A Framework for Identifying and Analyzing the Effective Drivers on the Future of Financial Technology Startups

Document Type : Original Article

Authors

1 PhD. Student, Department of Management, Qom Branch, Islamic Azad University, Qom, Iran.

2 Associate Professor, Department of Industrial Management, Tarbiat Modares University, Tehran, Iran

3 Assistant Professor, Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran.

4 Associate Professor, Department of Management, University of Qom, Qom, Iran.

Abstract

Purpose: Financial technology, through the use of advanced innovations and technologies,
has profoundly transformed the financial industry and its traditional institutions, such as banks and insurance companies. FinTech startups operate across various sectors, including payments, lending, financing, investing, cryptocurrencies, regulation, banking, and insurance. Numerous drivers are influencing the future of these startups. This study aims to identify and analyze the key drivers shaping the future of FinTech startups.
Methodology: This study is applied in its orientation and employs a mixed-methods approach. Both quantitative and qualitative techniques were utilized to analyze the data. The Fuzzy Delphi method was employed to screen the drivers, while the Fuzzy DEMATEL method was used to assess the effects of these drivers. The Best-Worst Method (BWM) was employed to determine the weights of the drivers based on the severity of uncertainty. Subsequently, the Root Definition method was applied to present the desired research scenario. The theoretical population comprised experts in the field of fintech startups, and the sampling method employed was purposive. Data were collected through a literature review, expert interviews, and three fuzzy questionnaires: screening, impact assessment, and prioritization.
Results: Initially, 21 drivers were identified through a literature review and expert interviews. These drivers were subsequently screened using the Fuzzy Delphi technique. Eleven drivers, which had defuzzified values of less than 0.7, were excluded from the study. The remaining ten drivers were analyzed based on two indicators: effectiveness and severity of uncertainty, using the Fuzzy DEMATEL and Best-Worst Fuzzy methods. The drivers related to the familiarity of executive, legal, and regulatory institutions with fintech business models, along with the performance of regulators in legislation, exhibited the most significant net effects. Additionally, the drivers concerning regulatory performance in legislation, as well as the familiarity of executive, legal, and regulatory institutions with fintech business models, exhibited higher levels of uncertainty, as evidenced by their greater weights.
Conclusion: Considering the drivers related to regulatory performance in legislation, as well as the familiarity of executive, legal, and regulatory institutions with fintech business models, a preferred research scenario was developed. To develop this scenario, six components—namely, customers, actors, the transformation process, stakeholder worldview, problem environment, and problem owner—were utilized based on the Root Definition technique.

Keywords

Main Subjects

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Sciences and Techniques of Information Management
Volume 10, Issue 4
November 2024
Pages 103-136

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