Explains a study to model differences between Inventors who become Entrpreneurs and those who remain in their research/academic position

1. ABSTRACT
The purpose of this study is to develop a model that predicts whether technologists at Public Research
Organisations (PROs) will become entrepreneurs. This would enhance the ability of institutions and
governments in setting policies that enhance their technology commercialisation activities.
Using inventors employed at SA-based PROs and spin-off entrepreneurs as samples, this paper
investigates building a model based on individual inventors' attitudinal, situational and personal
characteristics that is used to establish the likelihood that such inventors may become entrepreneurs.
Inventors from these research institutions will be identified by the issuance of a patent, or pending
patents for their work. Inventors that have previously left the laboratories to start-up their own
companies will also be identified and both groups will be surveyed.
INTRODUCTION
Technological innovation is viewed as a driving force behind national economic growth, the
globalization of markets, firm competitiveness and a nation's standard of living through its effect on
increasing productivity.
There are a number of ways by which technologies invented in the research institutions may eventually
reach the market. For example, inventions may be licensed to existing firms, they may be developed by
joint ventures between the research institutions and existing firms, the research institutions could spin-off
the researchers into their own company, or the inventors themselves could leave the labs to start their
own companies.
Several studies have suggested that technology-based, high-growth companies are more innovative and
effective at job and wealth creation than large companies in capitalist economies (Birch 1987; Flender
1977; Grasley 1979; Kirchhoff 1988; Rothwell 1988.) According to a study by Reynolds (1987: 231):
"For public policy, the most significant implication is that a small percentage of new firms (30%) are
providing the majority (60%-80%) of the jobs and sales."
Rothwell (1989) claims that small, technology-based firms are more efficient job and wealth creators
because they are less bureaucratic, move more rapidly, and have lower costs for technology
development, along the lines of the high flex Silicon Valley-type of firm (Teece 1996). While this
distinction between new and more established firms is mostly stated than tested empirically (Chesbrough
and Rosenbloom 2002; Zahra et al. 2006) there is an intuitive appeal to the notion that such firms can
help reduce frictions in the marketplace and facilitate faster adaptation, or even the creation of new
markets (Teece 1996; Yoffie and Cusumano. 1999), infect the culture of larger, more static corporations
(Doz 1996) and invigorate product development pipelines of larger firms (Doz 1996; Powell 1998). In
fact, it is a standard model in the pharmaceutical biotechnology industry that small research firms
provide important input into the larger pharmaceutical firms’ pipeline (Fisken and Rutherford 2002;
Glick 2008). The recent phenomena of YouTube and Google Maps also offer corroborating anecdotal
evidence.
It should be emphasized that the technology-based, high-growth firm is a special segment of the small
business community with above average prospects for job and wealth creation. Roberts (1991) has
convincingly demonstrated the importance of technology-based, spin-off companies from MIT and other
universities and laboratories in the Greater Boston area for the development of the first American
technopolis. It is obvious that small high-technology firms are an important factor in the creation of
wealth.
Since Betz (1987) argues that the primary basis for high-growth new ventures is new technology, an
argument can be made that technology-based, high-growth new ventures are a significant factor in
economic development (Di Gregorio and Shane 2003). Thus the use of entrepreneurial spin-offs from

2. public research organizations (PROs) merits serious consideration as a preferred mode of technology
transfer. Indeed, the spin-off activity is likely to do more than commercialise a specific publicly-
sponsored invention; it may create a technical entrepreneur who then commercialises additional public or
private technology, or serve as role model for other academics and create a culture that is more favorably
disposed to seeking commercial outcomes from research. According to Ronstadt (Ronstadt 2007;
Ronstadt 1988), an entrepreneur is likely to initiate multiple ventures over their career.
If we are able to identify those inventors who are most likely to become entrepreneurs, we may also be
able to improve the efficacy of programs developed by the research institutions designed to
commercialise technological innovations (Di Gregorio and Shane 2003; Lerner 2004). Examples of
these kinds of programs include
a) Programs to grant entrepreneurial leave to inventors who wish to create spin-off companies or
b) Funds granted to technologists by research institutions to mature technologies toward a
commercial state if the associated scientists and engineers intend to start enterprises which will
create local jobs or
c) Incubators created that support the creation of new businesses based on the technologies of the
institution.
Other examples are also feasible.
In this paper, we report on a study that will address the question: a priori, using empirical data, is it
possible to identify likely entrepreneurs from those employees at the research institutions that have
produced inventions which (according to the patenting process used in the PROs) were thought to have
some commercial potential? We will use situational variables, attitudes and personal characteristics to
answer this question. The result is a proposed technique that can be used to determine the characteristics
of inventors and innovators who are likely to become entrepreneurs. The significant resources being
deployed to promote spin-off technology commercialisation could be focused upon individuals who have
the characteristics that imply a high probability of becoming entrepreneurs as was proposed by
Sarasvathy (2004.)
Alternatively, the results can be used for broader interventions, if certain widespread barriers to
entrepreneurship are discovered (Sarasvathy 2004), then allowing the more entrepreneurially disposed
researchers self-select for more specific programs.
CONCEPTUAL FRAMEWORK
The literature explains the entrepreneurial act through three major schools of thought: the first school
proposes that traits or personal characteristics determine who becomes an entrepreneur. Although Gartner
(1989a; 1989b) argues that the "traits" school of thought regarding entrepreneurship has been relatively
unproductive, others have found such an approach to be useful when applied to a specific subset of
entrepreneurs (such as PRO scientific and technical inventors) and their non-entrepreneurial counterparts.
Autio and Kauranen (1994), for example, found that technical entrepreneurs differed from technical non-
entrepreneurs in that they exhibited stronger internal personal motivations. In their study on innovation
radicalness, Marvel and Lumpkin (2007) confirmed that characteristics and human capital embodied in
technology entrepreneurs are related to outcomes. Trait-oriented studies have also been conducted to
explore differences between subgroups of technical entrepreneurs. Schrage (1994) found no correlation
between personal achievement motivation and degree of success of technical entrepreneurs. Perry (1982)
argues that further refinement of the taxonomy of technical entrepreneurs may reveal different motives.
Mosey and Wright (2007) found that academics with previous entrepreneurial experience had better
social and business networks, hence were better able to engage with businesspeople and potential
investors, although some discipline-base differences occur. For example, the biological sciences
appeared to have fewer opportunities for academic-industry interaction. Markman et al (2003) also
propose that personal characteristics can be a contributing factor in entrepreneurial success.
The second school of thought maintains that the environment or situation where an individual works, has
a major effect on the entrepreneurial act (Shane 2002; Di Gregorio and Shane 2003; Lerner 2004; O'Shea

3. et al. 2005; O'Shea et al. 2007). For example, Goldhar and Lund (1982) describe the necessity of
continued technical support from the technology source as well as the availability of resources from the
local business community in their case study of a technology commercialised after transfer from a
university. Donckels and Courtmans (1990) also note the extraordinary integration of the small firm into
the local business environment and its dependence upon support from this environment, while West and
Noel (2009) found that the state of development of the local community appears to affect how the CEO’s
previous knowledge can be leveraged. Roberts (1991) identifies the existence of role models as an
inducement for laboratory employees to become entrepreneurs. Hence, the perception of a significant
incidence of entrepreneurship from the laboratory is one of the composite situational variables used in
this study. Cooper (1986) argues for the importance of situational variables in the formation of new
firms; his variables are similar to those used in this study. Martin (1984) provides a comprehensive
descriptive model of the high-tech entrepreneurial act including the causal variable of perceived
supportive environment. Several studies (Louis et al 1989; Shefsky 1994) of the technology-based
entrepreneurial act from universities found that situational variables included herein were important in
the stimulation of spin-off activity and Dew (2009) argues that a combination of situational influences
can have an important bearing on entrepreneurial behaviour.
The third school of thought argues that the attitudes of individuals with respect to the act of
entrepreneurship are most effective in differentiating entrepreneurs from non-entrepreneurs and can be
used to predict which, upon performing suitable attitudinal testing, are most likely to become
entrepreneurs Robinson et al (1991.) This school of thought is derived from a substantial body of
literature in social psychology that addresses the relationship between attitudes and behaviors (Allport et
al, 1935; Ajzen and Fishbein 1980; Baggozzi 1984; Baggozzi 1981; Breckler 1984.) Social
psychologists' interest in the concept of attitude as a means of explaining social behavior dates from the
turn of the 20th century. However, until Robinson et al (1991), few attitudinal scales were developed
specifically to study entrepreneurial behavior. These authors also describe the difficulties inherent in the
first two schools of thought and argue, "A viable alternative to personality and demographic approaches
is the use of attitudes in predicting behavioral tendencies" (1991: 15.) In their paper, these authors used
the generally accepted tripartite model of attitudes (that is attitudes are comprised of three components:
affect, cognition and conation) to develop the Entrepreneurial Attitude Orientation Scale as a predictive
instrument. We have adopted this scale, along with the traits (and personal characteristics) and
situational variables alluded to earlier in the construction of the model. Markman et al (2002) have
found that self-efficacy and the type of regret are different between technological entrepreneurs and
technological non-entrepreneurs.
O’Shea et al. (2007) provide a multivariate explanation of why MIT has been so successful in its
commercialisation activities. They found four broad areas to be relevant
1 personal characteristics of the academics
2 environmental forces acting upon the academics
3 organisational culture that can influence commercialisation activity
4 environmental conditions outside the institution.
In summary, the relative effectiveness of the three schools of thought in separately predicting the
entrepreneurial act is not the subject of this paper. Empirical research continues based upon each of
these schools and their relative effectiveness will be debated for some time. It is our intent to combine
these three streams of research to produce an instrument that will optimize our chances of identifying
potential entrepreneurs, or generally assessing the likelihood of entrepreneurial outcomes from the
research domain.
THE STUDY
A questionnaire that queries respondents for demographic information, personal characteristics,
situational perceptions and attitudes will be administered to a sample from two populations. Both
populations were inventors at PROs. Inventors are defined by their act of applying for and receiving
patents, or generally being named on a patent. The two distinct populations are: 1) non-entrepreneur
inventors (NEI) who at the time of the study elected to remain employees of the laboratories and 2)
entrepreneur inventors (EI) who in the last five years had left the laboratory in order to commercialise

4. their technology. While different inventions obviously have different commercial value and thereby
represent varying degrees of entrepreneurial opportunity, invention disclosures are reviewed for
commercial potential. The expense of patent prosecution is incurred only if the invention is judged to
have such potential.
For this pilot we intend surveying SA-based inventors, expanding across Australia in the next round.
The questionnaire consists of three parts: in part I, standard questions are asked regarding the inventors'
demographic profile such as age, sex, education and years of experience. The second portion of part I
(personal characteristics) deals with the respondents’ involvement in the pursuit of entrepreneurial
activities such as spin-offs, consulting, receipt of royalties and other business activities.
Part II of the survey instrument consists of 49 questions designed to capture data about seven classes of
situational variables. Situational questions are derived from three sources: 1) the authors' experiences
with PRO spin-offs over the last two decades, 2) a pilot study involving six spin-off entrepreneurs, and 3)
a survey of the literature that discusses situational variables used in contexts similar to that of this study,
that is, scientists and engineers leaving technology sources to commercialise their inventions. The seven
factors are defined by factor analysis using principal components with varimax rotation and are extracted
from the 49 questions. The factors are: 1) the degree of support from laboratory management for
entrepreneurial spin-offs; 2) self-reported inclination to be an entrepreneur; 3) the extent of local
business support for new businesses; 4) personal resources available to become an entrepreneur; 5) the
desire to be involved in supporting the laboratory mission of technology commercialisation; 6) the
incidence of laboratory spin-offs or entrepreneurial activity; and 7) the seriousness of constraints to the
entrepreneurial act.
The Entrepreneurial Attitude Orientation Scale which was available in the published literature was used
as Part III of the survey instrument. Twelve composite attitudinal variables, derived from 75 survey
questions, are to be investigated using a Likert-type ten-point scale. The survey developed by Robinson
at al (1991) was selected because of its empirical validity and availability in the published literature.
Cronbach’s alpha will be calculated to ensure the reliability of the personal characteristic section.
Nunally (1978) indicates that a reliability measure of 0.70 or higher is acceptable indicating the
reliability of the survey instrument.
The Model used to Predict Entrepreneurs from PRO Inventors
The purpose of this study is to create a model that will predict future entrepreneurial acts on the part of
inventors who have not previously so behaved. Responses to the questionnaire provided data for a
discriminant analysis that sought to find a model that effectively predicts future entrepreneurs from a set
of technical laboratory inventors.
In this study, we will have respondents from two populations that differ in the consummation of the
entrepreneurial act. We will use various Fisher discriminant functions to assign each respondent in the
sample to one of the two groups that better reflects their personal characteristics, situational perceptions
and attitudes: the EIs or NEIs. The classification of NEIs as EIs is our method of predicting the inventors
who will become entrepreneurs because they have the characteristics, the environment (the situational
component) and the attitude to become entrepreneurs. Johnson and Wichern (1992) describe the Fisher
method of separating a sample made of two populations into two separate groups using Fisher's linear
discriminant functions. For example, the discriminant analysis to be run with the situational variables
will use the seven variables (inclination to be entrepreneur, lab support, business support, incidence of
entrepreneurship, personal resources, desire to support commercialisation and constraints) to define a
formula that assigns each respondent to a group depending on the respondent's score on each one of the
variables. The discriminant analysis is therefore a classification scheme that utilizes several statistical
techniques to determine ways in which the scores on each one of the variables can be used to
differentiate between the different groups and then assigns each respondent to a group based on their
scores so that they are defined by their scores rather than by their actual group.
Any NEIs that are classified by the discriminant functions as EIs are the ones that we assume to be our

5. predicted entrepreneurs.
To test for differences in demographic, personal characteristics, situational perceptions and attitudes, we
should have three groups:
1) NEIs predicted to become entrepreneurs: those who are predicted to become entrepreneurs.
2) NEIs not predicted to become entrepreneurs: the remainder of the NEIs.
3) the EIs who already had spun off their entrepreneurial firms.
The hypotheses that we want to test could be stated as
H1: There are differences among the three groups in personal characteristics.
H2: There are differences among the three groups in demographic information.
H3: There are differences among the three groups in situational perceptions.
H4: There are differences among the three groups in attitudes.
First an ANOVA test will be run to test for differences among the three groups. Mean differences
between each pair of groups will be tested using the Tukey-Kramer test of multiple comparisons. The
Tukey-Kramer test is an adjustment to the Tukey HSD pair wise multiple comparison test that is usually
exercised when the size of the samples is unequal (1990.) The Tukey-Kramer test controls for multiple
comparison errors when more than two variables are used in the differences tests.
CONCLUSIONS AND FURTHER RESEARCH
In the light of increasing government interest in the transfer of technology from PROs to commercial
markets, this study attempts to improve the efficacy of commercialisation efforts by developing a
methodology with which it may be feasible to identify those inventors that are most likely to become
entrepreneurs.
Once identified, these individuals can be invited to participate in special programs that facilitate their
entrepreneurial activities. On a related dimension, there is an argument for ‘creating’ a category of
professionals to take on the role of technology/commercialisation entrepreneurs, with solid science and
commercialisation capabilities in their profile, but who do not wish to be ‘behind the bench’ all the their
lives, who enjoy science communication and who wish to be at the cutting edge of business and wealth
creation, rather than mainstream markets, as described in the literature on surrogate entrepreneurship
(Radosevich 1995; Davidsson et al. 2001; Lockett et al. 2005).
Future repetitions of the survey to acquire intertemporal data will allow further testing of the
discriminant analyses as a tool to predict the propensity of inventors in public-sector technology sources
to become entrepreneurs involved in the commercialisation of their inventions. The authors intend to
repeat the survey, hopefully expanding it across Australia, in the first instance, every three years in order
to determine who among the NEI sample actually became EIs during the intervening period. Another,
more likely, outcome of longitudinal surveys is to allow monitoring and analysis of how the innovation
system is developing with respect to the propensity to create new ventures directly from the research
world. This in turn can be used for policymaking purposes, to address any concerns that arose, but also
to help direct the innovation system towards desired outcomes.
If the propensity to become entrepreneurs can indeed be identified and determined to be useful as a
predictor, there are major policy implications for the use of public-sector technology in industry. For
example, preference could be established for selecting as licensees those inventors identified with this
propensity. In the US, some laboratories use the existence of a business plan prepared by the inventor as
an indicator of potential commercialisation activities. Indeed, technical staff could be hired, promoted
and selected for training based upon their predicted likelihood to become entrepreneurs. Several
laboratories are now involved in expensive training programs designed to improve entrepreneurial
knowledge and skills for technical staff members. The success of these training programs can be
enhanced if participants can be selected based upon their propensity to become actual entrepreneurs.

6. The effectiveness of such programs also could be assessed by noting the degree to which the training
affects this propensity.
In recent years, the Australian government has introduced a Commercialisation Training Scheme for
students taking a higher degree by research. This entails undertaking courses that lead to a Graduate
Certificate in research commercialisation. Willingness to participate in such a program could be taken as
a partial indicator of willingness to engage in the entrepreneurial act. By the same token, longitudinal
surveys could provide support or otherwise for the use of such measures to enhance commercialisation
outcomes.
In addition to furthering the efforts for public-sector technology commercialisation, the ability to predict
future technical entrepreneurs could enhance research activities regarding the entrepreneurial act.
Almost all of the studies done to date are strictly ex post the actual act. If accurate predictions could be
made, a priori data would allow the examination of changes in attitudes, perceptions and personal
characteristics which result from the entrepreneurial experience. Such studies could significantly
enhance our understanding of technical entrepreneurship.
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