Combining the MDM and BWM Algorithm to Determine the Optimal Crowdfunding Tokenization Solution for Digital Assets Market Startups

Abstract

This study aimed to use the modified Delphi method and best worst method to establish an evaluation model for analyzing the perspectives and key influencing factors used in evaluating startups’ optimal token-financing solutions. In accordance with the modified Delphi method, a list of influencing factors was obtained through expert opinions and a literature review, and, further, adopted to construct an evaluation model and the subsequent weights. Thereafter, the relative weight of each factor in the best worst method framework was determined, to obtain the optimal token-financing solution. This study makes important contributions in theory and in practice by providing a decision-making model based on the modified Delphi method and the best worst method, which can serve as a valuable reference and measurement tool for startups to evaluate optimal solutions, when undertaking token financing. Academically, it contributes to the literature by providing an application process that integrates the modified Delphi method and the best worst method, and introduces an optimal evaluation framework for startups to use when undertaking token financing. In addition, it makes a practical contribution in the context of the rapid development of FinTech, as the evaluation model proposed in this study can be a valuable measurement tool for startup entrepreneurs who intend to use token financing to improve the capital turnover rate of their equity.