journal of Economics And Administrative Sciences

Abstract:
This Paper aims to analyze the impact of relationship marketing dimensions on customer satisfaction in the Jordanian banking sector. It uses a comprehensive measurement model based on confirmatory factor analysis (CFA) and structural equation modeling (SEM). The study's problem stems from the need to understand how relationship-marketing practices affect customer satisfaction in rapidly changing and highly competitive banking environment. The research relied on a field survey of 677 Jordanian bank customers. A standardized questionnaire measured the seven dimensions of relationship marketing and customer satisfaction. The results showed statistically significant positive effects for all dimensions on customer satisfaction. Customer bonding strengthening was the most influential dimension (β = 0.412), followed by commitment (β = 0.359), and then promise fulfillment (β = 0.343). The other dimensions also had positive effects. These results highlight the importance of relationship-based marketing practices in enhancing customer satisfaction and retention. The study underlines the need for an advanced analytical framework to help Jordanian banks focus on factors with a greatest impact, such us customer bonding, organizational commitment, and promise credibility. The study also suggests intensifying programs that support long-term relationships with customers. This approach can increase customer loyalty and improve the competitiveness of banks in the Jordanian market.

Abstract
This research aims to analyse the impact of economic and energy variables on carbon emissions in Germany during the period (1994–2024), within the framework of environmental economic modelling based on artificial intelligence techniques. The study is based on the hypothesis that the nature of the energy structure and the level of economic activity contribute to explaining changes in carbon dioxide emissions to varying degrees. To verify this hypothesis, an artificial neural network model was adopted to analyse the relationship between a set of independent variables represented by total energy consumption, total energy production, the proportion of renewable energy in total primary energy, GDP, and environmental taxes, and the dependent variable represented by carbon dioxide emissions. The analysis showed that total energy production and GDP are the two most influential variables in explaining changes in carbon emissions, accounting for a high percentage of the model's explanatory power. The results also showed an average effect of the renewable energy ratio. While the effect of environmental taxes was relatively limited, the effect of energy consumption appeared to be weaker than that of the other variables. The study concludes that promoting the transition to renewable energy and developing more effective environmental policies are key to reducing carbon emissions and advancing environmental sustainability.

Abstract
The electrocardiogram (ECG) signal is one of the primary biological signals used in the diagnosis of cardiac arrhythmias. However, conventional analysis and classification methods are often limited by the nonlinear and non-stationary nature of ECG signals. This research aims to evaluate the performance of a one-dimensional convolutional neural network (1D-CNN) and hybrid models that integrate Hilbert–Huang Transform (HHT)-based features with 1D-CNN for ECG signal classification. Five standard diagnostic categories (N, S, V, Q, and F) were considered. A comparative framework was developed, including a standalone 1D-CNN model and three hybrid models based on different HHT-derived features. Model performance was assessed using confusion matrices, ROC-AUC curves, and key statistical metrics, including accuracy, precision, recall, and F1-score. The results indicate that the standalone 1D-CNN model achieved the highest classification performance when applied to raw ECG signals. Among the hybrid models, the Mean_E + 1D-CNN model demonstrated superior performance compared to the other feature-based models. These findings suggest that while HHT features provide valuable time–frequency interpretability, direct learning from raw signals using deep learning models remains more effective for ECG classification.

Abstract
This research examines the dynamics and forecasting performance of currency in circulation (CIC) in Iraq. It uses a theoretical framework that recognizes long-run equilibrium relationships, short-run adjustment, volatility clustering, and nonlinear patterns. These complexities limit the adequacy of traditional linear models and motivate hybrid approaches. Accordingly, a triple hybrid model is employed. This combines autoregressive distributed lag (ARDL), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) with Generalized Error Distribution (GED) to capture heavy-tailed behavior, and Bidirectional Gated Recurrent Unit (BIGRU). Stationarity is assessed using ADF tests. Residual diagnostic tests confirm heteroskedasticity, thereby justifying the inclusion of a GARCH component. The proposed model is compared with ARDL–GARCH and ARDL–BIGRU benchmarks. Empirical results based on RMSE, MAE, and MAPE demonstrate the statistically superior out-of-sample forecasting performance of the triple hybrid model. Bank deposits negatively affect CIC by absorbing liquidity, while withdrawals positively impact CIC, reflecting the persistence of cash-based transactions. These findings highlight the importance of advanced hybrid models for monetary policy and liquidity management in rent-based economies. Future research should include macroeconomic variables and examine the possible impact of Central Bank Digital Currencies (CBDC).

Abstract
This study examines the impact of forensic accounting techniques, challenges, and accountants' skills on fraud detection effectiveness in the private sector of Sulaymaniyah City, Kurdistan Region of Iraq. The study employed a quantitative research design, with preliminary semi-structured interviews conducted to inform questionnaire development. In the qualitative phase, purposive sampling was used to interview eight forensic accountants with experience handling multiple fraud cases in Sulaymaniyah. Insights from these interviews guided the creation of a structured questionnaire, which was subsequently distributed to Seventy-four accounting professionals from private banks and merchandising firms for the main data collection. Data were analysed using SPSS Statistics 23, applying Cronbach's alpha to assess reliability, factor loading to validate constructs, and linear regression to test the hypotheses. The results showed that forensic accounting techniques, challenges, and professional skills each had a statistically significant positive effect on fraud detection effectiveness (R2 = 0.646, *p* < .001). Among these predictors, techniques had the strongest influence (β = .424), followed by challenges (β = .270) and skills (β = 0.226). The positive effect of challenges suggests that systemic barriers may prompt organizations to strengthen internal controls and adopt adaptive practices. The study underscores the need for structured forensic procedures, ongoing skill development, and context-specific approaches to strengthen fraud detection in transitional economies. These findings contribute to the limited literature on forensic accounting in the Kurdistan Region and underscore the need for future research on integrating advanced digital forensic tools alongside traditional practices to strengthen financial system resilience.