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Consequences of Implementing Blockchain Technology in Management Accounting: A Grounded Theory Approach.

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    • Abstract:
      Due to the ever-growing importance of transparency and security in financial processes, there is a necessity to explore the impact of blockchain, an influential innovation in the last decade, in the area of management accounting. The current study seeks to investigate the circumstances and factors that affect the effectiveness of blockchain in management accounting. The statistical population comprised international experts and professors in the area of management accounting. To identify the crucial components in the application of blockchain in management accounting, the current study adopted a systematic literature review and semi-structured interviews with a sample of seven international experts. The current 2024 study applied grounded theory and followed the process of expert sampling until theoretical saturation. The findings demonstrated that the current model can be grouped in six overarching categories: 1 core category, 22 causal conditions, 21 contextual conditions, 14 strategies, 24 intervening conditions, and 11 outcomes. The results also indicated that the application of blockchain leads to enhanced efficiency in the performance of organizations through the provision of transparency, cost-saving due to disintermediation, and automation. Additionally, the study results indicated that the application of blockchain enhances data security through encryption. The application of blockchain supports a transformation in the management accountant's role from traditional functions to data analysis and expert advisory roles. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of Journal of Empirical Research in Accounting is the property of Dr. Fakhri Sadat Hosseini, the Vice President for Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)