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Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

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。

More and more customers refer to online reviews before making any purchasing decisions thanks to the increasing popularity of social media and online shopping. This phenomenon has caught the attention of business managers who are increasingly aware that online reviews provide great opportunities to connect with current and potential customers. However, both practices and research on online review management from the businesses’ perspective are fragmented. The purpose of this paper is to develop an integrative framework that includes the key dimensions of an online review management system.

Based on the Grounded Theory approach, the authors conducted a multiple case study by analyzing the interviews with 11 hospitality services.

The authors found that an online review management system should go beyond the current norm of response management to incorporate key dimensions of formality, centralization, specialization, response customization, integration and review analytics.

The study provides a systematic guideline for online review management practices. The framework can be used as a tool for a business to evaluate existing online review management practices and develop/refine its online review management system.

The study contributes to online review management literature by developing a comprehensive framework to understand the structure and processes of online review management. The key dimensions of an online review management system identified in this study provide an initial measurement model for the online review management construct. Furthermore, the study provides a springboard for future empirical validation and refinement of the key factors for effective online review management.

This paper aims to analyze if and to what extent mechanical artificial intelligence (AI)-embedded in hotel service robots-influences customers’ evaluation of AI-enabled hotel service interactions. This study deploys online reviews (ORs) analytics to understand if the presence of mechanical AI-related text in ORs influences customers’ OR valence across 19 leading international hotels that have integrated mechanical AI – in the guise of service robots – into their operations.

First, the authors identified the 19 leading hotels across three continents that have pioneered the adoption of service robots. Second, by deploying big data techniques, the authors gathered the entire population of ORs hosted on TripAdvisor (almost 50,000 ORs) and generated OR analytics. Subsequently, the authors used ordered logistic regressions analyses to understand if and to what extent AI-enabled hospitality service interactions are evaluated by service customers.

The presence of mechanical AI-related text (text related to service robots) in ORs influences positively electronic word-of-mouth (e-WOM) valence. Hotel guests writing ORs explicitly mentioning their interactions with the service robots are more prone to associate high online ratings to their ORs. The presence of the robot’s proper name (e.g., Alina, Wally) in the OR moderates positively the positive effect of mechanical AI-related text on ORs ratings.

Hospitality practitioners should evaluate the possibility to introduce service robots into their operations and develop tailored strategies to name their robots (such as using human-like and short names). Moreover, hotel managers should communicate more explicitly their initiatives and investments in AI, monitor AI-related e-WOM and invest in educating their non-tech-savvy customers to understand and appreciate AI technology. Platform developers might create a robotic tag to be attached to ORs mentioning service robots to signal the presence of this specific element and might design and develop an additional service attribute that might be tentatively named “service robots.”

The current study represents the first attempt to understand if and to what extent mechanical AI in the guise of hotel service robots influences customers’ evaluation of AI-enabled hospitality service interactions.

While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.

The research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.

The study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.

Although only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.

This research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.

This study was to develop a methodology of online review analytics for hotel quality management at macro and micro levels. The macro level was for understanding the service properties critical to quality and future development. The micro level was for personalized quality diagnosis for individual hotels.

Textual reviews of superior hotels were studied using latent semantic analysis and Kano model to understand what service properties customers concern and expect. Taguchi's quality engineering was applied to establish a quality reference base using superior hotels for evaluating other hotels in the same semantic space. A decision tree algorithm was then used to identify the properties critical to quality discrimination, and the decision rules were used to diagnose individual hotels.

The service properties concerned by customers for superior hotels were identified. The market positioning and value of each property to customers were clarified. For individual hotels, the deficiencies of service properties were diagnosed. With reference to market positioning, deficient properties of priority in improvement and the strategies for enhancing competitiveness were suggested.

The proposed methodology demonstrated the potential value that review analysis can achieve a new and deeper understanding of customer voices and transform it into more specific business operation requirements. The research and application gap that most previous studies only stayed at the macro-level analytics was filled. Moreover, this study effectively applied the diagnostic techniques derived from quality engineering to online review analytics.

The main objective of this study is to determine the factors that have the greatest impact on travelers' opinions of airports.

11,656 customer reviews for 649 airports around the world were gathered following the COVID-19 outbreak from the website that rates airport quality. The dataset was examined using hierarchical regression, PLS-SEM, and the unsupervised Bayesian algorithm-based PSEM in order to verify the hypothesis.

The results showed that people’s intentions to recommend airports are significantly influenced by their opinions of how well the servicescape, staff, and services are.

By encouraging air travelers to have positive intentions toward recommending the airports, this research offers airport managers decision-support implications for how to improve airport service quality. This will increase the likelihood of retaining more passengers.

This study also suggests a quick-to-implement visual decision-making mechanism based on PSEM that is simple to understand.

The purpose of this paper is to establish a conceptual framework on how hospitality and tourism educators could incorporate new technology and big data analytics into program curriculum.

The research developed a logic model to visualize the benefits/impact of hospitality and tourism data analyst via a literature review approach.

The incorporation of statistics, research and the knowledge of data exploration, analysis and insight into hospitality programs would enhance students’ data analysis competencies.

This is a literature review paper, based on philosophical perspectives from literature review. It would be nice to conduct an empirical study with regard to data analytics in the hospitality and tourism industry.

The hospitality and tourism program coordinators and/or directors are urged to inspire more students who are interested in adding statistics and accounting studies to the hospitality and tourism field. Also, the hospitality and tourism data analyst would secure attractive job offers as well as enhance the average salary of hospitality and tourism graduates.

Hospitality and tourism data analytics would secure attractive job offers as well as enhance the average salary of hospitality and tourism graduates.

The paper explored the impact of big data analytics in the hospitality and tourism industry and made recommendations for hospitality and tourism data analytics curricula.

酒店/旅游课程设置是否做好了迎接大数据时代的准备？

本论文旨在建立一个概念模型, 以指导酒店旅游教育者们如何引进新科技和大数据分析到现有的课程设置里.

本论文通过文献综述的方式, 提出一个logic模型以描画酒店旅游数据分析的好处/影响。

酒店课程里加入统计、研究方法和数据勘探的内容对于帮助学生提高数据分析能力有帮助。

本论文采用概述的形式, 以文献综述的角度, 建立理论模型。如果可以加入实际模型测试, 比如针对酒店旅游业做实际的数据分析, 那么结果将更加丰富。

酒店旅游项目协调员和/或负责人应该鼓励更多对统计和会计有兴趣的学生从事酒店旅游业。此外, 酒店旅游数据分析员将获得令人羡慕的工作机会和优越的薪资以提高酒店旅游毕业生的平均薪资水平。

本论文探索了大数据分析在酒店旅游业中的影响, 以及对酒店旅游数据分析课程设置做出建议。

大数据分析, 酒店旅游数据分析员, 数据科学家, 统计学和研究能力, 酒店旅游教育家, 运动学分析

文献评论

In search of creating an extraordinary experience for customers, services have gone beyond the means of a transaction between buyers and sellers. In the event industry, where purchasing tickets online is a common procedure, it remains unclear as to how to enhance the multifaceted experience. This study aims at offering a snapshot into the most valued aspects for consumers and to uncover consumers' feelings toward their experience of purchasing event tickets on third-party ticketing platforms.

This is a cross-disciplinary study that applies knowledge from both data science and services marketing. Under the guise of natural language processing, latent Dirichlet allocation topic modeling and sentiment analysis were used to interpret the embedded meanings based on online reviews.

The findings conceptualized ten dimensions valued by eventgoers, including technical issues, value of core product and service, word-of-mouth, trustworthiness, professionalism and knowledgeability, customer support, information transparency, additional fee, prior experience and after-sales service. Among these aspects, consumers rated the value of the core product and service to be the most positive experience, whereas the additional fee was considered the least positive one.

Drawing from the intersection of natural language processing and the status quo of the event industry, this study offers a better understanding of eventgoers' experiences in the case of purchasing online event tickets. It also provides a hands-on guide for marketers to stage memorable experiences in the era of digitalization.