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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.