Call for Book Chapters: Big Data Marketing and Management in Tourism and Hospitality
Call for Book Chapters: Big Data Marketing and Management in Tourism and Hospitality
Editors:
Nikolaos Stylos (University of Bristol, [email protected])
Jeremy Zwiegelaar (Oxford Brookes University, [email protected])
Important Dates
Chapter short proposal submission deadline: 3rd February 2023
Notification of Proposal Acceptance: 15th February 2023
Full chapter submission deadline: 15th July 2023
Aim and Scope of the book
Tourism is a complex and challenging ecosystem. It is based on various operations, e.g., management and marketing offline and online activities. The tourism sector is very context dependent and requires real-time dynamic information to optimize business performance and customer experiences. Marketing analytic solutions using BD may benefit organizations for the broader tourism sector, including airliners, hotels, amusement enterprises and tourism destination organizations. All these organizations can greatly improve their operations via Big Data information inputs and produce corresponding transactions and synergetic outputs, such as online data for predictability on bookings and revenues (Antonio et al., 2019; Line et al., 2020). Big Data can also contribute greatly to a better understanding of tourist decision-making and to improve tourism marketing and management perspectives (Stylos et al., 2021).
The usefulness of Big Data has been acknowledged widely for creating effective customer targeting and service delivery in the tourism sector (Hadjielias et al., 2022). Improved conceptualization, alternative systems design, and effective use of BD need to be considered to further increase its usefulness for tourism business intelligence. The interdependence of various actors in the delivery of goods and services in the tourism sector makes Big Data a key asset for efficiently coordinating resources and tools among various stakeholders to ultimately improve business performances (Stylos, 2022; Zwiegelaar & Stylos, 2022).
Within the extant literature there is a need to explain how data and specifically big data is useful for delivery of customer value and ways to enhance customer benefits (Buhalis and Sinarta, 2019). Individuated customer value propositions, allowing companies to deviate from their competitors and develop other types of strategic value (Gonzalez et al., 2019; Günther et al., 2017). Big data for example, can provide for data-driven marketing practices such as, recommendations, geo-fencing, social Customer Relationship Marketing (CRM), market segmentation, personalization, and marketing-mix optimization (Sigala 2018b; Talón-Ballestero et al. 2018; Lehrer et al. 2018). Big data analytics can enhance decision-making and market research in tourism in various areas, such as predicting tourism demand, measuring tourists’ satisfaction, and designing personalized tourism experiences, destination management (Xiang & Fesenmaier 2017; Fuchs et al. 2014; Li et al. 2018; Reinhold et al. 2018). Big data does not only result in more efficient and effective operations and enhanced decision-making; big data support better strategizing and can support tourism firms to improve their business models and strategies (Sigala 2018a). When new value and service innovation elements are developed by organizations and customers together, the service provision optimization is expected to benefit of both sides (Benoit et al., 2020). BD intelligence is vital to provide service innovation and the appealing value of volatile and time-sensitive organizations in Tourism and Hospitality.
The “Big Data Marketing and Management in Tourism and Hospitality” forthcoming book seeks to offer a holistic view of both the strategic and operational aspects of Big Data implementation into this vibrant sector that can greatly benefit from optimizing the dynamic interoperability across the travel and tourism industry.
If you are interested in publishing a chapter, please prepare a short chapter abstract proposal of up to 500 words, (including a chapter title, and a set of 5 keywords) in one of the topic areas shown in the description, and send it via email to both Dr Niko Stylos ([email protected]) and Dr Jeremy Zwiegelaar ([email protected]) by 3rd February 2023.
We will notify proposers by 15th February 2023, and the authors of the selected abstracts will then be invited to submit full chapters by mid-July 2023.
General direction of the handbook contents: This handbook specializes in Big Data-related Managerial and Marketing theory and practice in the Tourism and Hospitality sector.
Each chapter should primarily provide current and contemporary aspects of theories, frameworks, debates and discussions on Big Data Marketing and Management as this has been introduced, managed, and experienced by various stakeholders in Tourism and Hospitality.
Book Structure and Topic Areas
Preface
Introduction: The Four Layers of BD application
[Section 1] - Big Data in Tourism and Hospitality Marketing
Example topic areas for chapters (but not limited to):
Ø Service marketing performance and BD
Ø Marketing communications and BD
Ø Sharing economy / collaborative consumption and BD
Ø Marketing and privacy/e-surveillance and BD
Ø Digital transformation and BD
Ø Business Model Innovation and BD
Ø Marketing operations and policies related to data use and BD
Ø Smart hospitality/tourism and BD
Ø Business Intelligence and Analytics using BD
[Section 2] - Big Data in Tourism and Hospitality Management
Example topic areas for chapters (but not limited to):
Ø Service management and BD
Ø Human resource management and BD
Ø Sustainable management and BD
Ø Artificial Intelligence management and BD
Ø Responsible innovation management and BD
Ø Knowledge production / intelligence and BD
Ø Open Innovation and BD
Ø Ethics and related policies to data usage and BD
Ø Decision-making with BD from an ecosystem perspective
Concluding Remarks
References
Antonio, N., de Almeida, A., & Nunes, L. (2019). Big data in hotel revenue management: Exploring cancellation drivers to gain insights into booking cancellation behavior. Cornell Hospitality Quarterly, 60(4), 298-319.
Buhalis, D. and Sinarta, Y. (2019). Real-Time Co-Creation and nowness service: Lessons from tourism and hospitality, Journal of Travel and Tourism Marketing, 36(5), 563-582.
Fuchs M, Höpken W, Lexhagen M (2014). Big data analytics for knowledge generation in tourism destinations–A case from Sweden. Journal of Destination Marketing & Management, 3(4), 198–209.
Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
Günther WA, Mehrizi MHR, Huysman M, Feldberg F (2017) Debating big data: a literature review on realizing value from big data. Journal of Strategic Information Systems, 26, 191–209.
Hadjielias, E., Christofi, M., Christou, P., & Drotarova, M. H. (2022). Digitalization, agility, and customer value in tourism. Technological Forecasting and Social Change, 175, 121334.
Lehrer C, Wieneke A, vom Brocke J, Jung R, Seidel S (2018) How big data analytics enables service innovation: materiality, affordance, and the individualization of service. J Manage Informat Syst 35(2):424–460
Li J. Xu L, Tang L, Wang S, Li L (2018) Big data in tourism research: a literature review. Tourism Manage 68:301–323
Line, N. D., Dogru, T., El-Manstrly, D., Buoye, A., Malthouse, E., & Kandampully, J. (2020). Control, use and ownership of big data: A reciprocal view of customer big data value in the hospitality and tourism industry. Tourism Management, 80, 104106.
Reinhold S, Laesser C, Beritelli P (2018) The 2016 St. Gallen consensus on advances in destination management. Journal of Destination Marketing and Management, 8, 426–431.
Sigala, M. (2018a). New technologies in tourism: from multi-disciplinary to anti-disciplinary advances and trajectories. Tourism Management Perspectives, 25, 151–155.
Sigala, M. (2018b). Implementing social customer relationship management: a process framework and implications in tourism and hospitality. International Journal of Contemporary Hospitality Management, 30(7), 2698–2726
Stylos, N. (2022). Big Data. In Encyclopedia of Tourism Management and Marketing. Edward Elgar Publishing.
Talón-Ballestero P, González-Serrano L, Soguero-Ruiz C, Muñoz-Romero S, Rojo-Álvarez JL (2018) Using big data from customer relationship management information systems to determine the client profile in the hotel sector. Tourism Management, 68, 187–197.
Stylos, N., Zwiegelaar, J., & Buhalis, D. (2021). Big data empowered agility for dynamic, volatile, and time-sensitive service industries: the case of tourism sector. International Journal of Contemporary Hospitality Management, 33(3), 1015-1036.
Xiang. Z, & Fesenmaier DR (2017). Big data analytics, tourism design and smart tourism. In: Analytics in smart tourism design, Springer, Cham, pp. 299–307.
Zwiegelaar, J. B., & Stylos, N. (2022). Data Analytical Tools. In Encyclopedia of Tourism Management and Marketing. Edward Elgar Publishing.