Journal Article

Big data empowered agility for dynamic, volatile, and time-sensitive service industries: The case of tourism sector


Purpose – Dynamic, volatile, and time-sensitive industries, such as tourism, travel, and hospitality, require agility and market intelligence to create value and achieve competitive advantage. The current study examines the influence of Big Data (BD) on the performance of service organizations, and probes for a deeper understanding of implementing BD, based on available technologies. Design/methodology/approach – An ethnographic study was conducted following an abductive approach. A primary qualitative research scheme was employed with 35 information technology and database professionals participating in five online focus groups of seven participants each. Analytical themes were developed simultaneously with the literature being revisited throughout the study to ultimately create sets of common themes and dimensions. Findings – BD can help organizations build agility, especially within dynamic industries, to better predict customer behavioral patterns and make tailor-made propositions from the BD. An integrated BD-specific framework is proposed to address value according to the dimensions of need, value, time, and utility. Research limitations/implications – Little research exists on the key drivers of BD use for dynamic, real-time and agile businesses. This research adds to the developing literature on BD applications to support organizational decision-making and business performance in the tourism industry. Originality/value – The study responds to scholars’ recent calls for more empirical research with contextual understanding of the use of BD to add value in marketing intelligence within business ecosystems. It delineates factors contributing to BD value creation and explores the impacts on the respective service encounters.

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Stylos, Nikolaos
Zwiegelaar, Jeremy
Buhalis, Dimitrios

Oxford Brookes departments

Oxford Brookes Business School


Year of publication: 2021
Date of RADAR deposit: 2021-01-12

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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