IDEAS home Printed from https://ideas.repec.org/a/bjz/ajisjr/2132.html

The Use of Big Data in Tourism: Current Trends and Directions for Future Research

Author

Listed:
  • Dimitrios Belias
  • Sawsan Malik
  • Ioannis Rossidis
  • Christos Mantas

Abstract

The aim of this research is to examine the new landscape that is taking shape in the tourism economy, due to the adoption of technological innovations. The technologies and systems used to make the most of the resulting interdisciplinary and multilingual big data, the methods by which heterogeneous elements from different sources are incorporated to bring new knowledge, and the modern services that are ultimately implemented and provided to the public, as well as the people involved in this process, and those who benefit from the new services are among the issues analyzed. The work is not limited to a simple mapping of space but concludes with an evaluation of the systems already implemented and the various methods of analysis and exploitation of large-scale data for this industry, which is based on detailed research of the current literature to identify the potential gaps for future research. This is a literature review. The authors have identified the content of this research on well-known online databases which include scholar, google and Scopus. They used the appropriate keywords such as “big data†& “tourism innovation†to reach the publications used in this research, also given attention on using recent papers which are derived from reliable journals. The research has concluded that the use of big data in the tourism sector is a rising trend. Big data creates expectations for a better understanding of tourism demand and the adjustment of supply by tourism companies to meet the demands of tourists and the profitable activity of tourism businesses. There is a need to examine how can big data help the hotels to deal with Covid-19 pandemic, which can be a topic for future research. Big data is one of the most recent trends on innovation, Information Communication Technologies (ICT) and knowledge management. Hence, there is a need to gather and analyze the existing publications which concern the tourist industry. The originality of this research stems from making an analysis of the current situation as well as it is a bridge to the future by making suggestions on how future research can be shaped. The key limitation of this research resides on that it focuses on the existing literature and used secondary data. However, it gives directions for the future research which can take place with primary data collection.

Suggested Citation

  • Dimitrios Belias & Sawsan Malik & Ioannis Rossidis & Christos Mantas, 2021. "The Use of Big Data in Tourism: Current Trends and Directions for Future Research," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, September.
    • Handle: RePEc:bjz:ajisjr:2132
    DOI: https://doi.org/10.36941/ajis-2021-0144
    as

    Download full text from publisher

    File URL: https://www.richtmann.org/journal/index.php/ajis/article/view/12660
    Download Restriction: no
    File URL: https://www.richtmann.org/journal/index.php/ajis/article/view/12660/12265
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.36941/ajis-2021-0144?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    2. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    3. Piera Centobelli & Valentina Ndou, 2019. "Managing customer knowledge through the use of big data analytics in tourism research," Current Issues in Tourism, Taylor & Francis Journals, vol. 22(15), pages 1862-1882, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Julia Eichholz & Thorsten Knauer & Sandra Winkelmann, 2023. "Digital Maturity of Forecasting and its Impact in Times of Crisis," Schmalenbach Journal of Business Research, Springer, vol. 75(4), pages 443-481, December.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    5. Zhang, Chonghui & Zhang, Na & Su, Weihua & Balezentis, Tomas, 2024. "Online commodity recommendation model for interaction between user ratings and intensity-weighted hierarchical sentiment: A case study of LYCOM," Omega, Elsevier, vol. 129(C).
    6. Zhitang Li & Cuihua Zhang & Ruxia Lyu, 2025. "The developer’s optimal distribution strategy in the differentiated platform: the value of user feedback data and negotiation," Electronic Commerce Research, Springer, vol. 25(1), pages 553-593, February.
    7. Adesuwa Omorede & Juan Francisco Prados-Castillo & Amalia Cristina Casas-Jurado, 2025. "Researching entrepreneurship using big data: implementation, benefits, and challenges," International Entrepreneurship and Management Journal, Springer, vol. 21(1), pages 1-22, December.
    8. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    9. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    10. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    11. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    12. Chotia, Varun & Khoualdi, Kamel & Broccardo, Laura & Yaqub, Muhammad Zafar, 2025. "The role of cyber security and digital transformation in gaining competitive advantage through Strategic Management Accounting," Technology in Society, Elsevier, vol. 81(C).
    13. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Law, Rob & Yang, Yating, 2020. "Group pooling for deep tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 82(C).
    14. Tiago Carneiro & Winnie Ng Picoto & Inês Pinto, 2023. "Big Data Analytics and Firm Performance in the Hotel Sector," Tourism and Hospitality, MDPI, vol. 4(2), pages 1-13, April.
    15. Weronika Jakubczak & Anna Golebiowska & Dariusz Prokopowicz, 2021. "The Legal and Security Aspects of ICT and Industry 4.0 Importance for Financial Industry 4.0 Development," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 169-181.
    16. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.
    17. Gianfranco Marotta & Phillipe Krahnhof & Cam-Duc Au, 2022. "A Critical Analysis of Budgeting Processes from the Pharmaceutical Industry and Beyond," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(3), pages 1-3.
    18. Dan Zhang & Loo G. Pee & Shan L. Pan & Jingyuan Wang, 2024. "Information practices in data analytics for supporting public health surveillance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(1), pages 79-93, January.
    19. Liu, Congzheng & Letchford, Adam N. & Svetunkov, Ivan, 2022. "Newsvendor problems: An integrated method for estimation and optimisation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 590-601.
    20. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjz:ajisjr:2132. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Richtmann Publishing Ltd (email available below). General contact details of provider: https://www.richtmann.org/journal/index.php/ajis .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.