JOURNAL OF ADMINISTRATION AND ECONOMICS

Life expectancy at birth is a key indicator of a population\'s health, social, and economic conditions, as it reflects the overall quality of healthcare, living standards, nutrition, education, working conditions, and public safety. In this context, the present study aims to measure and analyse the factors affecting life expectancy at birth in Iraq from 2005 to 2023. To achieve this, the Nonlinear Autoregressive Distributed Lag (NARDL) model was employed to capture the asymmetric effects of economic, social, and environmental factors on life expectancy at birth. Moreover, the Bounds Testing approach was applied to verify the existence of a long-run cointegration relationship among the study variables. The results revealed that GDP, unemployment, and inflation exert asymmetric effects on life expectancy, while a positive and significant relationship was found between life expectancy at birth and the age dependency ratio. In contrast, the effects of carbon dioxide emissions and average temperature on the dependent variable were statistically insignificant. The study recommends adopting economic and social policies to reduce unemployment, stabilise prices, and improve living standards, thereby enhancing life expectancy in Iraq.

Quality control is an essential statistical tool for monitoring and analysing the quality of production and service processes. Its primary objective is to detect, as early as possible, any deviations in the process under study. In this research, a simulation approach was employed to study the performance of parametric (MC1, MC2) and nonparametric kernel-based Multivariate Cumulative Sum (MCUSUM) control charts under three statistical distributions: multivariate normal, multivariate gamma, and multivariate chi-square. The efficiency of the two charts was evaluated using the proposed Composite Average Run Length (CARL) criterion across various mean-shift magnitudes. The results revealed that the nonparametric chart based on the kernel estimator was better at detecting small and moderate shifts. In contrast, the parametric chart was slower to identify deviations and process changes. Moreover, a comparison of the three kernel functions (Gaussian, Laplace, and Epanechnikov) showed that the Gaussian kernel was the most efficient at detecting small shifts, followed by the Epanechnikov kernel. In contrast, the Laplace kernel showed the lowest efficiency in this aspect. These findings indicate that both the choice of statistical distribution and the type of kernel function significantly influence chart performance. This emphasises the importance of adopting nonparametric methods when the data do not follow a normal distribution, the true underlying distribution is unknown, or the data follow alternative distributions.

This study aims to examine the Fractional Trigonometric Polynomial Regression Model as a nonlinear statistical model for representing complex relationships between dependent and independent variables. Two estimation methods were used: the Maximum Likelihood (ML) and Bootstrap methods. Simulation experiments with different sample sizes were conducted to compare their performance in parameter estimation. The results showed that the ML method performed better when the variance was low, whereas the Bootstrap method was more accurate when the variance was high. The model was also applied to real data of diabetic patients, and the findings confirmed that the data follow a normal distribution and that the proposed model provides a precise representation of the relationships among variables

The research aims to examine a problem that has recently emerged in society: healthy sleep, as most people, especially university students, experience irregular sleep for several reasons. The researcher used multiple logistic regression to identify factors affecting the quality of sleep among university students, given the categorical nature of the dependent variable (good, average, bad). Data were collected over thirty days using an electronic questionnaire distributed to more than 50 male and female students. Each respondent completed the questionnaire daily, based on their daily reports of sleep quality and psychological stress; monthly averages of these variables were then computed. The data were entered and coded in SPSS, and the analysis results demonstrated the model’s predictive efficiency, as evaluated by the -2 Log Likelihood test, the classification table, the pseudo R-squared statistic, and the ROC curve. The findings indicated that the variables use of a mobile phone before sleep, psychological stress, and caffeine intake were statistically significant predictors of sleep quality. In contrast, other variables — age, gender, sleep duration, physical activity, and sleep environment — were not statistically significant.

This research aims to explore the impact of spiritual marketing on customer well-being by surveying tourists' perceptions in the Iraqi tourism sector, The research problem is encapsulated in the following main question: What role does spiritual marketing play in enhancing customer well-being from the perspective of tourists in the Iraqi tourism sector? The main research hypothesis is that adopting spiritual marketing concepts and practices effectively contributes to achieving higher levels of customer well-being, whether psychological, emotional, or social, A descriptive-analytical approach was adopted, and an electronic questionnaire was used to collect data from a random sample of 102 tourists, The questionnaire was divided into two main dimensions: spiritual marketing (faith, ethics, realism, and humanity) and customer well-being, Statistical analysis using SPSS version 26 revealed a significant and positive correlation between the level of spiritual marketing implementation and customer well-being, The "humanity" dimension showed the strongest impact, while no significant effect of realism on customer well-being was observed, This may be attributed to weak transparency practices or a gap between the promises made by tourism companies and the actual services provided, The research recommends integrating spiritual marketing principles into tourism marketing strategies, particularly given increasing customer awareness of the importance of values and deeper messages in the tourism experience, This integration helps build long-term relationships and enhance the organisation's image.

This study aims to assess the extent of effectiveness of Islamic finance instruments in Saudi commercial banks using a descriptive-analytical methodology, Data were collected through a questionnaire using a five-point Likert scale (1–5), targeting both employees and customers to evaluate the level of utilisation, effectiveness, and customer satisfaction with these instruments, as well as the challenges that limit their optimal application, The results indicate a moderate level of effectiveness, suggesting there is room to further develop, enhance, and refine Islamic finance tools, The study contributes to the literature by combining employee and customer perspectives and employing MANOVA to examine differences across multiple dependent variables simultaneously.

This study aimed to analyse the extent of technological development in electronic banking services in Yemeni banks and to examine the level and indicators of financial inclusion in Yemen. A descriptive methodology was employed, using an inductive approach and a sample of historical data on the study variables for the period 2014–2023. The study reached several conclusions, the most significant of which is that the Yemeni banking sector relies on internet networks and telecommunication services to support its daily operations and provide electronic services. It also adopts electronic solutions to simplify banking processes, enhance operational efficiency, and gradually transition to electronic banking services, thereby increasing individuals’ and businesses’ access to financial services. Furthermore, the study highlighted the important role of the Central Bank of Yemen in keeping pace with the technological development of electronic banking services by working to strengthen the country’s financial infrastructure, facilitating banking services, establishing regulatory and legislative frameworks to ensure a secure and reliable environment for the provision of such services, and making efforts to improve financial inclusion in Yemen.

This research aims to predict temperatures using a time-series methodology, specifically, transformation function models. These models describe the dynamic relationship between input variables (solar radiation) and output variables (temperature). The model was initially built using the original data, and then reconstructed after applying data blurring to reduce noise and improve the quality of the time series. The two models were then compared using the prediction accuracy criteria (MAE and MSE). The results showed that the model after blurring performed better than the model using the original data, as it had lower values for the prediction accuracy criteria and achieved higher short-term accuracy. This finding underscores the importance of preprocessing techniques, such as data blurring, in enhancing the efficiency of statistical models and improving forecasting results in climate applications.

Joint models are advanced statistical methods that enable the integrated analysis of longitudinal and survival data, particularly when there is a structural association between a biomarker's temporal trajectory and the risk of a clinical event. In this context, the present study aims to develop a more efficient estimation of joint model parameters by adopting the Hybrid Genetic Algorithm–Expectation Maximization (HGA–EM) approach, which combines the global search capability of genetic algorithms with the optimization and stabilization properties of the EM algorithm, This approach was applied to clinical data from patients with thalassemia, including three longitudinal biomarkers (HB, MCV, and Urea) measured over three follow-up visits. The results showed that the HB index increased gradually over time (0.0551). Higher HB values were associated with a noticeable reduction in the risk of relapse, as indicated by a negative longitudinal–survival association parameter (−0.0823), The mean MCV was approximately 77.99 with a limited time effect (0.1205), and it demonstrated a slight risk-reducing survival association (−0.0088), In contrast, Urea was the most variable biomarker, increasing over time (0.2835) and showing a clearer association with an increased probability of the event, reflected by a relatively larger negative survival coefficient (−0.02745), Overall, these findings highlight the efficiency of the HGA–EM hybrid approach in achieving more stable and accurate estimates than conventional methods and in precisely capturing the dynamic relationship between longitudinal biomarkers and the risk of clinical events.

The study aims to examine the role of responsible leadership in achieving high-performance requirements. The importance of the study lies in the need to meet high-performance requirements in organisational activities within universities, which serve as platforms and driving forces for developing future capabilities, particularly in private universities that are experiencing substantial growth. The study sought to diagnose the levels of availability of the dimensions of responsible leadership, represented by (the ethical dimension, strategic dimension, economic dimension, environmental dimension, and social dimension), among managers of administrative units in the surveyed universities, and to test the impact of these dimensions on achieving high-performance requirements. The current study adopted the descriptive-analytical approach to examine and diagnose its variables. The study population consisted of administrative unit managers numbering 169, distributed across 13 private universities in the region, with a response rate of (72%). The questionnaire was used as a research instrument to collect field data and was distributed electronically via Google Forms, yielding 122 valid responses for analysis. The data were subjected to statistical methods to assess correlations and effects among the variables, and several statistical tests were applied to diagnose correlations and regression relationships among the study variables. The study reached several conclusions, the most important of which is that achieving high-performance requirements in the surveyed field is associated with responsible leadership among administrative unit managers, suggesting that the likelihood of achieving these requirements may be linked to the availability of responsible leadership dimensions among the study sample. In addition, the dimensions of responsible leadership can help meet high-performance requirements at private universities in the region. The social dimension ranked first among the dimensions of responsible leadership in terms of its strength in achieving high-performance requirements. The study recommended focusing on programs that enhance the availability of responsible leadership dimensions among administrative unit managers to meet high-performance requirements

Autoregressive Moving Average models with Exogenous inputs, denoted as ARMAX, are widely used to describe the dynamic behaviour of time series influenced by external variables. In these models, the (AR) component represents the autoregressive part of the output series, the (MA) component represents the moving average part associated with the random residuals, and the (X) component represents the exogenous component associated with the input series. Thus, these models effectively describe the stochastic and deterministic components of the data.
The parameters of these models can be estimated using both parametric and non-parametric estimation methods. However, parametric methods rely on stringent assumptions to achieve efficient estimation, whereas non-parametric methods do not impose such functional assumptions; hence, they are preferred by many researchers. Among these robust non-parametric techniques are Local Linear Regression (LLR) and Wavelet Estimation (WAVE). In this study, a comparison between these two methods was conducted using simulation experiments. The simulation results revealed a clear preference for the Local Linear Regression method, based on the Root Mean Square Error (RMSE), which yielded the lowest values among the methods.

The problem of drug addiction is considered one of the most dangerous health, social, and psychological issues facing the world.
The research aims to study the effect of some variables on addiction. Factors influencing addiction using the logistic regression model. Cases were classified into addicted and non-addicted based on variations in the level of addiction and the number of times the individual visited the hospital, which represents the dependent variable. The independent variables represent the factors influencing addiction.
The sample consists of 160 cases of both genders and various ages, based on a questionnaire that includes 20 questions about the causes of addiction. The results showed that the most significant factors leading to addiction were chronic diseases, imprisonment or detention, as well as smoking and hookah use. The SPSS statistical software was applied to obtain and analyse the results.

This research aims to develop work methods and approaches to administrative decisions by adopting the DACUM methodology as a foundation for transitioning to modern training programs and their applications in the workplace, These programs require digital literacy and high-performance skills, The research problem stems from the lack of familiarity with modern training concepts within the Baghdad Al-Rusafa Health Directorate, These concepts align with current needs and new global trends, including the DACUM methodology, Furthermore, the directorate lacks a digital infrastructure to enhance digital performance and facilitate digital transformation, The research employed several statistical tools for data analysis, including correlation, multiple linear regression, and a five-point Likert scale, The research methodology was descriptive and analytical, utilizing a questionnaire for data collection after review by experts, The sample consisted of 165 respondents, The researcher concluded that the DACUM methodology demonstrates a significant correlation and impact on improving the digital performance of human resources.