The Internet economy has been noted to have the ability “to deliver more value and wealth to more consumers and citizens more broadly than any economic development since the Industrial Revolution” in a recent report by the Boston Consulting Group [5:5].
In the United Kingdom the Internet economy accounted for 8.3% of GDP in 2010, which made it the sixth biggest industry sector and it is also predicted that this share will increase to 12.4% by 2016 [5:47–48]. Although the Internet economy is one of the largest industry sectors in the UK and the Internet itself has also been widely researched, the Internet as an economy is not yet well understood [7:4]. This industry sector is currently part of one of the Research Councils’ priority areas in the United Kingdom [13] and in order to be able to allocate the funding most effectively, the Research Councils UK are concerned about the impact of UK research [15], which is distinguished between academic and economic as well as societal impact [14].
In terms of economic and societal impact of universities, Hughes and Kitson [6] criticise the emphasise that is currently being given to, what they refer to as “hard commercialization”, technology transfer activities in forms such as university spin-outs and patenting. While it has been recognized that those activities are an effective way for some disciplines at academic institutions to collaborate with industry, it has also been argued that technology transfer only represents a small part of the variety of roles that those institutions can play in the business ecosystem. It has also been found that these “direct commercialisation pathways are in the distinct minority of all academic interactions with external organizations” [6:734]. While spin-outs potentially deliver high returns, the total number of ventures is very low [20:5]. The ease at which direct commercialization methods can be measured creates the risk of focusing too much on these specific activities when measuring academic impact, which ultimately effects the amount of funding that is being allocated [6:744–746].
This thesis seeks to better understand the influence of universities on Internet startups by focusing on the human as well as social capital theory. The thesis is going to explore the relationship between human capital and social capital and the influence that universities and the particular industry have. By gaining an insight into the role of universities, improvements can be made to the way entrepreneurial activities, especially besides hard commercialisation routes, can be measured and evaluated.
Formal education has generally been found to have an influence on engaging in entrepreneurship, however, when it comes to the success of the entrepreneurial activities, other than previous startup experience, education has not been found to have any significance [4:302]. It has been concluded, that “even the most specific type of explicit human capital, formal education as provided by business classes, only succeeded in increasing the pace of gestation activities, not in affecting critical outcomes” [4:322] and it has also been noted that higher levels of human capital may give entrepreneurs more confidence as they perceive that alternative employment can be easily found in case the venture fails [4:321].
In contrast, an earlier study came to the conclusion that entrepreneurs had significantly lower levels of education than corporate managers [18:202], which, in contradiction with Davidsson and Honig [4:321], has been interpreted with a possible increase in risk aversion due to being more knowledgeable by having higher levels of human capital [21:166]. A general myth about entrepreneurs being relatively uneducated existed, but several studies have now proven exactly the opposite [16:143].
It has also been found that entrepreneurs in the IT industry are generally very highly educated, with almost half of them holding a master’s degree, a quarter holding a bachelor’s degree and ten percent holding a PhD as their highest degree [8:430]. However, students from IT related disciplines have also been found to face a dilemma between pursuing a research degree and joining or creating a startup. In recent years, particularly with the growth of the digital economy, research and industry have come closer together in computer science disciplines. Consequently those two potential career pathways are commonly in competition with each other [22:229–230].
The human capital theory assumes that the performance outcome of an individual or a group is related to the skill and knowledge levels [9:211]. With increased knowledge, an increased performance and productivity level can be expected [17:8]. Personal traits are not considered as human capital because they cannot be transferred or developed over time. In entrepreneurship, human capital has also been identified as much more relevant than personal traits [23:791]. It is therefore assumed, that entrepreneurs with higher levels of human capital should also be more likely to identify entrepreneurial opportunities as referred to at the beginning of this chapter [4:305]. In addition, it has been assumed that human capital has got a positive influence on entrepreneurial exploitation, although it has also been noted that related empirical evidence is inconclusive [4:307,10:811]. However, it has also been noted that although the results of previous studies have not reached a common conclusion, researchers have only examined the direct effect of human capital rather than the indirect one as well [2:611].
Education and training could be considered as a direct investment in human capital and it has also been noted that expenditures that have both effects are most important while very difficult to measure at the same time [17]. Education has also been noted to be “the strongest human capital variable for identifying business continuance” [1:555].
By investing in education, an entrepreneur’s skill set and knowledge base can be increased which may ultimately have a positive influence on the exploitation and discovery of entrepreneurial opportunities. Many previous studies have relied on the years of formal education as a measure of human capital [10:811].
Nevertheless, Stuetzer et al. [19] came to the general conclusion that “traditional human capital indicators” like startup experience are less relevant than having a balanced skill set. It has been also suggested that the balanced skill set can be acquired by having a founding team with complementary skills [19].
Other than human capital, social capital refers to the multidimensional network of social structures as well as memberships and an individual’s ability of benefiting through social exchange. The network itself is built on trust and is structured through ties that can vary from weak to strong. While an entrepreneur can benefit from weak ties, such as memberships, by utilizing them as a source of additional information and support, strong ties, such as family, can give an entrepreneur consistent access to additional resources [4:307–308].
Consequently, Mosey and Wright [11] have identified a relationship between human capital and social capital. It has been noted, that entrepreneurs with higher human capital are also more effective in increasing their social capital. While previous business ownership experience has been found to have an influence, particularly on the quality of the social capital, it has also been concluded that the discipline of academic entrepreneurs has an impact on the ability of creating social capital, with engineering and material sciences showing better results than biological sciences [11:932] This result is also in accordance with an earlier study [8:426].
When it comes to the role of universities in the overall startup ecosystem, a large number of institutions have put policies and infrastructures in place for supporting student venture creation. However, while investments are being made in these initiatives, institutions have been found to have difficulties of assessing the entrepreneurial output of their programs, which ultimately causes problems when assessing the overall effectiveness of their efforts [12:392–393]. The lack of reliably monitoring entrepreneurial activities within higher education institutions has also been identified by other researchers [3]. The need for being able to demonstrate the entrepreneurial impact of higher education institutions is ultimately also important for receiving further investments in this sector. Nevertheless, it has also been recognized that there are difficulties to be overcome in order to collect reliable data about entrepreneurial activities [12:396].
This thesis is aiming to explore entrepreneurial activity by using an existing crowd-sourced data with mash-ups of several data sources based on the theories that have been identified.
This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/G037574/1) and by the RCUK’s Horizon Digital Economy Research Institute (RCUK Grant No. EP/G065802/1)