Why do firms collaborate with local universities?
Rune Dahl Fitjar
Professor of Innovation Studies
UiS Business School
University of Stavanger
Norway
e-mail: [email protected]
This paper examines collaboration between firms and local universities. Drawing on data
from 23 semi-structured interviews in four regions and a survey of 2002 firms across
Norway, it suggests three main reasons why firms choose to collaborate with local
universities: Firstly, knowledge transfer across distance is costly, and collaborating locally
reduces the risk of information loss when the knowledge is transferred. Secondly, if the
local university can make a useful contribution, firms might choose to look no further.
Thirdly, firms may take a less instrumental approach, interacting with the university
mainly in order to build up competencies at the university rather than in the firm. This
could reflect either a long-term strategy where the firm hopes to benefit in the future, or a
desire to contribute to the local community.
Introduction
Firms that collaborate with universities often do so locally. Data from a survey of 2002
Norwegian firms, presented in this paper, show that 21.6 percent of the firms
collaborate with universities in total, and 16.1 percent collaborate with local
universities. This is the case even though Norway does not have any world-leading
universities – the highest ranked Norwegian university in the 2014/15 Times Higher
Education Rankings (the University of Oslo) was in a lowly 186th spot. Even outside the
four largest city regions, 72 percent of firms that collaborate with universities do so with
local ones. Even if the universities may specialise in research areas that are particularly
well-suited to the needs of local industry, it is fairly safe to assume that the local
university in most cases does not represent the world-leading expertise in the areas of
knowledge that local firms wish to access. So, why do firms nonetheless tend to
collaborate mainly with local universities?
This paper presents three competing models that seek to explain this pattern. First, the
knowledge spillover model posits that transfer of knowledge from universities to firms
is costly, and the costs of transferring knowledge rise with geographical distance.
Collaboration that takes place in close geographical proximity can benefit from frequent
face-to-face contact that is helpful in transferring tacit knowledge. Thus, the quality of
the university must be weighed against the decreasing utility of knowledge that is
located at a geographical distance. Second, the bounded rationality model posits that
firms do not seek to maximise knowledge spillovers, but to satisfy their requirements
for external knowledge. Thus, they search for partners until they find a suitable
candidate, and if the knowledge requirements are met, they need not look any further.
These searches will often start at the local university or at other universities to which
management or employees are attached through personal education background. Local
universities are well-placed to benefit in such contexts. Third, the social responsibility
model holds that firms do not necessarily take an instrumental approach to interaction
with universities. This model recognises that knowledge transfer is not necessarily
always from the university to the firm – it may also go the other way. Firms may seek to
benefit in the longer term from helping to build up competencies at the local university,
or they may want to make a contribution to the local community.
The paper discusses these models based on data from two different sources: First, we
have conducted qualitative interviews with 23 firms involved in collaboration with four
Norwegian universities – Tromsø, Nordland, Stavanger and Haugesund 1. The interviews
reflect on why the firms have chosen to collaborate with these universities and how the
collaboration came about. Second, we present data from a survey of 2002 Norwegian
firms across all regions, in which we asked about collaboration with local and non-local
universities, among various other questions. These data are analysed through a
regression analysis seeking to uncover which firms tend to collaborate with local and
with non-local universities and how this corresponds to expectations derived from the
three models.
In the following section, the three models are explained in greater detail with references
to previous research. Then, we present the research strategy and findings from the
qualitative interviews. The fourth section presents the quantitative analysis, including
the operationalisation of variables and the findings from the regression analysis. The
final section concludes.
The geography of firm-university interaction
Universities are given a central role in regional development in various theories and
policy directions in the field of regional studies. The literature on knowledge spillovers
has highlighted the effects of academic research at universities on productivity growth
and innovation (Griliches 1979; Nelson 1986), emphasising – in particular with Jaffe
(1989) and Audretsch and Feldman (1996) – the geographically bounded nature of
these spillovers. This basic insight has inspired a raft of regional development theories
in which boosting knowledge production at regional universities and/or the efficient
transfer of this knowledge to firms and other users are the keys to innovation and
regional development. This includes theories of technology transfer, regional innovation
systems, triple helix systems, and – most recently – smart specialisation. In higher
education policy, these ideas have called attention to the so-called “third mission” of
universities, leading to the implementation of measures of impact or societal outreach.
A basic idea in this line of reasoning is that universities are the key institutions in
generating new knowledge, which is then transferred to firms and later put into use by
The term “university” is used in this paper to refer to all institutions of tertiary education. The
Norwegian system distinguishes between two categories of such institutions: Universities and høyskoler,
often translated as “university colleges”. The three former institutions are accredited as universities, while
Stord Haugesund is accredited as a university college under the Norwegian system. As a rule, the
university colleges are more heavily oriented towards education and less research intensive than the
universities. For the sake of simplicity, the term “university” will be used throughout this paper to refer
also to høyskoler, including Stord Haugesund University College.
1
these firms. In regional innovation systems theory, for instance, universities form part of
the knowledge exploration subsystem of the region, whereas firms form part of the
knowledge exploitation subsystem whose role is to apply the knowledge generated in
the former part of the system (e.g. Asheim and Coenen 2005). The emphasis is on the
transfer of knowledge from the university to the innovating firm.
What is the impact of geographical proximity in this process? Previous research has
offered various interpretations of why these knowledge spillovers seem to take place
more efficiently when the actors are located in close proximity. Although universities
tend to produce a lot of codified knowledge (e.g. in the form of journal articles) that
could in theory be transferred costlessly across a distance, they also produce a great
deal of tacit knowledge in the form of knowhow and operations (Audretsch et al. 2005).
For innovative firms, it is arguably more important to have access to this type of
knowledge than to knowledge that is universally available to everybody, including
competitors. Secondly, even if knowledge is codified, firms may not be able to access it.
In many cases, understanding scientific knowledge and putting it into practical use
require a fundamental understanding of the underlying research, a point which is
recognised e.g. in the concept of absorptive capacity (Cohen and Levinthal 1990). Having
access to the researchers who produced the work is often helpful in this regard.
Consequently, even firms in industries which draw predominantly on codified
knowledge tend to locate close to universities. Asheim et al. (2007) note that firms with
analytical knowledge bases cluster around major universities to get personal access to
leading researchers.
Nonetheless, for most firms, there is a trade-off between geographical proximity and the
quality of research that can be accessed. While firms do tend to cluster in the proximity
of world-class universities (e.g. Woodward et al. 2006), not all firms can be located close
to the best universities. This holds even though university research output and firm
knowledge demands are heterogeneous in terms of academic fields: While universities
may specialise in areas where the local industry has specific needs, and thus may
generate world-class knowledge in these particular fields, it will still be the case that not
all firms are located close to a leading centre of expertise in the field in which they
require such expertise.
Which options – besides relocation – are available to firms located close to universities
that are not internationally leading in the relevant fields? Do they still collaborate with
the local university, or do they more often link up with universities located at a greater
distance? Laursen et al. (2011) highlight the trade-offs that firms make between
geographical proximity and university quality in these contexts. Building on data from
the UK, they find that being located in proximity to a lower-tier university reduces the
propensity of firms to collaborate locally and increases the probability of collaborating
with universities outside the region. Thus, they conclude that firms tend to place more
weight on university quality than on geographical proximity when forced to make a
choice in the selection of university partners.
A common feature in all of these theories is their instrumental approach to universityfirm interaction: Firms connect with universities in order to access knowledge that they
need in their innovation processes. These connections may take different forms, from
the unidirectional technology transfer of the linear model to the more interactive
models popular in the current literature. Nonetheless, the basic idea in both cases is one
of firms using university research in a fundamentally instrumental way as an input in
their own innovation processes. Furthermore, firms are essentially rational in selecting
university partners that maximise the input to innovation, which involves weighing the
quality of knowledge production against the costs of transferring this knowledge across
geographical distance.
While these assumptions may hold in many cases, they run the risk of overlooking some
important reasons why firms choose to interact with local universities. In the rationalist
knowledge spillover model, firms will interact with local universities if the quality of
knowledge inputs from the local university is greater than the quality of knowledge
inputs from other universities, discounting for the loss of knowledge when it is
transferred across a distance.
However, going back to the seminal work of Simon (1947), we know that actors and
organisations tend not to be fully rational, but to display bounded rationality, acting on
the information that they have to reach a satisfactory solution within the limited time
available to make a decision. Applying this to the context of university-firm interaction,
firms will often have more information about research activities at the local university
and will not have time to scan all possible research group for potentially better partners.
Acting on the basis of the information at hand, they will in such contexts choose to
collaborate with the local university – or other specific universities of which they have
sufficient knowledge through educational background or otherwise – if the knowledge
inputs that can be obtained from this university is satisfactory to meet their needs. They
will try to access other universities only if the required inputs are not available from the
local university or from other universities that they know reasonably well.
Furthermore, interaction is often most effective in socially embedded relations. A certain
level of trust between actors is required in order for interactions to result in learning
and innovation. This requirement may constrain the ability of firms to choose university
partners that might otherwise have been preferable. It may furthermore lead to a
preference for interacting with the local university, where trust-based relations may
already exist or may be more easily built. For interactions taking place in geographic
proximity, social capital built up at the community level can in many cases provide a
solid basis for trust, even when actors do not know each other personally. Geographic
proximity is also associated with social and institutional proximity, which may further
simplify the process of building trust (Boschma 2005).
Finally, firms do not necessarily even view interaction with universities as instrumental
in their innovation activities. Interaction with universities may serve other purposes for
the firm, such as the need to appear socially responsible and embedded in the
community. Corporate social responsibility is an increasingly popular idea, creating new
expectations on firms to contribute to society, which may include contributions to
academic research. Corporate social responsibility is often directed to contributing to
local communities in particular, rather than to society at large (Marquis et al. 2007). This
is where firms need to maintain their social licence to operate, and it is where they may
perceive a greater responsibility for contributing. Firms’ and indeed individuals’
responsibilities towards society are tied to membership of communities with which they
identify and should contribute their fair share. Consequently, firms may under such
circumstances try to contribute to the development of the local university. In the case of
Heidelberg, Glückler and Ries (2012) find that academia emerged as the main
beneficiary of place-based philanthropy, which was based on regional affection and
commitment. However, contributions to the university may take place through a range
of mechanisms, ranging from pure philanthropy to joint research projects that are as
much of benefit to the university as to the firm. This may also contain an aspect of longterm investment, insofar as contributions to the university may help to build a stronger
research community that may become of greater benefit to the firm in the future.
To sum up, this review of the literature has highlighted three different models for why
firms collaborate with universities. In the knowledge spillover model, firms collaborate
with local universities because of the costs and difficulties of transferring knowledge
across geographical distance. In the bounded rationality model, firms collaborate with
local universities because they have more information about them and can establish
trust-based relations more easily. In the social responsibility model, firms collaborate
with local universities out of a sense of social responsibility to contribute to the
development of the university – and thereby the community – regardless of whether the
firms themselves benefit in the short term. It is worth noting that these models are not
mutually exclusive: Any and all of these considerations may be present in the decision of
a firm to collaborate with the local university.
The following section discusses findings from qualitative interviews with firms
collaborating with four local universities in Norway in light of these models.
Qualitative analysis
In the qualitative part of the study, we conducted interviews with firms that were active
in collaborating with four Norwegian universities, located in different city regions:
Stavanger (the University of Stavanger), Tromsø (the University of Tromsø – the Arctic
University), Bodø (the University of Nordland), and Haugesund (Stord Haugesund
University College). The main purpose of the interviews was to explore the reasons for
the establishment and maintenance of these relationships as seen from the firm partner.
We also conducted interviews in each of the universities, but this paper will focus on the
firm interviews as the research question addressed pertains to why firms choose to
consult local universities.
The four city regions all have a university or university college located in the central city
of the region, but all of the universities are also fairly small and less research intensive
than the largest universities of Norway. The University of Tromsø is the only that makes
it into the Times Higher Education Rankings – in 351th-400th spot. In 2011, the
universities in Stavanger and Tromsø had around 9000 students, Nordland had around
6000, and Stord Haugesund 2800. At the highest level of study, 114 PhD students
completed their degrees at the University of Tromsø, 28 in Stavanger and 8 in Nordland,
whereas Stord Haugesund graduated 5 PhD in collaboration with other institutions. At
the level of staff, the University of Tromsø was by far the largest with 2534 employees,
of which 250 full professors. The University of Stavanger had 1066 employees and 96
full professor, the University of Nordland 541 employees and 49 professors, and Stord
Haugesund University College had 273 employees and 6 professors. By way of
comparison, the University of Oslo had in 2011 a staff size of 6017, of which 790 were
professors, and more than 27000 students, graduating 425 new PhDs.
This set of cases thus provide an appropriate group for studying why firms choose to
collaborate with their local universities, even if the research quality available in other
universities – elsewhere in Norway or abroad – may be superior. In each of the
universities, we first conducted semi-structured interviews with five to seven
representatives of university management and senior faculty involved in collaboration
with industry. During these interviews, the informants were asked to identify firm
partners with whom they were currently collaborating, and also the names of
individuals within this firms who were involved in the collaboration. Subsequently, we
conducted semi-structured interviews with these individuals, covering five to eight
firms in each region for a total of 23 firm interviews. Two researchers participated in
each of the interviews to ensure the reliability of the study. The same two researchers
were involved in the interviews within any single region, and one researcher took part
in all the interviews across regions. The interviews were also recorded and transcribed
to ensure comparability and sharing of data within the project team.
The interviews were then analysed in a joint workshop of the full project team, including
all interviewers. During the workshop, important themes emerging from the interviews
were identified, one of which concerned the firms’ motives for collaborating with these
local universities. In the analysis, we classified different firm explanations of their
motives into the three categories identified in the theoretical section above, or any other
motives that firms may give. The intention of this is to explore the full range of possible
explanations for why the firms have decided to collaborate with their local universities.
Findings
The various rationales outlined above were all represented during the interviews
conducted. Unsurprisingly, the issue of knowledge spillovers was raised as a main
motivation by all firm representatives interviewed. When explaining how they selected
universities for collaboration, most informants emphasised university quality: “We
primarily go after the best expertise” [CEO, Nordland] and “we are always chasing
quality” [External affairs advisor, Stavanger] were typical examples of the kinds of
rationales given by firms. The importance of distance in promoting knowledge transfer
was raised by some informants: “It’s easiest for us to collaborate with communities in
Bodø due to the proximity” [Project director, Nordland]. “Besides, accessibility is
incredibly important to us. We can say: ‘Let’s meet in half an hour’. It’s amazing. You
can’t do that with someone from Bergen or Stavanger. Then you have to plan and travel”
[Development director, Haugesund]. For other firms, geographical distance does not
matter: “There are planes all the time” [Product manager, Haugesund]. “The world has
become smaller and distance matters less” [IP manager, Stavanger].
Several informants emphasise their relations to individual academics, rather than to the
university as an institution, as being important. According to one respondent, the
geographical distance to the university doesn’t matter; it’s all about relational proximity
to individuals: “That they are in Haugesund is very accidental. It’s because some of us
had a relation from before” [Project management director, Haugesund]. Firm
representatives were often on a first-name basis with individual academics involved in
collaboration, and noted how these individuals possessed precisely the knowledge or
competence which the firm required: “We had the researcher NN, it’s his scientific clout”
[General manager, Tromsø]; “The only positive person was OO, who’s still here, who’s
Norway’s best entrepreneur in fisheries” [CEO, Tromsø]; “We have a multi-year contract
with PP which we spend several million on. […] Now we are looking into collaborating
with QQ. […] PP is a leader in his area globally” [External affairs advisor, Stavanger]. “We
knew RR was still in Haugesund – he doesn’t have that many years left until retirement”
[Project management director, Haugesund].
In this context, geographical proximity matters not because of the costs of knowledge
transfer, but because local communities provide an environment in which universities
and individual academics can meet. When the search for appropriate partners focuses
on finding individual academics, it quickly becomes unmanageable to find global leaders
in the area. The firm starts at a university they know well, and search until they find the
competence that they seek. Local universities are well placed to benefit in this context,
given that firms will often know them fairly well and meet them in various arenas.
Personal experience as a student mattered in several cases: “I had NN [as a lecturer]
myself when I studied and therefore knew him” [Project management director,
Haugesund]. “[The initiative was taken based on] relations between former students and
the university” [CEO, Nordland]. “We have relations to universities through people who
are educated there” [Project director, Nordland]. In other cases, the contact is made at
other local events: “Haugesund isn’t the world’s largest city, so there are quite a few
arenas where most people meet” [Business innovation director, Haugesund]. “I guess it
started at Skarven [a pub] at some point” [General manager, Tromsø]. The relationships
are often ongoing, feeding further collaboration: “It’s reasonable to contact NN at UiN.
We’ve worked with him before” [Project director, Nordland].
In many cases, the contact is initiated by the universities rather than by the firms:
“Often, the universities come to us” [Research director, Stavanger]. “[In the projects you
have mentioned, who takes the initiative?] It’s the research institutions, every time”
[CEO, Nordland]. These contacts are often maintained through individual liaisons at the
universities, who are responsible for communicating with firms and connecting with
individual researchers at the university who might get involved in joint research or
contracts. “NN’s entry [liaison] at UiS has eased the relations with UiS tremendously”
[External affairs advisor, Stavanger]. “OO is a very good ambassador to the part of
industry that I am part of” [Business innovation director, Haugesund”. In other cases, a
lack of initiative from the university is blamed for the absence of ongoing research
projects: “Researchers with good ideas don’t come to us” [CEO, Tromsø].
In many cases, firms consider these proposals based on cost-benefit assessments. A
research director in Stavanger describes a typical process as follows: “Through the year,
we have collected all the project proposals that are in a database and then each project
is sent to the organisation as they come in. Depending on their recommendations, we
develop a proposal with all the proposals. We ask each person to present the project and
give an assessment of the value in the project”. These considerations are often based
heavily on potential benefits in the short term, with a strong emphasis on whether the
project can contribute to the company’s ongoing activities: “We have limited time and
capacity, we prioritise what’s closest to our productivity” [CEO, Nordland]. “Therefore,
we work perhaps best with those who want results in the short term and primarily want
results based on making it work rather than those who want to publish an article” [CEO,
Haugesund]. Several informants explicitly emphasised the longer-term horizon of the
universities as a problem for collaboration: “The universities lack a sense of urgency,
they’re too slow” [Research director, Stavanger]. “Those articles are nice, but they don’t
benefit our productivity” [CEO, Nordland].
However, other firms emphasised the potential benefits that could be reaped in the
longer term from contributing to the development of research communities at the local
university. A company in Stavanger that finances PhD positions at the university notes
that “we wanted to have a university with stronger quality and research” [External
affairs advisor]. Another firm explains that “we want to build competence at the
universities” [Programme director, Stavanger]. In Nordland, a firm gives as the main
motivation for participating in a project with the university that “we wanted to build up
a real-time lab in the local area” [CEO]. In these cases, the firm sees collaboration with
the local university as an investment in developing competencies at the university.
Better research quality at the local university might in the future benefit the firm, which
considers itself well-placed geographically to benefit from high-quality research on
topics relevant to its daily activities.
In other cases, explicit cost-benefit assessments – even in the long run – are absent, and
the firm emphasises the desire to contribute to the development of the region more
broadly. Several firms in each region referred to this rationale in explaining their
engagement with the local university: “[The founder] had an idealistic perspective. He
wanted this region to be the most innovative” [Research director, Stavanger]. “[The
founder] was very interested in getting a university to Tromsø and donated quite a bit of
money” [CEO, Tromsø]. “We want to have a role locally because we are a local firm, so
when you participate in something like this, you don’t always do it to get something
back, but to support HSH [the university college]” [Business innovation director,
Haugesund]. “Personally, I care about having a strong academic institution in the local
community for the consciousness of being a city, but also for educating exciting people
who will work and live in the city that we love” [CEO, Haugesund]. “We have an owner
who thinks it is fun to build new things, get more hands to work and develop local
communities” [Project director, Nordland].
To sum up, while knowledge spillovers, often in the very short term, is an important
consideration for firms in deciding whether to cooperate with universities, firms only
rarely seek to maximise such spillovers in their selection of partners. Firms tend to
search for partners that possess relevant competences, and stop searching once they
find an appropriate partner. Cooperation is often the result of ongoing relations and
experiences of successful cooperation in the past, usually at the individual level. In many
cases, firms do not seek out universities at all – rather, it is the university that takes the
initiative and develops the idea for the project. In these cases, geography matters less
because of the costs of knowledge transfer and more because the local area in many
cases represents the place where the partner search starts – by the firm or by the
university. If a suitable partner is found within the region, a relationship is often formed.
Geography also matters because of a sense of community responsibility displayed by
many firms. Local owners are often committed to the development of the city or local
area and want to make a contribution to the university, even if it does not benefit the
firm directly. In these cases, they collaborate with the university precisely because it is
local and part of a community that the owner identifies with.
Quantitative analysis
In the second step of the analysis, we examine data from a survey of firms across
Norway to investigate whether these finding can be generalised to a larger group of
firms. The survey was conducted during the spring of 2013 and included 2002 firms
from all sectors and across all regions of Norway. The sample was restricted to firms
with more than ten employees, and we imposed quotas for firms in the city regions of
Oslo (500), Stavanger (350), Bergen (300), and Trondheim (250) to ensure the inclusion
of a sufficient number of firms in these regions. The remaining 600 firms were sampled
from all regions, including rural areas. The firms were sampled from the Norwegian
Registry of Business Enterprises, which is a compulsory registry of all Norwegian firms.
The interviews were conducted by telephone with the CEO or general manager of each
firm, and were done by professional interviewers from the market research firm Ipsos
MMI.
In the survey, firms were asked whether they collaborated with any of seven different
types of partners, one of which was universities. For each type of partner identified,
firms were also asked whether they had collaborated with partners within the region,
elsewhere in Norway, and/or abroad. The answers to these questions will serve as the
dependent variables in this analysis. We include to dependent variables: First,
collaboration with local universities, coded 1 if the firm indicated that they collaborated
with a university within the region and 0 otherwise. Second, collaboration with nonlocal universities, coded 1 if the firm indicated that they collaborated with a university
elsewhere in Norway and/or abroad and 0 otherwise.
Overall, 16.1 percent of firms indicated that they collaborated with a university in the
region, and 9.7 percent of firms that they collaborated with a university outside the
region. While collaborating with universities is fairly uncommon in general, a
significantly higher proportion of firms collaborate with local universities than with
non-local ones. Further sub-dividing the non-local category, 7.7 percent of firms
collaborate with universities elsewhere in Norway, and 3.5 percent with universities
abroad. Some firms collaborate with universities both within and outside the region: In
total, 4.2 percent collaborated with both local and non-local universities, while 11.9
percent collaborated with local universities only and 5.5 percent with non-local
universities only.
The main question to explore in the regression analysis is whether the drivers of
collaboration are the same for local and non-local collaboration. In the knowledge
spillover model, local and non-local collaboration are expected to a function of the same
drivers, with the quality of the local university versus non-local universities and the
costs of knowledge transfer (which may vary across sectors) being the only
considerations. In the other models, collaboration with the local university is expected
to be – to some extent – the consequence of different processes that collaboration with
non-local universities. The bounded rationality model emphasises trust, which is tied to
local communities, and would expect firms whose managers have more social capital to
collaborate more locally, including with universities. Similarly, we would expect firms
whose managers are more open to trusting people from outside the region to
collaborate more with non-local universities. The social responsibility model
emphasises the importance of making a contribution to local communities through
collaborating with the university. If this model holds, we should expect firms whose
managers express a greater sense of orientation towards the regional community to
collaborate more with local universities.
Variables
Knowledge spillover model
The knowledge spillover model posits that collaborating with local universities will be a
function of the research quality at these universities and the costs of transferring this
knowledge, which is expected to increase with geographical distance. In this case, we
expect to see higher levels of collaboration with universities for firms located in regions
with more research intensive universities, ceteris paribus. In our data set, we expect in
particular higher levels of collaboration with universities in Oslo, Bergen and
Trondheim, whose universities are larger and more research intensive. In the
2014/2015 Times Higher Education Rankings, the universities in these three cities were
ranked 186th, 201-225th, and 276-300th, respectively. The only other Norwegian
university on the list – Tromsø – was ranked 351-400th. The Shanghai Jiao Tong and QS
rankings provide similar results.
For non-local collaboration, we should expect lower levels in these regions, as more of
the relevant research is available locally. In order to test this hypothesis, we include a
dummy for the city region in which the firm is located – Oslo, Stavanger, Bergen, or
Trondheim, each coded 1 if the firm is located in the region and 0 otherwise. These will
be compared against a baseline of firms located outside any of the four largest city
region.
The knowledge spillover model also posits that the costs of transferring knowledge
across geographical distances will vary for firms in different industries and relying on
different knowledge bases (Asheim et al. 2007). Thus, we would expect to see different
effects of firm sector on local compared to non-local collaboration with universities. In
particular, we would expect higher coefficients for local than for non-local collaboration
for sectors with more synthetic or symbolic knowledge bases, compared to sectors with
more analytic knowledge bases. In order to test this hypothesis, we include dummies for
each of ten different sectors, coded 1 if the firm is part of this sector and 0 otherwise.
The sector classifications used are (1) mining and quarrying, (2) manufacturing, (3)
utilities, (4) construction, (5) wholesale and retail trade, (6) transport and storage
services, (7) food and accommodation services, (8) information and communication
services, (9) financial and insurance services, and (10) other services. The latter
category will serve as the baseline.
Bounded rationality model
The bounded rationality model follows the knowledge spillover model in that
collaboration with local universities will be a function of the research quality and the
cost of transferring this knowledge. However, firms will only maximise this term over
the set of universities that they know and trust. In this model, we would expect
collaboration with local universities to be higher for firms with managers who express a
higher level of trust in other regional actors, and collaboration with non-local
universities to be higher for firms with managers who are more open to trusting more
distant actors.
A second aspect of the bounded rationality model is that firms may gradually learn to
collaborate with universities, and thus expand the number and potentially the
geographical scope of their interactions over time. In that case, we might expect firms
that collaborate with local universities to be more likely to engage in collaboration also
with universities outside the region. A significant proportion of the firms that
collaborate with the local universities may have collected relevant knowledge on the
nature of university-firm collaboration and on other potential partners, which may lead
to more collaboration also outside the region. Thus, we include a second model of
collaboration with non-local universities, where collaboration with local universities is
included as a predictor.
Social responsibility model
The social responsibility model emphasises attachment to local communities and the
desire to contribute to community development. In this model, we should expect firms
whose managers express a greater sense of commitment to the region to collaborate
more with local universities. Several of the informants in the qualitative interviews also
emphasised the embeddedness of the owner in their local community as an important
driver. Thus, we would expect locally owned firms to collaborate to a greater extent with
local universities, while this pattern would not extend to collaboration with non-local
universities. Local ownership is measured as a proportion of shares in the firm held by
individuals or organisations located within the region. In total, 67.2 percent of the firms
included in the survey were fully owned by local or regional owners, and another 9.5
percent were partly owned by local or regional owners.
Both the bounded rationality and the social responsibility model posit an effect of
owners’ attitudinal predispositions, including their degree of bonding and bridging
social capital and their regional attachment, respectively. These attitudes are measured
by a total of ten survey questions probing these attitudes. The questions are combined
into measures of regional and non-regional trust and regional attachment through a
principal components analysis in which all the ten indicators were included. In the
analysis, all components with an eigenvalue above 1 were extracted and varimax
rotated, and the resulting factor loadings are shown in Table 1.
Table 1: Principal components analysis of attitudinal dimensions
Question
Comp. 1 Comp. 2 Comp. 3
In general, I think most people can be trusted
0.49
0.01
-0.05
One cannot be too careful in dealing with other
-0.23
0.18
0.49
people
I trust other business managers in this region
It is important to maintain employment in the
region, even if it should hurt the business’
profits
In my experience, it is often easier to cooperate
with local or regional actors than people from
other parts of the country
I need to improve my understanding of other
countries’ cultures
I wish Norway and Norwegians were more open
to the world around us
I am most comfortable around people who are
open to change and new ideas
I trust business managers in other countries
I trust other business managers in my industry
Eigenvalue
0.53
0.13
-0.06
0.01
0.09
0.56
-0.02
0.56
-0.10
0.06
-0.06
0.13
0.34
0.52
2.24
-0.10
0.62
0.44
0.23
-0.02
1.54
0.59
0.02
0.15
-0.23
0.08
1.45
Unexpl.
0.46
0.53
0.38
0.51
0.48
0.50
0.42
0.59
0.51
0.40
0.48
The first component incorporates high loadings for the indicators related to withingroup trust. Trust in other business managers within the same region and within the
same industry, as well as in other people more generally, all have loadings around 0.5.
This component will be taken as an indicator of trust in actors that are close to oneself
either in terms of geography or sector, i.e. bonding social capital (Putnam 2000). The
second component includes high loadings for indicators related to openmindedness
towards more distant actors, including a desire to learn from foreign cultures and for
Norway to be more open to the outside world. Both of these indicators have loading
around 0.6. This component also includes a more general positive inclination towards
change and new ideas with a loading of 0.44. The component can therefore be taken as
an indicator of bridging social capital, or a desire or ability to link up to groups that are
more distant from oneself (Putnam 2000). The final component expresses sentiments
related to the region, with loadings around 0.6 for attitudes of regional social
responsibility (preferring to maintain regional employment over company profits) and a
preference for collaborating with regional actors. The component also includes a loading
of almost 0.5 for a cautious attitude in dealing with other people, suggesting that these
regional sentiments can also be linked to a distrust of outsiders. We consider this
indicator to be an expression of regional attachment.
Controls
In order to control for the effect of potentially confounding variables, we also include a
set of variables that have frequently been shown to correlate closely with firm
collaboration with universities. We include the following three variables: Firm size,
measured as the log number of employees in the firm. Educated staff, measured as the
log of the percentage share of employees that are university graduates. R&D expenditure,
measured as the log of the share of company turnover spent on research and
development.
Findings
Table 2 shows the results of the logit regression analyses. The two columns under model
1 present the basic model of collaboration for local and non-local universities as the
dependent variable. In general, the model has a somewhat higher explanatory power, as
measured by the Pseudo-R2, in predicting collaboration with non-local universities,
suggesting that local universities serve a broader population of firms. This is also
supported by the coefficients of the three control variables, which all have a larger effect
on non-local than local collaboration. Collaboration with non-local universities thus
seems to be restricted to larger and more R&D intensive firms with more educated
personnel to a greater extent than collaboration with local universities.
The knowledge spillover model predicted firms to collaborate more frequently with
local universities and less frequently with non-local ones where the local provision was
greater, i.e. in the regions of Oslo, Bergen and Trondheim. This only holds for the region
of Trondheim, where the coefficient for local collaboration is positive and significant,
while the coefficient for non-local collaboration is negative, although not significant. In
both Bergen and Oslo, the coefficient for local collaboration is actually negative
compared to the baseline of non-urban firms, and in Oslo the coefficient for non-local
collaboration is positive. However, none of these coefficients are significant.
A possible reason why the expected pattern is only found in Trondheim might be that its
university, the Norwegian University of Science and Technology (NTNU), is more
industry-oriented than the universities in Oslo and Bergen, and has its strengths in
research areas that might be more relevant to the population of Norwegian firms. In
2011, the NTNU gained 4.4 percent of its income from industrial research projects,
compared to 1.6 percent for the University of Bergen and 0.8 percent for the University
of Oslo. It participated in 101 projects under the SkatteFUNN R&D tax incentive scheme,
compared to 22 at the University of Oslo and only 2 at the University of Bergen (Ministry
of Education 2012).
There are also some differences between sectors when it comes to the use of local
versus non-local universities as collaboration partners. Compared against the baseline
of other services, firms in the manufacturing, wholesale and retail trade, and mining and
quarrying industries have a lower probability of collaborating with local universities
and a higher probability of collaborating with non-local ones. However, the differences
are mostly not statistically significant, although firms in wholesale and retail trade
collaborate to a significantly lower extent with local universities than firms in other
services.
In other industries, notably in food and accommodation services, information and
communication services and utilities, the likelihood of collaborating with non-local
universities is lower relative to other services than the likelihood of collaborating with
local universities. These may be characterised as industries with a dominance of
synthetic or symbolic knowledge bases, in particular food and accommodation, as well
as some areas of communication services. Thus, these differences are as expected.
However, once again, the differences are mostly not significant.
Table 2: Regression analysis
Knowledge spillover
model
Region: Oslo
Region: Bergen
Region: Stavanger
Region: Trondheim
Region: Other
Industry: Mining
Industry:
Manufacturing
Industry: Utilities
Industry: Construction
Industry: Trade/retail
Industry:
Transport/storage
Industry:
Food/accommodation
Industry:
Info./communication
Industry: Finance
Industry: Other services
Bounded rationality
model
Bonding social capital
Bridging social capital
Collaboration with local
university
Collaboration with nonlocal university
Social responsibility
model
Model 1
Local
Nonuniv.
local
univ.
Model 2
Local
Nonuniv.
local
univ.
Model 3
Local
Nonuniv.
local
univ.
-0.34
(0.21)
-0.12
(0.23)
-0.08
(0.22)
0.74***
(0.22)
Baseline
-0.13
(0.51)
-0.44
(0.24)
-0.11
(0.42)
-0.14
(0.28)
-0.53*
(0.25)
-0.31
(0.37)
0.70*
(0.28)
-0.15
(0.28)
-0.30
(0.31)
Baseline
0.31
(0.26)
-0.57
(0.34)
0.02
(0.28)
-0.47
(0.36)
Baseline
0.09
(0.61)
0.44
(0.29)
-0.69
(0.76)
-0.29
(0.45)
0.22
(0.30)
-0.26
(0.54)
0.13
(0.44)
-0.51
(0.36)
-0.52
(0.44)
Baseline
-0.42
(0.22)
-0.04
(0.23)
-0.10
(0.22)
0.81***
(0.22)
Baseline
-0.17
(0.52)
-0.52*
(0.25)
-0.04
(0.42)
-0.15
(0.28)
-0.60*
(0.26)
-0.30
(0.38)
0.68*
(0.28)
-0.07
(0.29)
-0.25
(0.31)
Baseline
0.46
(0.27)
-0.54
(0.35)
0.07
(0.29)
-0.71
(0.37)
Baseline
0.14
(0.62)
0.58
(0.30)
-0.68
(0.78)
-0.27
(0.45)
0.35
(0.31)
-0.13
(0.55)
-0.01
(0.45)
-0.48
(0.37)
-0.48
(0.46)
Baseline
-0.33
(0.21)
-0.11
(0.23)
-0.07
(0.22)
0.75***
(0.22)
Baseline
-0.06
(0.52)
-0.42
(0.24)
-0.13
(0.42)
-0.15
(0.28)
-0.48
(0.25)
-0.32
(0.37)
0.70*
(0.28)
-0.16
(0.28)
-0.30
(0.31)
Baseline
0.28
(0.26)
-0.58
(0.34)
0.00
(0.28)
-0.46
(0.36)
Baseline
0.08
(0.61)
0.43
(0.29)
-0.71
(0.76)
-0.30
(0.45)
0.24
(0.30)
-0.27
(0.54)
0.16
(0.44)
-0.53
(0.36)
-0.50
(0.44)
Baseline
0.10
(0.05)
0.08
(0.06)
0.12
(0.07)
0.07
(0.08)
0.08
(0.05)
0.07
(0.06)
0.09
(0.07)
0.04
(0.08)
1.33***
(0.20)
0.10
(0.05)
0.08
(0.06)
0.12
(0.07)
0.07
(0.08)
1.27***
(0.20)
Regional attachment
Local ownership share
Controls
Firm size
Educated staff
R&D expenditure
Foreign ownership
share
Constant
-0.02
(0.07)
-0.24
(0.17)
-0.19*
(0.09)
-0.43*
(0.21)
0.01
(0.07)
-0.18
(0.18)
-0.20*
(0.09)
-0.39
(0.21)
-0.03
(0.07)
-0.19*
(0.09)
0.32***
(0.07)
0.31***
(0.07)
0.33***
(0.08)
0.39***
(0.09)
0.43***
(0.10)
0.48***
(0.09)
0.27***
(0.07)
0.27***
(0.07)
0.26***
(0.08)
0.35***
(0.09)
0.37***
(0.10)
0.42***
(0.10)
-5.31***
(0.61)
0.17
1617
-3.47***
(0.45)
0.14
1617
-5.39***
(0.62)
0.21
1617
0.34***
(0.07)
0.32***
(0.07)
0.33***
(0.08)
0.00
(0.21)
0.40***
(0.09)
0.43***
(0.10)
0.49***
(0.09)
0.38
(0.24)
-3.63***
(0.45)
Pseudo-R2
0.11
N
1617
*: P<0.05, **: P<0.01, ***: P<0.001
-3.92***
(0.40)
0.11
1617
-5.71***
(0.56)
0.17
1617
When it comes to the bounded rationality model, we note that neither bonding nor
bridging social capital make a significant difference for the likelihood of collaborating
with either local or non-local universities. The coefficient for bonding social capital has a
fairly low P-value in several of the models (P=0.053 for local collaboration and P=0.076
for non-local collaboration in Model 1), but it does not meet the 95% requirement. More
importantly, however, there is certainly no significant difference in the effects of
bonding social capital on local versus non-local collaboration. Indeed, the coefficient for
non-local collaboration is higher than the coefficient for local collaboration in all the
models. Similarly, the coefficient for bridging social capital is higher for local
collaboration, although these coefficients are further from being statistically significant
in both cases.
However, the model does detect a strong association between collaborating with local
and non-local universities. In Model 2, which includes collaboration with non-local
universities as a predictor of local collaboration, and collaboration with local
universities as a predictor of non-local collaboration, the coefficients are positive and
significant at the 99.9% level. This might suggest that firms learn from collaborating
with universities and expand the number of universities that they work with. However,
it is not clear that this learning always starts with collaboration with local universities.
The coefficients are equally strong in both models, which is counter to what one might
expect to find if local universities were the starting point for most firms.
In the social responsibility model, none of the variables have a significant effect on local
collaboration, and indeed, the effect of both regional attachment and local ownership
tends to be on the negative side. However, both variables have a significant negative
effect on non-local collaboration. It is notable that both locally owned and strongly
regionally attached firms collaborate significantly less with non-local universities than
similar firms with fewer bonds to the region. It is possible that this pattern reflects some
unobserved heterogeneity that is picked up by these variables, such that locally owned
and regionally attached firms tend to be in general less oriented towards university
interaction than their R&D intensity and industry affiliation would predict. If this is the
case, the difference between the coefficients for local and non-local collaboration could
reflect some degree of regional social responsibility that makes these firms nonetheless
collaborate to a higher extent with their regional universities.
One possible cause of such unobserved heterogeneity could be that non-locally owned
firms include a fairly high share of foreign-owned firms – often multinational
enterprises that behave in a systematically different way. To test for this, Model 3
replaces the variable local ownership share with foreign ownership share. It is worth
noting that the coefficient for non-local collaboration remains almost identical to the one
in Model 1 (albeit reversed to reflect the reversed independent variable). However, the
coefficient for local collaboration is reduced to almost zero, suggesting that there is no
difference in the extent to which foreign-owned and Norwegian-owned companies in
general collaborate with local universities. The main driver of lower collaboration in
Model 1 thus seems to be locally-owned firms.
The control variables behave in the manner that one might expect. Larger firms, firms
with higher degrees of R&D intensity and firms with more educated personnel all
collaborate to a significantly higher extent with universities. These coefficients are all
significant at the 99.9% level in all the models. As mentioned in the first paragraph of
this section, the effects are higher for non-local collaboration than for local collaboration
in all the models.
Conclusion
Collaboration with local university can be the outcome of a diverse set of causes. The
neo-classically-inspired knowledge spillover model sees collaboration with universities
as a function of their research quality weighed against the cost of transferring
knowledge across a distance. The relevance of this model is broadly reflected in the
data: In the qualitative interviews, informants frequently emphasise the competence
and skills of their university partner as a main driver of collaboration. However, few
respondents reflect on the costs of knowledge transfer or difficulties in transferring
more tacit knowledge. In the survey data, we note that firms collaborate more locally in
Trondheim, where the local supply might be considered better – not necessarily in terms
of research quality, but in terms of more industry-relevant research.
However, firms do not necessarily seek to maximise the knowledge spillovers. The
qualitative interviews mainly reflect satisficing strategies: Firms try to find research
groups that possess relevant competencies and can make a contribution to the firm.
However, in most cases, they do not necessarily try to find the best competencies in the
world. If there are researchers locally or at well-known universities who fit the bill,
firms will often engage with these and look no further. In particular, smaller firms and
those with lower R&D budgets may have fewer resources to look beyond the local area,
which is reflected in the relatively lower effects of these variables on local collaboration
compared to non-local collaboration. However, more cultural variables, such as social
capital, do not tend to be associated with the probability of collaborating.
Finally, firms do not always have a strictly instrumental approach to interaction with
local universities. Some firms view interaction with local universities as an investment
in building up research competence at these universities, which may in turn benefit the
firm at some point in the future. This is a reasonable consideration when the university
is new or in an emergent phase and in contexts where regional industry is fairly strong
and knowledge-intensive. In such cases, the knowledge spillovers may run as much from
industry to the university as vice versa. In other cases, firms simply want to make a
contribution to the community. Local owners in particular may view themselves as part
of a community and with a social responsibility to make a contribution to the
development of the community when they have the resources and opportunities to do
so. Local universities are important pillars of the local community and well-placed to
benefit from such considerations. This effect does not show up to the same extent in the
survey data, suggesting that it might be limited to a few firms which could nonetheless
make an important contribution to the university.
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