Business intelligence and business analytics in tourism: insights through Gioia methodology

Author:

Jiménez-Partearroyo MontserratORCID,Medina-López AnaORCID,Rana SudhirORCID

Abstract

AbstractAlthough Business Intelligence (BI) and Business Analytics (BA) have been widely adopted in the tourism sector, comparative research using BI and BA remains scarce. To fill this gap in the literature, the present study explores how BI and BA contribute to strategic innovation, address operational challenges, and enhance customer engagement. To this end, using a dual-method approach that incorporates both quantitative and qualitative methodologies, we first conduct a bibliometric analysis using SciMAT. This sets the stage for the subsequent application of the Gioia methodology. Specifically, we perform an in-depth qualitative examination of a total of 12 scholarly articles on the tourism sector, evenly split between BI and BA. Upon synthesizing the findings on the roles of BI and BA, we outline distinct pathways through which they influence tourism sector management solutions. Based on the obtained evidence, we argue that, while BI focuses on technological advancement and operational integration, BA is more aligned with predictive analytics and data-driven customer engagement. These insights provide managers with a better understanding of the roles of BI and BA, serving as a guide for their strategic applications, from improving service quality to innovating in customer engagement. The novelty of this approach lies in its use of the Gioia methodology, in a comparative analysis to evaluate the separate yet complementarily roles of BI and BA, and in enhancing tourism industry practices.

Funder

Universidad Rey Juan Carlos

Publisher

Springer Science and Business Media LLC

Reference100 articles.

1. April, J., Better, M., Glover, F., Kelly, J., & Laguna, M. (2006). Enhancing business process management with simulation optimization. Paper presented at the Proceedings of the 2006 Winter Simulation Conference, 642–649. https://doi.org/10.1109/WSC.2006.323141.

2. Azvine, B., Cui, Z., Nauck, D. D., & Majeed, B. (2006). Real time business intelligence for the adaptive enterprise. Paper presented at the The 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE’06), 29. https://doi.org/10.1109/CEC-EEE.2006.73.

3. Berlanga, R., & Nebot, V. (2016). Context-aware business intelligence. Paper presented at the Business Intelligence: 5th European Summer School, eBISS 2015, Barcelona, Spain, July 5–10, 2015, Tutorial Lectures 5, 87–110. https://doi.org/10.1007/978-3-319-39243-1_4.

4. Better, M., Glover, F., & Laguna, M. (2007). Advances in analytics: Integrating dynamic data mining with simulation optimization. IBM Journal of Research and Development, 51(3.4), 477–487. https://doi.org/10.1147/rd.513.0477.

5. Bevacqua, A., Carnuccio, M., Folino, F., Guarascio, M., & Pontieri, L. (2014). A data-driven prediction framework for analyzing and monitoring business process performances. Paper presented at the Enterprise Information Systems: 15 h International Conference, ICEIS 2013, Angers, France, July 4–7, 2013, Revised Selected Papers 15, 100–117. https://doi.org/10.1007/978-3-319-09492-2_7.

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