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This study aims to integrate reviewers’ and readers’ discrepant perspectives on extremely negative reviews. Specifically, this study examines the relationship between negative emotion intensity levels and reviews helpfulness on two platforms: integrated websites and social networking sites (SNS) to emphasize the role of platform types on customers’ purchase decisions.

This research adopts a mixed-method approach of business intelligence approach and quasi-experimental design. Study 1 performed text mining and Welch’s t-test to compare reviewers’ negative emotion intensity levels on two platforms. Study 2 adopted a 2*2 factorial quasi-experimental design to examine how intense negative emotions impact the perceived reviews helpfulness on two platforms. A 3*2 factorial design in Study 3 also tested social tie strength’s moderating effect between the intensity of negative emotions and review helpfulness.

The current study reveals that integrated website reviewers tend to express more extreme negative emotions than SNS reviewers. SNS and integrated website readers deem reviews that embed severe negative emotions as less helpful. The moderating role of social tie strength between extremely negative emotions on review helpfulness was insignificant in the study.

This study enriches the online review literature by comparing writers’ and readers’ perspectives on online reviews with extremely negative emotions across two online platform types: integrated websites and SNS. From the writers’ perspective, this study highlights anonymity and the presence of an audience as essential factors that reviewers consider in selecting an online review platform to express themselves. This research also sheds light on how readers’ perspectives on extremely negative reviews conflict with the presumptions of writers of extremely negative reviews on integrated websites by demonstrating that content embedding extremely negative emotions is less helpful regardless of platform type.

This research provides online negative review management strategies to platform and hotel managers. The findings suggest hotel and review platform managers should consider adopting review alignment or monitoring systems based on negative emotions intensity levels since readers on both platforms perceive reviews embedding extremely negative emotions as less helpful. Additionally, hotel managers can progress promotions to guests who share online reviews on SNS since SNS reviewers are more likely to attenuate their extremely negative emotions when writing reviews.

This research innovatively provides a comprehensive overview of negative reviews’ production and consumption process from reviewers’ and readers’ perspectives. This research also provides practitioners insight into the nature of two different platform types and the management of negative reviews on these platforms.

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.

Electronic word of mouth in the form of user-generated content (UGC) in social media plays an important role in influencing customer decision-making and enhancing service providers’ brand images, sales and service innovations. While few research studies have explored real content generated by hotel guests in social media, business analytics techniques are still not widely seen in the literature and how such techniques can be deployed to benefit hoteliers has not been fully explored. Thus, this study aims to explore the significant factors that affect hotel guest satisfaction via UGC and business analytics and also to showcase the use of business analytics tools for both the hospitality industry and the academic world.

This study uses big data and business analytics techniques. Big data and business analytics enable hoteliers to develop effective and efficient strategies improving products and services for guest satisfaction. Therefore, this study analyzes 200,431 hotel reviews on Tripadvisor.com through business analytics to explore and assess the significant factors affecting guest satisfaction.

The findings show that service, room and value evaluations are the top-three factors affecting overall guests’ satisfaction. While brand type and negative emotions are negatively associated with guests’ satisfaction, all other factors considered were positively associated with guests’ satisfaction.

The current study serves as a great starting point to further explore the relationship between specific evaluation factors and guests’ overall satisfaction by analyzing user-generated online reviews through business analytics so as to assist hoteliers to resolve performance-related problems by analyzing service gaps that exist in these influential factors.

以消费者评论为主体的社交网络口碑营销对于影响消费者决策和提高服务提供商的品牌形象、销量、和服务创新起到重要作用。然而, 很少研究探索社交媒体上的真正酒店客人评论。因此, 商务分析技术在文献中还是很少使用的, 这种技术应该更多得到科研上的应用以给酒店从业人员给与启示。因此, 本论文旨在探究影响酒店顾客满意度的因素, 通过消费者评论和商务分析, 以展示商务分析技术是如何为酒店业和科研界来使用的。

本论文使用大数据和商务分析技术来进行数据分析。大数据和商务分析能够为酒店从业人员开发有效战略以提高产品和服务质量, 最后达到顾客满意。因此, 本论文分析了Tripadvisor.com的200, 431酒店评论数, 通过商务分析技术, 以探索和审视影响顾客满意度的重要因素。

研究结果显示服务、客房、和价值比成为影响顾客满意度的前三项因素。品牌类型和负面情绪是影响顾客满意度的负面因素。其他因素成为影响顾客满意度的正面因素。

本论文是利用消费者评论的商务分析来探究影响顾客满意度与具体衡量因素之间关系的起点范例, 以此, 帮助酒店从业商来解决服务中的欠缺因素, 提高绩效。

This paper aims to explore the underlying structure of wellness in upper-upscale and luxury hotels and the roles wellness attributes play in customer satisfaction and dissatisfaction.

This study uses a mixed methods approach consisting of content analysis and social media analytics. In Study 1, the authors integrate and review the structure of wellness attributes by conducting a literature review on prior research on wellness and analyzing websites of upper-upscale and luxury hotels. In Study 2, the authors implement text analytics and regression analysis to determine the roles of wellness attributes in customer satisfaction and dissatisfaction by examining the final data gathered from 141,973 reviews of 226 upper-upscale and luxury hotels in NYC.

This research introduces the underlying structure of wellness in the upper-upscale and luxury hotels. Findings demonstrate a significant relationship between wellness attributes and customer satisfaction/dissatisfaction. This study shows each wellness attribute’s specific roles in customer satisfaction and dissatisfaction through the Kano model.

The current study extends the research on wellness by discovering the underlying structure of wellness in the upper-upscale and luxury hotels. Based on the Kano Model, the study reveals specific roles of wellness attributes regarding their dichotomous impact on customer satisfaction and dissatisfaction. The study makes a novel approach to the topic of wellness through a mixed methods approach consisted of content analysis and social media analytics. Analyzing online customer reviews derived from TripAdvisor.com, the study provides an in-depth insight and understanding of customers’ perceptions of wellness attributes.

The study guides hotel operators to perform wellness attributes by defining the unique roles of wellness attributes in customer satisfaction and dissatisfaction. Using the findings of the current study, hotel operators can prioritize wellness attributes regarding their core strategies and provide satisfying wellness attributes to customers.

Prior research merely focuses on hotels in wellness destinations or wellness-focused hotels with a lack of research on wellness offered in the general lodging industry. This research fills the gap by discovering the underlying wellness structure embedded in the general lodging industry, specifically in the upper-upscale and luxury hotels.

This study aims to uncover how heuristic information cues (HIC) and systematic information cues (SIC) of online reviews influence review helpfulness and examine a moderating role of social influence in the process of assessing review helpfulness. In particular, this study conceptualizes a theoretical framework based on dual-process and social influence theory (SIT) and empirically tests the proposed hypotheses by analyzing a broad set of actual customer review data.

For 4,177,377 online reviews posted on Yelp.com from 2004 to 2018, this study used data mining and text analysis to extract independent variables. Zero-inflated negative binomial regression analysis was conducted to test the hypothesized model.

The present study demonstrates that both HIC and SIC have a significant relationship with review helpfulness. Normative social influence cue (NSIC) strengthened the relationship between HIC and review helpfulness. However, the moderating effect of NSIC was not valid in the relationship between SIC and review helpfulness.

This study contributes to the extant research on review helpfulness by providing a conceptual framework underpinned by dual-process theory and SIT. The study not only identifies determinants of review helpfulness but also reveals how social influences can impact individuals’ judgment on review helpfulness. By offering a state-of-the-art analysis with a vast amount of online reviews, this study contributes to the methodological improvement of further empirical research.

本论文旨在揭示网络评论的启发性信息源和系统性信息源对于评论有用性的影响, 以及检验社会影响在评论有用性的调节作用。其中, 本论文基于双重历程理论和社会影响理论来构建理论模型, 并且利用实际数据来验证假设, 通过分析一系列实际客户评论数据。

本论文样本数据为2004年至2018年Yelp.com上面的4,177,377网络评论。本论文采用数据挖掘和文本分析的方法来提取自变量。本论文采用零膨胀负二项回归模型来验证假设。

研究结果表明, 启发性和系统性信息源都对网络评论有用性有着显著作用。规范性社会影响加强了启发性信息源对评论有用性的作用。然而, 规范性社会影响对系统性信息源与评论有用性的关系并未起到有效的调节作用。

本论文对现有评论有用性的文献有着补充贡献, 其采用双重历程理论和社会影响理论来构建理论模型。本论文不仅指出评论有用性的影响因素, 而且展示了社会影响如何影响个人对评论有用性的判断。本论文的样本数据庞大, 数据分析夯实, 这对于进一步的实际测量研究有着方法改进方面的贡献。

This paper aims to explore how emotional expressions embedded in online hotel reviews influence consumers’ helpfulness perceptions. In particular, this study develops and tests hypotheses analyzing empirical data with a text-mining method in the context of hotels to investigate how review valence influences the perceived helpfulness of online hotel reviews and to examine the role of negative emotional expressions embedded in online consumer reviews with respect to perceived helpfulness.

This study collected 520,668 online reviews involving 488 hotels in New York City (NYC) on Tripadvisor.com. Of these reviews, 69,202 reviews (13.29 per cent) that had received helpfulness votes were analyzed by a text mining method and negative binomial regression.

This study demonstrates that negative reviews are considered more helpful than positive reviews when potential customers read online hotel reviews for their future stay. However, when intensively negative emotions were expressed, the degree of helpfulness regarding negative reviews was diminished.

While emotional expressions prevail in online consumer reviews, surprisingly little attention has been devoted to the consequences of emotional expressions in consumers’ information processing and decision-making. Due to the nature of service, given the inseparability of production and consumption, which often hinders the execution of flawless service, consumers tend to be more dependent on reviews to minimize any potential failures they may encounter later on. Therefore, this study fills a gap by demonstrating that negative reviews and emotional expressions play a more crucial role in consumers’ information processing and decision-making.

Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data.

The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making.

By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity.

The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results.

To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

This study aims to investigate the effect of customers’ multisensory service experience on customer satisfaction with cognitive effort and affective evaluations using big data and business intelligence techniques.

Online customer reviews for all New York City hotels were collected from Tripadvisor.com and analyzed through business intelligence and big data analytics techniques including data mining, text analytics, sentiment analysis and regression analysis.

The current study identifies the relationship between affective evaluations (i.e. positive affect and negative affect) and customer satisfaction. Research findings also find the negative effect of reviewer’s cognitive effort on satisfaction rating. More importantly, this study demonstrates the moderating role of multisensory experience as an innovative marketing tool on the relationship between affect/cognitive evaluation and customer satisfaction in the hospitality setting.

This study is the first study to explore the critical role of sensory marketing on hotel guest experience in the context of hotel customer experience and service innovation, based on big data and business intelligence techniques.

Information and communication technologies have been widely implemented and made radical changes for several decades in the hospitality and tourism industry. This rapid development also generates considerable data in social media. This trend opens the door to analyze unstructured data and gain increased attention of a qualitative research approach from hospitality and tourism researchers and industry professionals. Therefore, this paper aims to describe how a computer-assisted qualitative data analysis (CAQDA) approach can be used in the hospitality and tourism technology literature to uncover the trends and thematic concepts of hospitality and tourism technology research and their dynamics in Journal of Hospitality and Tourism Technology (JHTT)

To achieve the proposed research goals, the current study used CAQDA software, Leximancer, to analyze 218 articles published in JHTT between Volume 1(1) in 2010 and Volume 10(4) in 2019. Based on the rigorous CAQDA processes, the study performed the thematic analysis using all articles and subgroup analyses in the five-year periods.

Using CAQDA, the study reveals the critical research trends and insights on hospitality and tourism technology for 10 years in the JHTT. The findings of this study can provide strong evidence of what hospitality and tourism technology research topics have been examined and how these topics were connected and changed over time. More importantly, the current study illustrates how the CAQDA approach can be applied to uncover the hidden trends and thematic concepts from text data in the hospitality and tourism literature.

This study is the first attempt to apply CAQDA software to identify research trends and thematic concepts and gain insights from past JHTT’s articles. Moreover, this study applies this software to describe how hospitality and tourism researchers can use one of the modern computer-assisted qualitative techniques. Based on the findings of this study, theoretical and methodological implications for hospitality and tourism researchers are provided. More importantly, the current study presents the specific guidelines of how the CAQDA approach can be used for the literature review.

在酒店与旅游研究中, 信息和通讯技术被广泛应用了数十载, 也在发展中经历了巨大变革。如此迅速的发展在社交媒体中产生了大量数据。此项趋势为分析非结构化的数据以及为酒店旅游界增加定性研究打开了大门。因此, 以 Journal of Hospitality and Tourism Technology（JHTT） 发表的论文为例, 本研究旨在介绍在酒店旅游文献中如何运用计算机辅助的定性研究方法来发现研究趋势, 主题概念, 以及发展进程。

为达到研究目的, 本研究运用 CAQDA 软件, Leximancer, 来分析了 JHTT 从2010 年第1卷第1期截止到 2019 年第 10 卷第4期之间发表的 218 篇论文。基于 CAQDA 的严谨分析, 本研究对所有发表的论文进行了以5年为阶段的主题分析和分主题分析。

通过 CAQDA, 本研究发现了 10 年间在 JHTT 发表论文中凸显出来的酒店和旅游科技的研究趋势。研究结果为了解酒店旅游研究中出现的以科技为核心主题, 以及主题之间是如何关联以及发展提供了科研证据。更重要的是, 本论文为阐述 CAQDA 是如何可被应用到酒店旅游研究的文本分析中提供了例证。

本论文首次应用了 CAQDA 软件来发现科研趋势和研究主题, 从 JHTT 发表的论文中得到了深刻见解。此外, 通过应用此软件, 本研究也为酒店旅游学者展示了如何运用此项现代计算机辅助定性分析技术。本文结果对理论和方法论具有深远意义。更重要的是, 本研究提供了在文献综述中如何运用 CAQDA 提供了指导方针。

定性研究, 文献综述, 主题分析, 文本分析, 酒店和旅游科技, 计算机辅助定量分析, CAQDA, Journal of Hospitality and Tourism Technology, JHTT.

文章类型 研究论文

This study aims to provide an understanding of the concept of service innovation resulting from emerging technologies and suggest areas for future hospitality and tourism research. By thoroughly reviewing previous literature, this study provides the basis for improving customer service with service innovation.

This study examines the existing body of knowledge from leading hospitality, tourism and business journals by performing content analysis.

This study reveals the multifaceted aspects of service innovation practices using emerging technologies. Findings provide an evidence base to future studies by highlighting the role of technology in hospitality and tourism service innovation.

The major contribution of this study is the demonstration of an approach for both academic researchers and service providers how they can use the technology to improve customers’ perceived value, experience and engagement.

本论文旨在讨论新兴科技对服务创新的应用以及酒店和旅游管理领域中的未来发展方向。本论文通过全面回顾文献，对服务创新中的客户服务提供基础理解。

本论文通过对酒店、旅游、以及商业领域顶尖期刊文献做文本分析，以达到研究目的。

本论文提供了新兴科技对服务创新措施的多方面讨论。研究结果强调了科技对酒店和旅游管理创新中的重要地位，对未来研究做出了指导性意见。

本论文的主要贡献在于向学术研究人员和服务提供商展示，如何运用科技来加强客户感知价值、体验、以及客户参与。

服务创新、顾客价值、顾客体验、顾客参与、价值共创、科技、批判性文献综述

文献综述