Finally, 12 key factors are selected after examining the extent to which factors affect each other and the status of sustainable startup ecosystems by direct and indirect methods. The results of applying the cross-impact analysis method reveal that employment, business ownership and scale, income and saving, reforming laws, access to information, the existence of NGOs, and awareness and understanding of risk are among the factors affecting the system sustainability. Results indicate that ecological, economic, and institutional dimensions were of greater importance in a sustainable startup ecosystem. In the second part, the data were analyzed using MICMAC software. For this purpose, 25 key experts in sustainable entrepreneurship were identified. Then, the existing criteria and sub-criteria were weighted using the fuzzy analytic hierarchy process. Sustainable entrepreneurship ecosystem dimensions were first extracted based on the summative content analysis. In this regard, the present study aims to identify and determine the relationships between the indicators of a sustainable entrepreneurship ecosystem for agricultural startups. The findings have implications for research and practice concerned with incubation and university-based entrepreneurial support.Ī sustainable entrepreneurial ecosystem focuses on sustainable development and how entrepreneurs can work to achieve innovative, risky, and profitable entrepreneurial activity while maintaining economic, environmental, social, and cultural factors. We draw on value creation and capture to explain these differences. External legitimacy was more important to TB entrepreneurs, while internal social capital mattered more to CI entrepreneurs. In contrast, CI entrepreneurs benefit from physical capital supporting product development, financing of smaller-scale projects, and knowledge capital focused on business basics. Focusing on university BIs, our qualitative analysis reveals that TB entrepreneurs benefit from physical capital supporting proof of concept, financial grants, and technical and industry-specific knowledge capital. This is the first comparative study addressing this gap. Yet these differences may affect their ability to benefit from incubation. Little is known about how support needs differ between entrepreneurs pursuing technology-based (TB) and creative-industry (CI) opportunities. Additionally, greater rates of new venture formation were found following critical moments in the life of incubator organizations.īusiness incubators (BIs) provide entrepreneurs with access to critical resources (for example, physical, financial, knowledge, social, legitimacy, and so on) that support venture development. Findings indicate that incubator organizations, spin-offs, informal and formal networks, the physical infrastructure, and the culture of the region are related uniquely and interact to form a system conducive for dense high-technology entrepreneurial activity. This taxonomy depicts the relationship among components in one entrepreneurial system, Boulder County, Colorado, that encourages, supports, and enhances regional entrepreneurial activity. Second, semantic structure analysis (Spradley 1980) based on semi-structured interviews with founders is used to develop a taxonomy. First, a genealogy of high-technology companies is presented depicting a high spin-off rate resulting from the presence of seven incubator organizations.
This paper reports the results of a two-phase study that explores new venture creation within the context of an entrepreneurial system.
0 kommentar(er)
