The Knowledge Spillover Theory of Entrepreneurship (KTSE) focuses on exploring how entrepreneurs use uncommercialised knowledge spillovers into funding a new venture. This phenomenon explores the role of geographical proximity on the exploration of entrepreneurial opportunities that result in the creation of start-ups that promote the evaluation of the economic growth in regions. However, the definition of knowledge spillovers and the mechanisms measurements to evaluate high-tech entrepreneurs during the first years of operation continues to be an elusive research area in the field of entrepreneurship and innovation. This doctoral thesis seeks to shed light on the effects of knowledge spillovers, incubators, and accelerators on high-tech start-ups performance and survival.
Knowledge Spillovers research focuses on the effects of economics and the characteristics of countries on start-ups. However, there is a clear gap in stating a definition of knowledge spillovers and taxonomy with other disciplines. Research so far assumes that entrepreneurs automatically absorb knowledge spillovers. This work takes a different approach by identifying the processes, mechanisms and companies that facilitate using knowledge spillovers towards innovation.
The doctoral research focused on obtaining primary data from entrepreneurs at the individual level. The study conducted a sequential mixed method exploratory design to empirically develop a model that identifies the types of knowledge spillovers used by companies at the seed and growth stages. A qualitative phase conducted a multiple-case study approach involving 32 semi-structured interviews with chief executive officers and co-founders of high-tech start-ups that attended incubator and accelerator programmes in Greater London, United Kingdom. The resultant conceptual model identified the start-up's strategic decisions to form alliances and partnerships through accelerator programmes, incubators and networking events. The results also suggest that entrepreneurs are likely to allocate Research and Development (R&D) budgets to hire human capital and invest in training to implement information technologies that allow them to overcome geographical proximity and engage in product innovation. The qualitative phase's objective was to identify the mechanisms, processes, definitions of knowledge spillovers, and to guide factor analysis to generalise the findings.
The qualitative findings guided the development of incoming and network knowledge spillovers formative constructs that evaluate alliances with organisations and information sources. The results led to quantitative models' development to evaluate the start-up's absorptive capacity and product innovation. The quantitative phase conducted a validation and generalisation of the qualitative model using factor analysis from a sample of 556 founders of high and medium-tech start-ups operating in the United Kingdom. The findings highlighted that tacit and explicit knowledge spillovers positively affect the company's creation during the process of potential absorptive capacity. The results suggested that the entrepreneur valuation of the business idea based on their experience, or by conducting market research through interviews, surveys, and asking experts in the field. The entrepreneurial journey is supported by incubators or accelerator programmes through networking events and the provision of headquarters. The activities undertaken in these programmes provide access to investment from venture capitalists, and headquarters for start-ups to run their operations. This process leads to the development of alliances and partnerships that enable access to knowledge spillovers.
Entrepreneurs wound to take the managerial decisions to hire highly skilled human capital and incorporate technological tools and conduct R&D. Furthermore, the model three variant of KST-QNCM proves that the founder's start-ups type of industry's background and academic qualifications influence start-ups operations and objectives.
The research's main contribution to knowledge is the developed Knowledge Spillovers model of High-Tech Start-ups (KMS-HTS). The model states propositions and the statistical effects from constructs and variables during the phases of identifying the business idea and creation of the company, establishment and development, and scaling up and the company's future. The model provides a clear description of entrepreneurs' processes and mechanisms to implement knowledge spillovers towards innovation. The model also provides a taxonomy and sources of knowledge under the classification of network and incoming knowledge spillovers that can be implemented in disciplines not linked to economic and econometric models.
The thesis provides strong empirical evidence on different approaches taken by entrepreneurs based on the type of industry. The model revealed that high-technology start-ups follow a unidirectional process of absorption and implementation of knowledge spillovers to develop new products through exploratory innovation. Thus, high-tech start-ups become potential sources of knowledge spillovers for entrepreneurs and companies through R&D that generate research outputs, patents, and academic publications. On the other hand, Medium-high technology and knowledge-intensive companies aim to engage in a product development cycle focused on developing a product prototype from existing technology to participate in local and international markets. Under this category, companies can engage in exploratory or exploitative innovation by using information technologies to acquire additional knowledge spillovers.