Digital transformation in the Central Bank of the Islamic Republic of Iran (with a focus on identifying knowledge areas and indicators)

Document Type : Original Article

Authors

1 PhD student in Information and Knowledge Management, Faculty of Management and Economics, Tarbiat Modares University,Tehran, Iran

2 Department of Management & Economics, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

3 Assistant Professor, Department of Information Science and Knowledge, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

4 Postdoctoral Research in Data-Driven Digital Transformation, Faculty of Management and Economics, Tarbiat Modares University,Tehran, Iran

Abstract

Objective: Rapid advancements in emerging technologies have triggered profound societal changes. Consequently, banking and credit institutions, alongside the Central Bank, have undergone significant technological transformations. Leveraging these technologies offers a solution to existing challenges and creates opportunities to improve services within the Central Bank. This article aims to identify the knowledge areas and indicators of digital transformation within the Central Bank of the Islamic Republic of Iran to establish a systematic framework for digital transformation in the Central Bank and other financial institutions.
Method: This study employs a two-fold approach: a systematic literature review to identify indicators of digital transformation, and the Delphi method to refine these findings through expert consultation. The identified components and indicators were presented in a questionnaire to a panel of 24 experts—comprising managers, researchers, and faculty members—who possessed both relevant academic backgrounds (at least a bachelor’s degree) and significant professional experience (minimum five years). Ultimately, 15 subject matter experts, selected via purposive sampling, refined and validated the initial indicators. During this process, the relative importance of each element was determined using a 5-point Likert scale. A consensus was reached among the Delphi panel, and the Kendall correlation coefficient was calculated to measure the level of inter-rater agreement and determine the termination point of the survey rounds.
Findings: Initial qualitative analysis identified 16 components and 101 indicators. Following the Delphi process, these were refined into 14 main components and 51 indicators. The results indicate that the most significant factors for the Central Bank are: intelligentizing data collection and statistical analysis (mean: 4.93), management of analytical tools for opportunity identification (mean: 4.66), strategic IT architecture management (mean: 4.66), and intelligentizing business processes to prevent fraud and illicit transactions (mean: 4.60).
Conclusion: Through the synthesis of the study's findings, a conceptual model for the digital transformation of the Central Bank was developed. This model can serve as a foundation for future research in designing banking transformation frameworks. Furthermore, identifying these core knowledge areas acts as an enabler for the Central Bank, fostering greater dynamism, agility, flexibility, and innovation.
Contribution to Knowledge: This research underscores the importance of the Central Bank’s commitment to adopting transformative technologies, which is critical for enhancing the overall financial performance of banking and credit institutions.

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References

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Sciences and Techniques of Information Management
Volume 12, Issue 1
March 2026
Pages 61-83

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