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The purpose of this paper is to propose a model to test whether the combined effects of valence and objectivity/subjectivity of online review have an effect on consumer judgment and whether e-WOM platforms have a moderating effect.

In total, 480 respondents participated in online experiments with a four (positive+objective, positive+subjective, negative+objective, and negative+subjective online review) by two (marketer-generated vs consumer-generated brand community web sites) between subject design.

The experiment showed that: an objective negative online review was rated higher in terms of message usefulness compared to the other types of online reviews; positive reviews, whether they are objective or subjective, were rated higher in terms of attitudes toward and intention to purchase the reviewed product, and the effects of online reviews moderated by e-WOM platforms on consumer judgment were supported.

The present study, based on an established theoretical foundation, will help the research community to gain a deeper understanding of the combined effects of online review valence and attributes on consumer judgment and whether user-generated web community is better for consumers to consult product experience.

The findings of this study can provide interested firms with useful strategies and tactics to enhance users’ acceptance of online reviews in terms of who operates the web sites.

With increasing use of consumers’ online reviews, the present study proposed and tested a comprehensive research model integrating both the valence and objectivity/subjectivity of online review, which has rarely been addressed in previous research.

This paper aims to explore the relationships between website quality – through consumer-generated media stimuli-, emotions and consumer-brand engagement in online environments.

Two independent studies are conducted to examine these relationships. Study 1, based on a sample of 366 respondents, uses a structural equation modelling approach to test the research hypotheses. Study 2, based on 1,454 online consumer reviews, uses text-mining technique to examine further the relationship between emotions and consumer-brand engagement.

The findings show that all the consumer-generated media stimuli are positively related to the dimensions of emotions. However, only pleasure and arousal are positively related to the three variables of consumer-brand engagement. The findings also show cognitive processing as the strongest dimension of consumer-brand engagement providing positive sentiments towards brands.

The findings provide marketers with an understanding of how valid, useful and relevant content (i.e. information/content) creates a greater emotional connection and drive consumer-brand engagement. Marketers should be aware that consumer-generated media stimuli influence consumers’ emotions and their reaction.

This study is one of the firsts to adapt and apply the S-O-R framework in explaining online consumer-brand engagement. This study also adds to the brand engagement literature as the first study that combines PLS-SEM approach with text-mining analysis to provide a better understanding of these relationships.

Given the importance of performance consistency of chain hotels in customers’ decision-making and service evaluation, this study aims to explore how consistently chain hotel brands offer quality service and carry out their performance from the eyes of customers through online reviews on TripAdvisor of the top five US hotel chains (i.e. Choice, Hilton, InterContinental, Marriott and Wyndham) and their brands.

The research objectives were achieved through methodological triangulation: business intelligence, data visualization analytics and statistical analyses. First, the data collection and pre-processing of consumer-generated media (CGM) (i.e. TripAdvisor online reviews) were performed using business intelligence for further analyses. Using data visualization analytics (i.e. box-and-whisker plot by region and brand), the geographic patterns of performance attributes (i.e. online review ratings, including location, sleep, cleanliness, room and service) were depicted. Using a series of analyses of variance and regression analyses, the results were further assessed for the impacts of brand performance inconsistency on consumers’ perceived value, sentiment and satisfaction.

The empirical results demonstrate that there are significant performance inconsistencies in performance attributes (location, sleep, cleanliness, room and service) by brands throughout the six regions in the US hotel market. More importantly, the findings confirm that brand performance consistency significantly influences consumers’ perceived service quality (i.e. perceived value, satisfaction and sentiment).

This study is one of the first attempts to empirically explore hotel brand performance consistency in the US hotel market from customer reviews on CGM. To measure hotel brand performance in the US hotel market, this study collected and analyzed user-generated big data for the top 5 US hotel chains through business intelligence, visualization analytics and statistical analysis. These integrated and novel research methods would help tourism and hospitality researchers analyze big data in an innovative data analytics approach. The findings of the study contribute to the tourism and hospitality field by confirming hotel brand performance inconsistency and such inconsistent performance affected customers’ service evaluations.

This study demonstrates the significant impact of hotel brand performance consistency on consumers’ perceived value, emotion and satisfaction. Considering that online reviews are perceived as a credible source of information, the findings suggest that the hotel industry pays special attention to brand performance consistency to improve consumers’ perceived value, emotion and satisfaction.

The present study aimed to understand the relationships between the various kinds of feedback received by hospitality operators. Information from guests, experts, and internal sources are often received, valued, and processed in various ways. The researchers sought to further explore the usage of such feedback and implications for theory and practice.

A survey was sent to hotel general managers of four‐ and five‐diamond properties around the USA using the listing of the American Automobile Association (AAA). A total of 140 responses were received. The researchers utilized correlations and canonical correlation analysis to help understand the relationships among the variables.

The results of the study revealed moderate to strong correlations between improvement in consumer‐generated feedback and customer satisfaction; between improvement in AAA ratings and customer satisfaction and mystery shopping scores. There were also moderate to high correlations among value placed in consumer‐generated and that placed on other electronic forms of electronic feedback such as social networking, blogs, and online travel agency feedback. Canonical correlation was also performed among the variables in the various correlation matrices. Two statistically significant dimensions emerged. The most influential variables in the first dimension were value placed on TripAdvisor and value placed on meeting planner feedback. The second dimension featured three influential variables: value placed on reviews in TripAdvisor, value placed on social networking, and perceived improvement in consumer‐generated ratings.

The present research revealed two distinct sets of general managers: those who have a strong preference towards online feedback and those who place greater value in traditional sources of feedback such as letters from customers. Additionally, the researchers discovered some similarities between improvement in scores of experts and consumers. This in turn, points out to the existence of some universal aspects of service that appeal to both stakeholder groups. The different levels of value placed on various kinds of feedback points out to the need for tourism and hospitality operators to adopt a more comprehensive strategy to collect, analyze, and take appropriate actions based on such information.

The researchers contribute to the nascent literature on consumer‐generated feedback by exploring its relationship to other variables. Furthermore, the study of various sources of feedback (i.e. guests, experts, and operators) is often studied separately in the tourism literature. It was the aim of this study to explore all of these together in order to better understand their relationships, value, and uses.

The purpose of this paper is to examine the influence of perceived information and entertainment value, perceived credibility and perceived value on Generation Y consumers’ usage frequency of online consumer reviews.

The paper proposes and tests, with structural equation modelling analysis of moment structures, a research model using data from a large sample of Generation Y consumers.

The results confirm that Generation Y consumers perceive online reviews to be informative, entertaining, credible and valuable, and that they frequently consult such reviews. More specifically, the empirical analysis confirms that perceived information value, perceived entertainment value and perceived credibility significantly influenced the perceived value that Generation Y attach to online consumer reviews, which, in turn, was a significant predictor of their usage frequency of such reviews.

The results highlight the strategic importance of integrating online consumer reviews into the marketing communication mix when targeting Generation Y, together with the necessity of having filtering mechanisms to ensure that only authentic reviews are published and the need to implement tactics to ensure that such reviews are informative and entertaining and, consequently, of value.

This study contributes to marketers’ comprehension of strategically using online consumer reviews when targeting the Generation Y segment.

El objetivo de esta investigación es examinar la influencia del valor de la información, del valor del entretenimiento, la credibilidad y el valor percibido en la frecuencia de uso de las revisiones online de los consumidores por parte de los consumidores de la Generación Y.

Este documento propone y contrasta, a través de los modelos de ecuaciones estructurales (AMOS), un modelo de investigación que utiliza datos de una gran muestra de consumidores de la Generación Y.

Los resultados ponen de relieve la importancia estratégica de integrar las reseñas de consumidores en línea en la combinación de comunicaciones de comercialización al dirigirse a la Generación Y, junto con la necesidad de disponer de mecanismos de filtrado para garantizar que sólo se publiquen reseñas auténticas y la necesidad de aplicar tácticas para asegurar que dichas reseñas sean informativas y entretenidas y, por consiguiente, de valor.

Los resultados destacan la importancia estratégica de integrar las revisiones de los consumidores en línea en el mix de comunicación de marketing cuando se dirigen a la Generación Y, junto con la necesidad de contar con mecanismos de filtrado para garantizar que solo se publiquen revisiones auténticas y también se implementen tácticas para garantizar que las reseñas sean informativas y entretenidas y, en consecuencia, de gran valor.

Este trabajo ayuda a los especialistas de marketing a comprender como pueden utilizar estratégicamente las opiniones de los consumidores online para dirigirse a los consumidores de la Generación Y.

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

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

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

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

Building on the three-factor theory, this study aims to unravel how the role of hotel attributes such as basic, excitement and performance factors could differ in accordance with different hotel star ratings and distinct customer segments.

This study explores the asymmetric effects of hotel attributes on customer satisfaction by extracting 412,784 consumer-generated reviews from TripAdvisor across different cities in China.

By taking into account the origins of customers and hotel star ratings, the study uncovers that guests’ expectations of hotel performance differ with respect to their origins (domestic and international guests) and the star ratings of the hotels being reviewed, thereby moderating the asymmetric impact of hotel attributes on customer satisfaction.

The study compares and contrasts the determinants of customer satisfaction for domestic and international guests in the context of Chinese hotels. Care should still be exercised when generalizing the insights gleaned from this study to other contexts.

The findings from this study translate into actionable guidelines for hotel operators to make informed decisions regarding service improvement.

The study extends previous work by offering a deeper understanding of the asymmetric impact of hotel attributes on customer satisfaction. Specifically, this study provides a deep understanding of the different hotel attributes such as basic, performance and excitement factors in explaining customer satisfaction among different hotel customer segments. Findings from this study can not only inform hotel operators on the significance of various hotel attributes in determining customer satisfaction but also guide the formulation of business strategies to retain customers by inducing delight and not frustration.

Online reviews derived from peer communications have been increasingly viewed as an important approach for consumers to gather pre-purchase information. This study aims to examine factors affecting online reviews adoption in social network communities and then indicates the underlying mechanism of this process based on an extended information adoption model (IAM).

Using the data collected from 242 users of a social network community via an online survey, the proposed model is empirically assessed by partial least squares-based structural equation model (PLS-SEM).

The results show that both perceived diagnosticity and perceived serendipity are drivers of online reviews adoption in social network communities. Meanwhile, community identification is not only an antecedent of diagnosticity and serendipity perceived by community members, but also motivates source credibility which, in turn, positively influences argument quality. Finally, the importance of argument quality and source credibility in reviews adoption process is also presented.

This study extends the IAM and enriches the literature regarding online reviews adoption. It deepens the understanding of serendipitous experiences and community identification in social networking context by addressing their important roles in the authors' extended IAM.

Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC.

A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words.

Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains.

Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach.

This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.

When shopping for hotels online, consumers usually follow a sequential process of search, click-through and book. How to maximize consumer conversion on the path to purchase and prevent potential customers from giving up the online search remains an important topic to hotel marketers and online travel agents (OTAs). The purpose of this study is to understand how informational cues displayed in an online hotel search process, including quality indicators, brand affiliation, incentives (discounted price and promotion) and position in the search results, influence consumer conversion from one stage to another.

The authors collected clickstream data of hotel search from Expedia. The data include information on individual consumers’ click-through and booking, as well as events leading up to the conversions (or failure to convert) from search, click-through to book. It contains 940,164 hotels searched and displayed in 39,574 online search queries made by users in a regional US market between November 1, 2012 and June 20, 2013. The modeling strategy comprised the Heckman model and random effects model, which integrated sequential consumer behavior in different problem-solving stages while accounting for heterogeneity across different hotels online.

The authors find that consumers rely on informational cues displayed online to make decisions about hotel booking. Specifically, consumers tend to click through hotels with higher consumer-generated ratings and industry-endorsed ratings. However, they tend to rely on consumer-generated ratings rather than industry-endorsed ratings when committing to a booking. Moreover, consumers are strongly responsive to incentives (discounted price and promotion) when clicking-through and booking a hotel. Finally, the likelihood of consumer conversions from search to click-through and booking is higher for hotels with brand affiliation and higher positions in the search results.

This research provides critical managerial implications of online search for hotel marketers and OTAs. The results inform hotel marketers and OTAs on how consumers respond to informational cues displayed in their search process and how these informational cues influence consumer conversion from one stage to another. The sequential problem-solving process of search, click-through and booking disclosed in this study also helps hotel marketers to identify customer conversion opportunities using effective informational cues.

当在线酒店预定时, 消费者往往遵循一系列流程, 搜索, 点击查询, 到最后预定。对于酒店营销商和线上旅游社（OTAs）来说, 如何最大化提高消费转化, 使得消费者不会半途中断, 最后预定酒店, 是一个重要话题。本论文的研究目的就是理解酒店在线搜索过程中, 信息线索如何影响每个阶段的消费转化, 其中涉及的因素有：信息质量、品牌、激励（折扣和促销）、以及搜索结果排名等。

研究样本数据采集于Expedia酒店搜索点击流。其中包括个人消费者点击和预定信息、以及由搜索、点击查询到预定过程中的消费转化（或者中途转化失败）的各种事件。样本容量包括940,164家酒店, 其涉及到由美国局部市场消费者在2012年11月1日到2013年6月20日之间做出的39,574条搜索结果。 我们采用Heckman模型和随机效应模型来整合不同线性时间上的消费者行为, 同时考虑不同酒店的多样性。

研究发现消费者使用在线信息线索来做酒店预订决策。具体来说, 消费者倾向于对于消费者评价高和行业认证高的酒店进行点击查询。然而, 相比行业认证, 消费者更倾向于借鉴消费者评价, 来做出最后预定决策。此外, 在点击查询和预定时, 消费者对于激励（折扣和促销）反应强烈。最后, 品牌和搜索排名靠前的酒店往往获得从搜索、点击查询到最后预定中更高的消费转化率。

本论文对酒店营销商和OTAs有重要的在线搜索启示。研究结果向酒店营销商和OTAs证明消费者在搜索过程中对信息线索如何反应, 以及这些信息线索如何影响每个阶段之间的消费转化。本论文展示的从搜索、点击查询、到预定的线性决策过程对于酒店营销商们有着重大帮助, 帮助其使用信息线索找出各种消费转化机遇。