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.