Document Type : Original Article
Authors
1 Phd Student, Department of Public Administration, Kish International Branch, Islamic Azad University, Kish Island, Iran.
2 Assistant Professor, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 Assistant Professor, Faculty of Management, Parand Branch, Islamic Azad University, Parand, Iran
Abstract
Purpose: This study aims to identify and prioritize the factors influencing the establishment of network governance in the banking system, with the goal of addressing inefficiencies arising from traditional hierarchical structures within this system.
Methodology: This applied research employs a descriptive-analytical survey method with a quantitative approach for data collection. The statistical population consists of all employees and managers of Bank Sepah nationwide. Based on Morgan's table, the sample size was estimated at 384 individuals and selected using simple random sampling. The data collection instrument was a researcher-developed questionnaire comprising 8 main factors (economic, managerial, legal, infrastructural, structural, technological, behavioral, and mechanistic) and 31 indicators. Data were analyzed using Structural Equation Modeling (SEM) and factor loading analysis.
Findings: The results indicated that all eight identified factors have a significant effect on network governance at the 95% confidence level. Furthermore, all 31 indicators exhibited high effect coefficients, and the validity of the factors was confirmed through factor loading analysis. The effect coefficients for each factor are as follows, respectively: behavioral (0.365), mechanistic (0.204), infrastructural (0.200), managerial and structural (0.150), technological (0.096), economic (0.088), and legal (0.021).
Conclusion: Based on the obtained coefficients, behavioral factors exert the greatest impact, while legal factors have the least impact on network governance in the banking system. Overall, all eight factors are recognized as determining variables in designing and implementing network governance in the country's banking sector, and special attention to behavioral and mechanistic components is essential for the success of this model.
Value: The novelty of this research lies in presenting a local quantitative model of multidimensional factors affecting network governance within the context of Iran's banking system. By numerically prioritizing these factors, it fills the existing research gap regarding the application of network governance in financial institutions and provides an operational framework for banking managers and policymakers to optimally allocate resources and reform structures.
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