Mobilising big data analytics capabilities to improve performance of tourism supply chains: the moderating role of dynamic capabilities

Gupta, Yuvika, Farheen Mujeeb, Khan, Kumar, Anil, Luthra, Sunil and Queiroz, Maciel (2023) Mobilising big data analytics capabilities to improve performance of tourism supply chains: the moderating role of dynamic capabilities. International Journal of Logistics Management. ISSN 0957-4093 (In Press)

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Abstract / Description

Purpose: With the emergence of big data analytics and the importance of analytics-driven decisions, the travel industry is swiftly jumping on and adopting the bandwagon. However, research in this domain is limited. Accordingly, the present research seeks to understand how Big Data Analytics Capabilities (BDAC) add value to Tourism Supply Chains (TSCs) and can Dynamic Capabilities (DC) improve the triple bottom line.
Design/methodology/approach: Data from 218 valid responses were collected from different Indian tourism industry units. Confirmatory factor analysis (CFA) was applied to confirm the constructs, followed by partial least squares structural equation modelling (PLS-SEM) to check the mediating effect of dynamic capabilities (DC) on TSCs performance.
Findings: The findings show that BDAC significantly influences the performance of TSCs and that DC plays a critical role in strengthening the impact of BDAC on TSCs’ economic performance. These results corroborate that DC plays a key moderating role.
Research limitations/implications: This study contributes significantly to the tourism sector in India, where tourism is a key contributor to the country’s gross domestic product. Theoretically, this study contributes to the resource-based view (RBV) and practically encourages professionals in the tourism sector to promote the use of BDAC to enhance the performance of TSCs.
Originality: The originality of the study is that it has tried to comprehend the moderating role of dynamic capabilities which impact BDAC to improve TSC performance.

Item Type:	Article
Additional Information:	This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com
Uncontrolled Keywords:	Big data analytics capability; tourism supply chains; dynamic capabilities; resource-based view; firm performance
Subjects:	300 Social sciences > 330 Economics 600 Technology > 650 Management & auxiliary services
Department:	Guildhall School of Business and Law
Depositing User:	Anil Kumar
Date Deposited:	29 Aug 2023 08:41
Last Modified:	29 Aug 2023 08:41
URI:	https://repository.londonmet.ac.uk/id/eprint/8715

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