The collaborative frontiers of social networks and opportunity recognition in convergent technologies.
Opportunity recognition has long been considered a key aspect in entrepreneurship process. This leads to exploring the influential role of social networks and information exchanges in opportunity recognition in convergent technologies that will be a dominant force in the global markets in the next decades. In this paper the effects of collaborative frontiers of key social network players on opportunity recognition in convergent technologies is studied. Further the effects of information generated from convergent technology key players and the role of knowledge structure as a potential mediator in opportunity recognition process are discussed. Building on this framework, a model is developed and a set of propositions is offered for the future research.

Entrepreneurship (Technology application)
Social networks (Forecasts and trends)
New business enterprises (Innovations)
New business enterprises (Services)
New business enterprises (Customer relations)
Ozgen, Eren
Pub Date:
Name: Academy of Entrepreneurship Journal Publisher: The DreamCatchers Group, LLC Audience: Academic Format: Magazine/Journal Subject: Business, general Copyright: COPYRIGHT 2009 The DreamCatchers Group, LLC ISSN: 1087-9595
Date: Jan-July, 2009 Source Volume: 15 Source Issue: 1-2
Event Code: 010 Forecasts, trends, outlooks; 360 Services information; 240 Marketing procedures Computer Subject: Technology application; Market trend/market analysis
Geographic Scope: United States Geographic Code: 1USA United States
Accession Number:
Full Text:

Opportunity recognition has long been considered as a crucial aspect of the entrepreneurial process. (Ventakaraman, 1997; Gaglio, 1997; Hills, 1995). To understand the influential role of social networks in entrepreneurial opportunity recognition in emergent technologies first it is essential to review the essence of opportunity recognition in prior literature. To date much prior opportunity recognition research has in common that "innovation" and "information" are two important elements in this process.

Opportunity recognition involves having specific information regarding market, industry and technology conditions and other factors (Ozgen & Baron, 2007) and the discovery of an innovative idea to create new businesses (Schumpeter, 1934; Kirzner, 1973; Kirzner, 1997). Opportunity recognition is also defined as an act of innovation in new service, process and production (Schumpeter, 1934; 1943) that involves seizing new marketable ideas (Kuratko & Hodgetts ,1998).

To date researchers applied various approaches in opportunity recognition. Some focused on the role of active and systematic search for information in the identification of entrepreneurial ideas and innovations for new markets, products, services, methods or processes (Schumpeter, 1942). In other words, entrepreneurs recognize opportunities by scanning the environment for information and focusing on markets, industry, customers and technological developments (Bhave, 1994; Reitan, 1997; Busenitz & Barney, 1996).). Prior research found a strong link between the industry change, i.e. growth of sales, entry barriers and manufacturing possibilities, or market structure such as demographics and socio-cultural factors lead resigning of opportunities and creation of new ventures (Drucker, 1985; Dean & Meyer, 1992). Changing industry or market conditions may shift the demand and production possibilities and lead to entrepreneurial inventions (Timmons, 1999; Kuratko & Welsch, 2001; Dean & Meyer, 1992). Especially, scanning the environment focusing on technology, consumer economics, social values and governmental regulations play an important role in the recognition of opportunities (Stevensen & Gumpert, 1985). Therefore, it was suggested that systematic search based on specific information leads the discovery of entrepreneurial possibilities (Shaver & Scott, 1991; Kaish & Gilad, 1991; Herron & Sapienza, 1992; Fiet, et al., 2000, Fiet el al., 2002).

Some researchers suggested that possession of information or alertness is critical in identifying and exploiting market opportunities (Kirzner, 1973) and claimed that entrepreneurial opportunities exist because people possess different information (Hayek, 1945). Possession of different information leads individuals to discover entrepreneurial possibilities because any given individual cannot identify all opportunities (Kirzner, 1973). Therefore asymmetries of information (Shane & Venkataraman, 2000) lead entrepreneurial innovations.

A stream of research applying a cognitive approach focused on the way people think and process information (Baron & Markman, 1999) and examined entrepreneurs who evaluate and discover opportunities and explored the skills, cognitive processes (Baron, 1998). These researchers found that "alertness", in other words, the way entrepreneurs perceive information and process knowledge play a key role in opportunity recognition. Therefore, opportunity recognition is a cognitive process and in the mind of certain individuals and opportunities are identified by being "alert" to possibilities that the market presents (Woo et al., 1992; West & Myer, 1997; De Koning, 1999).

Opportunity recognition is also defined as a joint function of individual and the environment and entrepreneurs' interaction with the environment shapes the evolution of ideas (Vesper, 1990; De Koning, 1999; Shane & Ventakaraman, 2000). In other words, opportunities are both out there and also in the minds of certain individuals. Therefore, opportunity recognition is a joint function of personal (background, experience, education) and external factors (contextual and environmental factors) (Singh, 2000).

Although different approaches were applied in prior opportunity recognition studies much of all these earlier investigations have in common that "innovation" and "information" play a central role in this process. Consequently, the recognition of entrepreneurial opportunities is linked to having specific information regarding with market, industry and technology conditions and innovative ideas. Since information and innovation are the essence of opportunity recognition much research has investigated possible sources of these two crucial factors in opportunity recognition. One potential source of these two key elements that has received much attention has been social networks.


Social Network Theory suggests that network ties provide access to resources and information that is critical to opportunity recognition and venture formation (Butler & Hansen, 1988; Burt (1992). According to the Social Capital Theory social network ties increase an entrepreneur's capacity in attracting, employing and circulating resources and providing possible access to information (Aldrich & Zimmer, 1986; Dubini & Aldrich, 1991; Zhao & Aram, 1995; Birley, 1985; (Simon, 1976; Floyd & Wooldridge, 1999; Sexton & Bowman-Upton, 1991). Social network ties connect structural holes, link lack of connections, increase resource supply; ease information transfer and facilitate innovation (Burt, 1992; Johannisson, 1990; Johannisson, 1996; Ostgaard & Birley, 1996). Earlier research found that social networks provide knowledge for new possibilities opportunity to manage information to overcome barriers in entrepreneurial process (Johannisson, 1990; Johannisson, 1996; Ostgaard & Birley, 1996; Butler & Hansen, 1988).

Christensen and Peterson (1990) reported that in addition to other factors social networks are often a source of venture ideas and are positively linked to opportunity recognition for creating viable new ventures. Entrepreneurs who used network sources identified significantly more opportunities than those who developed their venture ideas individually (Hills, et. all 1997). Social network contacts allow entrepreneurs to have an access to a wide range of information (Singh, Hills, Hybels, & Lumpkin, 1999; (Shane & Cable, 2002) and create linkages between resources and opportunities (Singh, 2000). Access to diverse commercial social networks updates entrepreneurs with changes in industry and market conditions from customers, government regulations and competitors (Almeida & Kogut, 1999; Shane & Cable, 2002; Gargiulo & Benassi, 2000) and underpins entrepreneurs' knowledge base (Bozeman & Mangematin, 2004) leading to innovations and idea generation and recognition of opportunities (De Carolis & Saparito, 2006; Ozgen & Baron, 2007).

Social networks and opportunity recognition in convergent technologies

Social network ties are especially crucial in recognizing opportunities and enhancing innovation capabilities in technology domain. Network alliances increase innovative performance of high tech industries (Soh & Roberts, 2006) and play an influential role in idea generation, transferring knowledge and opportunity recognition (Greve & Salaff, 2003, Dimov, 2007). Therefore, it is logical to assume that a fundamental understanding of the social networking strategies in opportunity recognition process in emerging technologies will be as of timely and worthwhile for entrepreneurship researchers.

Convergent technologies are emergent technologies that combine a set of technologies and various scientific disciplines. The term "convergent technologies" indicates the convergence of new emerging technologies, such as nanotechnology, biotechnology, information technology and new human technologies. It is expected that convergent technologies will revolutionize science and technology in the next decade and lead to dramatic developments in a variety of fields such as medical science, material science, electronics, military, healthcare, chemical plants, transportation, pharmaceuticals, manufacturing, agriculture, energy, environmental management, etc. (Roco & Bainbridge, 2001).

As the emergent technologies will create remarkable advances over the globe in the next decade it is considered worthwhile to further explore opportunity recognition in convergent technologies. Innovation and information have long been considered as the essence of opportunity recognition and social networks are found as one possible source of these two crucial elements. Although prior research contributed our understanding of opportunity recognition immensely to-date how socially generated information lead to opportunity recognition within the complex and multifaceted system of convergent technologies has not, as yet, been carefully explored.


Dealing with innovation and opportunity recognition in convergent technologies requires exploitation of social networking, social cohesion and networking strategies among major players, such as businesses, universities and government (Sorenson & Waguespack, 2005; Roco & Bainbridge, 2001; Mazzola, Nature Biotech, 2003; Liebeskind, Oliver, Zucker & Brewer, Org Sc., 1996; Powell, Kogut & Smith-Doerr, ASQ, 1996).

The multifaceted nature and complex structure of convergent technologies heavily depend upon the accumulated knowledge of multitude disciplines, such as science, engineering and technology, across boundaries between disciplines and a vibrant set of industry, government and university activities (Roco & Bainbridge, 2001). Since the complexity of methods and procedures increase in convergent technologies the need for collaborations among a variety of partnerships with different skills and capabilities increase. Therefore we suggest that it is crucial to investigate key social network players and information generated from these sources in transferring knowledge as predictors of innovation and new ideas (Sorenson & Waguespack, 2005) in opportunity recognition research in convergent technologies.

Information generated from interdisciplinary collaborations in Academia

Previous entrepreneurship research found that teams with multidisciplinary background lead to idea generation and innovation especially in high tech industries (Alves, Marques, Saur, Marques, 2007; Kamm, Shuman, Seeger & Nurick, 1990; Timmons, 1994). Currently convergent technologies are driven by academic research. Consequently, academic social network that consists of multidisciplinary teams of scientists, engineers and technicians may be particularly critical in opportunity recognition in convergent technology domain (Roco & Bainbridge, 2001).

As the process of developing new products and processes get progressively more complex in convergent technologies, collaboration of cross-functional team members in academia will be crucial in facilitating resource accumulation, idea generation and creating linkages to entrepreneurial ideas.

Convergent technology domain connects basic science and engineering disciplines and accents diverse scientific and technical information and knowledge accumulation to support research infrastructure. Recent entrepreneurship research suggests that knowledge transfer, large-scale multidisciplinary collaboration and outreach activities across various disciplinary lines (Brush, Duhaime, Gartner, Stewart, Katz, Hitt, Alvarez, Meyer & Venkataraman, 2003; Shook, Priem, & McGee, 2003) will be critical in future entrepreneurship research (Dean, Shook & Payne, 2007).

An academic social network consisting of highly trained people with expertise in diverse disciplines such as physics, chemistry, biology, materials and engineering will increase diffusion of information, broaden knowledge and research base (Roco & Bainbridge, 2001). Social network of academicians will foster new knowledge creation and access to wide range of scientific and technical information that will lead radical innovations, which stimulate opportunity recognition and increase entrepreneurial behavior.

It was found that information and prior knowledge add significant insights into entrepreneurial discovery. Prior knowledge of a particular field provides individuals the capacity to recognize entrepreneurial opportunities (Shane, 2000). Therefore social network of academicians with different background and expertise will help entrepreneurs combine diverse pieces of knowledge to recognize an entrepreneurial opportunity. Based on this reasoning we propose that:

P1. The greater the extent to which entrepreneurs in academia with different background and expertise team up in new projects, the more likely they will be to discover opportunities for new ventures in convergent technology.

Information generated from industry-academia collaboration

Convergent technology domain involves interaction among scientists and diverse links between academic and industrial participants. Therefore an effective use of social networking strategies constitutes a valuable tool to interweave diverse links and bridge the gap between academic and industrial participants (Adler & Kwon, 2002) and is considered a critical aspect in opportunity recognition within the ecosystem of convergent technologies.

To date it was found that developing social capital is a major challenge for academic entrepreneurs in universities compared to entrepreneurs in commercial environments (Mustar et al., 2006; Lockett & Wright, 2005; Nicolaou & Birley, 2003). Accessing social networks is crucial to commercialize technological innovation (Delmar & Shane, 2004). Yet at present much convergent technologies research is constrained to university and national lab environment (Wolff, 2006). Convergent technology innovations are driven by academia and therefore to be able to identify opportunities with commercial market applications (Lockett, Wright, & Franklin, 2003) and capture the full potential of nanotech innovations entrepreneurs in universities need to broaden their scientific networks in university environment to commercial networks in industry environment. Commercialization potential of innovations in convergent technologies across a wide range of projects and creating new materials, products and techniques for industrial manufacture require coordinated efforts and interaction between university and industry sector.

Collaboration between academic and industry participants is a crucial path in recognition of opportunities in convergent technologies. Developing partnerships and forming social networks between academic institutions and the industry private sector will strengthen ties between academic researchers and the industry sector and create a broader approach in cross fertilization of ideas. Industry-academia social network will support easing the transfer of mutual intellectual input and feedback and fostering establishment of convergent technologies industry hubs (PMSEIC, 2005).

We suggest that entrepreneurs in academia can often obtain valuable information from persons that they meet or contact in the industry (these individuals might include but not restricted to venture capitalists, investors, suppliers, manufacturers, producers, etc.) on changing trends in the market, social behavior patterns, consumer economics, changing structure of the industry and current market circumstances. Further social network collaborations between private sector and academia will also assist academicians in making contacts in the field, such as with financiers, suppliers, and customers, keeping industry specific knowledge up-to-date and accessing to informed decision-making. Therefore, social networks formed between university and industry private sector will help entrepreneurs in convergent technologies scan the environment and identify opportunities by using different types of information about the market.

Social networks formed between large firms and universities enhance industry-wide applications ideas of convergent technologies and this will create new demand and supply curves for new products, methods and processes replacing existing ones. As it is suggested in creative destruction theory innovative processes in new products, production or organizational methods, markets, sources of input and market structures (Schumpeter, 1934) through applications of convergent technologies projects will induce implementation of new applications in a range of industries and bring innovations to the market. Increase demand for new technologies and methods will enhance opportunity recognition in various markets and boost entrepreneurial behavior and innovations in convergent technologies.

Companies investing in university research funds through joint projects gain access to new technological developments and innovation (Chesbrough, 2003). Social networking with academia and private sector will ease the transfer of the nanotech research results into commercial application and facilitate entrepreneurial opportunity recognition. Further, university spin-outs have long been considered as an important source in generating technology development and transferring university-invented technology to industry and markets (Roberts, 1991).

Universities are increasingly becoming the center of convergent technology development. Therefore, through private sector and academia network universities transfer nanotech innovations and more in-depth reflections of emerging technological developments back into the market and industry in the form of new entrepreneurial venture opportunities. Universities on the other hand through joint development agreements will be given a range of initiatives to support and strengthen the academic research along with financial support and access to information on possible commercial market applications. This leads to the next propositions:

P2. The greater the extent to which entrepreneurs in academia collaborate with the private sector in industry as a source of information, the more likely they will be to identify opportunities for new ventures in convergent technologies.

Government generated information

Opportunity recognition in emerging technologies is closely linked with the government collaboration. Prior research findings show that university and government research institutions play a significant role in advancing scientific activities (Miyazaki & Islam, 2007). In this study "government" refers to "government generated information and funding for convergent technology projects".

When new technologies start developing the infrastructure required for that technology needs to be developed as well including regulations, legitimation and resource fundings (Van de Ven, 1993). That means, the legal requirements of entrepreneurship in convergent technologies not only include patents, licensing, intellectual property rights and commercial application issues but also a wide range of law and policy requirements and regulations that engage environment, safety and social responsibility (Reinert, Andrews, & Keenan, 2006). Therefore information obtained from government on regulations and standardization of the new technology will be helpful for entrepreneurs in prioritization and coordination efforts to bridge knowledge gaps in recognition of opportunities.

Further, convergent technology initiatives involve high cost and start-up funding initially and could create an obstacle in launching new research programs. Prior research found that more entrepreneurial firms were founded in states with high science and technology and economics initiatives than those without such initiatives (Woolley & Rottner, 2008). Therefore, we expect government generated information and funding for any convergent technology projects will not only give an access to research funding for promoting new technology and build confidence in the capital market but also ease understanding of the regulatory frameworks (PMSEIC, 2005) for the new technology.

In sum, government generated information and funding for convergent technology projects will facilitate innovations and rapid commercialization of research results and lead to meaningful progress in recognition of opportunities (NNI, 2008). This leads to the next proposition:

P3. The greater the extent to which entrepreneurs receive government related information and funding for convergent technology projects the more likely they will be to discover opportunities in convergent technologies.


Knowledge structure is a cognitive factor that is derived from "schemas", i.e. mental frameworks that give structure to and organize information in memory and enable individuals to perceive connections between events and knowledge (Baron, 2006).

Schema theorists imply that individuals remember information that fits well with the schema (Hastie & Kumar, 1979). Applying pattern recognition theory and the prototype model, Baron (2006) proposed that mental frameworks play an essential role in opportunity recognition because individuals more likely notice information relevant with their existing schemas. Baron (2006) suggested that training, previous experience and learning shape individuals' mental frameworks, which influence their perception of external world. Thus, it might be easier to notice and identify opportunities through information relevant to individuals' existing schemas than information irrelevant to existing schemas (Baron, 2006).

For instance, individuals with better-developed schemas related to an industry can "access signals from information services or channels, to which they subscribe, based on their prior knowledge" (Fiet, Piskounov, & Gustavson, 2000, pp.198). Their background may help them to filter signals from the environment and transform them into information, carry out data handling, adapt complex technical developments and utilize available information processing.

Prior research findings suggest that alertness schemas play an important role in opportunity recognition Gaglio and Katz, 2001) and the more developed entrepreneurs' schemas for knowledge in a particular field, the more likely they use this information in opportunity recognition (Ozgen & Baron, 2007).

Extending previous research finding on the importance of schemas in opportunity recognition we suggest that further studying the extent of knowledge structure, referring the way in which individuals organize knowledge in "three specific schema layers", will be valuable in understanding the opportunity recognition process. Knowledge structure refers to three basic schemas called "declarative", procedural" and "structural" under which knowledge may be organized. Declarative knowledge includes information about concepts, names and things. Procedural knowledge contains how to steps to be able to do a task; and structural knowledge refers to deeper understanding of a material or a concept (Grotzer, 2002). We suggest that studying the extent of entrepreneur's knowledge structure will provide us a better understanding on "how" entrepreneurs employ information that they obtain from various sources in recognizing opportunities in convergent technologies. We predict that the better developed declarative, procedural and structural knowledge of an entrepreneur in a given technological field the better and more organized knowledge structure the individual has. As a result, better developed knowledge structure will ease processing information and informed individuals are more likely to identify stimuli relevant with their existing knowledge.

Based on this reasoning we propose that the impact of social network collaboration mechanism on opportunity recognition is partially mediated by the strength of entrepreneurs' knowledge structure for convergent technology related information. Entrepreneurs who have better developed knowledge structure in convergent technologies are more likely recognize new business opportunities through social network collaboration compared to those who have less developed knowledge structure in convergent technologies. These individuals with better developed knowledge structure will better apply and utilize information generated from these social sources in recognition of opportunities for viable new ventures.

P4. The effects of social network mechanism on opportunity recognition in convergent technologies will be partially mediated by knowledge structure.

The proposed model is shown in Figure 1 on the following page.


As the field of convergent technologies is still developing it is suggested to study nascent entrepreneurs in convergent technologies in opportunity recognition research. Nascent entrepreneurs are those who are involved in independent business start-up efforts and/or trying to start a new venture (Delmar & Davidson, 2000) either alone or with others (Reynolds, 1999). This involves any behavior associated with starting a new firm such as earning money on sales, doing market research and saving money to start business (Delmar &Davidson 2000). Individuals who qualify as nascent entrepreneurs expect to be owners or part owners of a new venture; have been active in trying to start-up a new venture in the past 12 months and the effort is still in the start-up or gestation phase (Reynolds, 1999. pp.170).

Opportunity recognition measure could be adapted from one used in previous research (Singh et al., 1999) that includes entrepreneurs' self assessments and also from various other quantitative and verifiable methods such as "the number of companies started; number of patents held; number of opportunities recognized and cross-validation of entrepreneurs' self-assessments by persons who know them well and are familiar with their actual success in identifying opportunities". (Ozgen & Baron, 2007, p. 24). Knowledge structure measure could be adopted from previous research (Grotzer, 2002). The other measures could be developed by the researchers as needed.

Structural Equation Modeling Analysis is suggested to analyze the data as it is a reliable straightforward method of dealing with multiple and interrelated dependence relationships simultaneously while providing statistical efficiency (Hair, 1998).



At present convergent technologies are still in infancy. Consequently, initial costs of new applications are expected to be high in the beginning. The high cost of research and start-up packages may pose some difficulty in recognition of opportunities for cutting edge ideas and transferring these opportunities into new research programs. Therefore, until it builds up impetus initially convergent technology initiatives require very high funding and investment.

As much convergent technology research is carried out in university environment at present convergent technology researchers in academia use information stem from similar academic or scientific environment such as scientific conferences, journals, reports and workshops and thereby are constrained with diversity of information sources (Wolff, 2006). The main challenge in convergent technologies includes forming intense social network collaborations among industry, academia, government and other sectors that help to build a knowledge base and convert knowledge into commercial outcomes, in other words transferring research results from academia or laboratories to industry (NNI, 2008). The multifaceted challenges in opportunity recognition in convergent technologies involve the collaboration of many disciplines, managing diverse social networks, working across many different fields, integrating different perspectives among partners, handling massive diverse volume of information and assessing business implications and commercial results of nanotech innovations (Bean, Chapas, Collins & Kingon 2005).

Exploring various other information sources with commercial market applications could expose convergent technologies to many possible application areas. Scholars suggest further studying collaborative efforts between industry and academia that lead to identifying commercial needs to project for large-scale market impact opportunities (Osman, Rardon, Friedman & Vega, 2006). Further, opportunity recognition researchers could explore the influence of diversity of information coming from various social network sources.

A longitudinal study on academic entrepreneurs' social network ties revealed that differences in the human capital and prior business experience of academic entrepreneurs could play a significant role in forming social network ties with industry (Mosey & Wright, 2007). Regardless of academic discipline the lack of prior business experience could create structural holes in the social connections between university and private sector and form a barrier for academicians in building effective social network ties with investors and managers in industry (Mosey & Wright, 2007) . Therefore, having business experience or developing human capital could be regarded as a significant factor for academicians in emerging technologies. Hence, the lack of prior business experience in academia could pose difficulty in forming social network ties with industry and recognition of entrepreneurial opportunities with commercial market applications.

U.S. National Science and Technology Council Report proposed that fostering R&D infrastructure, promoting vital research areas, encouraging and developing the scientific and technical human capital are among the major challenges in convergent technologies domain (NSTC, 2000). Scholars suggest implementing considerable changes in academia to offer courses, training and degrees in convergent technologies and build interdisciplinary centers of expertise to provide depth and broad approaches in social network formation which lead to recognition of opportunities for viable new ventures (Greg, 2004).

In conclusion, the conceptual model presented here is only a step towards understanding the collaborative frontiers of social networks in opportunity in convergent technologies. It is hoped that the ideas suggested here may provide insights for the future opportunity recognition research in convergent technologies.


Adler, P. & Kwon, S. (2002). Social capital: Prospects for a new concept. Academy of Management Review, 27, 17-40.

Aldrich, H. & Zimmer, C. 1986. Entrepreneurship Through Social networks. In D. Sexton and R. Smilor (Eds.), The Art and Science of Entrepreneurship (pp. 3-23). Cambridge, MA: Ballinger.

Alves, J., Marques, M.J., Saur, I. & Marques, P. (2007). Creativity and Innovation through Multidisciplinary and Multisectoral Cooperation. Creativity and Innovation Management, 16 (1), 27.

Aiman-Smith, L., Bean, A. S., Cantwell, A., Chapas, R., Collins, M. J., Kingon, A. I., & Mugge, P. C. (2006). Social networks key to harnessing nanoscience knowledge explosion. Research Technology Management, 49(3), 2-4.

Almeida, P., & B. Kogut. (1999) Localization of Knowledge and the Mobility of Engineers in Regional Networks. Management Science, 45, 905-918.

Baron, R.A. (2006). Opportunity recognition as pattern recognition: How entrepreneurs "connect the dots" to identify new business opportunities. Academy of Management Perspective, 20, 104- 119.

Bean, A.S.; Chapas, R; Collins, M.J. and Kingon, A. I. (2005). Nanoscience and technology: firms take first steps. Research-Technology Management, 48(3), 3-5.

Bhave, M.P. (1994). A process model of entrepreneurial venture creation. Journal of Business Venturing, 9, 223-242.

Birley, S. (1985). The role of networks in the entrepreneurial process. Journal of Business Venturing. 1, 107-117.

Bozeman, B. and V. Mangematin. (2004). Editor's introduction: building and deploying scientific and technical human capital. Research Policy, 33(4), 565-568.

Brush, C., Duhaime, I., Gartner, W., Stewart, A., Katz, J., Hitt, M., Alvarez, S., Meyer, GD., & Venkataraman, S. (2003). Doctoral education in the field of entrepreneurship. Journal of Management, 29(3), 309-331.

Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.

Busenitz, L.W., and Barney, J.B. (1997). Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making. Journal of Business Venturing, 12, 9-30.

Butler, J.E., and Hansen G.S. (1988). Managing social network evolution and entrepreneurial benefits. Frontiers of Entrepreneurship Research, 403-404.

Bygrave, W.D., and Hofer, C.W. (1991). Theorizing about entrepreneurship. Entrepreneurship Theory and Practice, 16(2), 13-22.

Chesbrough, H. (2003). Open Innovation. Cambridge, MA: Harvard Business School Publishing.

Christensen, P.S., and R. Peterson (1990). Opportunity Identification: Mapping the Sources of New Venture Ideas. Presented to the 10th annual Babson Entrepreneurship Research Conference. Aarhus University Institute of Management, Denmark.

Christensen, P.S. (1989). Strategy, Opportunity Identification and Entrepreneurship. Institute of Management, Aarhus University, Denmark.

De Carolis, D.M. & Saparito, P. (2006). Social capital, cognition, and entrepreneurial opportunities: A theoretical framework. Entrepreneurship Theory and Practice, 30(1), 41-56.

De Koning, A.J. (1999). Opportunity Formation From A Socio-Cognitive Perspective. Frontiers of Entrepreneurship Research, 258.

Dean, T.J., and Meyer, G.D. (1992). New Venture formations in Manufacturing Industries: A conceptual and empirical analysis. Frontiers of Entrepreneurship Research,

Dean, M.A.; Shook, C.L., and Payne, G.T. 92007). The Past, Present, and Future of Entrepreneurship Research: Data Analytic Trends and Training. Entrepreneurship Theory and Practice, 31(4), 601-618.

Delmar, F. & Davidson, P. 2000. Where do they come from? Prevalence and characteristics of nascent entrepreneurs. Entrepreneurship and Regional Development, 12, 1-23.

Delmar, F., & Shane, S. A. (2004). Legitimating first: Organizing activities and the survival of new ventures. Journal of Business Venturing, 19, 385-410.

Dimov, D. (2007). From Opportunity Insight to Opportunity Intention: The Importance of Person-Situation Learning Match. Entrepreneurship Theory and Practice, 31(4), 561-583

Drucker, P.F. (1985). Innovation and Entrepreneurship: Practices and Principles. Oxford, Butterworth: Heinemann.

Dubini, P., and Aldrich, P.H. (1991). Personal and Extended Networks are Central to the Entrepreneurial Process. Journal ofBusiness Venturing, 6, 305-313.

Fiet J. O., Clouse, V. G. H. and Norton, W. I. (2004). Systematic search by repeat entrepreneurs. In J. Butler (Ed.), Research in Entrepreneurship and Management (vol. 4). Greenwich, CT: Information Age Publishing.

Fiet, J.O., Piskounov, A. and Gustavson, V. (2000). How to decide to search for entrepreneurial discoveries. Frontiers of Entrepreneurship Research, 198.

Floyd, S.W., and Woolridge, B. (1999). Knowledge creation and social networks in corporate entrepreneurship: The renewal of organizational capability. Entrepreneurship Theory and Practice, 21(3), 123-143.

Gaglio, C.M. (1997). Opportunity identification; Review, critique and suggested research directions. In J.A. Katz (Ed.), Advances in Entrepreneurship, Firm Emergence and Growth (pp. 139-200). Connecticut: JAI Press.

Gargiulo, M. and Mario B. (2000). Trapped in Your Own Net? Network Cohesion, Structural Holes, and the Adaptation of Social Capital. Organization Science, 11(2), 183-196.

Greg, T. (2004). Convergent technologies: the technology for the twenty-first century. Foresight: The Journal of Futures Studies, Strategic Thinking and Policy, 6(6), 364.

Greve, A., & Salaff, J. (2003). Social networks and entrepreneurship. Entrepreneurship: Theory & Practice, 28(1), 1-22.

Grotzer, T.A. (2002). Expanding Our Vision for Educational Technology: Procedural, Conceptual, and Structural Knowledge. Educational Technology, 42(2), 52-59.

Hair, J. F. (1998). Multivariate data analysis. Upper Saddle River, N.J: Prentice Hall.

Hastie, R., and Kumar, P.A. (1979). Person Memory: Personality traits as organizing principles in memory for behavior. Journal of Personality and Social Psychology, 37, 25-38.

Hayek, F. (1945). The use of knowledge in society. American Economic Review, 35(4), 519-530.

Herron, L., and Sapienza, H.J. (1992). The entrepreneur and the initiation of new venture launch activities. Entrepreneurship Theory and Practice, 49-55.

Hills, G., and G. Lumpkin (1997). Opportunity recognition research: Implications for entrepreneurship education. Presented to the International Entrepreneurship Conference, Monterey Bay, California.

Hills, G.E., Lumpkin, G.T., and Singh, R. (1997). Opportunity recognition: Perceptions and behaviors of entrepreneurs. Frontiers of Entrepreneurship Research, 17, 168-182.

Hills, G.E. (1995). Opportunity Recognition by Successful Entrepreneurs: A Pilot Study. Frontiers of Entrepreneurship Research, 105-117.

Johannisson, B. (1990). Building an Entrepreneurial career in a mixed economy: Need for social and business ties in personal networks. Presented to the Academy of Management Annual Meeting. San Francisco, CA.

Johannisson, B. (1996). The Dynamics of Entrepreneurial Networks. Frontiers of Entrepreneurship Research, 253-267.

Kaisch, S., and Gilad, B. (1991). Characteristics of opportunities search of entrepreneurs vs. executives: sources, interest, and general alertness. Journal of Business Venturing, 6, 45-61.

Kamm, J. B., Shuman, J. C., Seeger, J. A. & Nurick, A. J. (1990). Entrepreneurial teams in new venture creation: A research agenda. Entrepreneurship Theory and Practice, 14(4), 717.

Kirzner, I. (1979). Perception, Opportunity and Profit. Chicago, IL: University of Chicago Press.

Kirzner, I. (1997). Entrepreneurial discovery and the competitive market process: An Austrian approach. Journal of Economic Literature, 53, 60-85.

Kirzner, I.M. (1973). Competition and Entrepreneurship. Chicago, IL: University of Chicago Press.

Koller, R.H. (1988). On the source of entrepreneurial ideas. Frontiers of Entrepreneurship Research. 194-207.

Kuratko, D., and Hodgetts, R. (1998). Entrepreneurship: A Contemporary Approach (Fourth Edition). Fort Worth, Texas: Dryden Press.

Kuratko, D., and Welsch, H.P. (2001). Strategic Entrepreneurial Growth.. Orlando, Florida: Harcourt Inc. Lachman, R. (1980). Toward measurement of entrepreneurial tendencies. Management International Review, 20 (2), 108-116.

Liao, J., and Welsch, H.P. (2001). Social Capital and growth intension: The role of entrepreneurial networks in technology-based ventures. Frontiers of Entrepreneurship Research, 315-327.

Liebeskind, J., Qliver, A., Zucker, L. and M. Brewer, 1996, Social networks, learning, and flexibility: Sourcing scientific knowledge in new biotechnology firms. Organization Science, 7, 428-443.

Lockett, A. & Wright, M. (2005). Resources, capabilities, risk, capital and the creation of university spin-out companies. Research Policy, 34, 1043-1057.

Lockett, A., Wright, M., & Franklin, S. (2003). Technology transfer and universities' spin out strategies. Small Business Economics, 20, 185-203.

Mazzola, L. (2003). Commercializing Convergent technologies. Nature Biotech, 21(10), 1137- 1143.

Miyazaki, K., and Islam, N. (2007). Convergent technologies Systems of innovation--An analysis of industry and academia research activities. Technovation, 27(11), 661.

Mosey, S., and Wright, M. (2007). From Human Capital to Social Capital: A Longitudinal Study of Technology-Based Academic Entrepreneurs. Entrepreneurship Theory and Practice, 31(6); 909.

Mustar, P., Renault, M., Colombo, M.G., Piva, E., Fontes, M., Lockett, A., Wright, M., Clarysse, B. and Moray, N. (2006). Conceptualizing the heterogeneity of research-based spin-offs: a multi dimensional taxonomy. Research Policy, 35, 289-308.

Nahapiet, J., and Ghoska, S. (1998). Social capital, intellectual capital and the organizational advantage. Academy of Management Review, 23, 242-266.

Nicolaou, N. & Birley, S. (2003). Academic networks in a trichotomous categorization of university spinouts. Journal of Business Venturing, 18, 333-359.

Osman, T.M., Rardon, D.E., Friedman, L,B., Vega, L.F. (2006). The Commercialization of Nanomaterials: Today and Tomorrow. JOM, 58(4), 21-25.

Ostgaard, T. & Birley, S. (1996). New Venture Growth and Personal Networks. Journal of Business Research, 36, 3750.

Ozgen, E., Baron, R.A. (2007). "Social sources of information in opportunity recognition: Effects of mentors, industry networks and professional forums," Journal ofBusiness Venturing. 22(2), 174- 192.

Powell, W.; Kogut, K.W.; Smith-Doerr, L. (1996). Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology. Administrative Science Quarterly, 41, 116-145.

Reitan, B. (1997). Where do we learn that entrepreneurship is feasible, desirable and/or profitable? Paper presented for USASBE conference.

Reinert, K., Andrews, L., and Keenan, R. (2006). Convergent technologies Nexus-Intersection of Research, Science, Technology, and Regulation, 12(5). 811-819.

Reynolds, P.D.1999. National Panel Study of U.S. business startups: Background and Methodology. Databases for the study of Entrepreneurship, V.4. 153-227.

Roberts, E. (1991). Entrepreneurs in high technology: Lessons from mit and beyond. New York: Oxford University Press.

Roco & Bainbridge, 2001). Societal Implications of Nanoscience and Convergent technologies. National Science and Technology Council Workshop Report.

Sapienza, H. (1992). When do Venture Capitalists Add Value? Journal ofBusiness Venturing, 7(1), 9-28.

Schumpeter, J.A. (1942), Capitalism, Socialism and Democracy (Fifth Edition). London: Allen & Unwin, 1976.

Schumpeter, J.A. (1961). The Theory of Economic Development. New York: Oxford University Press.

Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organizational Science, 11, 449-469.

Shane, S., and Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1), 217-226.

Simon, H.A. (1976). From Substantive to Procedural Rationality. In. S.J. Latis (Ed.), Method and Appraisal in Economics, (pp. 129-148). Cambridge University Press.

Sexton, D.L. and Bowman-Upton, N.B. (1991). Entrepreneurship. Creativity and Growth. New York: MacMillan Publishing Company.

Shane, S. & Cable, D. (2002). Network ties, reputation, and the financing of new ventures. Management Science, 48(3), 364-382.

Shaver, K.G., and Scott, L.R. (1991). Person, Process, Choice: The Psychology of New Venture Creation. Entrepreneurship Theory and Practice, 16 (Winter): 23-45.

Shook, C.L., Priem, R.L., and McGee, J.E. (2003). Venture Creation and the Enterprising Individual: A Review and Synthesis. Journal of Management, 29(3), 379-400.

Singh, R. (2000). Entrepreneurial Opportunity Recognition through Social Networks. New York: Garland publishing, Inc. Taylor and Francis Group.

Singh, R., Hills, G.E. Hybels, R.C., & Lumpkin, G.T. (1999). Opportunity recognition through social network characteristics of entrepreneurs. Frontiers in Entrepreneurship Research, 228-241.

Soh, P., and Roberts, E.B. (2005). IEEE Transactions on Engineering Management. Technology Alliances and Networks: An External Link to Research Capability. 52(4), 419-428.

Sorenson, O., Waguespack, D. M. (2005). Research on social networks and the organization of research and development: An introductory essay. Journal of Engineering and Technology Management, 22(1), 1-7.

Stevenson, H., and Gumpert, D. (1985). The Heart of entrepreneurship. Harvard Business Review, 63, 85-94.

Timmons, J.A. (1994). New Venture Creation: Entrepreneurship For The 21st Century (Fourth Edition). Homewood, IL: Irwin.

Timmons, J. (1999). New Venture Creation (Fourth Edition). Irwin: Chicago, IL.

Van de Ven, A.H. (1993). The development of an infrasure for entrepreneurship. Journal of Business Venturing, 8(3), 211-230.

Ventakaraman, S. (1997). The distinctive domain of entrepreneurship research. In J. Katz (Ed.), Advances in Entrepreneurship: Firm Emergence and Growth, (pp. 119-138). Emerald Publishing.

Vesper, K.H. (1990). New venture strategies. Englewood Cliffs, NJ: Prentice Hall.

West, P.G., and Meyer, G.D. (1997). Temporal dimensions of opportunistic change in technology-based ventures. Entrepreneurship Theory and Practice, 22(2), 31-52.

Wolff, M.F. (2006). Social networks key to harnessing nanoscience knowledge explosion. Research-Technology Management, 49(3), 2-3.

Woo, C.Y., Folta, T., and Cooper, A.C. (1992). Entrepreneurial search: Alternative Theories of behavior. Frontiers in Entrepreneurship Research, 31-41.

Woolley, J.L., and Rottner, R.M. (2008). Innovation Policy and Nanotechnology Entrepreneurship. Entrepreneurship Theory and Practice, 32(5), 791-811.

Zhao, L., and Aram, J.D. (1995). Networking and growth of young technology intensive ventures in China. Journal of Business Venturing, 10, 349-37.

Eren Ozgen, Troy University, Dothan Campus
Gale Copyright:
Copyright 2009 Gale, Cengage Learning. All rights reserved.