Exploring TAM and DOI Constructs as Predictors of Cybersecurity Data Analytics System Success

Organizations rely on data analytics systems to collect, analyze, and visualize patterns and trends that provide insights into effective and efficient resource allocation and innovation. Conversely, securing organizations using cybersecurity data analytics systems to detect anomalies, patterns, and trends indicative of malicious activities is also necessary. While cybersecurity threats are increasingly sophisticated, so are cybersecurity data analytics systems used to counteract advanced persistent threats. Incorporating innovative technologies such as artificial technologies and blockchain is increasingly considered part of the implementation of cybersecurity data analytics systems. This study aims to fill the gap by providing an empirical investigation of the success factors of cybersecurity data analytics systems implementations. This study integrated the technology acceptance and diffusion of innovation theories as lenses for investigating the success of cybersecurity data analytics systems. This study examines the impact of Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Compatibility (COMP), and Trialability (TRI) on the success of cybersecurity data analytics systems (CDASS) within United States (US) government organizations. Data from cybersecurity and information technology professionals were analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The results indicate that PEOU, PU, COMP, and TRI significantly impact system success. These findings underscore the importance of acceptance and diffusion factors when implementing CDAS to improve the likelihood of success within US government agencies.