Improve Innovation and College Research Funding 1
A STUDY TO IMPROVE INNOVATION FOR SMALL BUSINESS AND
RESEARCH FUNDING FOR COLLEGES
by
Dr. Terry James
© April 2009
ALL RIGHTS RESERVED
Improve Innovation and College Research Funding 2
ABSTRACT
A survey instrument was developed to measure business interest in funding college
research. Regressions between business interest in funding college research, and the
independent variables of collaboration, innovation challenges, and innovation level were
constructed. The theory of academic capitalism predicts growth in business-education
collaboration to secure external funds (Slaughter & Rhoades, 2004). Data supported the
theory of academic capitalism. The F statistic was used to evaluate the regression model.
F(3, 67) = 13.2, p < .001 provided overwhelming support for rejecting the null hypothesis
that no statistically significant regression model could be constructed to predict business
interest in funding college research using the variables collaboration and innovation
challenges: Business funding = 0.25 + 0.06 (collaboration) + 0.05 (finance).
Note: This paper summarizes a dissertation to be published in 2009.
Improve Innovation and College Research Funding 3
INTRODUCTION
College administrators believe substantial research funds from business may be
available for applied research by community colleges (Association of Canadian
Community Colleges [ACCC], 2006). While anticipation for a research contribution by
colleges was evident, the problem was that business demand for community college
research was unknown. Many people in government, industry, and educational
institutions are more interested in market demand than academic-driven research
initiatives (ACCC, 2007a). Disappointment will result if community college applied
research does not result in innovation. The purpose of the current exploratory,
quantitative, cross-sectional survey was to examine the relationships between small
business interest in funding community college research and three independent variables,
namely, innovation, innovation challenges, and collaboration. Multiple regression tests
were used to build an equation to predict business interest in funding college research.
The Organization for Economic Co-operation and Development (OECD)
publishes regular reports about investments in research because research affects the wellbeing of a nation (Sharpe & Smith, 2005). The OECD estimates a 12% increase in
income per person for every percentage point increase in business research as a
proportion of gross domestic product (Organization for Economic Co-operation and
Development [OECD], 2003). Research can provide innovations, new products, and new
processes for productivity and commercial success. Governments put emphasis on
research because the innovation outcomes appear responsible for much of the rise in
material living standards and economic progress (OECD, 2006a). The purpose of the
Improve Innovation and College Research Funding 4
current study was to improve small business innovation by studying applied research
funding at Canadian community colleges.
Note: From College/Institute Technology Transfer Initiative Conceptual Framework by Association of
Canadian Community Colleges (ACCC), 2007, Annexure 1. Ottawa, Canada: Author. Copyright by ACCC.
Reprinted with permission by ACCC Communication and Information Services Unit, L. Malcolmson.
Figure 1. Framework for research and commercialization inputs and outputs.
Improve Innovation and College Research Funding 5
Slaughter and Leslie (1999) published a theory of academic capitalism showing
world trends toward more market-orientated educational research. Applied, as opposed to
basic, research in postsecondary institutions grew quickly because of increased external
funding by partners in industry. Slaughter and Leslie discovered trends in policies at all
levels of government across many countries that increasingly emphasized the commercial
potential of funding educational research. The theoretical foundation used in the current
study was academic capitalism. From a theoretical perspective, the conceptual framework
shown in Figure 1, from the ACCC (2007a), showed two forces in the commercialization
of research: a technology push and a market pull. The area of strongest interest for many
policy makers is market pull or demand. The strong interest in market pull data was a
central consideration for choosing a survey of business rather than a survey of educators.
A knowledge gap was evident with the scope of market pull for community
college applied research. Market pull may determine the speed with which collaboration
between colleges and businesses occurs, as suggested by academic capitalism. Research
about universities that make early penetration into commercialized research showed that
early-start universities maintained enduring competitive advantage and improved
capacities to commercialize research (Owen-Smith, 2005). No empirical data accessed
suggested that the early-starter advantage would not apply to community college applied
research.
Universities have a tradition of research but community colleges focus almost
exclusively on teaching. With the increasing emphasis on applied rather than basic
research, community colleges anticipated an opportunity to participate in research. With
the promise of external funds from businesses and government, community colleges saw
Improve Innovation and College Research Funding 6
an opportunity to contribute to the national research and innovation targets. The
Association of Canadian Community Colleges (ACCC), an advocacy group representing
colleges, lobbied for a government mandate to conduct commercial research. Academic
capitalism predicted increased market participation by postsecondary institutions if
external funds were available (Slaughter & Rhoades, 2004).
The ACCC was successful with government regulators in promoting research in
addition to teaching as an available mission for community colleges. The ACCC
consistently advocated the goal of community college research in collaboration with
small and medium enterprise (SME) partners to improve innovation (ACCC, 2007b).
After a pilot program in 2004, the government changed the National Sciences and
Engineering Research Council to include a College and Community Innovation Program,
allowing community colleges to compete for research funding that promoted SME
innovation (National Sciences and Engineering Research Council, 2008).
While anticipation exists for a significant research contribution by community
colleges, the business demand for community college research was unknown. The policy
means government, industry, and educational institutions are more interested in market
demand as opposed to academic or curiosity-driven initiatives (ACCC, 2007a). Few
studies about community college research as compared to university research are evident.
No quantitative studies about the scope of market demand to fund community college
research were apparent.
The need for innovation in Canadian SMEs was documented in numerous studies
(Coates, 2004; Conference Board of Canada, 2005; Earl, Gault, & Bordt, 2004). The
reason for government sponsorship of research is to promote commercial innovations.
Improve Innovation and College Research Funding 7
What was less clear is how business funding of community college research correlates to
business innovation. The problem was to correlate the market demand for community
college research with the innovation challenges of SMEs.
Initial studies using academic capitalism theory by Slaughter and Leslie (1999)
showed a smaller academic capital trend in Canada than in the other countries examined.
A later book by Slaughter and Rhoades (2004) focused on the United States rather than
Canada. Many qualitative studies about academic capitalism theory exist; often as
doctoral dissertations (Bullard, 2007; Hutchinson, 2005; Mars, 2006; Machado, 2005).
Limited quantitative studies about academic capitalism are evident (Nixon, 2003). The
largest quantitative work (Slaughter and Leslie) was focused on universities and
examined a 10-year trend in the percentage of research funding from government versus
external sources such as business.
Background
The historical impetus of a more applied and commercial focus in research began
in the USA with the passage of the Bayh-Dole legislation in 1980 (Etzkowitz, Webster, &
Healy, 1998; Rahal, 2005). The Bayh-Dole Act improved U.S. productivity by promoting
innovation. The legislation allowed universities to patent the results of federally funded
research. Universities licensed inventions to companies to raise royalties.
The ties between corporations and universities grew substantially from 1980
(Rahal, 2005). Universities’ patents averaged 250 a year before Bayh-Dole and by 1998,
grew to 4,800 patents a year (Newman, Couturier, & Scurry, 2004). Corporate
sponsorship of university research grew from $850 million in 1985 to $4.25 billion within
a decade. In 2005, AUTM reported 15,115 U.S. patent applications (Association of
Improve Innovation and College Research Funding 8
University Technology Managers [AUTM], 2005). Shea, Allen, Gorman, and Roche
(2004) reported that spin-off companies from American academic institutions between
1980 and 1999 provided 280,000 jobs and $33.5 billion in economic activity to the U.S.
economy (p.22).
Governments in many countries learned educational institutions could create
wealth if government and institutional policies promoted research. Universities using
patents, licenses, and spin-off companies generated considerable wealth (Chang, Chen,
Hua, Yang, 2005). The Association of Universities and Colleges of Canada (2005)
reported that between 1999 and 2003, commercialization of research doubled gross
income from $23.4 to $51 million Canadian dollars.
Slaughter and Leslie (1999) stated in a germinal work that higher education
institutions would increasingly become involved in academic capitalism to attract
external revenue. Academic capitalism implies direct competition between higher
education institutions to attract external funding. In the germinal work by Gibbons,
Limoges, Nowotny, Schwartzman, Scott, and Teow (1994), Gibbons et al. stated that the
organization, location, ownership, and stability of academic research would change
significantly. The primary goal of universities as educators would expand to include a
new mission of contributing to the economy. Commercial and consumer interests would
increasingly dominate research. Knowledge would develop on many sites, not just
universities, with ever-finer specialties in networked, problem-oriented teams, where
researchers increasingly operate as contract workers.
The promotion of commercialized research is controversial. Collaboration of
businesses and universities in public-private partnerships may mean some loss of
Improve Innovation and College Research Funding 9
university control over the direction and quality of research because of different values
by academia and industry. A business push to proceed more rapidly for competitive
advantage is apparent. Business is more reluctant to publish or share results (Godin,
Dore, & Lariviere, 2002; Ylijoki, 2003).
Canadian research spending was 12th among the OECD countries and well below
the OECD average (OECD, 2006b). The target is for Canada to rank in the top five
countries in research performance by 2010 (Sharpe & Smith, 2005). Changes in policy
allow Canadian community colleges to perform applied research and commercialize
technology transfers to businesses (ACCC, 2006). College administrators believe
substantial research funds may be available from local businesses. The first problem was
business demand for community college research was unknown. No quantitative studies
were found about the scope of market demand for community college research.
The need for innovation in Canadian SMEs is documented in numerous studies
(Coates, 2004; Conference Board of Canada, 2005; Earl et al., 2004). Less clear was how
business funding of community college research correlated to business innovation. The
second problem was to correlate the market demand for community college research to
the innovation challenges of SMEs.
Many qualitative studies of academic capitalism theory exist, often as doctoral
dissertations (Bullard, 2007; Hutchinson, 2005; Mars, 2006; Machado, 2005). Limited
quantitative studies exist (Nixon, 2003). The largest quantitative study conducted was
historical and compared government and business funding of universities. No known
quantitative and forward-looking study of community college research opportunities was
apparent. Academic capitalism predicted increased opportunities for educational
Improve Innovation and College Research Funding 10
institutions to secure external funds when collaborating with market forces. The third
problem addressed by the current study was to correlate the level business funding of
college research with the level of community college-business collaboration.
Research directed at the three problems described provided the grounds for a
multiple regression test of academic capitalism theory. Multiple regression analysis of the
variables tested the predictions of academic capitalism about the growth of businesseducation partnerships for research funding.
Methodology and Variables
The study described small business community demographics, the level of small
and medium enterprises (SME) innovation, the innovation challenges for small business,
and the interest in applied research partnerships with community colleges on the part of
business. The survey targeted SMEs as defined by Industry Canada (2006): fewer than
500 employees. The survey included both profit and nonprofit organizations regardless of
business sector or organizational mission. The sampling method used the simple random
sampling technique from published e-mail lists. According to Creswell (2005), the crosssectional survey is appropriate for collecting data about attitudes, beliefs, opinions,
practices, community needs, and program evaluations.
The dependent variable was business interest in funding community college
research and was any exchange of resources between businesses and colleges (Slaughter
& Rhoades, 2004). Business interest in funding research was circumscribed by a sevenpoint Likert-type scale from low interest to high interest across seven funding options. An
index was constructed as a percentage of the maximum score from the combined value of
all seven funding options. Table 1 shows the funding options.
Improve Innovation and College Research Funding 11
Table 1
Funding Options
1. Would you like to fund College research to solve your business challenges using
college students? (Students can research a business problem to earn grades instead of a
salary).
2. Would you like to fund College research to solve your business challenges using a
student contest? (A business could offer a contest with the best student winning a prize
such as summer employment or a scholarship).
3. Would you like to fund College research to solve your business challenges using
royalties? (A contract to pay the school a percentage of future sales as a royalty for
College innovations permits funding without immediate business investment).
4. Would you like to fund College research to solve your business challenges using
equipment, space, or data? (College researchers work in exchange for the donation of
your business equipment, space, or data. Older models of expensive technology may be
useful for teaching purposes. A file with millions of data items may be useful too).
5. Would you like to fund College research to solve your business challenges using a
government matching grant? (Government agencies will provide research grants to
colleges if a business sponsor is involved. The cost of research is reduced by the
government grant).
6. Would you like to fund College research to solve your business challenges by
providing a cash donation? (If properly structured, a cash donation to a school for a
scholarship or other resources could be tax deductible).
7. Would you like to fund College research to solve your business challenges using
college faculty? (Most College faculty are 100% allocated to teaching. If a business paid
a portion of the salary of a teacher for a semester, the business would have access to a
senior person with decades of experience).
Innovation was defined as a significantly new product, process, or service
(OECD, 2005; Statistics Canada, 2006). A difference was apparent between the periods
used in some studies, with popular choices being one, three, or five years. The current
study used five years. The innovation variable provided an ordinal measure of innovation
from three to zero. Innovation was an ordinal variable with the standard options of worldfirst innovator, nation-first innovator, organization-first innovator, or non-innovator
Improve Innovation and College Research Funding 12
(Statistics Canada, 2006). Some studies used a dichotomous measure classifying
participants as innovators or non-innovators (Conference Board of Canada, 2005).
Innovation challenges are obstacles slowing down or causing problems for
innovation activities (Statistics Canada, 2006). The participant ranked innovation
challenges from the list of time, project management, marketing, rapid change, finance,
technical expertise, globalization, Internet, and staffing. The survey collected the
responses ranking the most important challenge as one, the second most important
challenge as two and so on, but coded the data by reversing the numbers so the most
important challenge was given the highest value and the lower the value the lower the
ranking.
The collaboration variable was defined as networking between business and
educational institutions to promote economic competitiveness by exchanging information
and research (Slaughter & Rhoades, 2004; Watt, 2003). Studies vary in the terminology
used for collaboration with terminology such as partnership, linkage, or networking. The
context in the current study was community college applied research, and would small
business innovators collaborate with a community college on business research. The
collaboration variable in the survey used a seven-point Likert-type scale from low interest
to high interest in measuring interest in collaborating with a college.
The validity and reliability of the instrument was tested with a multiple phase
pilot study (Sproull, 2004). A small focus group with experts from small business,
college administration, and college researchers validated the new instrument. Experts
were selected by the Seneca College Research Department. The focus group contributed
insights about the value and validity of the survey questions. The discussion covered the
Improve Innovation and College Research Funding 13
language and cognitive interpretations of the wording, order of questions, descriptions,
and appropriateness of the questions (Fowler, 1995). The discussion was taped with video
for analysis. The focus group was the first phase of pilot study to pretest the reliability of
the definitions, wording, and the validity of the questions.
A second phase improved the questionnaire with a field test of 16 participants
from the target population. The technique used was cognitive interviewing (Dillman,
2007). For each question, the coding sheet provided a category to count if (a) errors were
made, and if errors were major, where the meaning was misconstrued, or minor, such as
misreading words, (b) instructions about which questions to skip were correct and
understood, (c) questions were repeated, (e) the participant asked for clarification, (f)
probes were needed by the interviewer to obtain an answer, and (g) comments by
interviewer were made (Fowler, 1995).
The Scott’s Directory was used to identify the sampling frame of small business
leaders (Business Information Group, 2008). The Scott’s online directory included a
search manager that filtered the list of business contacts in the City of Markham with less
than 500 employees. The Scott’s Directory included the names and positions of the
business principals. Where several business contacts were listed for a company, the most
senior leader was selected. The Scott’s Directory was supplemented with the Markham
Board of Trade business directory (Markham Board of Trade, 2008). The Markham
Board of Trade business directory included information about the number of employees,
business contact number and e-mail address. If an overlap between the two directories
gave a duplicate business selection, the duplicate listing was discarded. Simple random
sampling without replacement was used as the sampling technique. Each business in the
Improve Innovation and College Research Funding 14
directory was numbered. Microsoft Excel’s random number generator was used to
generate a list of random numbers to determine which business was sampled. Over
sampling was employed. The email directories showed the number of employees. If the
business had hired more employees, any business with over 500 employees was excluded
with a filtering question in the survey. Once the instrument was finalized, the data for the
sample was collected using an online survey. The survey was developed using Enterprise
Feedback Management survey software by Vovici. The survey software, previously
known as Websurveyor, provided software tools to create and manage the secure
distribution of the survey instrument.
Results
The survey invitation cover letter was sent in increments of 500 or more to 2,236
businesses until at least 120 completed responses were collected. The initial response rate
was only 3%. Follow-up invitations increased the response rate to 8%. Malhotra (2007)
claimed mail survey’s typical response rates are less than 15% and internet surveys are
even lower (p.199). Richard Cunningham, President of the Markham Board of Trade,
stated the business community had strong sentiments about survey fatigue (personal
communication, September 1, 2008).
The 2,236 invitations included 112 invalid e-mail accounts. An informed consent
electronic signature was required before access to the survey, and 53 participants
declined consent. One company exceeded the 500 employee filter. Participants could skip
any question, and in 22 cases, a question pertaining to the dependent variable was
skipped. Any survey that included a skipped key variable was removed. After managing
the data issues, 115 was the sample size.
Improve Innovation and College Research Funding 15
Norusis (2006) suggested a sample size of 10 to 20 participants for each
independent variable as a sample minimum for multiple regression equations. With three
independent variables, a minimum sample size of 30 to 60 would be needed. The sample
size was also calculated using a formula for the standard error of the mean, where N is the
sample size, SD is the standard deviation of the sample, z is standard score for a
confidence level of 95%, and D is maximum permissible difference between sample and
population mean. The minimum sample size using the dependent variable, funding
interest, was established to be at least 113 as shown in Equation 1.
N = (SD.)2 z2
D2
=
(0.271)2 1.962 = 113
0.052
(1)
The standard deviation for business funding calculated using Excel was 0.27
using the 115 completed surveys. The calculated value for z, standard score at 95%
confidence, was 1.96. The permissible difference for business funding of college research
was selected as 5%. The final usable sample collected was 115, and the minimum sample
size calculated using the business funding dependent variable was 113.
Validity and Reliability
The instrument was tested first with a focus group. The group had eight attendees
made up of six business leaders and two research experts. The business leaders by title
included one owner, three chief executive officers, and four presidents. The focus group
of business and research experts provided the first level of validity and reliability
analysis. Major improvements were made to the questions’ wording, the flow of
questions, the categories of answers, and the types of questions. The second phase of
Improve Innovation and College Research Funding 16
reliability and validity testing used individual cognitive interviews in a field test.
Improvements to validity based on the field test included 16 areas. Both phases improved
the validity and reliability of the survey instrument.
For the dependent variable, business funding, seven questions were used to create
a composite index as described in chapter 3. A common measure of reliability to assess
internal consistency is Cronbach’s Alpha. Intra-item consistencies within an index
provide evidence that the same underlying construct or single dimension are included in
the index. Norusis (2006) recommends a Cronbach Alpha measure of more than 0.8 to
ensure a good scale. Cronbach’s Alpha, calculated using SPSS, for the seven items used
to create the business funding dependent variable, measured .91.
Findings
The business sectors included retail, construction, professional services, foodaccommodation, manufacturing, real estate, health, finance, waste management,
wholesale, transportation, education, and information technology. The survey data
showed that 92% of the participating companies were small and 8% were of medium size
with over 50 but under 500 employees. National data for the population showed that 95%
of companies were fewer than 50 employees, which compares well with the sample
(Industry Canada, 2007). Table 2 shows most business leaders participating were
experienced managers.
Improve Innovation and College Research Funding 17
Table 2
Management Experience of Participants
Category
Frequency Percent
______________________________________
Less than a year
3
2.6%
Between 3 – 5 years
13
11.3%
Between 6 – 10 years
10
8.7%
More than 10 years
89
77.4%
_______________________________________
The titles of participants included 43 presidents, 9 owners, 11 general managers, 3
chief executive officers, 8 directors, 4 partners, and 3 principals. The participants
appeared to be senior leaders. Table 3 provides data showing most companies existed for
at least five years although new companies also participated. Industry Canada (2007) data
suggested that most small companies fail within the first three years of existence.
Table 3
Age of Organization
Category
Frequency Percent
______________________________________
Less than 1 year
2
1.7%
Between 1 and 5 years
12
10.4%
Over 5 years
101
87.8%
_________________________________________
Seniority and experience do not necessarily predict educational level. Figure 2 is a
bar chart showing the level of management education. Educational levels of the sample
covered all educational levels from high school to doctoral levels.
Improve Innovation and College Research Funding 18
Management Education Level
50
Percent
40
30
46.09%
20
20.87%
10
16.52%
15.65%
0.87%
0
Undergraduate
degree
Master degree
High school
College Diploma
Doctorate
Figure 2. Education level of participants.
A number of assumptions are needed to ensure valid correlation or regression
tests. The distribution of values must follow a normal distribution. All research questions
included the same dependent variable. In Figure 3, the distribution of the dependent
variable, business funding, was shown. While not a perfectly normal distribution, the
histogram does not provide enough evidence to rule out regression or correlation tests.
Only innovators can have innovation challenges. A more pronounced normal distribution
for the business funding variable was visible when using the subset of data responses
from innovators as shown in Figure 4.
Improve Innovation and College Research Funding 19
Distribution Histogram for Business Funding
18
16
14
Percent
12
10
8
6
4
2
1.
1
1.
0
0.
9
0.
8
0.
7
0.
6
0.
5
0.
4
0.
3
0.
2
0.
1
0.
0
0
Figure 3. Distribution of business funding dependent variable N = 115.
Distribution of Business Funding Variable for Innovators
25
Percent
20
15
10
5
1.
10
1.
00
0.
90
0.
80
0.
70
0.
60
0.
50
0.
40
0.
30
0.
20
0.
10
0.
00
0
Figure 4. Distribution business funding dependent variable subset of innovators n = 68.
Hypothesis 1: Collaboration
H1: A relationship exists between small business interest in funding community
college research and the level of collaboration with community colleges.
Improve Innovation and College Research Funding 20
A scatter plot was drawn with Excel as shown in Figure 5. A positive relationship
between business funding and collaboration was apparent. Both variables are interval
data. The Excel Add-in called Data Analysis calculated the correlation and regression
measures. Using simple regression, the coefficient of correlation or r, coefficient of
determination or r2, and p-value were r = .56, r2 = .31, and p < .001. A p-value of less
than .001 indicated strong evidence for rejecting the null hypothesis that no relationship
existed between small business interest in funding community college research and the
level of collaboration with community colleges.
Business Funding and Collaboration
1.20
Business Funding
1.00
0.80
0.60
0.40
0.20
0.00
0
1
2
3
4
5
6
7
Collaboration
Figure 5. Funding interest and collaboration level. N=115.
Hypothesis 2 Innovation Challenges
H2: A relationship exists between small business interest in funding community
college research and business innovation challenges.
Improve Innovation and College Research Funding 21
One type of innovation challenge may obscure a relationship by another type of
innovation challenge. An analysis into the specific types of innovation challenges was
required to filter out those innovation challenges that were not related to the business
funding dependent variable. For example, only two participants selected the Internet
innovation challenge. While SPSS showed a high correlation between the Internet
challenge and business funding, a scatter plot of two points was not meaningful. The
Internet challenge was removed.
Using SPSS software and multiple linear regression backward elimination as
recommended by Norusis (2006), the regression backward elimination method calculated
the p-value for each challenge innovation against the business funding dependent
variable. All challenge variables except finance and marketing were filtered as not
significant as shown in Table 3. The calculations used a subset of data as only innovators
have innovation challenges. The number of participants who are innovators was 68.
As the marketing p-value was greater than .10, the marketing challenge was not
significant. A scatter plot was drawn with Excel as shown in Figure 6 between business
funding and the finance innovation challenge. A positive relationship between business
funding and the finance innovation challenge was apparent. The coefficient of the finance
innovation challenge was .06 indicating that each unit of increase in the finance challenge
resulted in a .06 increase in business funding on average. The r2 value of .10 indicated
that the finance challenge accounted for 10% of the variation in the business funding
dependant variable. The p-value of .007 indicated strong evidence that the null hypothesis
be rejected that no relationship existed between small business interest in funding college
research and the finance innovation challenge
Improve Innovation and College Research Funding 22
Table 2
Backward Elimination Regression Model (n = 68)
Challenge
P-value 1
P-value 2
P-value 3
Finance
.10
.02
.00
Marketing
.09
.11
.14
Globalization
.26
.21
Staffing
.41
Project Manage
.48
Rapid Change
.70
Technology
.55
Time
.69
Internet
.NA
.
.
Business funding and finance innovation challenge
1.20
Business funding
1.00
0.80
0.60
0.40
0.20
0.00
0
0.5
1
1.5
2
2.5
3
3.5
Finance
Figure 6. Business funding and the finance innovation challenge.
Improve Innovation and College Research Funding 23
Hypothesis 3: Level of Innovation
H3: A relationship exists between small business interest in funding college
research and small business level of innovation.
A scatter plot was drawn with Excel as shown in Figure 7. A positive linear
relationship between business funding and level of innovation was apparent. Innovation
was an ordinal variable so required a Spearman rather than Pearson correlation. SPSS
was used to run Spearman’s rank correlation which provided the Spearman’s rank
correlation coefficient of .33 and a p-value of less than .001. A p-value of less than .001
indicated strong evidence to reject the null hypothesis that no relationship existed
between business funding in college research and level of innovation.
BusinessFunding and Level of Innovation
1.20
Business Funding
1.00
0.80
0.60
0.40
0.20
0.00
0
1
2
3
4
Innovation Level
Note. Level of innovation is ordinal data with 0 as no innovation, 1 as new to organization, 2 as new to
Canada, and 3 as new to the world. N = 115.
Figure 7. Funding interest and business level of innovation.
A simple regression was also run in addition to the correlation. A simple
regression test using Excel calculated values of r = .33, r2 = .11, and p < .001. The r2
Improve Innovation and College Research Funding 24
value of .11 indicates that the level of collaboration accounts for 11% of the variation in
the funding index. The coefficient for level of innovation was .08 indicating that each
unit of increase in level of innovation resulted in a .08 increase in business funding on
average. The p-value of less than .001 indicated strong evidence that the null hypothesis
be rejected that no relationship existed between small business funding in community
college research and the level of innovation.
Hypothesis 4: Regression equation
H4: A statistically significant regression model can be constructed to predict the
degree of business interest in funding community college research using the
variables collaboration, innovation, and innovation.
A regression equation must assume certain characteristics to be valid. The data
must be normally distributed, the variance should be the same for all values of the
independent variable, the observations should be independent, and the relationship should
be linear. A check for normality is possible using a histogram of standardized residuals.
Figure 8 does not permit us to rule out a normal distribution. As a check for equal
variance, Figure 9 provides a plot of residuals that showed no pattern of increasing or
decreasing values. Independence was not a concern because the survey software
permitted only one response per invitation, and the survey was conducted at one point in
time. The collinearity shown in Table 4 shows a tolerance of close to one. If collinear
tolerance was close to zero, the affect of one independent variable would be difficult to
separate from another.
Improve Innovation and College Research Funding 25
Standardized Residual Histogram
Dependent Variable: Business Funding
12.5
Frequency
10.0
7.5
5.0
2.5
Mean =4.91E-16
Std. Dev. =0.993
N =68
0.0
-2
0
2
Regression Standardized Residual
Figure 8. Residual histogram check for normality.
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: Business Funding
Expected Cum Prob
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
Observed Cum Prob
Figure 9. Residual plot of business funding.
0.8
1.0
Improve Innovation and College Research Funding 26
Table 3
Coefficients, Significance, and Collinearity
Unstandardized
Coefficients
Model
1
Standardized
Coefficients
t
Sig.
4.596
.000
4.314
.000
.976
1.024
(Constant)
B
.269
Std. Error
.059
Collaboration
.060
.014
.064
.022
.319
2.957
.004
.904
1.107
-.049
.025
-.208
-1.938
.057
.912
1.096
Finance
Marketing
Beta
Collinearity Statistics
Tolerance
.447
VIF
MegaStat software was used to run all possible regressions using the
collaboration, innovation, and all innovation challenge variables with sufficient data.
MegaStat provided the adjusted r2, p-value, and the Mallow C-p calculation for every
possible combination of variables. A model with more variables may not be a better fit
even if the value of r2 increased. Mallow’s C-p was a measure of goodness-of-fit that was
less dependent on the number of variables than r2.
The largest r2 was .31, C-p of 2.4, and p-value of less than .001 using the
variables of collaboration, innovation, and innovation challenges of time, finance, and
marketing. A smaller r2 of .3, C-p of 1.9, and p-value of less than .001 occurred using the
variables of collaboration, finance, and marketing. From the MegaStat all-possible
regression analysis, the collaboration variable was dominant with finance providing some
influence as a predictor of research funding.
Using collaboration and finance as independent variables, the adjusted r2 = .27
meaning 27% of the variation in the dependent variable was explained by the
independent variables chosen. F(3, 67) = 13.2, p < .001 provided overwhelming evidence
to reject the null hypothesis that a statistically significant regression model cannot be
constructed to predict the degree of business interest in funding community college
Improve Innovation and College Research Funding 27
research using the variables collaboration, innovation, and innovation. A multiple
regression equation for external business funding, as suggested by the theory of academic
capitalism, was possible given the data. The academic capitalism regression equation for
funding college research follows:
Business funding = 0.25 + 0.06 (collaboration) + 0.05 (finance)
(2)
Research and Innovation
Figure 10 shows the average business interest in each of the research funding
options. The highest scoring choices were having students perform research work with a
possible scholarship or summer employment as examples of how to reward outstanding
student work. The second highest funding interest was the government matching grants.
The least attractive funding choice was paying cash for the research work, and the second
least attractive was giving the school a royalty against future business earnings in
exchange for college innovation research.
Figure 11 provides a bar chart of how participants ranked innovation challenges.
The higher ranked challenge was scored with the higher number, and total challenge
rankings were summarized for each category. The largest innovation challenges are time
and financing. The least challenging issues were globalization, rapid change, and the
Internet.
Improve Innovation and College Research Funding 28
Business Funding Options (Averages)
4.00
3.45
3.44
Interest from 0 to 6
3.50
2.83
3.00
2.44
2.63
2.63
2.20
2.50
2.00
1.50
1.00
0.50
0.00
Student
Contest
Royalty
Data
Grant
Cash
Faculty
Business Funding Option
Figure 2. Mean for each research funding option.
Innovation Challenges
Summarized rankings
90
80
70
60
50
40
30
20
10
Type of Challenge
Figure 3. Innovation challenges rankings summarized
St
af
fin
g
In
te
rn
et
Fi
na
nc
e
Te
ch
no
lo
gy
G
lo
ba
l iz
at
io
n
Ti
Pr
m
e
oj
ec
tM
an
ag
e
M
ar
ke
tin
R
g
ap
id
C
ha
ng
e
0
Improve Innovation and College Research Funding 29
Table 4
Cross Tabulation Percentage of Participants who Innovate or have Ideas to Innovate
Innovator
_______________
Yes
No
Total
________________________________________________
Ideas
Yes
.35
.15
.50
No
.25
.25
.50
Total
.60
.40
1.00
________________________________________________
Note. N = 115
Table 5 provides a cross-tabulation of innovation and ideas as a percentage of
participants. Innovators comprised 60% of the Markham sample, and leaders with ideas
for innovation included 50% of the sample. Statistics Canada (2003) data recorded that
53% of small businesses were innovators in national population. The Markham
community innovates slightly more than the national average, which is expected given
Markham is an urban environment. Leaders with no innovations and no new ideas for
future innovation included 25% of the SME businesses surveyed.
Conclusions and Recommendations
Hypothesis 1: Collaboration
A relationship existed between small business interest in funding community
college research and the level of collaboration with community colleges. The result was
significant with respect to the p-value, which indicated strong evidence of a relationship.
Improve Innovation and College Research Funding 30
The coefficient of determination indicated that about 22% of the variation in funding
interest in college research was explained by collaboration.
The theory of academic capitalism supported the result by predicting closer
partnerships between industry and educational institutions (Slaughter & Rhoades, 2004).
The finding that business leaders who innovate and collaborate are willing to provide
external funds to college research is significant to leaders as noted earlier. Innovative
firms are more likely to collaborate (Conference Board of Canada, 2005). The result is
important for anyone with a goal of improving college research funding. College leaders
may wish to use the new knowledge by tapping into existing business collaborations with
innovators to enhance college external funding.
Hypothesis 2: Innovation Challenges
A relationship between small business interest in funding community college
research and business innovation challenges, such as money or marketing, was supported
by the data. Granular tests run on each type of innovation challenge using SPSS showed
some specific types of innovation challenges were significantly correlated to research
funding. The innovation challenges of finance and marketing were found to have some
significance. Finance provided strong evidence with p = .007.
The relationship of finance with business funding of college research was positive
which may be counter-intuitive. As a business has more financing challenges, the
expectation might be for less interest in funding college research. The explanation may be
that college research is cost-effective, so as financing challenges increase, the interest in
funding college research increases. Innovative companies invest more in research, and
Improve Innovation and College Research Funding 31
innovation was a top priority in 40% of companies according to past studies (Gregoire,
2006).
Hypothesis 3 Innovation Level
A small relationship existed between small business interest in funding college
research and level of innovation for small business, explaining about 11% of the variation
in business funding. The significance of the finding is limited. Given the emphasis by
government leaders on innovation, if evidence had supported a strong relationship
between college research funding and level of innovation, the result would be important
and welcomed by leaders (Godin et al., 2002). Unfortunately, other variables and avenues
of study may provide more understanding about college research funding. An alternative
approach, given the collaboration results for hypothesis 1, would be an examination of
trust in industry-education partnerships. If college research funding consistently delivered
quality business solutions, the trust that developed may be more important than the level
of innovation in promoting business funding of college research (Watt, 2003).
Hypothesis 4: Regression Equation
The last hypothesis developed a multiple regression equation to predict business
interest in funding community college research using the independent variables of
collaboration, innovation level, and innovation challenges. MegaStat software was used
to run all possible regressions using collaboration, innovation level, and the innovation
challenges. Collaboration and the finance innovation challenge appeared as important
independent variables with collaboration as dominant.
Improve Innovation and College Research Funding 32
The theory of academic capitalism predicted closer ties between business and
education, so the ability to build a regression equation could test the theory (Slaughter &
Rhoades, 2004). A multiple regression was built with an adjusted r2 = .27. The
collaboration coefficient p < .001 and finance coefficient was p = .02. The F-significance
was less than .001 providing overwhelming support for rejecting the null hypothesis and
providing evidence to support the theory of academic capitalism.
Business funding = 0.25 + 0.06 (collaboration) + 0.05 (finance)
(2)
The result was important in adding quantitative support to the many qualitative
studies supporting the theory of academic capitalism (Bullard, 2007; Hutchinson, 2005;
Mars, 2006; Machado, 2005). The result was also important in providing limited
predictive ability for leadership decisions around the external funding of college research.
Given that the explanatory portion of the relationship was only 27%, the potential for
improvement is perhaps more important than the findings.
Research and Innovation
The broader social significance of the research question was to turn new ideas
into business products that can dominate international competitors. In Table 5, the data
showed 15% of non-innovators have ideas for innovation but are not proceeding with
innovation due to some challenge. The ideas are ready. Many innovations are waiting for
important actors, perhaps government or educational leaders, to facilitate business ideas.
Studies show innovation is important for building economic and social value (Conference
Board of Canada, 2006). If leaders can put research behind the new ideas, the social
Improve Innovation and College Research Funding 33
potential would mean turning ideas into products for the marketplace. The study data
showed 25% of innovators with successful products in the market have additional new
ideas that are not progressing due to some challenges. The sum of the two types, noninnovators with new ideas and innovators with additional ideas, means in Markham a
40% chance exists when contacting a business that an idea for innovation is waiting on a
research facilitator. The social implications of assisting some small portion of these ideas
forward would likely be significant (OECD, 2006b).
Recommendations for Future Research
First, a measure of the intensity of each innovation challenge on an interval scale
may provide a more useful association with college research funding than an ordinal
ranking of the challenges. Second, although time was a top innovation challenge for
business leaders, no linear relationship was found. More exploration into the time
challenge would be helpful. Third, an interval variable providing dollar amounts of
funding rather than a measure of attitude would be pragmatic for administrator budgets.
Recommendations for Stakeholders and Leaders
Academic leaders who wish to improve external funding of community colleges
by small business leaders should promote collaboration. The college may wish to act as a
hub for research networking between key actors in the research community. Data from
the study provides overwhelming evidence of a relationship between business interest in
funding college research and collaboration between industry and educational partners.
One controversy is academic leaders must exercise good judgment that any activities
Improve Innovation and College Research Funding 34
pursuing external research funding enhances rather than undermines the primary mission
of educating students.
If community colleges want to expand community involvement with business
leaders, the opportunities are available to collaborate. If college administrators want more
external funds, the business interest in funding college research is available. An important
lesson from universities is a poorly led project may cost more capital than earned (Bok,
2004). Another important lesson from university research partnerships with industry is a
demonstrable and lasting advantage to early starters (Owen-Smith, 2005).
Improve Innovation and College Research Funding 35
References
Association of Canadian Community Colleges. (2006, May). Applied research at
Canadian colleges and institutes. Ottawa, Canada: Author.
Association of Canadian Community Colleges. (2007a). College/Institute technology
transfer initiative conceptual framework. Ottawa, Canada: Author.
Association of Canadian Community Colleges. (2007b). ACCC annual report 2006-2007.
Ottawa, Canada: Author.
Association of Universities and Colleges of Canada. (2005). Momentum: The 2005 report
on university research and knowledge transfer. Ottawa, Canada: Author.
Association of University Technology Managers. (2005). AUTM U.S. licensing survey.
Retrieved April 15, 2008 from http://www.autm.org
Bok, D. (2004). The benefits and costs of commercialization of the academy. In D. Stein
(Ed.), Buying in or selling out: The commercialization of the American research
university. Piscataway, NJ: Rutgers University Press.
Bullard, D. (2007). Academic capitalism in the social sciences: Faculty responses to the
entrepreneurial university. (Doctoral dissertation, University of South Florida,
2007). Retrieved March 12, 2008, from ProQuest Dissertations and Theses
database.
Business Information Group. (2008). Scott’s directories Ontario boundary, Vol. II.
Toronto, Canada: Author.
Chang, Y., Chen, M., Hua, M., & Yang, P. (2005). Industrializing academic knowledge
in Taiwan. Industrial Research Institute. Retrieved May 27, 2006, from
EBSCOhost database.
Improve Innovation and College Research Funding 36
Coates, T. (2004). The development of core competence through the development of
emerging technology innovation: An empirical investigation. (Doctoral
dissertation, Rensselaer Polytechnic Institute, 2004). Retrieved March 13, 2007,
from ProQuest Dissertations and Theses database.
Conference Board of Canada. (2005). 6th annual innovation report: Don’t overlook
“MEs”; Medium-sized firms show the way to global success. Ottawa, Canada:
Author.
Conference Board of Canada. (2006). 7th annual innovation report: Lessons in publicprivate research collaboration. Ottawa, Canada: Author.
Creswell, J. (2005). Educational research: Planning, conducting, and evaluating
quantitative and qualitative research. (2nd ed.). Upper Saddle River, NJ: Prentice
Hall.
Dillman, D. (2007). Mail and Internet surveys: the tailored design method. Hoboken, NJ:
Wiley.
Earl, L., Gault, F., & Bordt, M. (2004, December). Summary: Meeting on
commercialization measurement, indicators, gaps, and frameworks. Ottawa:
Statistics Canada.
Etzkowitz, H., Webster, A., & Healy P. (1998). Capitalizing knowledge: New
intersections of industry and academia. New York: State University of New York.
Fowler, F. (1995). Improving survey questions: design and evaluation. Thousand Oaks,
CA: Sage.
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Teow, M. (1994).
The new production of knowledge. London: Sage.
Improve Innovation and College Research Funding 37
Godin, B., Dore, C., & Lariviere, V. (2002). The production of knowledge in Canada:
Consolidation and diversification. Journal of Canadian Studies, 37(3), 56-70.
Gregoire, E. (2006). This year 72 percent of companies will increase spending on
innovation. Boston: Boston Consulting Group.
Hutchinson, B. (2005). Academic capitalism, organizational change, and student
workers: A case study of an organized research unit in a public university.
(Doctoral dissertation, University of Arizona, 2005). Retrieved March 12, 2008,
from ProQuest Dissertations and Theses database.
Industry Canada. (2006). Key business statistics. Retrieved April 10, 2007, from
http://strategis.ic.gc.ca/epic/site/sbrp-rppe-nsf/en/rd00226e.html
Industry Canada. (2007, January). Key small business statistics. Retrieved 28 April, 2007,
from http://www.strategis.gc.ca/sbstatistics
Machado, M. (2005). Entrepreneurial activities in the Brazilian federal universities: A
case study of the federal University of Ceara. (Doctoral dissertation, University of
Arizona, 2005). Retrieved March 12, 2008, from ProQuest Dissertations and
Theses database.
Malhotra, N. (2007). Marketing research: an applied orientation. Upper Saddle River,
NJ: Pearson Education.
Markham Board of Trade. (2008). Markham business and buyer’s guide. Markham,
Ontario: Author.
Mars, M. (2006). The emerging domains of entrepreneurship education: Students,
faculty, and the capitalist academy. (Doctoral dissertation, University of Arizona,
Improve Innovation and College Research Funding 38
Texas, 2006). Retrieved March 12, 2008, from ProQuest Dissertations and Theses
database.
National Sciences and Engineering Research Council. (2008). College and community
innovation program. Author. Retrieved May 23, 2008 from
http://www.nserc.ca/colleges/ci_e.asp
Newman, F., Couturier, L., & Curry, J. (2004). The future of higher education: Rhetoric,
reality, and the risks of the market. San Francisco: Jossey-Bass.
Nixon, G. (2003). Academic capitalism forces and successful college leadership.
(Doctoral dissertation, University of Calgary, 2003). Retrieved December 24,
2007, from ProQuest Dissertations and Theses database.
Norusis, M. (2006). SPSS 15.0 Statistical procedures companion. Upper Saddle River,
NJ: Prentice Hall.
Organization for Economic Co-operation and Development. (2003, March). The sources
of economic growth in OECD countries. Paris: Author.
Organization for Economic Co-operation and Development. (2005). Oslo manual:
Guidelines for collecting and interpreting innovation data (3 ed.). Paris: Author.
Organization for Economic Co-operation and Development. (2006a). OECD economic
surveys—Canada. Retrieved April 16, 2007 from InfoTrac OneFile.
Organization for Economic Co-operation and Development. (2006b, December). Main
science and technology indicators. Retrieved April 15, 2007, from
http://www.oecd.org/document/26/0,2340,en_2825_497105_1901082_1_1_1_1,0
0.html
Improve Innovation and College Research Funding 39
Owen-Smith, J. (2005). Trends and transitions in the institutional environment for public
and private science. Higher Education, 49, 91-117.
Rahal, A. (2005). Assessment framework for the evaluation and prioritization of
university technologies for licensing and commercialization. (Doctoral
dissertation, University of Central Florida, 2002). Retrieved March 12, 2006,
from ProQuest Dissertations and Theses database.
Sharpe, A., & Smith, J. (2005). Measuring the impact of research on well-being: A
survey of indicators of well-being. Ottawa, Canada: Center for the Study of Living
Standards.
Shea, R., Allen, T., Gorman, C., & Roche, F. (2004). Universities and technology
transfer: A review of the entrepreneurship literature. Irish Journal of
Management, 25(2), 11-29. Retrieved May 11, 2007, from ProQuest database.
Slaughter, S., & Leslie, L. (1999). Academic capitalism: Politics, policies and the
entrepreneurial university. Baltimore, MD: John Hopkins University Press.
Slaughter, S., & Rhoades, G. (2004). Academic capitalism and the new economy:
Markets, state, and higher education. Baltimore, MD: John Hopkins University
Press.
Sproull, N. (2004). Handbook of research methods: a guide for practitioner and students
in the social sciences (3rd ed.) NJ: Scarecrow Press.
Statistics Canada. (2003). Business Register. Ottawa, Canada: Author.
Statistics Canada (2006). Survey of innovation for 2002 to 2005. Ottawa, Canada: Author.
Improve Innovation and College Research Funding 40
Watt, D. (2003). 2003 Partnerships survey report: Findings from the 1st global businesseducation-community partnerships issues and trends survey. Ottawa, Canada:
Conference Board of Canada and International Partnership Network.
Ylijoki, O. (2003). Entangled in academic capitalism? A case study on changing ideals
and practices in university research. Higher Education, 45, 307-335.