The Impact of Strategic Intelligence on the Quality of Decision-Making: An Empirical Study at the Ministry of Endowments and Religious Affairs

Abstract

The study aimed to examine the impact of strategic intelligence practices on decision-taking quality within the Ministry of Endowments and Religious Affairs in the southern governorates. To achieve the study’s objectives, the descriptive-analytical method was adopted. The study population consisted of all employees in supervisory positions, totaling (139) employees. A comprehensive survey approach was utilized, distributing questionnaires to all members of the study population. A total of (107) valid questionnaires were retrieved for statistical analysis, yielding a response rate of (77%). The Statistical Package for the Social Sciences (SPSS) was used to analyze the collected data. The most important findings of the study are: The level of strategic intelligence was highly rated, whereas Decision-Taking quality received a moderate rating. There is strong positive and statistically significant relationship between the dimensions of strategic intelligence and decision-taking quality. The findings indicated that strategic intelligence had a statistically significant impact on decision-taking quality, explaining (75.2%) of the variance in decision- taking quality. However, (the foresight and motivation dimensions) showed no significant impact. There are no significant differences in respondents' perceptions of (strategic intelligence and decision-taking quality) based on gender, academic qualifications, or years of service. However, significant differences were found based due to job title, in favoring of (Director General and above). The main recommendations of the study: Activating the future foresight system through the adoption of data analysis tools and trend identification, and organizing specialized training programs to develop employees’ capabilities in analyzing future scenarios, which would enable the ministry to confront unexpected challenges. It is also necessary to enhance the use of quantitative methods such as statistical models, data analysis, and forecasting tools to support evidence-based decision-making characterized by accuracy and objectivity.