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.