Drivers of cybersecurity adoption in developing countries: a data-driven approach

What the paper says

Purpose Using a data-driven approach, this study explores the drivers of cybersecurity (CS) adoption in developing countries. It highlights the importance of public policy, regulation, industry associations and risk awareness and provides key insights for organizations and policymakers to improve CS practices. Design/Methodology/Approach A mixed-methods sequential approach was used to analyze the factors driving the adoption of the CS role. The classification algorithms addressed unbalanced data, while SHapley Additive exPlanations (SHAP) identified key variables. Econometric models estimated the likelihood of CS adoption, providing a comprehensive analysis of internal and external motivators and balancing predictive power with interpretability. Findings The findings highlight the need for public policy to strengthen CS in small and medium enterprises (SMEs), as well as a stronger regulatory ecosystem. The study found minimal influence from the business sector and a gap in the role of academia and industry associations. Furthermore, the IT department is the key to identifying risks, while technologies such as cloud services and Radio-Frequency Identification (RFID) influence implementation. Research limitations/implications Although the data were collected in 2018, the findings provide valuable insights into CS adoption. Despite evolving threats, regulation and IT involvement remain critical. Research limitations include potential bias, which may have been exacerbated by increased reliance on digital systems during the COVID-19 pandemic. Future studies should further explore this relationship to better understand how pandemic-driven technological shifts may have influenced bias in CS practices and tools. Practical implications This study helps organizations assess their readiness to adopt CS. It highlights the importance of regulatory frameworks, industry support and the IT department in driving adoption, particularly in SMEs. The findings provide valuable guidance for strengthening CS strategies and aligning them with best practices in regulation and organizational structure. Social implications The study highlights the need for sector-specific initiatives to promote CS adoption and establish security standards across industries. It calls for increased public awareness of the impact of CS breaches on critical infrastructure and for public policies to support CS adoption in SMEs. Originality/Value This study explores the drivers of CS adoption using machine learning (ML) and econometrics for unbalanced data, with a focus on developing regions. It highlights regulatory, technological and structural challenges and advances knowledge of CS motivators in emerging markets.