مدل کارکردی اکوسیستم کارآفرینی مبتنی بر پذیرش هوش مصنوعی: از زیرساخت فناورانه تا شبکه‌سازی و حمایت نهادی

Objective: The rapid acceleration of technological advancements in recent decades—particularly in the field of artificial intelligence (AI)—has fundamentally transformed the structure, functioning, and dynamics of entrepreneurial ecosystems. Intelligent technologies have not only reshaped decision-making models, value-creation processes, and innovation patterns, but have also redefined the interactions among key ecosystem actors, including government, universities, industry, startups, policy-making institutions, and support organizations. Despite the widespread integration of AI in various industries, a comprehensive understanding of how this technology influences the functional components of entrepreneurial ecosystems—and the multi-dimensional mechanisms that shape its adoption—remains limited. Previous studies have predominantly focused on technological or organizational dimensions while overlooking critical aspects such as digital infrastructure, cultural and social mechanisms, institutional policy-making, multi-level interactions, and ecosystem support networks. Accordingly, the present study aims to develop a comprehensive functional model of the entrepreneurial ecosystem based on AI adoption, one that identifies, explains, and structures the key components to provide an integrated and multi-layered perspective on the process of AI adoption in digital entrepreneurship contexts. This research seeks to address the existing gap in the literature, which largely stems from the absence of a systemic and functional approach to the infrastructural, cultural, policy, institutional, and network dimensions of AI adoption, and offers a scientific and practical guideline for managers, policymakers, and entrepreneurs.

Method: This study is applied in purpose and descriptive–analytical in nature, employing a qualitative research approach. To accurately identify the functional dimensions of an entrepreneurial ecosystem based on AI adoption, data were collected through semi-structured interviews with 15 experts in the fields of management, innovation, digital technologies, entrepreneurship, policy-making, and technology development in Iran. Purposive and snowball sampling methods were used to select participants with substantial practical experience and deep familiarity with technology-driven entrepreneurial ecosystems. All interviews were recorded, transcribed verbatim, and coded using MAXQDA software. Data analysis followed the thematic analysis method in three stages: open coding, axial coding, and selective coding.

To enhance the accuracy and validity of the findings, several reliability and validity assessments were conducted, including Cohen’s Kappa (0.78) to evaluate inter-coder agreement, Krippendorff’s Alpha (0.74) to assess coding reliability, all of which confirmed the appropriate fit of the thematic structure. To enrich the conceptual analysis, more than 50 domestic and international academic articles were reviewed and incorporated into the theoretical comparison process. All stages of data collection followed research ethics principles, and informed consent was obtained from all participants.

Results: The qualitative analysis led to the extraction of 270 initial codes, which were refined and integrated into 41 conceptual indicators, 14 sub-themes, and ultimately 5 core categories. These main categories include:

Technological Infrastructure (digital infrastructure capacity, data infrastructure, cloud computing, access to digital platforms),

Technological Adoption (cultural readiness for AI, technological knowledge management, technological learning, and analysis of failure),

Technological Innovation (technological leadership, data-driven value creation, business model scalability, personalization, and digital market development),

Policy-Making and Institutional Support (facilitating policies, ethical standards, regulatory frameworks, and university–industry–government interactions),

Ecosystem Networking and Support (support networks, accelerators, angel investors, enabling institutions, and access to financial resources).

The findings show that successful AI adoption is a multi-dimensional and systemic phenomenon, in which digital and data infrastructures form the foundational layer. Factors such as cultural readiness, societal attitudes toward AI, human capability, technological learning, analysis of failure, and digital skill development are essential for sustainable adoption. At the institutional level, facilitating policies, transparent ethical standards, data governance mechanisms, and regulatory structures play a vital role in reducing risks and enabling adoption. The results further reveal that multi-level interactions and network-based collaborations among government, universities, industry, startups, and innovation institutions constitute one of the most influential drivers of AI adoption and institutionalization. Indicators such as inter-ecosystem collaboration, venture capital investment, development of international digital markets, and active support networks contribute significantly to the resilience and sustainability of AI-driven entrepreneurial ecosystems.

Conclusion: The final model demonstrates that AI adoption within entrepreneurial ecosystems is not merely a technological or organizational decision but rather an integrated phenomenon requiring synergy among digital infrastructures, human capacities, cultural readiness, effective policy-making, and strong ecosystem support networks. The model provides a comprehensive framework for analyzing and strengthening AI-driven entrepreneurial ecosystems and can serve as a foundation for designing technology development strategies, support programs, national policies, and practical action plans. Furthermore, the findings enrich the theoretical foundations of technology management and entrepreneurial ecosystems and pave the way for future research on systemic interactions, data governance, and dynamic modeling within AI-based entrepreneurial systems.