Digital Transformation and Administration Innovation In Press 2026 09 01 Identification and Ranking of Criteria and Sub-Criteria for Digital Asset Valuation Using Factor Analysis and Multi-Criteria Decision-Making Identification and Ranking of Criteria and Sub-Criteria for Digital Asset Valuation Using Factor Analysis and Multi-Criteria Decision-Making 1 17 EN Journal Article 2026 01 06 <p>The present study aimed to identify and rank the criteria and sub-criteria for digital asset valuation using factor analysis and multi-criteria decision-making techniques. In terms of purpose, this study is classified as applied research, while in terms of nature and methodology, it falls within the category of descriptive-correlational research. The statistical population consisted of capital market experts as well as investors in cryptocurrency markets. A total of 154 participants were selected through a non-probability purposive convenience sampling method. It should be noted that, for the confirmatory factor analysis section, the sample size was determined using G*Power software. Furthermore, for the multi-criteria decision-making section, 15 individuals were selected as the sample based on the theoretical foundations of multi-criteria decision-making techniques. Smart PLS version 4 software was employed for confirmatory factor analysis, while Microsoft Excel was used for the multi-criteria decision-making section. Based on the review of the theoretical foundations, four criteria were identified, including identification of digital assets, recognition of digital assets, structural supervision of digital assets, and institutional supervision of digital assets. These criteria comprised twenty-five sub-criteria. The results of the confirmatory factor analysis indicated that the constructs related to determining impairment losses of digital assets exceeding their recoverable amount relative to their carrying amount, internal supervision and controls concerning the accounting information system for the timely disclosure of digital assets, and the evaluation of digital asset recognition according to Paragraph 3 of Accounting Standard No. 8 concerning inventories and goods, exhibited weak factor loadings. Consequently, these three sub-criteria were removed from the model. The Shannon entropy method was employed to rank the main criteria, and the findings revealed that institutional supervision of digital assets was identified as the most important criterion. The WASPAS method was used to rank the sub-criteria, and the results demonstrated that the sub-criteria of estimating cryptocurrency volatility based on market value at the time of digital asset identification, evaluating the recognition of digital assets according to compliance with Accounting Standard No. 15 regarding investments, and internal supervision and controls based on assurance testing for the calculation of the fair value of digital assets ranked first through third, respectively. Based on the obtained findings, it is suggested that the greatest attention in digital asset valuation should be devoted to the criterion of institutional supervision of digital assets.</p> https://journaldtai.com/index.php/jdtai/article/download/263/248