This study presents a decision support model for venture capital (VC) funds operating on crowdfunding platforms, aiming to enhance objective project evaluation and optimize investment strategies. As crowdfunding continues to grow as an alternative financing method, alongside venture capital and angel investments, its expansion across developed and less-developed economies highlights the need for structured, data-driven decision-making frameworks that can assist investors in identifying high-potential startups while mitigating financial risks. Despite the rapid adoption of crowdfunding, existing research has primarily focused on isolated success factors rather than analyzing their interplay and how they influence investor decision-making. This study seeks to address this gap by providing a holistic understanding of these factors and their role in shaping crowdfunding-based VC investment strategies.,One of the key challenges in crowdfunding research is the quality and reliability of secondary data, which often limits the accuracy of investment assessments. To overcome this limitation, the study incorporates experimental methodologies, including observations and in-depth interviews, to gain deeper insights into decision-making processes. By applying qualitative and quantitative techniques, the research provides a more comprehensive evaluation framework for investors assessing startups on crowdfunding platforms. The proposed decision support model is designed to improve the selection process for venture capital funds, enabling them to make data-driven, unbiased decisions that reduce investment risks and enhance financial security.,The model itself integrates fuzzy logic assessments to evaluate key aspects of a startup, including its innovative idea, risk profile, and team competence. This structured approach provides a more nuanced evaluation method that goes beyond traditional, subjective decision-making processes. To validate its effectiveness, the model will be tested on a specific startup project, ensuring its practical applicability in real-world crowdfunding environments. If successful, the model could serve as a benchmark for evaluating crowdfunding-based investments, improving transparency and investor confidence in early-stage financing.,The broader impact of this research extends beyond venture capital funds, as it also has significant implications for crowdfunding platforms, regulators, and policymakers. By enhancing the financial security of investments and supporting more effective capital allocation, the model contributes to strengthening national and international competitiveness in the startup ecosystem. Furthermore, the model’s adaptability allows it to be applied beyond the European Union, making it a versatile tool for evaluating startup investments in diverse economic and regulatory environments.,Ultimately, this study represents a major step forward in refining venture capital decision-making on crowdfunding platforms, bridging the gap between qualitative and quantitative investment analysis while ensuring a more structured, risk-mitigated approach to funding early-stage ventures. By integrating AI-driven fuzzy assessments, leveraging experimental methodologies, and addressing key research gaps, the proposed decision support model has the potential to transform how venture capitalists and crowdfunding platforms evaluate and invest in startups, leading to more sustainable and successful funding outcomes.,

This study is highly significant as it fills critical gaps in crowdfunding research by introducing a decision-support model tailored for venture capital (VC) funds, ensuring a structured, data-driven, and unbiased evaluation process for startup projects. Unlike traditional VC investment models, which rely heavily on individual experience, intuition, or fragmented financial metrics, this research provides a comprehensive framework that systematically evaluates key factors such as innovation potential, risk assessment, and team competence. By integrating these dimensions into a fuzzy logic-based decision model, the study ensures that investment decisions are more objective, scalable, and analytically sound, reducing reliance on subjective judgments that often introduce bias into early-stage funding decisions.,One of the most important contributions of this study is how it enhances investment strategies by offering a structured approach to screening startups on crowdfunding platforms, where information asymmetry and limited due diligence often create high-risk investment environments. By incorporating qualitative and quantitative assessments, the model enables VC funds to make more informed and data-backed decisions, ensuring that they allocate capital to high-potential startups with well-defined growth trajectories. The ability to quantify startup potential through structured evaluation metrics significantly improves investment outcomes, portfolio diversification, and overall fund performance, making venture capital investment more precise and risk-aware in a rapidly evolving crowdfunding ecosystem.,Additionally, the study’s model serves as a critical tool for risk reduction, addressing one of the biggest challenges in venture investing—capital loss due to inadequate due diligence. By systematically analyzing market feasibility, innovation quality, financial stability, and execution capability, the framework minimizes exposure to high-risk ventures while identifying startups with sustainable, scalable business models. This level of risk mitigation is especially valuable in crowdfunding environments, where a high volume of startups seek funding, but only a fraction demonstrate the long-term viability needed for investor success.,Beyond improving VC investment strategies, the model has broad economic implications by enhancing national and international competitiveness in the growing crowdfunding sector. As alternative financing methods such as equity crowdfunding, tokenized investment platforms, and digital venture marketplaces gain traction, having a standardized decision-support model ensures that investments remain structured, transparent, and aligned with long-term economic growth objectives. By enabling investors to navigate the complexities of global crowdfunding ecosystems with confidence, the study supports greater capital flow into innovation-driven sectors, accelerates startup success rates, and strengthens the financial infrastructure of emerging entrepreneurial markets.,Ultimately, this research sets a new benchmark for VC investment in crowdfunding environments, offering a scalable, adaptable, and empirically validated model that bridges the gap between traditional venture funding strategies and the rapidly growing digital investment landscape. By integrating analytical rigor with real-world applicability, the study provides fund managers, policymakers, and startup founders with a crucial resource for making smarter, more strategic investment decisions in the evolving world of alternative finance.,