ارائه مدل مدیریت مالی هوشمند در استارتاپ‌ها با تأکید بر عدم‌قطعیت تورمی و فناوری‌های نوظهور

ابوالفضل بعیدی

Authors

Abolfazl Baeidi Department of Finance and Banking, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran https://orcid.org/0009-0007-3981-2953

Keywords:

Smart Financial Management, Startup, Inflationary Uncertainty, Emerging Technologies, Data-Driven Financial Intelligence, Financial Flexibility, Financial Decision-Making

Abstract

This study aimed to develop and validate a smart financial management model for startups with emphasis on inflationary uncertainty and emerging technologies. This applied study employed a sequential exploratory mixed-method design consisting of qualitative and quantitative phases. In the qualitative phase, 18 experts in financial management, startup management, fintech, economics, and investment in Tehran were selected through purposive sampling, and data were collected through semi-structured interviews and analyzed using thematic analysis. In the quantitative phase, 384 chief executive officers, founders, co-founders, financial managers, senior managers, and financial experts from startups operating in Tehran participated. Data were collected using a researcher-developed questionnaire. Reliability and validity were assessed using factor loadings, Cronbach’s alpha, composite reliability, average variance extracted, and HTMT. The model was tested using partial least squares structural equation modeling with 5,000 bootstrap resamples. Technological capabilities had a significant positive effect on data-driven financial intelligence (β = 0.514, p < 0.001) and financial decision quality (β = 0.356, p < 0.001). Data-driven financial intelligence positively affected financial decision quality (β = 0.327, p < 0.001). Inflationary uncertainty positively affected financial flexibility (β = 0.421, p < 0.001) and smart risk management (β = 0.337, p < 0.001), while negatively affecting financial decision quality (β = -0.176, p = 0.001). Financial flexibility (β = 0.241), smart risk management (β = 0.219), and smart liquidity and cost management (β = 0.194) also had significant positive effects on financial decision quality. The model explained 68.4% of the variance in financial decision quality, and the SRMR value was 0.061. Smart financial management in startups is shaped by the interaction of technological capabilities, data-driven financial intelligence, financial flexibility, risk management, and liquidity management, and the simultaneous development of these capabilities can mitigate the adverse effects of inflationary uncertainty and improve financial decision quality.

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