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Purpose-This study examines the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach-The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; data reporting and visualization. Findings-Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of Business Intelligence and Big Data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big datadriven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications-This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value-This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on Business Intelligence and Big Data. To the best of our knowledge, it is the first systematic literature review within hospitality and tourism research dealing with Business Intelligence and Big Data.

This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data‐driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross‐fertilization of theories used across fields, and neglected research topics.

In the advancing coopetition strategy literature, firms are often seen as organizations making coopetitive arrangements in an intentional fashion. Drawing on the received distinction between deliberate and emergent strategies, this paper innovatively analyzes the formation of coopetition as an unintended and therefore emergent strategy. More specifically, an in-depth case study on a renowned consortium of Italian opera houses is proposed to illustrate the role played by the external environment (e.g., the institutional one) in triggering coopetitive strategies through the imposition of cooperation. Accordingly, the specific strategic learning processes that actually intervene in the formation of coopetition are identified, and the two related brand-new concepts of imposed cooperation and induced coopetition are introduced and discussed. 97

This study taps into managers' perceptions of coopetition antecedents to better understand why firms adopt coopetition. By analyzing and synthesizing findings from systematic reviews of coopetition literature we integrate knowledge on coopetition antecedents. We develop and validate a scale measuring behavioral coopetition antecedents: strategic rationale and coopetition mindset. Based on a random sample of 368 Polish tourism firms, we run exploratory and confirmatory factor analyses to find that antecedents used in coopetition literature converge into two latent, behavioral constructs. Our data substantiate the view that coopetition is an intentional strategy, driven by a strategic rationale. Managers are found to pursue coopetition in order to reach clearly defined benefits with fitting partners. Moreover, three elements are found to converge in the coopetitive mindset latent construct: orientation to cooperation, trust, and experience in coopetition. We contribute to the methodological advancement of measurement instruments with applicability potential in future research examining the behavioral antecedents of coopetition. We also advance the behavioral stream of research in strategy by empirically identifying the connection between rational and behavioral antecedents of firms' coopetitive strategic behavior.

This study examines the role played by submission devices (mobile vs. desktop) in online travel reviewing behaviour. We analyse over 1.2 million online reviews from Booking.com and detect the presence and distinctive features of online reviews submitted by mobile devices. Our findings indicate that 1) the share of online reviews submitted by mobile increased at a very high rate over time (higher than the growth rate of those submitted by desktop); 2) there is a systematic and statistically significant difference between the features and distributions of online reviews submitted through mobile devices vs. online reviews submitted through desktops. We raise awareness of the role played by submission devices in online travel behaviour research and present implications for future research.

The "industry 4.0" phenomenon is expected to influence almost every aspect of business value chains, and hence it has been increasingly analyzed by management scholars. However, the overarching intellectual structure emerging from this new stream of literature has not yet been synthesized in a framework nor critically discussed. Furthermore, despite being part of the rhetoric in several recent industrial governmental plans, industry 4.0 in service sectors has not been systematically reviewed to date. By leveraging a systematic quantitative literature review, a data-driven approach and a quantitative methodology-embedding both bibliographic coupling and network analysis techniques-this study provides a clear visualization of the emerging intellectual structure of industry 4.0 in management studies. We also develop a framework based on the most recurrent themes emerging from the results of bibliometric and network analyses-the latter could be used by management scholars to understand studies surrounding industry 4.0. As service businesses can create and capture value generated through the 4 th Industrial Revolution as well as manufacturing firms, we suggest that scholarly attention should also be directed toward the service industries and provide a research agenda.