International Journal of Metaverse & Virtual Transformation

Abstract This study develops a framework for integrating the Metaverse into the banking industry to enhance customer experience, improve efficiency, and support digital transformation. With the growth of the Fourth Industrial Revolution and the advancement of digital technologies, banks must adopt innovative tools to remain competitive and deliver superior services.
A mixed-method design was applied, combining qualitative meta-synthesis and quantitative analysis. From 200 studies on Metaverse in banking, 35 were selected for detailed review. In the qualitative stage, MAXQDA 2020 enabled initial, axial, and pattern coding to identify major themes. The Fuzzy Delphi Method was then conducted with 15 experts in banking technology and information systems across two rounds to refine and validate indicators. In the final stage, a structured questionnaire was administered to 300 banking professionals, and the data were analyzed using descriptive statistics and structural equation modeling (SEM).
Findings validated 98 indicators organized into nine categories: customer experience, digital services, security and privacy, infrastructure and technology, marketing and branding, human resource development, data analytics, applied technologies, and operational features. Together, these categories form a structured roadmap for applying Metaverse solutions in banking, balancing customer-centered innovation with internal process optimization. The study concludes that successful Metaverse adoption in banking depends on strategic alignment, governance, and active institutional participation in digital ecosystems. The proposed framework offers practical guidance for leveraging Metaverse opportunities to improve efficiency, strengthen customer engagement, and accelerate digital transformation in banking.

Abstract The increasing development of management information systems provides the ability to use customer data in the form of large databases. Generally, many effective marketing insights are hidden under customer characteristics and their purchasing patterns, and knowledge-based marketing management can help to reveal them. Recent emphasis on customer relationship management has made the marketing function an ideal application area for analyzing customer data.Therefore, considering this sense of need, the general objective of the present study, namely the relationship between knowledge management and customer relationship management, was examined. According to the results of the study, knowledge management has a positive and significant effect on customer relationship management. This relationship is 1.88 percent, and a positive value indicates that the effect is positive and direct. Considering the multiple correlation coefficient, it can be said that the customer relationship management variable is explained by knowledge management. Finally, considering the relationships in the model, research suggestions were presented.

Abstract The study aims to assess and rank smart supply chain risks in the electricity industry by incorporating digital transformation technologies into a multi-criteria decision-making framework. The research is developmental–applied in nature and adopts a descriptive–survey design using a mixed-method approach. In the qualitative phase, semi-structured interviews with experts from the electricity industry were analyzed through thematic analysis to identify the principal supply chain risk criteria and strategic mitigation approaches. The analysis resulted in nine evaluation criteria: probability of supply disruption, severity of disruption impact, supply chain resilience, system recovery time, supply reliability, supply chain flexibility, total supply chain cost, economic efficiency of supply, and risk management cost. Three strategic responses were also identified: strengthening supply chain resilience, digitalizing and intelligently monitoring supply chain processes, and localizing and diversifying supply sources. In the quantitative phase, the Step-wise Weight Assessment Ratio Analysis (SWARA) method was employed to determine the relative importance of the identified criteria. The findings revealed that probability of supply disruption (0.232), severity of disruption impact (0.176), and supply chain resilience (0.136) were the highest-priority risk factors, followed by system recovery time, supply reliability, and supply chain flexibility. The proposed framework supports data-driven risk prioritization and demonstrates how digital transformation technologies can improve supply chain resilience, proactive risk management, and strategic decision-making in the electricity industry, thereby contributing to the development of more intelligent and sustainable digital supply chain ecosystems.

Abstract The aim of the study is to fit a public health index model based on smart data with an emphasis on population aging and retirement age in Iran. The study examines the fitting of the public health index model (life expectancy) considering retirement age and population aging in Iran during the years 1990 to 2024 using the quantile method (quartile regression). The present study is in the category of post-event research from the perspective of applied purpose and analytical-descriptive nature. First, reliability tests (PP) were conducted on the variables, which showed that the variables were stationary at the level of one after one order of difference. The results of the cointegration test also indicate the existence of at least one cointegration vector between the variables under study. The results of the quantile model estimation show that the retirement index has a negative and significant effect on the life expectancy index (health index) in the lower quartiles (25Q and 50Q), and the intensity of this negative effect increases as it moves towards the higher quartiles (75Q and 95Q). Also, factors such as public health expenditures, private health sector expenditures, capital formation, and literacy rate have a positive and significant effect on life expectancy, while the real exchange rate, inflation rate, and liquidity volume have a negative and significant effect on it. The impact of the retirement index, inflation rate, liquidity volume, and real exchange rate on the health index increases in the higher quartiles (75Q and 95Q).

Abstract This study aimed to develop and empirically examine an integrated model linking psychological factors, digital empowerment, and internal audit quality in organizations operating in environments shaped by emerging technologies. The research was applied in purpose and employed a descriptive–analytical, cross-sectional field design. In the qualitative phase, the fuzzy Delphi technique was used to identify and refine the components of the proposed model. Purposive sampling was followed by theoretical sampling until data saturation was achieved. After reviewing the 28 experts’ opinions, four additional specialists were included to address conceptual gaps. The three-round fuzzy Delphi process showed that all identified indicators exceeded the acceptance threshold and were retained in the final model. The proposed framework comprised sense of worth, autonomy, perceived impact, trust and psychological security, independence, integrity, confidentiality, objectivity, professional competence and due care, and idealism. In the quantitative phase, robust structural equation modeling with resampling and quantile structural equation modeling were employed to examine the associations among the proposed constructs. Gender, membership in the Iranian Association of Certified Public Accountants, education level, and work experience were included as control variables. The findings revealed statistically significant associations between education level, work experience, digital empowerment, and internal audit quality. Furthermore, the quantile structural equation modeling results indicated that the strength of these associations varied across different levels of internal audit quality, suggesting heterogeneous relationships among the study variables. Model assessment based on SRMR and NFI indicated an acceptable overall model fit, supporting the adequacy of the proposed conceptual framework.

Abstract This study investigates the threshold effects of digital innovation on the interaction between credit risk, ICT, and monetary policy effectiveness in Iran, using seasonal data covering the period 2009 to 2024. Monetary policy effectiveness is proxied by the ratio of loan balances to deposits. A robust digital innovation index is constructed via principal component analysis, incorporating key indicators such as bank account density, ATM usage, and the growing value of internet and mobile financial transactions. The empirical model incorporates several control variables, including credit risk, bank loan growth, bank size, inflation, short-term interest rates, ICT development indices, and return on assets. Preliminary diagnostic analysis, utilizing the Phillips–Perron stationarity and Johansen cointegration tests, confirms that all variables are integrated of order one and maintain long-term cointegration relationships. To specifically analyze nonlinear behavior and threshold effects, a smooth transition regression model with a logistic transition function (LSTR) is employed. Linearity tests identify the digital innovation index as the optimal transition variable, favoring the LSTR1 specification. Empirical findings demonstrate that in the nonlinear regime, digital innovation exerts a positive and statistically significant effect on monetary policy effectiveness, indicating that higher levels of innovative financial activity improve policy performance in Iran. Conversely, credit risk shows a significant negative impact; rising non-performing loans effectively weaken the monetary policy transmission mechanism. This research underscores how digital transformation can mitigate structural barriers to policy efficacy, offering crucial insights for policymakers aiming to enhance economic stability through technological integration.