A Model of Factors Influencing the Implementation of Artificial Intelligent in Crisis Management: A Case Study of National Crisis and Emergency Management Authority (NCEMA)

Abstract

This paper outlines the development of a structural equation model focusing on factors influencing the implementation of AI in crisis management within the UAE National Crisis and Emergency Management Authority. Literature has identified 28 factors which are categorized into seven domains that influencing the implementation of AI in crisis management for the model. The model was constructed and evaluated using SmartPLS software. The model was evaluated at its measurement and structural components. The results revealed that at the measurement component, the model met all evaluation criteria. While, at the structural component, the relationship between 'CoV' and 'CrM' was statistically significant (T-statistic = 2.633, P-value = 0.009), indicating a robust connection. However, the links between 'ReF' and 'CrM' and 'LSM' and 'CrM' were not statistically significant (P-values = 0.999 and 0.949, respectively), suggesting limited impact on 'CrM.' Relationships between 'RoB,' 'IoT,' 'DeL,' and 'NLP' with 'CrM' showed moderate evidence but lacked statistical significance, possibly due to data limitations. Furthermore, the model demonstrated a strong fit, with an R-squared (R²) value of 0.761, explaining approximately 76.1% of the variance in "CrM" with the seven independent variables. Lastly, for predictive relevance, the "CrM" as a dependent construct displayed a Q² value of 0.608, indicating that around 60.8% of the variation in "CrM" is explained by the model beyond random chance, confirming its strong predictive value.