Quarterly Studies in Banking Management and Islamic Banking

Abstract This study examines the effect of modern banking services on the level of bank output in Iran and specifically analyzes the asymmetric nature of these effects within the framework of the NARDL model. For this purpose, panel data from 13 banks during 2016-2023 were used and a composite index of modern banking services was constructed using principal component analysis (PCA). To control for structural heterogeneity of banks and macroeconomic conditions, control variables derived from bank balance sheet characteristics and macroeconomic indicators were also added to the model using principal component analysis in the form of two composite factors. The results of the linear ARDL model show that modern banking services have a positive and significant effect on bank output only in the long run and that their effect is not significant in the short run. The results of the asymmetric NARDL model provide a more accurate and deeper picture of the relationship between modern banking services and the level of output. The long-term findings indicate a significant asymmetry in the impact of new banking services such that an increase in new banking services (+T) increases bank output but that a decrease (-T) causes a sharper and larger decline in output. Based on the results, technology plays a role as a long-term driver of bank output. Negative technology shocks have more severe destructive effects than the benefits from positive shocks, which is consistent with theoretical concepts such as technology stickiness and technological risk and policymakers should prioritize the sustainable maintenance and improvement of technology infrastructure and the prevention of negative shocks.

Abstract Evaluating bank efficiency in high-risk and uncertain environments requires analytical frameworks that can simultaneously account for the internal structure of banking operations and environmental fluctuations. This study proposes an integrated empirical framework for assessing the efficiency of listed and over-the-counter banks in Iran by combining deterministic network data envelopment analysis (NDEA) with its fuzzy extension. Using data covering the period 2018–2023, the efficiency of 16 Iranian listed and OTC banks was estimated through deterministic efficiency scores, as well as the lower bound, upper bound, and mean values of fuzzy efficiency. The results reveal a statistically meaningful gap between deterministic and fuzzy efficiency measures, indicating persistent structural and institutional inefficiencies. These inefficiencies are primarily driven by weak corporate governance, low-quality decision-making, and ineffective risk management, while environmental volatility plays an amplifying role. Substantial heterogeneity in bank efficiency was observed, largely concentrated in key stages of the financial intermediation process. The findings highlight the necessity of institutional reforms and the redesign of supervisory mechanisms to enhance banks’ resilience and performance under conditions of uncertainty

Abstract The proliferation of automated systems and the emergence of a new generation of artificial intelligence, commonly referred to as agentic artificial intelligence, have fundamentally transformed traditional models of decision-making and interaction between banks and their customers. Endowed with capacities for autonomous agency, goal-oriented decision-making, and continuous learning, these systems are playing an increasingly central role in processes such as credit scoring, fraud detection, risk management, and the provision of digital financial services. However, delegating sensitive and rights-affecting decisions to agentic systems has generated novel legal challenges concerning liability, attribution of decisions, procedural transparency, and institutional accountability. Adopting an analytical and legal-regulatory approach, this study examines the implications of deploying automated systems and agentic artificial intelligence for the protection of banking customers’ rights. To this end, it first elucidates the distinctive characteristics of agentic AI and its impact on the architecture of banking decision-making. Next, it analyzes the principal liability-related challenges arising from automated decisions, including ambiguity in the attribution of responsibility, the erosion of traditional fault-based concepts, diminished decision transparency, and constraints on the effective right to contest adverse outcomes. Drawing on a four-layer analytical framework and a review of international regulatory developments and policy instruments, the study identifies the regulatory requirements necessary to safeguard banking customer rights. The findings show that without a focus on institutional bank responsibility, risk-based regulation, process transparency, and meaningful human oversight, existing legal frameworks are unable to effectively respond to the challenges posed by proactive AI. Finally, pathways for developing adaptive and responsible regulation in digital banking are suggested.

Abstract In this study, given the increasing expansion of digital banking and the increasing role of new technologies in improving the efficiency of organizations, the issue of human resource digitization and how artificial intelligence affects this process has been considered. The aim of the present study is to present a model to explain the effects of artificial intelligence on the digitalization of human resources in the banking industry. This study was conducted with a theoretical-analytical approach based on a literature review and an examination of concepts related to digital transformation, artificial intelligence, and human resource management, attempting to identify and explain the dimensions of the impact of this technology in various areas of human resources. The findings of the study show that artificial intelligence plays an important role in the digitalization of human resources by automating human resource processes, facilitating decision-making, increasing accuracy in recruiting and retaining employees, improving training and development, and improving the speed and quality of services. The results also indicate that the use of artificial intelligence in banking can lead to organizational agility and improving employee experience, while increasing productivity and reducing costs. Accordingly, the proposed research model can be used as a framework to better understand the relationship between artificial intelligence and digital transformation in human resource management.

Abstract This study aims to examine the impact of supervisory and regulatory policies on interaction with the international banking system, with financial transparency serving as a mediating role. The research is descriptive-correlational and applied in nature, with the statistical population comprising all employees of banks in Tehran province. The sample size was determined using Cochran’s formula and considering the number of parameters in the structural equation modeling (SEM) framework, ultimately, 384 participants were selected, and questionnaires were distributed and collected. Data were gathered using standardized questionnaires on regulatory policies and regulations (Bart et al., 2013), financial transparency (Chen et al., 2010), and international banking interaction (Buch and Neugbauer, 2017), each covering multiple dimensions assessed via a 5-point Likert scale. The validity of the instruments was confirmed by experts in banking, financial management, and academia, and reliability was established with satisfactory Cronbach’s alpha coefficients. Descriptive statistics were employed to describe demographic characteristics, and SEM analysis was conducted using SPSS version 27 and PLS version 3. The results indicated that regulatory policies and regulations have a significant positive effect on domestic banks’ interaction with the international banking system. Furthermore, financial transparency plays a significant mediating role between regulatory policies and international banking interaction such that increased transparency strengthens the impact of regulatory policies on international interactions. Based on these findings, it is recommended that banking supervisory bodies and policymakers improve regulatory frameworks and enhance financial transparency to facilitate more effective engagement of domestic banks with the international banking system.

Abstract Today's world has witnessed the emergence of a new paradigm in the banking industry, where competitive advantage has shifted from what (product) to how (interaction), and in this space, brand voice as the linguistic manifestation of brand personality plays a crucial role in trust-building. While previous research has focused on the quantitative applications of artificial intelligence in marketing, considering it a tool for quantitative optimizations and aiming to fill this gap, this research presents an innovative, theory-based hybrid model for designing and implementing brand voice using artificial intelligence. To this end, this study used a mixed-methods research design with an explanatory case study approach. In the qualitative phase, the desired identity was extracted by integrating service-dominant logic (S-D Logic) and Aaker's brand personality model. In the quantitative phase, by analyzing 4000 units of textual data extracted from digital communications (including 1200 outbound messages and 2800 interactive comments), along with examining organizational documents and generating 500 simulation scenarios with artificial intelligence, the semantic distance between the current and desired voice was measured. The findings from the quantitative analysis show that the current brand voice of Bank Refah Kargaran has a significant deviation from the desired personality and focuses 85% on the competence dimension. Applying the proposed framework in the simulated environment was able to increase the linguistic empathy index by 220% and significantly reduce the deviation from the brand identity.