Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Abstract

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability, customer demand forecasting, cheaper development of products, and improved stock control. Small and medium sized enterprises (SMEs) are the backbone of the global economy, comprising of 90 % of businesses worldwide. However, only 10 % SMEs have adopted big data analytics despite the competitive advantage they could achieve. Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics. The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK. This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners. The results of the evaluation are presented with a discussion on the results, and the paper concludes with recommendations to improve the scoring tool based on the proposed framework. The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

Keywords

Big data analytics / Evaluation[ Small and medium sized enterprises (SMEs) / Strategic framework / Big data analytics / Evaluation[ Small and medium sized enterprises (SMEs) / Strategic framework

References

[1]	The World Bank.Small and medium enterprises (SMEs) finance [Online]. Available https://www.worldbank.org/en/topic/smefinance, September 2022.
[2]	M. Ward, G. Hutton, Business statistics [Online]. Available, https://commonslibrary.parliament.uk/research-briefings/sn06152/, January 2023.
[3]	Organisation for Economic Cooperation and Development. Financing SMEs and entrepreneurs2022: An OECD scoreboard [Online]. Available (September 2022). https://www.oecd-ilibrary.org/sites/8ae4e97den/index.html?itemId=/content/component/8ae4e97d-en.
[4]	European Commission, Entrepreneurship and small and medium-sized enterprises (SMEs)| Internal market, industry, entrepreneurship and SMEs [Online]. Available, https://ec.europa.eu/growth/smesen, May 2021.
[5]	M. Bianchini, V. Michalkova, OECD SME and entrepreneurship papers, No. 15 data analytics in SMEs: Trends and policies, [Online]. Available, https://doi.org/10.1787/f493861e-en, June 2019.
[6]	M. Willetts, A.S. Atkins, C. Stanier. Quantitative study on barriers of adopting big data analytics for UK and Eire SMEs. N. Sharma, A. Chakrabarti, V.E. Balas, A.M. Bruckstein (Eds.), Data Management, Analytics and Innovation, Springer, Singapore (2022), pp. 349-373.[7] K.-H. Tan, Y.-Z. Zhan. Improving new product development using big data: A case study of an electronics company. R. Manag., 47 (4) (Sept. 2017), pp. 570-582.[8] Editorial Team, How Amazon used big data to rule E-commerce [Online]. Available, https://insidebigdata.com/2019/11/30/how-amazon-used-big-data-to-rule-e-commerce/, December 2019.
[9]	M. Willetts, A.S. Atkins, C. Stanier. A strategic big data analytics framework to provide opportunities for SMEs. Proc. of 14th Intl. Technology, Education and Development Conf., Valencia (2020), pp. 3033-3042.[10] M. Willetts, A.S. Atkins. Software positioning tool to support SMEs in adoption of big data analytics using a case study application. International Journal of Software Engineering & Computer System, 9 (1)(Apr. 2023), pp. 46-58.
[11]	M. Willetts, A.S. Atkins, C. Stanier. A teaching and learning case study on data mining using association rules for SMEs. Proc. of 16th Intl. Technology, Education and Development Conf., Valencia (2022), pp. 1401-1410.[12] M. Willetts, A.S. Atkins, C. Stanier. Teaching and learning case study on social media analytics for small and medium-sized enterprises. Proc. of 14th Annual Intl. Conf. of Education, Research and Innovation, Seville (2021), pp. 3158-3167.[13] F.D. Davis. A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Ph.D. dissertation, Massachusetts Institute of Technology, Cambridge, USA (1986).
[14]	M. Willetts, A.S. Atkins, C. Stanier.Big data, big data analytics application to smart home technologies and services for geriatric rehabilitation. M.A. Choukou, S. Syed-Abdul (Eds.), Smart Home Technologies and Services for Geriatric Rehabilitation, Academic Press, London(2022), pp. 205-230.
[15]	A. De Mauro, M. Greco, M. Grimaldi. A formal definition of big data based on its essential features. Libr. Rev., 65 (3) (Apr.2016), pp. 122-135.
[16]	N.A. Ghani, S. Hamid, I.A.T.Hashem, E. Ahmed. Social media big data analytics: A survey, Comput.Hum. Behav., 101(Dec. 2019), pp. 417-428.
[17]	P. Mikalef, M. Boura, G. Lekakos, J. Krogstie.Big data analytics and firm performance: Findings from a mixed-method approach. J.Bus. Res., 98(May 2019), pp. 261-276.
[18]	M. Ward, C. Rhodes. Small businesses and the UK economy, London, United Kingdom (December2014).
[19]	J.-M. Song, S.-M. Xia, D. Vrontis, et al. The source of SMEs’ competitive performance in COVID-19: Matching big data analytics capability to business models. Inform. Syst. Front., 24 (4) (May2022), pp. 1167-1187.
[20]	S. Coleman, R. G?b, G. Manco, A. Pievatolo, X. Tort-Martorell, M.S. Reis. How can SMEs benefit from big data? Challenges and a path forward. Qual. Reliab. Eng. Int., 32 (6) (Oct.2016), pp. 2151-2164.[21] M. Willetts, A.S. Atkins, C. Stanier. Barriers to SMEs adoption of big data analytics for competitive advantage. Proc. of 4th Intl. Conf. on Intelligent Computing in Data Sciences, Fez (2020), pp. 1-8.[22] S. Brandy. Overcoming challenges and unlocking the potential: Empowering small and medium enterprises (SMEs) with data analytics solutions. Intl. Journal of Information Technology and Computer Science Applications, 1 (3) (Sept. 2023), pp. 150-160.[23] M. Iqbal, S.H.A. Kazmi, A. Manzoor, A.R. Soomrani, S.H. Butt, K.A. Shaikh. A study of big data for business growth in SMEs: Opportunities & challenges. Proc. of Intl. Conf. on Computing, Mathematics and Engineering Technologies, Sukkur(2018), pp. 1-7.
[24]	N. C?rte-Real, T. Oliveira, P. Ruivo. Assessing business value of big data analytics in European firms. J. Bus. Res., 70(Jan. 2017), pp. 379-390.
[25]	V. Braun, V. Clarke. Using thematic analysis in psychology. Qual. Res. Psychol., 3 (2) (Jan.2006), pp. 77-101. Your institution provides access to this article.
[26]	M. Willetts, A.S. Atkins. Performance measurement to evaluate the implementation of big data analytics to SMEs using benchmarking and the balanced scorecard approach. J. Digit. Inform. Manag., 5 (1) (Apr.2023), pp. 55-69.[27] A. Alaboudi, A. Atkins, B. Sharp, A. Balkhair, M. Alzahrani, T. Sunbul. Barriers and challenges in adopting Saudi telemedicine network: the perceptions of decision makers of healthcare facilities in Saudi Arabia. J. Infect. Public Heal., 9 (6)(Dec. 2016), pp. 725-733.
[28]	D. Sekayi, A. Kennedy. Qualitative Delphi method: A four round process with a worked example. Qual. Rep., 22 (10) (Oct.2017), pp. 2755-2763.
[29]	E. Wenger, B. Trayner-Wenger.Communities of practice: A brief introduction [Online] Available.
[30]	J. Nielsen.Usability Engineering. Academic Press, Boston (1993).
[31]	Hootsuite. Social media management dashboard [Online] Available,.https://www.hootsuite.com/en-gb/,%20January%202021.
[32]	IT Governance.Brexit, GDPR & data protection: What you need to know [Online] Available,. https://www.itgovernance.co.uk/eu-gdpr-uk-dpa-2018-uk-gdpr, October 2020.
[33]	GOV.UK.Data protection: the data protection act [Online] Available. https://www.gov.uk/data-protection, September 2023.
[34]	F. Duarte. Amount of data created daily (2023) [Online] Available.
[35]	A. Gandomi, M. Haider. Beyond the hype: big data concepts, methods,analytics. Int. J. Inform. Manag., 35 (2) (Apr.2015), pp. 137-144.
[36]	B. Kahan.Exerpts from: Review of evaluation frameworks.
	[Online], Available. Saskatchewan Ministry of Education (2008). https://kau.edu.sa/GetFile.aspx?id=170971&Lng=AR&fn=evaluation-frameworks-review.pdf, March 2008.
[37]	S. Beguería.Validation and evaluation of predictive models in hazard assessment and risk management. Nat. Hazards, 37 (3)(Mar. 2006), pp. 315-329.[38] J. Nielsen. 10 usability heuristics for user interface design [Online] Available. https://www.nngroup.com/articles/ten-usability-heuristics/, November 2021.
[39]	J. Nielsen.Severity ratings for usability problems [Online] Available. https://www.nngroup.com/articles/how-to-rate-the-severity-of-usability-problems/, November 1994.
[40]	C. Jimenez, P. Lozada, P. Rosas. Usability heuristics: A systematic review. Proc. of 11th Colombian Computing Conf. Popayan (2016), pp. 1-8.[41] A. Abulfaraj, A. Steele. Detailed usability heuristics: a breakdown of usability heuristics to enhance comprehension for novice evaluators. Proc. of 22nd Intl. Conf. on Human-Computer Interaction, Copenhagen (2020), pp. 3-18.[42] V. Brock, H.U. Khan. Big data analytics: Does organizational factor matters impact technology acceptance?. J. Big Data, 4 (1)(Jul. 2017), pp. 1-28.
[43]	R. Rokhim, P. Wulandari, I. Mayasari. Small medium enterprises technology acceptance model: A conceptual review. Int. J. Bus. Soc., 19 (S4) (Dec.2018), pp. 689-699.
[44]	J.P. Chin, V.A. Diehl, K.L. Norman. Development of an instrument measuring user satisfaction of the human-computer interface. Proc. of SIGCHI Conf. on Human Factors in Computing Systems (Washington, 1988), pp. 213-218.
[45]	V. Venkatesh, F.D. Davis. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci., 46 (2) (Feb.2000), pp. 186-204.
[46]	V. Venkatesh, F.D. Davis. A model of the antecedents of perceived ease of use: Development and test. Decis. Sci. J., 27 (3) (Sept.1996), pp. 451-481.
[47]	S. Beecham, T. Hall, C. Britton, M. Cottee, A. Rainer. Using an expert panel to validate a requirements process improvement model. J. Syst. Software, 76 (3) (Jun.2005), pp. 251-275.
[48]	J. Nielsen.Why you only need to test with 5 users [Online] Available. https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/, October 2021.
[49]	M. Willetts, A.S. Atkins. Qualitative study on barriers of adopting big data analytics for UK SMEs. Intl. Journal of Big Data Management, 3 (1) (Jan.2023), pp. 28-50.