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

Abstract

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.