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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.

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.

By adopting network analytic techniques, this paper aims to examine interlocking directorates among firms operating in the hospitality services sector in seven major Italian tourism destinations.

The authors collected information for all the hotel corporations whose headquarters are located in the seven top Italian destinations: Florence, Milan, Naples, Rimini, Rome, Turin and Venice. Data come from the Analisi Informatizzata delle Aziende Italiane database by Bureau Van Dijk and were used to build a network where the nodes are board members (people) and corporations (hotels) and the links represent the membership of individuals in the boards. From this, with a one-mode projection, the authors obtain two networks: people and corporations. The overall networks’ structures are analysed by assessing their connectivity characteristics.

The findings indicate a relatively low number of interlocks that signals a high degree of fragmentation, showing that the interconnections (both within and between destinations) are scarce. This suggests that in absence of formalized cooperation arrangements, corporations might collaborate informally.

This work extends previous research on complexity in business settings, focusing specifically on service companies whose output depends on multiple interactions and helps clarifying coopetition practices of hospitality service firms. Policymaking perspectives are discussed as well as managerial viewpoints.

Not many studies of the interlocking directorates in the hospitality domain exist. This paper uses network analysis for a better understanding of the cooperative practices and the formal social structures of the Italian hospitality industry and derives a series of implications important for both researchers and practitioners while also looking at potential future studies.

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.

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.

The purpose of this study is twofold. First, this study elaborates an integrative conceptual framework of tourism destination competitive productivity (TDCP) by blending established destination competitiveness frameworks, the competitive productivity (CP) framework and studies pertaining to big data and big data analytics (BDA) within destination management information systems and smart tourism destinations. Second, this study examines the drivers of TDCP in the context of the ongoing 4th industrial revolution by conceptualizing the destination business intelligence unit (DBIU) as a platform able to create sustained destination business intelligence under the guise of BDA, useful to support destination managers to achieve the tourism destination’s economic objectives.

In this work, the authors leverage both extant literature (under the guise of research on CP, tourism destination competitiveness [TDC] and destination management information systems) and empirical work (in the form of interviews and field work involving destination managers and chief executive officers of destination management organizations and convention bureaus, as well as secondary data) to elaborate, develop and present an integrative conceptual framework of TDCP.

The integrative conceptual framework of TDCP elaborated has been found helpful by a number of destination managers trying to understand how to effectively and efficiently manage and market a tourism destination in today’s fast-paced, digital and hypercompetitive environment. While DBIUs are at different stages of implementation, often as part of broader smart destination initiatives, it appears that they are increasingly fulfilling the purpose of creating sustained destination business intelligence by means of BDA to help tourism destinations achieve their economic goals.

This work bears several practical implications for tourism policymakers, destination managers and marketers, technology developers, as well as tourism and hospitality firms and practitioners. Tourism policymakers could embed TDCP into tourism and economic policies, and destination managers and marketers might build and make use of platforms such as the proposed DBIU. Technology developers need to understand that designing destination management information systems in general and more specifically DBIUs requires an in-depth analysis of the stakeholders that are going to contribute, share, control and use BDA.

To the best of the authors’ knowledge, this study constitutes the first attempt to integrate the CP, TDC and destination management information systems research streams to elaborate an integrative conceptual framework of TDCP. Second, the authors contribute to the Industry 4.0 research stream by examining the drivers of tourism destination CP in the context of the ongoing 4th industrial revolution. Third, the authors contribute to the destination management information systems research stream by introducing and conceptualizing the DBIU and the related sustained destination business intelligence.

Online consumer reviews are increasingly used by third-party e-commerce organizations to shed light on the positive and negative sides of the brands they sell. However, the large number of consumer reviews requires these organizations to shortlist the most helpful ones to cope with information overload. A growing number of scholars have been investigating the determinants of review helpfulness; however, little is known about the influence of cultural factors in consumer's evaluation of review helpfulness.

This study has adopted Hofstede's cultural values framework to assess the influence of cultural factors on review helpfulness. We used a sample of 570,669 reviews of 851 hotels published by reviewers from 81 countries on Booking.com.

Findings reveal that reviewers from cultural contexts that score high on power distance, individualism, masculinity, uncertainty avoidance and indulgence are more likely to write helpful reviews.

This is one of the first cross-cultural studies in marketing using a big data approach in examining how users of reviews from different countries evaluate the helpfulness of online reviews.

Taking stock of extant hospitality and tourism research using social network analysis approaches, this study highlights why using either quantitative or qualitative approaches to examine social networks can be misleading and generate potentially biased findings. Indeed, purely qualitative and purely quantitative studies display limitations. The purpose of this study is to provide methodological insights by suggesting that mixed methods can be suitably used, depending on the specific research questions.

The study consists of an analysis and critical discussion of the methods used in a number of papers leveraging social network approaches to study social networks in tourism and hospitality. The authors describe the benefits and limitations of each method studies considered are examined based on a number of aspects.

More than half of the studies classified as network studies adopt quantitative designs and quantitative methods including statistical analyses and observational data. Mixed methods study is a minority and they are almost never labeled as mixed methods. A relevant portion of qualitative studies increasingly embeds a number of rudimentary statistical analyses. With an example, the authors also discuss that purely quantitative or purely qualitative methods can lead to discrepant results, and thus, the authors encourage scholars to embrace mixed method research designs such as explanatory or exploratory sequential designs. Advanced researchers might attempt in the future to embrace transformative, embedded or multiphase mixed methods.

This study is based on academic papers and research published before 2019. A rich research agenda is designed.

This study contributes to explore the way social networks have been dealt with in tourism and hospitality research so far, by advancing a proposal to adopt mixed methods in the form of explanatory or exploratory sequential designs. To the best of the knowledge, it is the first study addressing methodological pitfalls in extant network-based research within the tourism and hospitality domain.

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 paper aims to investigate antecedents of using non-travel-specific social media (specifically Facebook) for travel decision-making before a leisure trip.

Based on an online survey of 426 young travel consumers from Italy and Sweden, this work applies structural equation modeling and multi-group analysis.

The study finds support for most of the conventional TAM-related constructs: perceived usefulness, perceived enjoyment and intention, while ease of use is not found relevant in this context.

Results shed light on the antecedents of using non-travel specific social media in two countries. Future research might focus on validating the factors identified and add others that might shape usage in the selected countries. Future studies could further investigate possible differences arising from culture, country of origin and age. The analysis can also be extended to other countries.

The analysis might help managers in the hospitality and tourism sector by providing an understanding of the cognitive factors which determine travelers’ decision to use Facebook for trip planning. Thus, managers should get to know these factors in their effort to influence social media in hospitality and tourism settings.

The findings offer interesting perspectives on the applicability of conventional models to the context of non-travel-specific social media platforms. The exploration of cross-cultural differences also adds to the extant body of knowledge.