Digital transformation dimensions for evaluating SMEs' readiness for big data analytics and artificial intelligence: A review

Abstract

Assessing the readiness and maturity of small and medium enterprises (SMEs) is a foundation for implementing emerging technologies like big data analytics and artificial intelligence to drive their digital transformation endeavours. This study emphasises that readiness and maturity dimensions offer descriptive and prescriptive guidelines for gauging the current and desired levels of preparedness and maturity required to achieve desired digital transformation outcomes. However, prevailing readiness and maturity models overlook the diverse stages of advancement in big data analytics and artificial intelligence. This research explores the dimensions essential for assessing SMEs' readiness to adopt big data analytics and artificial intelligence. This paper identifies the key dimensions for evaluating SMEs' readiness and maturity across different categories of big data analytics and artificial intelligence by conducting a systematic literature review and employing cluster analysis. The study's principal findings underscore that SMEs' readiness for maturity is influenced prominently by strategic leadership and organisational culture, closely trailed by information technology, security, and business model transformation. Additionally, three pivotal dimensions encompass data analytics and governance, cost-benefit and risk management, and environmental factors. Consequently, proposing that evaluating digital readiness and maturity for SMEs should encompass these six dimensions, thoughtfully considering various prerequisites related to analytics and artificial intelligence.

Keywords: {'name': 'Small and medium enterprise'}, {'name': 'digital transformation,'}, {'name': 'big data analytics,'}, {'name': 'artificial intelligence'}

Authors & affiliations

Ignitia Motjolopane
Affiliation not supplied

Martin Chanza
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

Adrian, C., Abdullah, R., Atan, R., & Jusoh, Y. Y. (2016). Towards developing strategic assessment model for big data implementation: a systematic literature review. International Journal of Advances in Soft Computing and its Applications, 8(3), 173 -192.

DOI: 10.1007/978-3-319-57870-5_4

DOI: 10.1016/j.ijpe.2016.08.018

DOI: 10.3390/bdcc7010002

DOI: 10.7249/RR3136

DOI: 10.1080/13675567.2017.1369501

Berger, S., Bitzer, M., Hackel, B., & Voit, C. (2020, 15-17 June). Approaching digital transformation-development of a multi-dimensional maturity model. Twenty-Eighth European Conference on Information Systems (ECIS2020), Virtual Conference

DOI: 10.1109/ACCESS.2018.2821445

DOI: 10.1108/IMDS-12-2015-0495

DOI: 10.1080/2573234X.2018.1543535

De Bruin, T., Rosemann, M., Freeze, R., & Kaulkarni, U. (2005, November 30 - December 2). Understanding the main phases of developing a maturity assessment model. Australasian Conference on Information Systems (ACIS), Sydney, Australia.

DOI: 10.1016/j.dss.2012.05.044

Drus, S. M., & Hassan, N. H. (2017, 25-27April,). Big data maturity model–a preliminary evaluation. ICOCI Kuala Lumpur. Univ. Utara Malaysia, 117, pp.613-20. 6th International Conference on Computing and Informatics, ICOCI 2017, Kuala Lumpur,Malaysia.

El-Darwiche, B., Koch, V., Meer, D., Shehadi, R. T., & Tohme, W. (2014). Big data maturity: An action plan for policymakers and executives. In B. Bilbao-Osorio, S. Dutta, & B. Bruno Lanvin (Eds.), The Global Information Technology Report 2014 Rewards and Risks of Big Data (pp. 51). World Economic Forum https://www3.weforum.org/docs/WEF_GlobalInformationTechnology_Report_2014.pdf

Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals concepts, drivers and techniques. Printice Hall.

Farah, B. (2017). A value-based big data maturity model Journal of Management Policy and Practice, 18(1), 11-18.

Felch, V., & Asdecker, B. (2020, 20-24 June). How to Make Business Process Maturity Models Better–Drawing on Design Science Research. 24th Pacific Asia Conference on Information Systems (PACIS), Dubai, United Arab Emirates.

DOI: 10.1177/0008125618811931

DOI: 10.3934/mbe.2022400

DOI: 10.1007/s12599-021-00703-1

Hinterhuber, A., & Liozu, S. (2012). Is it time to rethink your pricing strategy. MIT Sloan Management Review, 53(4), 69-78.

DOI: 10.1007/978-981-13-2766-7_116

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Retrieved 10/11/2021, from file:///C:/Users/25509934/Downloads/Kitchenham-2007Systematicreviews5-8.pdf

DOI: 10.3390/info11030142

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21-32.

DOI: 10.1016/j.ijinfomgt.2016.12.003

. International Journal of Information Management,, 37(3), 114 -124.

Lustig, I., Dietrich, B., Johnson, C., & Dziekan, C. (2010, November/December 2010). The Analytics Journey : An IBM view of the structured data analysis landscape: descriptive, predictive and prescriptive analytics. Analytics http://analytics-magazine.org/the-analytics-journey/

DOI: 10.1007/s12599-015-0401-5

DOI: 10.1080/20421338.2021.1977088

DOI: 10.1109/ICDS50568.2020.9268720

DOI: 10.1007/978-3-319-32877-5_11

DOI: 10.1016/j.cor.2017.07.004

Osterwalder, A. (2004). The business model ontology a proposition in a design science approach Lausanne].

DOI: 10.1111/jpim.12544

Ramsden, R., & Chowdhury, M. (2019 ). The business readiness levels:The simple benchmark for business ventures. Independently published.

DOI: 10.1016/j.omega.2014.11.009

Rozados, I. V., & Benny, T. (2014, 10-13 December). Big data analytics in supply chain management: Trends and related research. 6th International Conference on Operations and Supply Chain Management, Bali, Indonesia

DOI: 10.1109/CITSM.2018.8674323

DOI: 10.47852/bonviewAIA2202336

Schoeman, W., Moore, R., Seedat, Y., & Chen, J. Y. J. (2021). Artificial intelligence: Is South Africa read. Retrieved 12/05/2022, from https://repository.up.ac.za/bitstream/handle/2263/82719/Schoeman_Artificial.pdf?sequence=1&isAllowed=y

DOI: 10.24251/HICSS.2017.648

DOI: 10.2478/orga-2020-0001

DOI: 10.1109/ICCED53389.2021.9664881

DOI: 10.1016/j.procs.2019.11.052

DOI: 10.1016/j.procir.2020.03.023

DOI: 10.1109/IEEM55944.2022.9989860

DOI: 10.1016/j.infsof.2016.01.010

Ustaoglu, N. (2019 ). A maturity model for digital transformation [ Master's, Sabanc? University].

DOI: 10.1016/j.jbusres.2020.06.057

Vesset, D., Girard, G., Feblowitz, J., Versace, M., Burghard, C., O’Brien, A., Olofson, C., Schubmehl, D., McDonough, B., Woodward, A., & Bond, S. (2015). IDC MaturityScape: big data and analytics 2.0 [ IDC MaturityScape White Paper]. Retrieved 05/06/2021, from https://www.cacp.ca/index.html?asst_id=1637

DOI: 10.26493/1854-4231.15.137-155

DOI: 10.1201/1078/43196.18.3.20010601/31289.6

Wittine, N., Sutherland, R., Wenzel, S., & Amaral Bicalho, A. L. (2021, 10-11 August ). Analysing the state of digitisation in SME–A survey based on an SME-specific maturity model 2nd Conference on Production Systems and Logistics, Virtual Conference

DOI: 10.3390/joitmc5020035

DOI: 10.1109/VAST.2012.6400554