JOURNAL OF ADMINISTRATION AND ECONOMICS

This study aimed to diagnose individual motivation to adopt artificial intelligence (AI) and its role in entrepreneurial decision-making among IT professionals in Duh Governor ate. The research was grounded in the Expectancy-Value Theory, considering AI adoption as the independent variable with four main dimensions: achievement value, utility value, intrinsic value, and cost. Entrepreneurial decision-making was the dependent variable, encompassing three thinking styles: intuitive, analytical, and adaptive.
Employing a descriptive-analytical methodology, the study utilised a structured questionnaire to collect data from IT professionals operating private offices and IT specialists engaged in business activities within Duhok Governorate. A total of 90 valid responses were analysed. The central research question addressed whether the expected value of AI adoption significantly influences entrepreneurial decision-making among IT professionals in the region. To explore this, the study formulated hypotheses examining the relationships between variables and developed a hypothetical model reflecting these connections. Statistical tools, including Spearman correlation, simple regression analysis, Cronbach's alpha for reliability, and normal distribution tests, were applied using SPSS V.26.
The findings revealed a significant positive correlation and impact between the dimensions of expected value from AI adoption and entrepreneurial decision-making. Based on these results, the study recommended enhancing the use of AI in entrepreneurial environments and proposed future research directions to examine these relationships across different sectors.

The primary objective of this study was to utilise the Bayesian method and maximum likelihood estimation in Poisson regression to model the incidence of lung cancer in Erbil City, Iraq. Poisson regression is commonly used to analyse count data, which makes it suitable for analysing disease rates in medical data. The study compares the performance of the maximum likelihood method with the Bayesian method, which incorporates a prior distribution in the count for parameter estimation. The data set used in this study, which includes cases of lung cancer with potential risk factors, was obtained from Rizgari Hospital in Erbil City.
The Bayesian estimation employs the Markov Chain Monte Carlo technique to generate a posterior distribution. Both processes are evaluated for effectiveness, and goodness of fit is assessed to determine model performance.
The results indicated that both methods effectively identified significant factors of lung cancer and have approximately reached the same factors that have an impact on lung cancer data in Erbil city (according to the MLE method, the most critical factors influencing lung cancer disease are (Grade, Laterality, Surgery, Chemotherapy, Hormone, Isotope, and Family History). For the Bayesian methods, the factors influencing lung cancer in our data set are (Grade, Laterality, Surgery, Chemotherapy, Radio, Hormone, Isotope, and Family History). Bayesian methods give better performance and provide more interpretable quantifications. The results were obtained by using the statistical packages (SPSS v.26, Stata, and R).

The research aims to identify the role of external governance mechanisms in enhancing oversight work inside and outside the research sample company by improving compliance with the implementation of instructions in various operations. Weaknesses in oversight work were also addressed by enhancing the effectiveness of external governance mechanisms within the research sample. The inductive approach was employed to complete the theoretical aspect and apply the quantitative approach in the practical element, utilising various statistical analyses of the research-designed questionnaire data, which included multiple questions directed at the sample. The research reached a set of conclusions, the most important of which are (weak experience in the field of control and auditing in the research sample, The highest percentage of respondents were those with less than five years of experience in that field (65.4%), and the title of auditor had the lowest rate of job titles in the sample (18.3%), in addition to the low use of modern technology in audit work, which may affect the improvement of audit performance. The research concluded with a set of recommendations, the most important of which are: There should be a balance in the administrative organisational structure of the research sample by working to increase specialisations for audit and control job titles, in addition to the necessity of designing electronic programs specialised in control work, and that all control cadres be trained on how to use these programs.

The study uses the Lindley distribution to estimate parametric survival models using leukemia survival data in Erbil City, Iraq. We examine two estimation methods for modelling time-to-event data: the Maximum Likelihood estimate (MLE) and the Method of Moments (MoM). The MLE approach produces asymptotically efficient estimates, whereas using sample moments, the MoM provides a more straightforward, non-iterative approach. The goodness of fit tests, Akaike Information Criteria (AIC), Bayesian Information Criterion (BIC) and Mean Square Error (MSE) are used to assess model performance.
The results show that MLE outperforms MoM in terms of precision and resilience, especially with censored data. However, the findings demonstrate the relevance of the Lindley distribution in medical survival analysis and provide insight into leukemia survival patterns in Erbil City.
This study adds to the growing literature on parametric survival modelling and helps clinical decision-making for leukemia therapies. The data set of this study was obtained from Rizgari Hospital in Erbil city, the results obtained by utilising the statistical packages (MATLAB V. 23.2 and R Software V. 2025.05.1+513).

The research aims to determine the impact of the smart supply chain, with its four dimensions (equipped with devices, interconnected, collaborative, and flexible), on enhancing sustainable performance in its three dimensions (economic, environmental, and social). The research was applied in the General Company for Electrical and Electronic Industries as a study population, and a random sample of 285 individuals was selected, with data analysis and extraction of results. The researchers employed a descriptive analytical approach. Ready-made programs (SPSS V. 28), (AMOS V. 26), (Microsoft Excel V.2010) were used to analyses the data using statistical tools such as the weighted arithmetic mean, standard deviation, coefficient of variation, correlation coefficient (Pearson), regression coefficient, and (T) and (F) tests. The results revealed a statistically significant correlation between the smart supply chain and sustainable performance, confirming the importance of transitioning towards a smart supply chain as a means of enhancing the sustainable performance of institutions. Several conclusions were drawn from the results, the most important of which is that organizations that adopt the smart supply chain are more likely to build sustainable collaborative partnerships and foster a culture of corporate innovation, which is a critical factor in achieving long-term sustainability and competitiveness goals.

Time series are considered an essential statistical topic, especially financial series that are characterised by high volatility, which means that the conditional variance in these series is not constant and depends on previous data. One of the most important models used in modelling financial series is the autoregressive model conditional on heteroscedasticity. These models have been adopted in previous studies. This research presents a survey of asymmetric GARCH models. The STGARCH model was employed as an autoregressive conditional heteroscedasticity model with a smoothed transition. The model was offered with stability conditions and the definition of logistic and exponential transition functions. The model parameters were estimated and compared with the GJR-GARCH model. The research concluded, based on the comparison criteria, that the STGARCH model was the most effective in representing the data, as a time series was used that defines the closing prices of the shares in the National Bank of Greece index.

The present study aims to examine the impact of job stability, represented by its dimensions (job satisfaction, salaries and incentives, and job security), on supporting human resource capabilities, which include (knowledge, skills, attitudes, and job practice), within the General Directorates of Zakho and Soran in the Kurdistan Region of Iraq as the field of the current research. The research problem is identified in addressing the central question: Does job stability play a role in supporting human resource capabilities? The significance of the study lies in highlighting the role of job stability in enhancing human resource capabilities, which in turn contributes to achieving more efficient and effective performance. The research employed a descriptive-analytical method and used a questionnaire as the primary tool for data collection from a sample of 352 administrative unit managers in the targeted directorates. The research hypotheses were tested using the (AMOS V.24) software package. The findings revealed that job stability has a direct impact on supporting human resource capabilities by providing a work environment characterised by stability and reassurance, which reflects positively on workplace interaction, organisational commitment, and institutional performance improvement. Based on these conclusions, the researchers propose several recommendations, the most important of which emphasise the need to enhance job stability through employee care and the creation of a safe work environment, to strengthen employees’ sense of trust and security, reduce psychological stress, and ultimately support human resource capabilities.

The private universities in Erbil face rapid transformations and increasing challenges within higher education institutions, necessitating the adoption of modern leadership styles characterised by flexibility, quick responsiveness, and performance quality. The importance of this study stems from the need for these universities to enhance their competitive capabilities by adopting leadership approaches based on humility, wisdom, and continuous improvement—core dimensions of agile leadership. The study employed a descriptive-analytical method and distributed a questionnaire to a sample of 150 administrative leaders from private universities in Erbil. The results revealed a high level of practising agile leadership among the leaders, alongside a significant correlation and impact between the dimensions of agile leadership and organisational excellence in these universities. The findings confirmed that these universities possess a solid foundation in leadership and managerial practices; however, there are clear opportunities to enhance leadership wisdom, promote staff and student participation in decision-making, and develop knowledge management mechanisms, thereby raising the levels of organisational excellence. The study concluded that adopting agile leadership patterns effectively contributes to improving performance, increasing adaptability to changes, and building a positive organisational culture that supports organisational sustainability. The study recommended firmly establishing agile leadership principles within the private university environment, developing specialized training programs aimed at building leadership capacities, and creating a flexible administrative environment that embraces change and supports sustainable development in the long term.

The current research aims to determine the role of electronic human resources management in achieving organisational excellence through a survey of the research community, comprising all employees of the Ministry of Higher Education and Scientific Research in Iraq. The research sample was randomly selected to represent the community, comprising 156 employees, enabling the collection of accurate and reliable data reflecting employees’ opinions on the variables under study. The research was based on the central problem represented by the question (What is the role of electronic human resources management in achieving organizational excellence in the Iraqi Ministry of Higher Education and Scientific Research?), and from it emerged a group of sub-questions aimed at clarifying the theoretical foundations and intellectual propositions related to these variables, as they are among the vital topics imposed by contemporary reality. To achieve the research objectives, the questionnaire was used as the primary tool for collecting data. After verifying the scale's validity, a set of statistical methods was used to test the research hypotheses, including the arithmetic mean, standard deviation, Pearson's correlation coefficient, and the simple linear regression test. The study reached many conclusions, the most prominent of which is (the weak level of interest in the dimension of leadership excellence from the point of view of sample members, due to its connection to key aspects including weak communication channels, resistance to change, and a lack of training in modern leadership skills, in addition to the absence of effective mechanisms for evaluating performance and motivating employees). At the proposal level, the study proposes adopting strategic leadership programs that focus on developing modern leadership skills and building effective communication channels, thereby enhancing leadership excellence within the ministry and supporting its efforts to achieve organisational goals efficiently and effectively.

This research aims to integrate innovative manufacturing technologies (sensors, the Internet of Things, artificial intelligence, etc.) into manufacturing activities (planning, design, scheduling, monitoring, control, and quality) to develop more efficient manufacturing processes. The research's significance lies in providing a comprehensive overview of innovative manufacturing activities and identifying the technologies used in each activity. To achieve this objective, a 51-item questionnaire was designed as a data collection tool for analysis and to determine the role of innovative manufacturing activities in promoting sustainability. The research employed a descriptive-analytical approach, which facilitates a comprehensive analysis of the research problem. Baghdad Soft Drinks Company was selected for the study's practical application, and the sample comprised 171 employees, including senior management, factory managers, engineers, technicians, and machine operators. The research concluded that the company's management has a weak understanding of the concept of smart manufacturing, its activities, and the technologies applied within them. Therefore, the research recommends that the company's management adopt modern manufacturing concepts supported by the technologies of the Industrial Revolution 4.0.

This research examines how emotional intelligence (EI) and ethical leadership (EL) influence employees’ mental health (EMH) and how EMH contributes to sustainable development (SD) in organisational settings. It also investigates the moderating role of organisational culture (OC) in strengthening the relationship between mental health and sustainability. A quantitative, cross-sectional survey design was employed, targeting branch-level employees (Customer Service Officers, Bank Tellers, Loan Officers, Credit Officers, and Relationship Officers) in Iraqi private banks. A total of 298 responses were collected using validated measurement scales. Data were analysed using SPSS (Version 28) with multiple and hierarchical regression techniques to test the hypothesised relationships. The results indicate that EI significantly enhances EMH, emphasising the role of self-awareness, self-regulation, and social awareness in fostering psychological and emotional well-being and effective stress management. Conversely, EL did not show a significant direct effect on mental health, suggesting that fairness, integrity, and ethical guidance alone may not directly influence employee well-being in high-pressure work environments. EMH significantly contributed to SD, and OC positively moderated this relationship, highlighting the role of involvement, consistency, adaptability, and mission in strengthening sustainability outcomes. This study contributes to organizational behavior and sustainability literature by validating the mental health–sustainability link in a developing country financial sector. The findings indicate that the widely held assumption that EL consistently enhances mental health may not always hold true, and extend existing frameworks by highlighting the moderating role of OC.

Multi-level regression models provide a robust framework for analysing hierarchical data characterised by nested structures and mixed effects. These data are commonly available in many fields, including education, social sciences, and others. This research aims to compare the performance of classical regression models and robust multilevel regression models in analysing educational data, and to identify the most effective ones for addressing statistical challenges such as nested data, non-normal distributions, and outliers. A multi-level hierarchical regression model was constructed by conducting exploratory and confirmatory analyses of the latent variables, based on which the hierarchical model was developed across three levels: student level (academic achievement), professor level (incentives, teaching quality), and university level (educational programs). The hypothesised model was estimated using two methods: the restricted maximum likelihood method and the robust design adaptive scale method. The study examines the impact of incentives, teaching quality, and academic programs on student achievement in Iraqi universities. Both methods were applied to data taken from a sample of Iraqi universities, comprising 540 responses. The results showed that the robustly estimated model outperformed the classical method, reducing the impact of outliers and providing more accurate estimates. It was also concluded that developing academic programs and incentives, along with improving the quality of teaching, are key drivers of raising educational achievement and reducing disparities between universities.

This study investigates and compares estimators of the Weibull distribution parameters for fuzzy data derived from survival times of patients with liver cirrhosis using the Maximum likelihood and Bayesian methods. The fuzzy data were converted to conventional data using a trigonometric function. The results demonstrated that the Bayesian method outperformed the Likelihood method, yielding more accurate and stable estimates than the probability method. The results showed that the Bayesian method outperformed the probability method as the cutoff level increased, while the maximum likelihood method fluctuated, achieving its best results at a cutoff level of 0.3.