Explore publications.

While African big businesses are increasingly utilising big data analytics to enhance their competitiveness, many small to medium enterprises (SMEs) across the continent remain hesitant to adopt these tools. Although SMEs could also gain significant benefits from big data analytics, the factors driving their potential adoption for information security management are not yet well understood. This paper presents a qualitative grounded theory study, focusing on SMEs in South Africa. Through in-depth analysis, this research generated a theory that reveals key factors influencing the adoption of big data analytics amongst these SMEs. Using semi-structured interviews, data were collected from 28 owner-managers of SMEs, then transcribed and systematically coded using the grounded theory approach. The resulting substantive theory provides valuable insights into the core aspects driving big data adoption for information security management among South African SMEs – specifically, ability, opportunity, and motivation. The study implies that by understanding the impact of these three factors, SMEs can make more informed decisions to enhance information security performance.

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.

Business intelligence (BI) can assist businesses with the analysis of information to make better decisions to improve business performance; however, a lack of research with respect to the adoption of BI tools specifically in the SMME apparel sector has been observed. Developments in information technology (IT) have led to an increase in competitiveness among providers, resulting in a plethora of offerings for customers to choose from. As such, businesses are operating in evolving and complex environments where business intelligence systems (BIS) have become essential. The aim of this study was to establish the factors that influence the adoption of BI by micro-small apparel retailers in KwaZulu-Natal. The study was a cross-sectional survey that sampled 132 apparel business owners who were selected using purposive sampling. A survey questionnaire was used to collect the data. The data was analyzed using the Statistical Package for Social Sciences (SPSS) Version 13.0. This study found that the main inhibitors to the adoption of BI systems among small apparel retailers are cost-related. However, an interesting finding was that although cost had a negative relationship to adoption in the results, most of the respondents still indicated that they were able to make financial plans to adopt BI. The study recommends that small apparel business owners prioritize the adoption of BI as a tool for business operations. The adoption of such tools would have a net positive effect on the operations of such businesses.

Big data (BD) adoption is still relatively slow despite the numerous business opportunities that it embodies. Within the context of financial institutions in South Africa, the specific drivers of big data adoption remain largely indeterminate. Consequently, this study explores the possible relationships between perceived usefulness and perceived ease of use and actual big data adoption, while being duly cognisant of the role that behavioural intention to adopt big data could play. The study employed a survey research approach and relied on data collected from a purposive sample of 195 management level employees of financial institutions. Regression analysis to test the hypothesised relationships in the study revealed that perceived usefulness and perceived ease of use were statistically significant predictors of big data adoption. Instructively, this relationship was subject to the extent of behavioural intention of respondents. For respondents with a low behavioural intention to adopt big data, perceived usefulness and perceived ease of use displayed a statistically insignificant relationship with big data adoption. Conversely, the regression model for the group of respondents with a high behavioural intention to adopt big data is statistically significant. These findings enrich literature related to big data from a developing country context while concurrently identifying veritable antecedents of big data adoption in financial institutions.