Identifying obstacles to evaluating business intelligence in Micro-Small Apparel Enterprises: a case study in Durban, South Africa
Winiswa Mavutha ORCID ↗
International Journal of Research in Business and Social Science · 2024 · Vol. 13 / Issue 5 · 121-132
Publication metrics
External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.
Dimensions
Live provider badge. If unavailable, use the Dimensions link above.
Observed 21 Oct 2026, 07:22 UTC
Observed 21 Oct 2026 07:22 UTC
Observed 22 Oct 2026 00:43 UTC
OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.
Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.
2017–2026 Annual history last updated:
Citations received
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
View data & coverage
2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.
Abstract
The escalating financial burdens faced by the Small Medium and Micro Enterprise (SMME) sector necessitate a perpetual pursuit of enhanced managerial efficacy. For enhanced financial management, client relationship management and inventory control, it is necessary for businesses to utilise various technologies and methodologies. One approach involves the utilisation of business analytical technologies, such as Business Intelligence (BI). Business Intelligence (BI) serves as a valuable tool for enterprises, aiding in the examination and interpretation of data to facilitate informed decision-making processes aimed at enhancing overall business efficacy. The eThekwini region encompasses a significant presence of micro-small enterprises within the apparel manufacturing and retail sector. The primary aim of this research was to identify and analyse the many issues that hinder the evaluation of business intelligence (BI) in micro-small apparel enterprises located in Durban, a city in Kwa-Zulu Natal, South Africa. A cross-sectional quantitative study was undertaken to examine a cohort of 161 small apparel business owners. The participants were chosen through the utilisation of a judgemental sampling technique. The data was gathered through the utilisation of anonymous questionnaires. The data was analysed by utilising multiple linear regression analysis to determine the characteristics that hinder the evaluation of business intelligence (BI) in the small apparel industry. The study employs the Technology-Organisation-Environment (TOE) framework as its conceptual framework. The researcher identified several obstacles that hinder the evaluation of business intelligence in micro-small textile enterprises in South Africa. These factors include perceived relative advantage, cost, organisational data environment, organizational decision-making culture, and external support. However, there are numerous governmental institutions that are intended to provide support for micro-small companies. Therefore, it is recommended that these institutions establish training initiatives aimed at educating apparel SMMEs on BI. The implementation of target-driven training has the potential to significantly contribute to the achievement of vision 2030 for fostering the growth of small, medium, and micro enterprises (SMMEs) to enhance the South African economy.
Keywords: {'name': 'Small Medium Micro Enterprises (SMMEs)'}, {'name': 'Business Intelligence (BI)'}, {'name': 'Technology-organisation-environment (TOE) framework'}
Authors & affiliations
Winiswa Mavutha
Affiliation not supplied
Deposited references
Supplied references, not a list of publications citing this article.
DOI: 10.1080/09537325.2019.1702637
DOI: 10.3390/su12187674
DOI: 10.1016/j.dss.2019.113113
DOI: 10.4018/978-1-7998-9155-0.ch044
DOI: 10.11591/eei.v9i1.1459
DOI: 10.1016/j.techfore.2021.120880
DOI: 10.3390/buildings12050522
DOI: 10.4102/sajesbm.v11i1.193
DOI: 10.1108/BL-02-2018-0008
FinMark Trust FinScope MSME Survey South Africa 2020
DOI: 10.3390/su13158379
DOI: 10.1016/j.ijinfomgt.2018.08.017
DOI: 10.17705/1jais.00462
DOI: 10.1016/j.ijinfomgt.2020.102190
Mavutha, W. (2024). Determining business intelligence adoption stages for apparel SMMEs in Durban, South Africa: Received: 09th October 2023; Revised: 26th October 2023, 18th December 2023; Accepted: 20th December 2023. Socialis Series in Social Science, 6, 31-47.
McCombes, S. (2019, September 19). Sampling methods. Scribbr. https://www.scribbr.com/methodology/sampling-methods/
DOI: 10.1016/j.jmsy.2018.10.005
Nxumalo, N. K. M. (2021). The effects of capital structure on the operational efficiency of Small and Medium-Sized Manufacturing Enterprises (SMSME) in Pietermaritzburg, South Africa (Doctoral dissertation).
Owusu, A. C. H. E. A. M. P. O. N. G., Agbemabiasie, G. C., Abdurrahaman, D. T., & Soladoye, B. A. (2017). Determinants of business intelligence systems adoption in developing countries: An empirical analysis from Ghanaian Banks. The Journal of Internet Banking and Commerce, 24(2), pp.1-25.
DOI: 10.1108/IMDS-05-2017-0170
DOI: 10.4324/9781003216896-2
Santos, A. dos, du Toit, J., Faasen, N., Quesada, L. L. (2021). Research Methodology 2e: Business and Management Contexts ([edition unavailable]). Oxford University Press Southern Africa.
Schiffman, L. G., & Wisenblit, J. L. (2019). Consumer behavior, global edition: Consumer behavior (12th ed.). Pearson Education.
SEDA. (2022). SMME Quarterly Update 3rd Quarter 2021. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://www.seda.org.za/Publications/Publications/SMME%20Quarterly%202021Q3%20(002).pdf
DOI: 10.1177/09721509221137199
Simkus, J. (2021). How Does the Cross-Sectional Research Method Work? Simply Psychology. www.simplypsychology.org/what-is-a-crosssectional-study.html
Worldwide big data and analytics spending guide (2022) IDC: The premier global market intelligence company. Available at: https://www.idc.com/getdoc.jsp?containerId=IDC_P33195 (Accessed: June 17, 2023).