1,720,997 research outputs found
Introduction to the Handbook on Big Data Marketing and Management in Tourism and Hospitality
No full textThe evolution of big data as a vehicle for better business intelligence is fuelling different practices and advances for decision making for tourism and hospitality professionals and the ecosystem. This introduction introduces big data and its context in the tourism and hospitality industries. The overall focus of this book is presented here by providing a holistic view of both the strategic and operational aspects of Big Data implementation into this vibrant sector that can benefit from optimizing the dynamic interoperability across the travel and tourism industry. The beginning section introduces the evolution of big data and its usefulness to organisations in creating strategic and operational value. The chapter then unfolds with explanations of the academic context of the current theories and aspects of big data applied to decision making, technologies, applications of big data for various stakeholders in the tourism and hospitality ecosystem. The usefulness and utility of big data is addressed but much of the book is dedicated to showing how value can be created/ co-created with the use of the big data for various users. The chapter concludes with naming and giving a brief statement of the content of each of the 17 chapters comprising this book
Big data as a propeller for the tourism sector:Present opportunities and Future Perspectives
No full textAlthough Big Data have opened a whole new world of opportunities for scientists, managers, businesses, and governments towards achieving increased effectiveness in decision-making processes (Morabito, 2015; Phillips-Wren and Hoskisson, 2015). Still there are various challenges that need to be addressed, such as the use of Big Data in achieving effective systems responses, as well as dealing with individual and societal aspects of Big Data-related technological advancements. Key issues have already started being raised among scholars, i.e. the potential of Big Data to effectively contribute to decision-making, the conjunction of Big Data with the Internet of Things, as well as various concerns about value creation, privacy, and e-Surveillance (Wong et al., 2023). The current chapter discusses these vital issues and offers some insights for the future
Big data in marketing services and products:Impacts of Marketing and Ethics
No full textThere has been a growth in studies focused on the implication that the use of Big Data may enhance firms’ performance by delivering streamlined resources and customer services. There is a need for more research focused on understanding how big data influences marketing to provide better customer value which is the aim of this chapter. It also explores the ethical aspects of big data applications and processing. To do so, we discuss issues pertaining to the usefulness and challenges for the adoption of marketing analytics, ethics considerations in the hospitality and tourism industry. Improved customer services, product offerings and tailor-made solutions to upskill and deliver better value to businesses are investigated. Then, ethical issues such as data ownership, customer identification, data governance were highlighted.Overall, there are current concerns about the use of big data within the hospitality and tourism industry as to its usefulness and corresponding investments required. There are different understandings of the usefulness of big data, due to varying definitions of the phenomenon. In this chapter we emphasize specifically on marketing analytics for big data tourism analytics focusing on the use of big data analytics, marketing, and ethics issues. Finally, suggestions on how tourism managers can create value with big data analytics will be provided and some ethical considerations will be discussed.<br/
Adoption and Assimilation of Big data in Tourism organizations
No full textThe combined use of data coming from various sources that would cover different aspects of individuals’ lives is a reality now through digital networks and data analytical tools offering big opportunities for improved business competitiveness; this is the Big Data evolution. For tourism and hospitality organizations to be analytical and use Big Data approaches, a specific set of frameworks are needed to enable the organizations to participate in the Big Data evolution. Much of the current thinking about big data focuses on the usefulness and the potential that it offers for enhancing business performance within a tourism setting. The main aim of this chapter is to explore and illustrate the big data flows comprising the business ecosystem decision making processes. What would contribute to the literature is to illustrate how big data shapes decision making. This chapter focuses on showcasing the various internal and external big data streams and highlights the added benefits for improving decision making within and across the tourism enterprise
Issues and Opportunities for Tourism SME Businesses:How is Big Data changing business models
No full textThis chapter focuses on the key issues and opportunities that Big Data affords in SME tourism businesses. There is a strong need to consider changes in the business models, business model innovation, dynamic capabilities and how they should evolve based on digital transformation. In particular, there is a need to change processes and prepare for change in small to medium sized tourist and hospitality related businesses. Business model innovation is seen as a key driver to change offerings that meet customer and supplier requirements showcasing the value creation from innovation. The issues of size and scale are often touted as major problems for SMEs to digitalise with the focus on Big Data, there are new options suggested, given advances in machine learning and artificial intelligence. These issues are considered and potential options for resolution are offered based on prevailing views with suggestions for addressing these changes and creating strategic business decision making options. The chapter concludes with a discussion and some suggestions for further research
Big Data Sustainability Network:Marketing Intelligence for activating Sustainability in the Tourism industry
No full textThis chapter seeks to create a tourism industry-specific framework that explains how sustainability should be: a) conceptualized at different system levels, b) mapped with respect to various tourism industry stakeholders/actors and c) integrated according to a tourism product life cycle philosophy, by drawing on Big Data (Stylos, 2022a; Stylos, 2022b; Stylos et al., 2021). It is argued that contemporary social marketing can serve as the overarching frame to bring the change that is urgently needed in the field of sustainability. Drawing on network theory and influenced by social marketing and life cycle sustainability, we develop a holistic sustainability framework to be applied in the tourism sector. The various interconnections of the actors at distinct levels are demonstrated, and the behavioral change tools that function as an array of network dynamics are exemplified via a step-by-step conceptual synthesis that is based on a series of propositions. The current chapter combines several aspects of literature to produce a robust theoretical framework to be utilized in tourism and hospitality sustainability research. A network analysis representation shows the interactions between UK tourism industry actors, as well as proposed behavioural interventions to activate sustainability. The proposed framework is accompanied by an illustrative example of network analysis and practical guidelines for materialization that offers useful insights to policy makers, the respective organizations and the UK tourism economy at large
Shaping the online destination image of Venice:Comparative perspectives of international and domestic tourists
No full textLittle has been recorded so far about the influence of flagship events on tourists’ online images of an urban destination. The current chapter compares international and domestic tourists’ interpretations of Venice, based on the online posts exchanged during the Venetian carnival period, using machine learning algorithms. To this end, 4928 online posts – two sets of 2452 online posts of international tourists and 2476 of domestic tourists, respectively – were collected and then analysed via sentiment and automatic content analysis. The carnival event dominates tourists’ communications, both international and domestic ones, as city’s major attraction. Findings also reveal the unique city architecture and the world renown carnival events as the main similarities between the two tourist segments. International tourists focus on carnival too, yet they seem to prioritize over local cuisine, representing a major difference between the two groups in shaping destination images. For domestic tourists, safety and security measures are of utmost importance. The results contribute to the literature on the formation of online urban destination images via further clarifying its constituent elements as these are indicated by two diverse tourist segments. Managers would also benefit from it
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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