlet allocation topic recognition and SnowNLP sentiment analysis were used to extract the topics and analyze the sentiments from visitors' online reviews of Wuyishan National Park. The conclusions were as follows: (1) The tourists mainly expressed positive emotions toward Wuyishan National Park, and the tourists acknowledged its ecological environment and natural and cultural heritage value. (2) The tourists' comments focused on four themes: tourism activities and facilities, natural and cultural heritage value, characteristic tourism products, and tourism management and services. Natural experience was the main tourism activity in Wuyishan National Park, while cultural activities were related to the tea culture. (3) The tourist facilities, ticket and reservation mechanism, and management and services of Wuyishan National Park were the main concerns of the tourists. The study suggested that Wuyishan National Park could be transformed from a tourist destination into a comprehensive national park that provides recreational experiences and environmental education. This should be conducted by (1) developing detailed natural and cultural education and experience products and (2) improving public service functions and enhancing the public welfare of the national park. [ABSTRACT FROM AUTHOR]

Purpose – Global adoption of the internet and mobile usage results in a huge variation in the cultural backgrounds of consumers who generate and consume electronic word-of-mouth (eWOM). Unsurprisingly, a research trend on cross-cultural eWOM has emerged. However, there has not been an attempt to synthesize this research topic. This paper aims to bridge this gap. Methodology – This research paper conducts a systematic literature review of the current research findings on cross-cultural eWOM. Journal articles published from 2006 to 2021 are included. This study then presents the key issues in the extant literature and suggests potential future research. Findings – The findings show that there has been an upward trend in the number of publications on cross-cultural eWOM since the early 2010s, with a relatively steeper increase toward 2020. The findings also synthesize cross-cultural eWOM research into four elements and suggest potential future research avenues. Value – To the best of the authors’ knowledge, there is currently no exhaustive/integrated review of cross-cultural eWOM research. This research fills the need to summarize the current state of cross-cultural eWOM literature and identifies research questions to be addressed in the future. – El boca a boca electrónico cross-cultural: una revisión sistemática de la literatura Objetivo – La adopción global de Internet y los móviles da lugar a una enorme diferencia en el origen cultural de los consumidores que generan y consumen el boca a boca electrónico (eWOM). No es de extrañar que haya surgido una tendencia de investigación sobre el eWOM transcultural. Sin embargo, no se ha intentado sintetizar este tema de investigación. El objetivo de este artículo es subsanar esta carencia. Metodología – Este trabajo de investigación realiza una revisión bibliográfica sistemática de las investigaciones realizadas sobre eWOM transcultural. Se incluyen artículos de revistas publicados desde 2006 hasta 2021. A continuación, el estudio presenta las cuestiones clave de la literatura existente y sugiere posibles investigaciones futuras. Resultados – Los resultados muestran que ha habido una tendencia al alza en el número de publicaciones sobre eWOM intercultural desde principios de la década de 2010, con un aumento relativamente creciente hacia 2020. Los resultados también sintetizan la investigación sobre eWOM intercultural en cuatro elementos y sugieren posibles vías de investigación futuras. Valor – Actualmente no existe una revisión exhaustiva/integrada de la investigación sobre el eWOM cross-cultural. Esta investigación satisface la necesidad de resumir el estado actual de la literatura sobre eWOM cross-cultural e identifica las cuestiones de investigación que deben abordarse en el futuro. – 跨文化电子口碑研究：系统性文献回顾 摘要 目的 – 在互联网全球化以及移动手机的广泛使用的背景下, 不同文化背景的消费者都在贡献电子口碑（eWOM）。这使得电子口碑存在文化差异。然而, 还没有人试图对这个研究课题进行综合分析。本文的目的就是要弥补这一空白。 方法 – 本研究论文对目前关于跨文化eWOM的研究成果进行了系统的文献回顾。包括2006年至2021年发表的期刊文章。然后, 本研究提出了现有文献中的关键问题, 并提出了潜在的未来研究。 研究结果 – 研究结果显示, 自2010年初以来, 关于跨文化eWOM的出版物数量呈上升趋势, 到2020年时增幅相对较大。研究结果还总结了跨文化eWOM研究的四个要素, 并提出了潜在的未来研究途径。 价值 – 目前还没有关于跨文化eWOM研究的详尽/综合的回顾。这项研究填补了总结跨文化电子WOM文献现状的需要, 并确定了未来要解决的研究问题。

Regardless of the diversity of travellers, meeting customer expectations is essential for the success of hotel business worldwide. How do hoteliers cope with these challenges to meet guest expectations before arrival in terms of different regions and travel purposes? This study analyses travellers’ complaints retrieved from the hotel review website ‘TripAdvisor’ to gain insight. A total of 1,868 travellers’ complaints were analysed using a Two-Way ANOVA factorial design to examine the differences among the home regions of travellers, travel types, and other hotel service attributes. The findings indicate that the home region of travellers has a higher main effect than traveller type on hotel service attributes regarding online complaint behaviour. Finally, family travellers are more likely to complain about cleanliness than couples. [ABSTRACT FROM AUTHOR]

During the coronavirus disease 2019 (COVID-19) pandemic, non-face-to-face e-commerce has become a significant consumer channel for customers to buy fresh food. However, little is known about customer opinion changes in fresh food e-commerce (FFEC) products and services during COVID-19. This study investigated the changes in expectations and preferences of FFEC customers on products and services before and during the pandemic from online reviews through a text mining approach. We divided the pandemic into two phases, acute and recovery, and found that eight attributes affect customers' opinions. Some logistic service-related attributes gained customer attention during the acute phase, but product-related attributes gained more attention in the recovery phase. Customers showed a great level of forgiveness on many attributes during the acute phase, but customers' dissatisfaction was expressed during the recovery phase. Finally, the results of the comparative importance–performance analysis provide improvement strategies for FFEC and help optimize their resource allocation of FFEC and enhance sustainable operation capacity in the case of a crisis. [ABSTRACT FROM AUTHOR]

To enhance the service quality and sustainable development of urban transport hubs, a comprehensive understanding of passengers' emotional inclinations and satisfaction levels is paramount. This study analyzes online reviews from passengers at three different types of transport hub in Changsha, China. We aim to understand passengers' experiences by analyzing word frequency, semantic networks, and sentiment. Our analysis shows that passengers' words can be grouped into four categories. Core words are more important in shaping passenger evaluations than edge words. The sentiment and satisfaction analysis reveals passengers are generally satisfied with the convenient transit options and the cleanliness of the transport hubs. The study also shows that passenger satisfaction levels have steadily increased over the years across different transport hubs. During holidays, passengers at airports and high-speed train stations express more positive sentiments. Passengers with shorter comments tend to be more satisfied than those with longer comments. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the impact of various internet marketing methods like Online Travel Agents (OTA), Online Reviews (ORV), social media (SM), and Web GIS (WG) and examine the mediating role of Destination Image (DI) on tourist Homestay Booking (HB) located in the Himalayan region of India. Primary data was collected by using a structured questionnaire from 340 tourist respondents. Structural Equation Modelling (SEM) was used to analyse the data using SPSS and AMOS 21. The study validates the considerable role of OTA, ORV, SM, and WG on Homestay booking. The result emphasizes OTA roles, the importance of utilizing positive reviews, leveraging the power of social media, the significance of superior destination image, and implementing Web GIS in Homestay marketing. [ABSTRACT FROM AUTHOR]

The research note discusses the potential effects of contemporary technologies, such as blockchain and artificial intelligence (AI), in enhancing consumers' trustworthiness of online reviews in the tourism and hospitality context. The study postulates that three characteristics of blockchain – decentralization, immutability, and timestamping – ensure transparency in the online review process. Using a socio-technical systems approach, the current study provides insights into how blockchain technology, AI agents, and human factors interact to facilitate such transparency and thereby enhance consumers' trustworthiness of online reviews for their decision-making. Therefore, the study proposes that perceived transparency mediates the relationship between these three blockchain characteristics and the trustworthiness of online reviews. In proposing an investigation of the characteristics of blockchain and its interaction with AI in online reviews, the study highlights that researchers and practitioners pay attention to a specific characteristic of contemporary technologies to actualize potential benefits. [ABSTRACT FROM AUTHOR]

Studies have confirmed the ineffectiveness of sentiment expressions generated by sellers in improving guests' purchasing intentions. However, how sentiment expressions can influence guest behavior and host performance remains unclear, given the relative importance of seller-generated content in peer-to-peer rental platforms. After collecting data from Airbnb and developing empirical models, this study confirmed that hosts' sentiment expressions largely benefit from their online performance. This case is especially true when their properties did not receive high-quality negative reviews. To further reveal the mechanism behind this effect, we further conducted two experiments. Results show that trust plays an intermediary role in the relationship between hosts' sentiment expressions and guests' purchasing intentions. This work contributes to tourism literature and property owners on peer-to-peer rental platforms in practice. [ABSTRACT FROM AUTHOR]

Applying dual-process theory, the current research examines the impact of written style (i.e., fact-based vs. emotion-based) and subject focus of online reviews (i.e., property-focused vs. host-focused) on customer trust and booking intention in the peer-to-peer (P2P) accommodation context. Following a theory-led conceptualization of customer trust in the P2P accommodation context, a series of three scenario-based experimental studies are conducted to examine the proposed hypotheses. Study results show that fact-based (vs. emotion-based) reviews of the property/host enhance customers' booking intention, which is mediated by trust in the property/host, respectively. Moreover, among fact-based reviews, host-focused (vs. property-focused) reviews lead to a higher level of booking intention among female customers, but male customers show no significant difference in booking intention when reading either host-focused or property-focused reviews. The present research findings bear important theoretical and managerial implications for researchers and P2P accommodation practitioners alike, which are discussed at the end. [ABSTRACT FROM AUTHOR]

The rapid development of text analytics enables marketers to obtain the information extracted from the narrative content in user-generated content (UGC). Recent studies have also demonstrated that people with different cultural backgrounds may express their opinions about their purchase in diverse manners. This study focuses on the impact of the narrative content of consumers' perception of helpfulness. It first identifies four contextual dimensions to propose a theoretical model, demonstrating that perceptions of helpfulness may differ in respect to the consumers' varied cultural backgrounds (e.g., individualism vs. collectivism). By using Linguistic Inquiry and Word Count (LIWC), the study empirically tests the hypotheses by analyzing 111,857 movie reviews collected for 167 American movies released both in the United States and in China from 2013 to 2016. The results reveal that individualist consumers perceive an online review that contains more self-description and future-focus content as helpful, whereas collectivist consumers rely more on online reviews containing social description and past-focus content. [ABSTRACT FROM AUTHOR]

The expansion of the e-commerce market has led to the challenge of information overload, necessitating the development of recommender systems. The recommender system aids users in decision-making by suggesting items that align with their preferences. However, conventional recommendation models rely solely on quantitative user behavior data, such as user ratings, and lead to limitations in recommendation performance due to the sparsity problem. To address these issues, recent research has leveraged convolutional neural networks (CNNs) to extract and incorporate semantic information from user reviews. However, several prior studies have a disadvantage in that they fail to account for the intricate interactions between users and items directly. In this study, we introduce a novel approach, the Review-based recommender system using Outer Product on CNN (ROP-CNN) model, which adeptly captures and incorporates semantic features from reviews to address the complex interactions between users and items using CNN. The experimental results, using real user-review datasets, demonstrate that the ROP-CNN model outperforms existing baseline models for prediction accuracy. And this study presents a novel theoretical and methodological perspective in recommendation research, suggesting a method that integrates user preference information from reviews into recommender systems by leveraging rich user-item interaction information.

With the growth of the internet and e-commerce, online reviews have become a prevalent and rich source of information for personalized recommendations. Review text typically contains user preferences regarding various aspects of items or services. However, most previous recommendation models that used online reviews focused on general opinions, without considering the various aspects of the review text. Although previous approaches have effectively reflected the overall preferences embedded in reviews, capturing the preferences for various aspects is limited. This paper proposes an attentive aspect-based recommendation model with a deep neural network (AARN), which can capture user preferences regarding various aspects embedded in review text. The proposed model uses an advanced aspect-based sentiment analysis (ABSA) method. Unlike previous approaches that extract overall preferences, ABSA offers the advantage of extracting preferences for various aspects embedded in review texts. An attention mechanism is then applied to measure the unique attention weights for each aspect. Extensive experiments were conducted on real-world online review datasets, showing that the proposed model outperformed baseline models in effectively predicting user ratings for target items. Furthermore, this study demonstrated the influence of the ABSA method on personalized and explainable recommendations.

Based on 1,087,248 individual online reviews for 2007–2020 collected from the websites of AutoHome and Sohu Auto using the Python method, rating, volume, variance, and many other characteristics of online reviews are generated for 808 automobile products sold in mainland China. Using the observational learning (OL) framework to establish the relationship between automotive consumers’ purchasing behaviours and the display of online reviews, the econometric results reveal that, at the aggregate level, automotive consumers seem to respond more significantly to the volume/variance of online reviews (i.e. hedonic cues) rather than the average rating (i.e. utilitarian cues) for producing adequate OL. However, at the sub-categorical level, consumers are more likely to produce adequate OL regarding ratings representing utilitarian attributes while effects of most of the variance (i.e. hedonic cues) are limited. These findings imply that consumers are more likely to perceive automobile products as ‘more hedonic and less utilitarian’, but an overly complicated online review system forces consumers to simplify the OL process that prioritizes utilitarian attributes, as hedonic information is difficult to be understood. This study provides theoretical and empirical insights about how consumers respond differently to various online review characteristics for products with no clear boundary in a hedonic/utilitarian distinction. [ABSTRACT FROM AUTHOR]

In today's dynamic business landscape, retailers are progressively turning to omnichannel strategies to offer integrated shopping experiences across various touchpoints. This study endeavors to elucidate the determinants driving loyalty among omnichannel consumers. A comprehensive research framework encompassing technological elements, in-store attributes, online reviews, and behavioral variables was formulated. A sample of 252 customers familiar with omnichannel shopping was evaluated. Structural equation modeling was utilized for data analysis. Results indicate that personal interactions and merchandise variety significantly influence crowd perception. Perceived ease of use was found to significantly affect perceived usefulness, attitude, and satisfaction. Furthermore, both crowd perception and attitudes toward online reviews markedly influence the overall attitude toward omnichannel. The data underscores the pivotal role of attitude in enhancing satisfaction and fostering loyalty among consumers. In conclusion, this research not only sheds light on the intricate dynamics of omnichannel customer loyalty but also offers valuable insights for both academia and industry, underscoring the importance of integrated multi-channel strategies in contemporary retailing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management & Systems is the property of Business Administration Association of R.O.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, online user-generated data have emerged as a valuable source for consumer product research. However, most studies have neglected the brand effect, although it is a significant factor in conventional market research. This paper demonstrates the importance of brands in data-driven design using online reviews. Specifically, this study utilizes game theory and suggests a game setting representing market competition. Elements of the game are determined based on online data analysis. The proposed approach consists of four stages. The first stage divides online customers into different segments and analyzes them to extract the feature importance of each brand in each segment. The importance is based on the positive term frequency of features, and it becomes the customer's partial utility for each feature. The second stage defines the specification of product candidates and calculates their costs. This study refers to real market datasets (bill of materials) available online. At this point, the game is all set. The third stage finds the Nash equilibrium of the designed game, and the final stage compares the optimal strategy for a product portfolio with and without brand consideration. The suggested approach was tested on smartphone reviews from Amazon. The result shows that the lack of brand consideration leads a company to choose a non-optimal product strategy, illustrating the significance of the brand factor. [ABSTRACT FROM AUTHOR]

Online recommendations, comments and ratings are today an important source of information for tourists who need to be informed and demanding. The bold review itself is very influential in the tourism industry, especially in the branding of a tour, because the bold review gives a clear idea to the service provider. This research is located in Kampong Batik, which has the potential for batik education tourism, and requires branding so that tourism can still exist, especially after being affected by the COVID-19 pandemic. Firstly, this study uses observation to identify the existing conditions of the research site, after that the conditions will be compared with tourist attraction criteria. Secondly, this research uses content analysis techniques to determine visitor perceptions of reviews on websites such as Google Review. After that, the result from primary observation will be compared with the result in content analysis. The observation uses tourist attraction criteria to determine whether or not Kampong Batik already meet the tourist attraction criteria. The results showed that many of the visitors felt that Kampong Batik already fit in tourist attraction criteria. Still, there's one criterion that has not been fulfilled which is supporting transportation criteria. The results of this research are expected to be an input for the City Government of Semarang and related stakeholders. [ABSTRACT FROM AUTHOR]

The 7P marketing mix encompasses various business facets, notably the Process element governing internal operations from production to customer service. With the surge in online customer feedback, assessing machine learning efficacy, especially ensemble learning, in classifying 7P-related customer review data has gained prominence. This research aims to fill a gap in existing literature by evaluating ensemble learning's performance on 7P classification, an area not extensively explored despite prior sentiment analysis studies. Employing a methodology merging Natural Language Processing (NLP) with ensemble learning, the study processes restaurant reviews using NLP techniques and employs ensemble learning for precision and accuracy. Findings demonstrate that DESMI yielded the highest performance metrics with accuracy at 0.697, precision at 0.699, recall at 0.697, and an F1-score of 0.684. These outcomes underscore ensemble learning's potential in handling complex datasets, signifying its relevance for marketers and researchers seeking comprehensive insights from customer reviews within the 7P marketing mix domain. This study sheds light on how ensemble learning outperforms its foundational methods, indicating its prowess in extracting meaningful insights from diverse and intricate customer feedback. [ABSTRACT FROM AUTHOR]

The tourism industry is rapidly adapting to new conditions and the growth of online and smart technologies. Since user-generated content provides relevant data to find out about the image of a destination among tourists, this study aimed to investigate the status of Iran's destination image on TripAdvisor, one of the most popular online tourism platforms. The study adopted a quantitative content analysis research method. To cover different opinions, two important parts of this website, namely Things to Do and Iran Travel Forum, were considered as the statistical population in the three-year period from 2019 to the beginning of 2022. Multistage random cluster sampling was used, and coding was done by coders based on the compiled codebook. The content of TripAdvisor social network was analyzed in three cognitive, emotional, and behavioral dimensions. The findings indicated that potential foreign tourists are most concerned about Iran's infrastructural affairs, especially transportation, visa, currency exchange, and sometimes hosting business services. Signs of insufficient information, lack of access to up-to-date and comprehensive information online, contradictions in available information, and instability of procedures in the field of visa issuance and transportation services can also be seen in these contents. Although users considered Iran's tangible heritage and historical and cultural attractions very attractive, the connection of these attractions with the human and social attractions of contemporary host society had brought a double surprise and attraction. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Practical implications: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

Online travel agency (OTA) attributes are significant to travelers in deciding which travel agency to select for their trip since different attributes represent different levels of perceived value to the travelers. The study aims to investigate the OTA attributes generated perceived value effect on OTA selection intention by Bangladeshi tourists. Data were collected by following a purposive sampling technique from 307 tourists of Bangladesh who buy from OTA and analyzed by utilizing SPSS version 24 integrated with AMOS version 26, applying structural equation modeling (SEM). Information quality, price, privacy and security, ease of use and online review are considered the features of OTA that influence perceived value and perceived value influence OTA selection intention. The result showed that the information quality, price, privacy and security, and ease of use significantly and positively influence perceived value, while online reviews were found to have no significant relationship. The perceived value also positively influences the OTA selection intentions of tourists. This study will provide important directions for OTA service providers in developing better strategies to improve OTA performance.

Tiket.com is an Online Travel Agent (OTA) that provides online hotel bookings. This study aims to analyze the effect of visual presentation and online reviews on hotel booking intention on the Tiket.com application. This research integrates visual presentation and online reviews with the Technology Acceptance Model (TAM). The variables tested consist of Visual presentation, Perceived Ease of Use, and Online review as independent variables, Perceived Usefulness as a moderating variable, and Booking Intention as the dependent variable. The type of research is quantitative with a cross-sectional design and the research subjects are users of the Tiket.com application. Data was collected by distributing questionnaires to 119 respondents. The research hypothesis was tested using the Partial Least Squares Structural Equation Model (PLS-SEM) analysis technique. Based on the results of the hypothesis, H1, H3, and H8 are not in line with previous thinking because they do not positively influence buying interest in the tiket.com application. tiket.com applications should update booking features, and more attractive colors, update the system, reduce addsens, and update a more modern look.