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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

The purpose of this work is to survey the body of research revolving around big data (BD) and analytics in hospitality and tourism, by detecting macro topical areas, research streams and gaps and to develop an agenda for future research.

This research is based on a systematic literature review of academic papers indexed in the Scopus and Web of Science databases published up to 31 December 2020. The outputs were analyzed using bibliometric techniques, network analysis and topic modeling.

The number of scientific outputs in research with hospitality and tourism settings has been expanding over the period 2015–2020, with a substantial stability of the areas examined. The vast majority are published in academic journals where the main reference area is neither hospitality nor tourism. The body of research is rather fragmented and studies on relevant aspects, such as BD analytics capabilities, are virtually missing. Most of the outputs are empirical. Moreover, many of the articles collected relatively small quantities of records and, regardless of the time period considered, only a handful of articles mix a number of different techniques.

This work sheds new light on the emergence of a body of research at the intersection of hospitality and tourism management and data science. It enriches and complements extant literature reviews on BD and analytics, combining these two interconnected topics.

This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e. descriptive, exploratory, predictive, prescriptive and cognitive analytics) in their research. Only cognitive analytics, the latest and most advanced type, is based on artificial intelligence (AI) and requires machine learning (ML). As cognitive analytics constitutes the cutting edge in industry application, this study aims to examine in depth the extent cognitive analytics has been covered in the literature.

This study is based on a systematic literature review (SLR) of the hospitality and tourism literature on the topic of “analytics”. The SLR findings were complemented by the results of an additional search query based on “machine learning” and “deep learning” that was used as a robustness check. Moreover, the SLR findings were triangulated with recent literature reviews on related topics (e.g. big data and AI) to generate additional insights.

The findings of this study show that: there is a growing and accelerating body of research on analytics; the literature lacks a consistent use of terminology and definitions related to analytics. Specifically, publications rarely use scientific definitions of analytics and their different types; although AI and ML are key enabling technologies for cognitive analytics, hospitality and tourism management research did not explicitly link these terms to analytics and did not distinguish cognitive analytics from other forms of analytics that do not rely on ML. In fact, the term “cognitive analytics” is apparently missing in the hospitality and tourism management literature.

This study generates a set of eight theoretical and three practical implications and advance theoretical and methodological recommendations for further research.

To the best of the authors’ knowledge, this is the first study that explicitly and critically examines the use of analytics in general, and cognitive analytics in particular, in the hospitality and tourism management literature.

This study aims to discuss the evolution of Big Data (BD) and Analytics in the tourism and hospitality field. It analyses the important role that BD has played so far in tourism and hospitality research and delineates how it might evolve in the future.

In line with the Platinum Jubilee Special Issue of Tourism Review, this work consists of a critical and conceptual analysis including a mini literature review of recent work in the area at the intersection of BD and tourism and hospitality research.

Findings suggest that tourism and hospitality scholars are increasingly aware of and adopting BD approaches to retrieve, collect, analyse, report and visualise their data. However, a number of avenues for improvement in the use and interpretation of BD and BD analytics as both sets of methods and technology need to be developed. Moreover, BD analytics promise to enhance a number of digital technologies in tourism and hospitality such as AI and IoT that heavily rely on data. As such, the authors envision that a new digital entrepreneurship field might be shaped within the tourism and hospitality literature. Research pathways for future inquiry at the intersection of BD and tourism and hospitality are outlined.

While thinking retrospectively about research revolving around BD and its role in the tourism and hospitality research field so far, this study also addresses the challenges pertaining to how BD research will be conducted in the next seven decades within tourism and hospitality.

Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe.

This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness.

The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers.

Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse.

This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.

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 explores the connection between intellectual capital (IC) components and the competitive advantage (CA) of the hospitality sector in Jordan through the mediating role of service innovation as well as the moderating role of big data analytics capabilities.

Data were collected through a self-administered questionnaire from the hospitality sector with a sample of 402 respondents. Data were analysed using SmartPLS, a bootstrapping technique was used to analyse the data. The mediating effect for service innovation and the moderating effect for big data analytics capabilities were performed.

The results showed that the proposed moderated-mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between human capital, structural capital and relational capital and the CA as well as a mediating effect of service innovation. The findings confirmed that there is a moderating relationship for big data analytics capabilities between service innovation and CA. The results illustrate the importance of IC and service innovation in enhancing CA in the Jordanian hospitality sector in light of the big data analytics capabilities.

This cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalisation of the limitation's results, and the results are limited to one sector.

This research developed a theoretical model to incorporate IC components, service innovation, big data analytics capabilities and CA. This paper offers new theoretical and practical contributions that add value to the innovation and CA literature by testing the moderated-mediation model of these constructs in the hospitality sector which has been greatly affected by the coronavirus disease 2019 (COVID-19) pandemic. This study is distinguished from other studies by highlighting the role of IC and service innovation in enhancing CA as service innovation contributes to the formation of many organisational advantages in the Jordanian hospitality sector.

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.

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.

This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.

Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).

Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.

Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.

To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.