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Quantitative Study on Barriers of Adopting Big Data Analytics for UK and Eire SMEs

Willetts, Matthew, ATKINS, Anthony and STANIER, Clare (2021) Quantitative Study on Barriers of Adopting Big Data Analytics for UK and Eire SMEs. In: Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, 71 . Springer, pp. 349-373. ISBN 978-981-16-2937-2

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Abstract or description

Big data analytics has been widely adopted by large companies, enabling them to achieve competitive advantage. However, small and medium-sized enterprises (SMEs) are underutilising this technology due to a number of barriers including financial constraints and lack of skills. Previous studies have identified a total of 69 barriers to SMEs adoption of big data analytics, rationalised to 21 barriers categorised into five pillars (Willetts et al. in A strategic big data analytics framework to provide opportunities for SMEs. In: 14th International technology, education and development conference, 2020a, [Willetts M, Atkins AS, Stanier C (2020a) A strategic big data analytics framework to provide opportunities for SMEs. In: 14th International technology, education and development conference, pp 3033–3042. 10.21125/inted.2020.0893]). To verify the barriers identified from the literature, an electronic questionnaire was distributed to over 1000 SMEs based in the UK and Eire using the snowball sampling approach during the height of the COVID-19 pandemic. The intention of this paper is to provide an analysis of the questionnaire, specifically applying the Cronbach’s alpha test to ensure that the 21 barriers identified are positioned in the correct pillars, verifying that the framework is statistically valid.

Item Type: Book Chapter, Section or Conference Proceeding
Uncontrolled Keywords: Big Data Analytics, SMEs, barriers to Big Data Analytics adoption, strategic framework, COVID-19.
Faculty: School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Event Title: Data Management, Analytics and Innovation. Singapore: Springer Singapore
Event Location: Online Virtual platform
Event Dates: 14-16/2022
Depositing User: Anthony ATKINS
Date Deposited: 19 Nov 2021 15:31
Last Modified: 19 Nov 2021 15:31
URI: http://eprints.staffs.ac.uk/id/eprint/7083

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