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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.

This study examines the impact of Big Data technologies on operational efficiency in DP World’s supply chain and logistics operations. Employing a mixed-methods approach, we surveyed 405 stakeholders and performed 20 semi-structured interviews to investigate the incorporation of advanced analytics, machine learning, and real-time processing capabilities. The study employs Technology-Organization-Environment (TOE) and economic frameworks to examine the technological, organisational, environmental, and economic determinants affecting Big Data adoption. Results indicate substantial positive effects on the reduction of operational complexity, enhancement of customer responsiveness, improvement of supplier connections, and strengthening of technical partnerships. PLS-SEM analysis utilising ADANCO software validated all hypotheses, demonstrating robust connections between Big Data deployment and supply chain efficiency measures. Essential findings indicate that governmental assistance, organisational culture, and information technology infrastructure are crucial success determinants. The report offers practical advice for supply chain managers and policymakers, emphasising Big Data's revolutionary capacity for competitive advantage in global logistics operations.

This study examines the impact of Big Data technologies on operational efficiency in DP World’s supply chain and logistics operations. Employing a mixed-methods approach, we surveyed 405 stakeholders and performed 20 semi-structured interviews to investigate the incorporation of advanced analytics, machine learning, and real-time processing capabilities. The study employs Technology-Organization-Environment (TOE) and economic frameworks to examine the technological, organisational, environmental, and economic determinants affecting Big Data adoption. Results indicate substantial positive effects on the reduction of operational complexity, enhancement of customer responsiveness, improvement of supplier connections, and strengthening of technical partnerships. PLS-SEM analysis utilising ADANCO software validated all hypotheses, demonstrating robust connections between Big Data deployment and supply chain efficiency measures. Essential findings indicate that governmental assistance, organisational culture, and information technology infrastructure are crucial success determinants. The report offers practical advice for supply chain managers and policymakers, emphasising Big Data's revolutionary capacity for competitive advantage in global logistics operations.

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.