Knowledge transfer in university–industry research partnerships: a review
Knowledge transfer in university-industry research partnerships: a review
Esther de Wit‑de Vries 0 1
Wilfred A. Dolfsma 0 1
Henny J. van der Windt 0 1
M. P. Gerkema 0 1
JEL Classification 0 1
Henny J. van der Windt 0 1
0 Glendonbrook Institute for Enterprise Development, Loughborough University London , London , UK
1 Science and Society Group, Faculty of Science and Engineering, University of Groningen , Groningen , The Netherlands
This paper identifies practices that can facilitate knowledge transfer in university-industry (U-I) research partnerships by systematically reviewing extant literature. We aim to contribute to the theoretical development in the field of academic engagement and propose that knowledge transfer provides a valuable perspective. We started our review with identifying barriers and facilitators of knowledge transfer. Extant literature identified knowledge differences and differences in goals resulting from different institutional cultures as important barriers to knowledge transfer. They result in ambiguity, problems with knowledge absorption and difficulties with the application of knowledge. Trust, communication, the use of intermediaries and experience are found as facilitators for knowledge transfer that help to resolve the identified barriers. Our analysis offers practical advice for the management of academic engagement. Finally, we identified questions for future research based on inconsistencies in extant research and open questions we encountered during our analysis.
Research collaboration; Academic partnerships; University-Industry; University-Business; Knowledge transfer; Knowledge management; Practices; Barriers; Facilitators; Ambiguity; Absorptive capacity; Cultural differences; Goals; Trust
D2 · D8 · D9 · L2
1.1 Knowledge transfer practices in U–I collaborations
Knowledge transfer between academia and industry is considered an important driver of
innovation and economic growth as it eases the commercialization of new scientific
knowledge within firms
(Bercovitz and Feldmann 2006; Mowery and Nelson 2004)
benefit from the interaction with industry as well, as it can inspire new research
directions and provides additional funding
(D’Este and Perkmann 2011)
. Over the past decades,
research into academic engagement increased. Most of this research studied academic
(Agrawal 2001; Shane 2005)
, which includes patenting, licensing, joint
ventures, spin-offs and so forth. However, there are other ways for academics to ensure
application of their knowledge these practices focus predominantly on knowledge exchange
(Salter and Martin 2001; Alexander and Childe 2013)
. These forms of interaction have
been referred to as academic engagement or academic partnership (Perkmann et al. 2013).
In this paper we focus on these kinds of academic engagement which we define as research
partnerships based on “high relational involvement in situations where individuals and
teams from academic and industrial contexts work together on specific projects and
produce common outputs”
(Perkmann and Walsh 2007, p. 263)
. This means that we will focus
on research partnerships, collaborative research, contract research and consulting while
collaborations with limited interaction or that require little or no new research are excluded.
Although university income from academic engagement outranks income derived from
selling intellectual property (IP)
(Perkmann et al. 2011)
and is valued higher by
industry (Cohen 2002), researchers into university–industry interactions have ignored these
forms of collaborations for a long time. Since 2006 research into academic engagement is
(Perkmann et al. 2013)
. Up till now, the field is still behind in the development
of theoretical perspectives. We propose that research into academic engagement can build
on theory on knowledge transfer to fill this gap. Academic engagement, after all, aims to
develop novel knowledge that benefits the academic and industrial partner. This requires
bidirectional knowledge sharing to identify relevant problems, share and develop new
insights, and the transfer and implementation of knowledge or technology.
In this paper we aim to map extant knowledge and perspectives on knowledge transfer
in academic engagement through a systematic literature review. Additionally, we identify
open questions for future research. Besides our aim to develop a theoretical perspective to
study academic engagement our focus on knowledge transfer adds to previous reviews on
academic engagement. As those have focussed on characteristics of researchers and
(Perkmann et al. 2013)
, factors that affect collaboration but did not focus on
academic engagement, its management or knowledge transfer
(Ankrah and AL-Tabbaa 2015;
, tried to define academic engagement (Perkmann and Walsh 2007) or
(Hagedoorn 2002; Hagedoorn et al. 2000)
In this review we discuss which theoretical frames could deepen our understanding of
university–industry (U–I) knowledge transfer, identify barriers and facilitators of
knowledge transfer and use a ‘practices perspective’ to identify ways to deal with these barriers.
This allows us to emphasize social interactions, managerial aspects and concrete
activities that enhance knowledge transfer. The term “practice” can refer to a broad range of
activities. We use the term to refer to institutionalized daily events at a workplace
. The abstraction level we use is such that it can be translated into managerial
implications. In adopting this focus we follow a developing interest in organization science that
seeks a detailed understanding of ‘what is actually done’, or “the micro-level”, and how to
make sense of those activities
To define knowledge transfer we used the definition by Bloedon and Stokes (1994, p.
44) who defined this as ‘the process by which knowledge concerning the making or doing
of useful things contained within one organized setting is brought into use within another
organizational context’. Knowledge transfer practices are then defined as the activities that
facilitate what is needed to bring knowledge into use in another organization’s context,
such as, teaching, the management of interactions and sharing data and technology.
This paper continues with a methodological section that describes our review process.
The third section outlines our analysis of the literature resulting from our review. In that
section we provide an overview of theoretical perspectives and activities that have been
described in previous research. We aim to realize generalization and accumulation of
knowledge and to identify issues which are inconclusive or have been ignored in the extant
literature and provide practices that facilitate the management of academic engagement in
practice. The paper concludes with translating these insights into an analytical framework
and research agenda.
Following previous research in the field of U–I research (for example Perkmann et al.
Ankrah and AL-Tabbaa (2015)
we used the principles and process of a
systematic literature review
(Tranfield et al. 2003)
. While conducting our review we
encountered some problems in our search process. The main problem was that there is little
consistency in the terminology used to describe research partnerships/academic engagement
and knowledge transfer. Secondly, the literature that focusses on knowledge transfer and
management of such collaborations is scarce. As a result, combining key words such as
academic engagement or research partnerships with knowledge transfer or knowledge
management provided limited results. We developed a methodology that overall followed the
analytical process of a systematic review but differs from other systematic reviews when
it comes to searching and identifying relevant literature. Therefore, the following section
describes our method in detail.
Previous reviews by
, Hagedoorn et al. (2000) and
concluded that there was a lack of research into transfer channels other than
commercialization. Also Perkmann et al. (2013) found that literature on academic engagement was mainly
published after 2006. Therefore, we did not expect to find many papers on knowledge
transfer before 2002 and selected the period 2002–2016 for our review. We also searched
the period 1997–2001 to verify the findings by Perkmann et al. (2013), but we did not
find relevant papers in this period. We only searched within English peer reviewed journal
articles. Instead of limiting our search to a list of prominent journals we decided to include
a wide range of economic and managerial literature. This was necessary as literature on
research partnerships is widespread. We used the following academic databases: Emerald,
Web of Knowledge and Business Source Premier. In the end we identified relevant papers
in 26 different journals. There were only six journals in which we found more than 1 paper,
three of those had published two papers and three published three papers.
1.4 Search protocol
The initial search strategy was to find papers discussing “research partnership*”, “academic
engagement” or papers that combined “scienc*, academi* or university” with
“industry* or business”, in combination with “knowledge management”, “knowledge transfer”
or “technology transfer”. This, however, did not provide many useful results. Therefore,
we changed the search strategy to an approach in which we used broad Boolean search
strings to identify papers on academic engagement from which we manually selected the
ones that discuss research partnerships in relation to knowledge transfer. We searched in:
titles, keywords and abstracts using the terms: ‘University–business’,
‘university–industry’ “academic engagement” and “research partnership” (other terms for university such
as ‘Academ*’ and ‘Higher Education’ ‘science’ did not yield additional results), combined
with one of the terms ‘collaborat*’, ‘cooperation*’, ‘partnership*’, ‘engage*’, ‘relation*
‘research’ ‘alliance*’. The term ‘research’ generated results for a broad range of terms used
to indicate collaborations such as joint research, collaborative research, contract research
and so on. Our search terms were based on previous reviews by
Ankrah and AL-Tabbaa
and Perkmann et al. (2013). The results from the Boolean search from the three
literature databases were combined in Rayyan
(Ouzzani et al. 2016)
. In total we found about
890 unique papers.
From these results we selected papers that could help us answer the following research
questions: What is known about knowledge transfer in academic engagement according
to the extant literature. How can failure and success of knowledge transfer be explained?
And what practices facilitate the transfer of knowledge in academic engagement? We used
the following steps and criteria (see Fig. 1). First, we excluded papers that focus solely on
entrepreneurial activities like patenting, liaison offices, science-hubs and other
intermediary organisations. Second, we excluded papers that were not related to knowledge transfer.
Third, we only included papers that gave theoretical explanations relating to effectiveness
of knowledge transfer, papers that identified factors that influence knowledge transfer and
papers that describe knowledge transfer practices and management practices that influence
NO: Discusses aspects related
to knowledge transfer?
YES: Discusses theory,
practices or factors?
knowledge transfer. If the abstract was unclear about the content, the decision to include a
paper was made after scanning the whole paper.
There are not many papers that focus explicitly on knowledge transfer in academic
(Bruneel et al. 2010)
. To find literature that discusses knowledge transfer we
first identified the factors that affect knowledge transfer in inter-organizational
collaboration. This can be justified when we follow the logic that academic engagement is a
specific form of inter-organizational collaboration or alliance
(see for example Galan-Muros
and Plewa 2016)
. Additionally, we looked for research that confirmed the relevance of the
factors we identified for academic engagement. To identify the inter-organizational factors
we used a paper by Van Wijk et al. (2008). This study combined results from 75 papers
on knowledge transfer to re-evaluate previous quantitative findings from inter- and
intraorganizational studies. We only used the factors that were relevant for inter-organizational
collaborations, absorptive capacity, ambiguity, cultural differences, differences in goals,
trust and tie-strength (Fig. 2).
These factors and their definitions (see below) were used to decide which of the papers
on academic engagement in our results discussed topics that could be related to
knowledge transfer. Van Wijk et al. (2008) identified “absorptive capacity” and “ambiguity” as
important factors. Given that these factors relate to differences in knowledge background
and the complexity of knowledge we included all research that discussed differences in
knowledge background and knowledge characteristics. “Cultural differences” and
“differences in goals” were also identified by van Wijk et al. (2008) and literature that
discussed such differences was therefore included. “Trust” and “tie-strength” were identified
as important facilitators. We therefore included literature that discussed these factors, but
also other forms of relational capital. We found 35 papers that discussed relevant insights
into knowledge transfer after applying the inclusion and exclusion criteria (for an overview
of the papers see Table 1).
1.5 Data analysis process
The next step was to analyse the papers we selected. First, we prepared a table which
summarized the research questions and answers. Second, we identified the information that
related to knowledge transfer and included this in our table. Third, we organized our
literature in line with three themes—cognitive difference, institutional differences and social
capital in a summarizing document that formed the basis for the analysis.
Fig. 2 Publications per year
2 Factors influencing knowledge transfer
Before turning to the analysis of the selected papers we will discuss the definitions of and
relations between the factors identified by Van Wijk et al. (2008) and relate them to
literature on academic engagement. The factors relating to cognitive differences are ambiguity
and absorptive capacity. They relate to differences in knowledge background between the
firm and the academics. Similarity in knowledge backgrounds makes it easier to understand
and absorb new knowledge that results from the collaboration. Knowledge ambiguity refers
to a situation where dissimilarities in knowledge result in “inherent and irreducible
uncertainty regarding what the underlying knowledge components and sources are precisely, and
how they interact”
(van Wijk et al. 2008)
. It is an aggregated term for various knowledge
characteristics of which the tacit nature
, complexity and the limited
possibilities for specification
are the most important. Knowledge that has these
characteristics is hard to identify, understand and transfer (ibid.). Hence, ambiguity is
negatively related to knowledge transfer and hard to resolve without on the job training
Wijk et al. 2008)
Absorptive capacity refers to the ability to recognize, assimilate and apply new external
(Cohen and Levinthal 1990)
. The capability of firms to absorb new knowledge
depends on the shared knowledge base of the academics and the firm employees. It has a
strong relationship with causal ambiguity, as it also strongly depends on a shared
(Cohen and Levinthal 1990)
The relevance of ambiguity and absorptive capacity in the context of U–I collaboration
was confirmed by
Santoro and Bierly (2006)
. They showed that technological relatedness
and technological capability (which increases absorptive capacity) were the most important
facilitators of knowledge transfer in U–I collaborations. In the same study, tacitness and
explicitness (related to knowledge ambiguity) moderated knowledge transfer negatively.
Institutional factors are cultural differences and shared goals. The term cultural
differences is used to indicate a lack of shared meaning and social conventions
. This complicates collaboration because different languages, opinions, social
behaviours, norms and beliefs make the interpretation of behaviour and knowledge more
(Lane and Lubatkin 1998; Mowery and Shane 2002; Simonin 1999)
Different goals relate to the different ways in which business and academia benefit
from knowledge. Shared goals are needed to reach a common understanding of the desired
output and the interpretation of results
(Tsai and Ghoshal 1998)
. When shared goals are
lacking it becomes more difficult to understand the implications and cause effect relations
of the knowledge developed, which causes ambiguity
(Partha and David 1994)
goals are also seen as an obstacle to build trust
(Davenport et al. 1998)
. The relevance
of cultural differences for U–I collaborations is confirmed by research from Bruneel et al.
Cyert and Goodman (1997)
Liyanage and Mitchell (1994)
Partha and David
Galan-Muros and Plewa (2016)
Ghauri and Rosendo-Rios (2016)
Social capital in the form of tie strength and trust reflects the closeness of a
relationship and positively influences knowledge transfer
(Bloedon and Stokes 1994; Bruneel et al.
2010; Davenport et al. 1998; Santoro and Gopalakrishnan 2001)
. Tie strength is a measure
for the frequency of interactions and communication. While trust is used to express the
reliability of a partner (Hansen 1999). Tie strength influences trust positively. Additionally,
trust and tie strength are associated with the commitment to help a partner to understand
(Hansen 1999; Inkpen 2000)
. Sherwood and Covin (2008) found that trust
is positively associated with tacit knowledge transfer, as trust increases open
communication and the willingness to share knowledge. The importance of social capital (trust and tie
strength) has been confirmed for academic engagement
(Amabile et al. 2001; Philbin 2008;
Plewa et al. 2013a; Schartinger et al. 2002)
As can be seen from the previous text, the factors that influence knowledge transfer are
interrelated. Trust is positively influenced by tie strength and shared goals, and negatively
by ambiguity and organizational differences. Tie strength improves absorptive capacity, as
more interaction provides more opportunities to exchange knowledge. Ambiguity can be
reduced by tie strength as well. Reduced ambiguity in return improves absorptive capacity
and the understanding of the goals and needs of the partner. When there are large
differences between organizational cultures, it is more likely that organizations have different
research goals and possibly also different knowledge backgrounds. This can result in more
ambiguity and less trust in that the partner will do what is right for you.
In the following part we will discuss the findings from the literature we reviewed. For each
of the three topics we identified we will discuss the theoretical insights, their implications
and the associated practices for successful knowledge transfer.
3.1 Cognitive differences
We start with a general discussion on knowledge flows in academic engagement. After this,
we turn to theoretical insights about how knowledge differences and characteristics
influence the effectiveness of knowledge exchange and absorptive capacity. Finally, we discuss
how different practices of knowledge exchange are influenced by these factors and which
practices help to improve knowledge transfer from a cognitive differences perspective.
Looking at the papers in our review, the overall picture is that the extant literature
pays little attention to the knowledge contribution of industrial partners. The majority of
the papers focuses on development and transfer of knowledge by the academic partner.
The knowledge contribution from the industrial partner is reduced to formulating
interesting research problems
(D’Este and Perkmann 2011; McCabe et al. 2016)
providing data and insight in the application context
(Barnes et al. 2002; Gertner et al. 2011;
Hadjimanolis 2006; McCabe et al. 2016; Wang and Lu 2007)
Ulhøi et al. (2012
) focus specifically at the knowledge contribution of the industrial
partner. They sketch a much more dynamic exchange process, in which the industrial
application of research outcomes directly influences academic research. This
discrepancy is partly explained by McCabe et al. (2016) who discusses three levels of
collaboration, low, high and deep, and links them to different knowledge exchange practices. In
collaborations with low engagement the firm is seen as data source, while all research
activities are controlled and conducted by the academic partner. In high collaborations
the firm contributes through the identification of research problems, grounding the
design and data collection in the application context and by assisting academics in
making decisions. In ideal circumstances during deep collaboration the industrial partner
would take a more equal role as the academics and contribute to the identification of
research problems, help with the selection of methods and is engaged in data gathering
and analysis. In practice, the role of the industrial partner in data analysis and theory
development is limited, even in deep collaborations. Because industrial partners lack the
time to dive into the data and feel unequipped to participate truly in the academic debate
(McCabe et al. 2016)
. Also, academics hardly use data that is produced by the industrial
partner due to a lack of quality signals of industrial data that is required for academic
(Canhoto et al. 2016)
. Additionally, academic knowledge and expertise is
valued higher than industrial knowledge. This makes industrial partners reluctant to
take part in the research and the academic debate
(McCabe et al. 2016)
The ease with which knowledge is transferred depends on the characteristics of
knowledge, similarities in knowledge background and knowledge management
capabilities. We will discuss each of these aspects in the following paragraphs.
The most important characteristic of knowledge is its explicitness
(Santoro and Bierly
. Knowledge that can be made explicit can be transferred through prototypes,
formulas or manuals. Such knowledge is often transferred through contractual agreements,
(Alexander and Childe 2013; Sandberg et al. 2015)
. In that case the
successful use of the knowledge depends on whether it can be appropriated to the application
(Alexander and Childe 2013; Sandberg et al. 2015; Wang and Lu 2007)
knowledge transfer requires interaction to develop competence (Johnson and Johnston
2004) and more direct collaboration
(Alexander and Childe 2013; Azevedo Ferreira and
Rezende Ramos 2015; Daghfous 2004; Gertner et al. 2011; Steinmo 2015; Wang and Lu
and interactional expertise
(Canhoto et al. 2016; Sandberg et al. 2015)
tacit knowledge is best transferred through academic engagement, instead of patenting
or licensing, as it includes more personal interaction.
developed the knowledge creation circle to explain tacit knowledge
transfer. Which shows that tacit knowledge is transferred in four steps; (1) through
creating shared experiences (socialization), after which knowledge is (2) externalized,
(3) recombined and (4) internalized.
Johnson and Johnston (2004)
explored how the
knowledge creation cycle affects knowledge transfer in academic engagement. They
found that all four steps of the knowledge creation cycle (socialization, externalization,
combination and internalization) were needed in the initiation phase, to formulate
relevant research questions and goals, and in the knowledge transfer phase, to absorb tacit
knowledge. The need to go through the whole knowledge cycle in both phases
distinguishes collaborative research from other learning processes.
The second important factor that influences knowledge absorption, is differences in
knowledge background, referred to as cognitive and epistemic difference. They result in
differences in ‘language’ and different logics regarding what methods should be used.
Therefore, relatedness of prior knowledge and technological competence help to
understand and integrate new knowledge
(Daghfous 2004; Santoro and Bierly 2006)
Although cognitive distance does not diminish the propensity to collaborate, it does
limit interaction during the collaboration. Resultantly, tacit knowledge transfer which
requires interaction is limited. But is might also be problematic for forms of engagement
that require interaction relating to the use of methods and technology, like joint research or
(Sandberg et al. 2015)
Studies on prior knowledge have asked how prior technological knowledge and
management capabilities are related. There seems to be agreement on the importance of general
collaboration experience, organizational capabilities, and experience with the particular
partners for overall collaboration success
(Buganza et al. 2014; Bjerregaard 2009; Canhoto
et al. 2016; Daghfous 2004; Sandberg et al. 2015)
. Studies that particularly studied
cognitive difference in relation to knowledge transfer are contradictory about the effect of
experience. Daghfous’s (2004) and
Muscio and Pozzali (2013)
found that cognitive differences
are not diminished by experience. To which Daghfous’s (2004) adds that systematic
learning in relation to management skills does not significantly increase learning capabilities.
, on the other hand, found that cognitive capital can be developed over time
at the organizational level. While research by
(Corley et al. 2006)
indicates that epistemic
differences can be reduced by strong organizational routines. Therefore, the role of
experience to mitigate knowledge differences remains unclear. If experience or management
capabilities do not reduce cognitive differences, identifying suitable partners with
matching knowledge backgrounds is an important success factor
(Galan-Muros and Plewa 2016)
Finding the right partners is especially difficult for SME (small and medium size
enterprises) as they have smaller networks
(Buganza et al. 2014)
The relevance of technical and organizational uncertainties in relation to learning
activities is unclear as well. A study by
indicated that prior knowledge was
only significant in case of high uncertainty about the organizational aspects for the
implementation of the new knowledge. His hypotheses is that in the case of radically new
technologies knowledge is so different from existing knowledge that knowing how to organize
the implementation of new technologies becomes more relevant. This needs to be
confirmed by future research.
We now turn to practices that can improve knowledge transfer. Communication is an
important facilitator to improve absorptive capacity. The channels for communication
during engagement are diverse and differ in their ability to transfer tacit knowledge and to
deal with differences in knowledge backgrounds
(Alexander and Childe 2013)
Knowledge transfer through rich, or interactive, media is preferred over indirect communication
through reports, presentations, patents and so forth, as the latter are unable to transfer tacit
(Alexander and Childe 2013; Sandberg et al. 2015)
We noticed that three reoccurring practices are important for rich communication
practices: boundary spanners
(Al-Tabbaa and Ankrah 2016; Barnes et al. 2002; Gertner
et al. 2011, Hadjimanolis 2006; Wallin et al. 2014)
(Alexander and Childe 2013;
Azevedo Ferreira and Rezende Ramos 2015; Daghfous 2004; Gertner et al. 2011; Wallin
et al. 2014; Wang and Lu 2007)
and the use of tools or objects
(Buganza et al. 2014; Wallin
et al. 2014)
. We will continue with discussing how each of these practices can be used
effectively to transfer knowledge according to the literature.
Boundary spanners are often personnel that is exchanged between academia and
industry during the course of the collaboration. For instance, the outplacement of personnel
from the firm, secondment and employment of graduates
(Galan-Muros and Plewa 2016;
Gertner et al. 2011; Harryson et al. 2007; Hadjimanolis 2006; Pinheiro et al. 2015; Ulhøi
et al. 2012; Wang and Lu 2007)
or (Ph.D.) students that do part of their research at the
(Gertner et al. 2011; Galan-Muros and Plewa 2016; Hadjimanolis 2006; Harryson
et al. 2007; Wang and Lu 2007)
. Such mobility can be limited by organizational differences
(Galan-Muros and Plewa 2016)
If partners mainly interact through periodical meetings instead of personnel exchange
the identification of suitable recipients within the firm, who have the right knowledge
background is essential. This requires time and commitment
(Mesny and Mailhot 2007; Plewa
et al. 2013a)
Boundary spanners are effective because they facilitate the knowledge conversion and
translation of academics results to the context of the firm and vice versa
Ferreira and Rezende Ramos 2015; Gertner et al. 2011)
. This requires the investment of time
to develop a shared language and discourse
(Al-Tabbaa and Ankrah 2016; Canhoto et al.
. Over time and through close collaboration the boundary spanner gets a better
understanding of the partner’s needs and knowledge background. This enables him to translate
results which facilitate application and implementation
(Gertner et al. 2011; Hadjimanolis
2006; Wang and Lu 2007)
. Firm employees who interact frequently with researchers and
follow the debate at academic meetings gain a deeper understanding of the working
methods and knowledge produced by the researchers. This helps to integrate the results of the
(McCabe et al. 2016)
Training and workshops help to transfer tacit, complex knowledge and build skills
(Azevedo Ferreira and Rezende Ramos 2015; Daghfous 2004)
. They provide a space for
deliberation and feedback which increases the comprehension of results
(McCabe et al.
. It is important to have the right people, with the right level of expertise, involved
in these meetings
(Azevedo Ferreira and Rezende Ramos 2015)
. The open and interactive
mode of communication in this kind of meetings gives industrial partners the possibility to
engage more and feel more comfortable about giving input
(McCabe et al. 2016)
creative chaos in interactive sessions provides a way to learn autonomic and recombine the
new insights with previous knowledge, which facilitates absorption (Johnson and Johnston
The use of prototypes and working in the facilities of the industrial partner helps to
integrate knowledge and learn about implementation challenges
(Daghfous 2004; Gertner
et al. 2011; Hadjimanolis 2006; Wallin et al. 2014; Wang and Lu 2007)
. Mostly because it
helps to see connections between different aspects of knowledge and this is an important
way to reduce ambiguity. Close interaction is also the most important way for researchers
to identify interesting questions for future research
(Perkmann and Walsh 2007; Ulhøi et al.
2012; Wang and Lu 2007)
3.2 Institutional differences
Differences in organizational goals and culture are a frequently mentioned, but not well
defined barrier to academic engagement. The literature we reviewed uses the term cultural
differences to indicate differences in project goals, expected outcomes, visions on required
research activities, the allocation of time and resources, management styles, social
conducts, cognitive differences, different ‘language’ and time perception
Galan-Muros and Plewa 2016; Ghauri and Rosendo-Rios 2016; Harryson et al. 2007)
spite of that, they are frequently mentioned as barrier, they are not well researched. It is
therefore much welcomed, that since 2013 more research has been conducted into how
institutional differences influence knowledge transfer and collaboration success.
There remains discussion about the extent to which cultural differences actually affect
collaboration in practice. On the one hand, it has been shown that increasing academic
convergence between companies and industry reduce the differences
On the other hand, the limited statistical research on cultural differences indicates that
cultural differences do affect collaboration success
(Galan-Muros and Plewa 2016; Ghauri and
. Ghauri and Rosendo-Rios (2016) found that especially market and
time orientation affect collaboration success.
Differences in goals originate from differences in market orientation
, priorities in norms
(Al-Tabbaa and Ankrah 2016; Mesny and
, and different logics for the sharing of knowledge (Steinmo 2015). Differences
in goals are best managed by improved communication
(Bjerregaard 2009; Plewa et al.
. Goals and outcomes should be established early in the project. The use of project
plans that outline goals and outcomes could facilitate this
(Canhoto et al. 2016; Morandi
. Project management tools can be helpful in the communication of progress and the
relation between goals and outcomes
(Wallin et al. 2014)
. A complicating factor here is
that differences in goals are often not recognized in the early, ‘honeymoon’, stage of a
collaboration, they become clear during the engagement phase
(Estrada et al. 2016; Plewa
et al. 2013a)
. In this phase the selection of actual research questions, methods and resource
allocation might provide problems, even if these matters seemed clear at the beginning
(Estrada et al. 2016; Mesny and Mailhot 2007; Plewa et al. 2013a)
Researchers are expected to put sufficient effort into understanding the needs of the
industrial partner; this becomes especially important during the engagement phase
(Canhoto et al. 2016; Ghauri and Rosendo-Rios 2016; Plewa et al. 2013a)
management about what can be achieved in the available time and when results can be expected is
also important to keep industrial partners satisfied
(Azevedo Ferreira and Rezende Ramos
2015; Barnes et al. 2002; Bjerregaard 2009; Sandberg et al. 2015; Steinmo 2015; Wallin
et al. 2014)
Frequent meetings and deliberation are key to recognize and solve differences
2013; Plewa et al. 2013b; Steinmo 2015)
. The possibility for interactive discussion for the
coordination of goals is important to keep industrial and academic expectations aligned
(Johnson and Johnston 2004). Experience with the collaboration partner has been found to
mitigate problems relating to differences in goals, because it leads to more realistic
expectations and better insight in the partner’s needs
(Azevedo Ferreira and Rezende Ramos
2015; Steinmo 2015; Wallin et al. 2014)
. Finally, looking for a higher common good can
help to re-unite goals if there seems to be no common ground
(Mesny and Mailhot 2007)
A highly valued academic norm is academic freedom, the autonomy to follow
interesting directions and choose one’s own research problems and methods. This may
conflict with making strict project plans and specifying deliverables that align with industrial
needs. A good understanding of a partner’s needs helps to take these needs into account,
also when novel directions are pursued, while open communication raises understanding.
Zhu and Hawk (2015)
show how academics at Stanford University and MIT (Michigan
Institute for Technology) managed to maintain their academic freedom. They focus on
fundamental research, but use market developments to inspire their research. This way they
manage to secure industrial funding. At the same time strict conflict of interest policies are
in place to prevent conflicts of interests.
Cultural differences that are referred to as institutional norms or organizational
routines relate to differences in project management and time orientation. Time orientation
relates to differences in what is considered an acceptable period to reach goals,
punctuality in meeting deadlines and the continuity of personnel
(Barnes et al. 2002; Ghauri and
. The industrial partner’s aversion to long term orientation of
academics and the fundamental nature of research can be managed by open communication and
good project management. This requires clarifying communication channels, providing
and updating project plans and punctuality from academics
(Barnes et al. 2002; Ghauri
and Rosendo-Rios 2016; Morandi 2013; Wallin et al. 2014)
. Estrada et al. (2016) found
that such ‘routine’ based differences, meaning dissimilarities in working methods, could
only be resolved when orientation based differences, meaning dissimilarities in goals, were
Cultural differences relating to the application of knowledge and willingness to share
knowledge relates to the academic habit to publish results, while industrial partners rather
keep knowledge secret. These differences can be handled through publication management
and upfront arrangement of IP (intellectual property) rights
(Azevedo Ferreira and Rezende
. However, arranging IP too early in the collaboration might negatively
influence trust between partners
(Canhoto et al. 2016)
. Publication management includes
arrangements regarding what data can be published and allows the industrial partner to
authorize publication, this ensure academics do not publish sensitive knowledge (Azevedo
Ferreira and Rezende Ramos 2015). Also, providing the industrial partner the possibility to
delay the publication to arrange IP rights reduces this barrier
3.3 Social capital
Trust has been shown to influence knowledge transfer in research partnerships
et al. 2010; Plewa et al. 2013b; Ulhøi et al. 2012)
. Mostly, because it reduces fear of
opportunistic behaviour and, resultantly, increases the willingness to share information
et al. 2013b; Philbin 2008; Sherwood and Covin 2008; Steinmo 2015)
. Trust increases with
frequent communication. Therefore, tie strength improves trust
(Al-Tabbaa and Ankrah
2016; Plewa et al. 2013b)
Trust in U–I collaboration is affected by two things. First, industrial partners fear that
the academic partner is not working on the same goals, due to institutional differences, and
that academics use the industrial partner as money cow
(Al-Tabbaa and Ankrah 2016;
Pinheiro et al. 2015; Ulhøi et al. 2012)
. Second, there is a fear that academic partners,
unintentionally, share sensitive knowledge with other companies, due to a lack of experience with
handling sensitive knowledge (
Ulhøi et al. 2012
). The latter can be prevented by
providing secrecy training and using a split management strategy. Meaning that academics who
work for different companies should not be mixed in research projects (
Ulhøi et al. 2012
Fear for a lack of common interests is reduced by building social capital, which includes,
tie-strength, and collaboration experience with the particular partner
(Pinheiro et al. 2015;
Sandberg et al. 2015)
. Frequent meetings in the initiation stage also help to merge goals,
keep them aligned and increase trust
(Plewa et al. 2013b)
What is needed to build trust also depends on the collaboration stage. In the
initiation stage trust is mainly based on the reputation of and previous experiences with the
(Plewa et al. 2013b)
. Resultantly, academic reputation and previous personal ties
are important drivers for establishing collaborations
(Pinheiro et al. 2015; Sandberg et al.
Muscio and Pozzali (2013)
found that research quality is less important for
establishing the collaboration than the applicability, in the sense of ‘readiness to use’, of
the knowledge that will be produced.
During the collaboration the quality of communication is important. Social capital is
built through frequent face-to-face communication and workshops that facilitate interaction
(Al-Tabbaa and Ankrah 2016; Plewa et al. 2013b)
. This kind of communication improves
insight in the partner’s goals. Spontaneously sharing interesting knowledge that is not
directly related to the specific project, experience and successful previous collaborations
make partners feel that the other is genuinely interested in what is needed and improves
insight in the partner’s needs
(Al-Tabbaa and Ankrah 2016; Pinheiro et al. 2015)
Therefore, it is often recommended to start with small projects, like student projects, and build to
more complex collaborations and more fundamental questions from there
(Buganza et al.
2014; Pinheiro et al. 2015)
. This way managerial capabilities can be developed and
academic work can be aligned with business challenges
(Buganza et al. 2014; Plewa et al.
2013a; Pinheiro et al. 2015)
Trust also influences the contractual and organizational management of the
collaboration. Trust results in less formal contractual agreements
(Chin et al. 2011; Morandi 2013;
Ulhøi et al. 2012)
. When there are no IP-rights expected the collaboration is often formed
by memoires of understanding (MoU) or standard documents from the technology transfer
office (TTO) instead of legal contracts (ibid). Additionally, trust is reflected in the absence
of formal control mechanisms. Coordination is often effected informally between project
managers from both sides
(Barnes et al. 2002; Chin et al. 2011; Morandi 2013)
. This can
lead to confusion when university partners have several senior researchers, and it is unclear
who is in control (Barnes et al. 2002). Appointing a single person from both organizations
as a liaison has therefore been recommended
Furthermore, trust influences the formalization of communication. Regular contact
during the collaboration is important to ensure that goals remain aligned
(Buganza et al. 2014;
Plewa et al. 2013a)
. To align goals, projects often start with a project plan, which allocates
tasks and responsibilities and milestones in detail
(Barnes et al. 2002; Morandi 2013)
These plans are rarely updated as the work develops and they soon become obsolete. The
risk in this kind of work is that projects deviate from original plans, or that changes in
plans are not well administrated and lead to discussion later on. Collaborations involving
mutually dependent research form an exceptions, these plans are more likely to be updated
to coordinate activities (Morandi 2013).
Although partners expect to be informed, reports play a minor role in this and are
usually only compiled at the end of each phase and perceived as archiving material
(Chin et al.
2011; Morandi 2013)
. Preferably, results are discussed in informal settings and regular
progress meetings, or informally by email (discussions) and telephone
(Chin et al. 2011;
Morandi 2013; Ulhøi et al. 2012)
This review aimed to explore the relevance of knowledge transfer as a concept for theory
development regarding academic engagement and to give an overview of literature that
addresses knowledge transfer in academic engagement. We found that research into
knowledge transfer in academic engagement is dispersed. This could be due to incoherence in
terminology at all levels; from terminology to indicate the form of engagement to the
factors and theoretical frames that are used to discuss knowledge transfer.
Nevertheless, knowledge transfer seems an interesting perspective for theory
development for research into academic engagement. Our framework and the factors found by van
Wijk et al. (2008) provided an interesting starting point for a more focused analysis, and
integration of concepts. We also found that especially qualitative research can benefit from
a better theoretical bedding for its analysis in order to provide better funded insights in the
mechanisms behind success and fail factors of academic engagement. And makes it easier
to build on previous research.
Bringing together this literature on knowledge transfer enabled us to develop a stylised
model that shows how different characteristics of knowledge transfer relate in the context
of academic engagement (Fig. 3). We could also compare previous research outcomes and
draw new conclusions by connecting empirical results with theoretical explanations and
by identifying dissimilarities that require more research. In the remainder of this paper
we present the stylised model and the implications of our analysis for future research and
We found two promising lines of research. The first, deals with the cognitive differences
and the adsorption of knowledge. The second, with differences in goals and the
applicability of knowledge. We also identified the most relevant factors and practices for the
mitigation of these differences. Trust and communication help to overcome both, cognitive
differences and differences in goals. Intermediaries mainly help to reduce cognitive differences,
and experience primarily helps to resolve differences is goals.
In relation to cognitive differences there seems to be agreement that secondment,
employee exchange and hiring graduates are important ways to (bi-directionally) transfer
the tacit aspects of knowledge and that Master and PhD students can play a particularly
important role in this
(Gertner et al. 2011; Harryson et al. 2007; Thune 2009)
similarity in knowledge background is so important for absorptive capacity, we
recommend that this is taken into account in partner selection. The use of prototypes and
models helps to resolve ambiguity and to connect new and extant knowledge. For absorptive
capacity, trust is foremost a mediating factor, because it increases the willingness to share
knowledge. Communication practices on the other hand are very important for the quality
of knowledge sharing. Communication should be open, interactive and bidirectional, for
instance in the form of workshops, to recognize and resolve cognitive differences. When
tacit knowledge needs to be transferred this requires on the job training or the hiring of
graduates who worked on the project. The use of prototypes to show underlying relations
can help to manage ambiguity. The role of experience to mitigate differences in knowledge
background remains unclear. We believe that experience can help overcome minor
differences through learning activities, but does not resolve fundamental differences in epistemic
background or knowledge background without extensive learning. For example, an
ICTprofessional will not learn fundamental physics through collaboration experience; this
requires extensive training.
The second line of research, applicability of knowledge, is highly dependent on goal
similarity of the partners. Industrial partners often feel (or fear) that differences in
knowledge application requirements might go at the costs of industrial needs if there is too much
focus on academic relevance and publication requirements. While the need to publish
might be at odds with the need to protect sensitive company knowledge and hamper trust.
Communication to determine goals and to discuss what information can be published is the
most important way to deal with these differences. Drawing up project plans that include
milestones and the use of management tools can improve trust in the willingness of the
academic to take into account industrial needs. Furthermore, experience with academic
engagement in general and the specific partner in particular will build understanding for
the needs of industry and the particular partner more specifically. Collaboration experience
with the specific partner also increases trust in that the partner will handle sensitive
4.1 Future research agenda
We can identify a number of avenues for future research into knowledge transfer related to
academic engagement. These suggestions are based on open questions we encountered
during our analysis and inconsistencies between the results in the papers discussed here.
Absorptive capacity, ambiguity and cognitive distance seem to be the most difficult
barriers to be resolved. There remains uncertainty over the relevance of experience and
management capabilities to solve transfer problems related to knowledge differences. This
requires more research. Also, there seems to be agreement that secondment, employee
exchange and hiring graduates are important ways to transfer the tacit aspects of knowledge
in both directions. However, there is a need for more insight into the firms’ perspective on
the involvement of students and Ph.D.’s in research partnerships
, as most
research discusses the academic perspective only.
We noticed that “cultural differences” is used as an aggregated term for different goals,
organizational and managerial differences and epistemic norms. This is problematic as it
makes it hard to understand the cause-effect relations of the individual aspects of cultural
differences on knowledge transfer. Research that differentiates between cognitive or goal
related differences and routine based differences indicates that these factors affect
(Corley et al. 2006; Estrada et al. 2016)
. A more structured approach is
required which distinguishes between the effects of single attributes of cultural differences
and their effect on collaboration success and knowledge transfer.
The extent to which cultural differences affect academic engagement is unclear, even
as the role of experience to reduce this barrier.
and Bruneel et al.
(2010) found that experience and academic convergence reduces differences. While
Muscio and Pozzali (2013)
found that more experience in
interaction with firms does not change the perception of cognitive distance. Firms indicated
that different logics remained problematic for the development of useful interaction
with universities, but that this disadvantage was outweighed by the benefits of the
. There seems to be a need for future research to improve our
understanding of how cultural differences are managed.
The relation between trust and knowledge transfer and the specific threats perceived
in U–I collaborations requires greater attention in future studies
(Plewa et al. 2013a,
. We noticed that trust issues for research partnerships differ from those for
businessto-business collaborations. Yet the trust scales used most frequently in the papers we
reviewed are the ones intended for analysing business-to-business relations, developed
by Saparito et al. (2004). Therefore, these do not fully reflect the trust related concerns
we encountered in our analysis. Secondly, trust is mainly researched in quantitative
research in relation to general collaboration success. Little attention has been paid to the
practices required to build trust or the effect of trust on knowledge transfer specifically.
From the papers we studied, it seems that U–I research partnerships are managed
informally (e.g. MoU instead of formal contract, informal reporting), or as
Powell et al.
) call it, irrational. This is in contrast with the findings of
Ankrah and AL-Tabbaa
, who argue that U–I collaborations are managed as rational process: focusing
on planned resource and knowledge transfer. This could be due to a difference in focus,
Ankrah and AL-Tabbaa (2015)
focus on negotiations in the pre-collaboration phase
and their data included many results related to academic entrepreneurship, while the
papers we analysed focus on the execution of the project and research partnerships. Yet,
we believe this difference taps into a broader debate, on the governance of university
knowledge transfer, presented by
Geuna and Muscio (2009)
, who argue that U–I
collaborations have a more informal irrational management style than is often assumed. This
is also confirmed by the papers in our review, which show a very informal management
style, based on high levels of trust. In our view, an increased understanding of when
informal or formal management mechanisms are used is needed.
We also found that the current literature is focussed on the responsibilities and
perception of the academic partner, with very limited attention for the role of the
industrial partner. While knowledge transfer is a bidirectional process. This could lead to an
underestimation of the importance of the firm’s efforts to absorb knowledge and
communicate its needs to the researcher. More attention for how firms manage research
partnerships is therefore needed. On the other hand, it would be interesting to gain greater
insight into what knowledge academics require from firms, to enables them to provide
relevant results and manage the knowledge needs of the firm. Also, the literature mainly
focuses on problems in the implementation phase. There is room left for research into
problem management during the initiation and collaboration phase.
Closing the gap between qualitative and quantitative research is another way to bring
the field forward. Qualitative and quantitative research has both identified factors which
influence knowledge transfer, but have not integrated their results. Such integration
would increase the understanding of the underlying mechanisms. Qualitative research in
this field is often very descriptive and does not refer to theoretical concepts.
Researchers who consider using results from this paper should be aware of the fact,
that the qualitative nature of most research papers we used in this review, the small
samples in both case study and quantitative studies we reviewed, and the sample bias of
the selected cases in these studies (discussing only one sector, one university or a
single research collaboration) might influence the validity of results we discussed.
Resultantly, the conclusions we draw about the relation between different factors require more
research. We therefore invite scholars to conduct more research into the relationships we
proposed in our model.
4.2 Managerial implications
In our analysis we identified a number of barriers to successful knowledge transfer in
academic engagement. We also identified the practices that could help to overcome those
barriers. We found that cognitive differences are hard to overcome without the presences of
boundary spanners or intermediaries. Therefore, we recommend to carefully select
knowledge partners and the persons who represent the company. During the collaboration
knowledge is best transferred through rich, meaningful, direct and bilateral interaction, especially
when tacit knowledge is involved. Attributing sufficient time for interaction is important
to reap the fruits of the partnership. Workplace mobility of employees during and after the
collaboration seems the best way to transfer and implement (tacit) knowledge, while these
employees also act as intermediaries to align goals.
Collaboration experience with a specific partner and learning how to deal with
differences seems the best way to overcome differences in logic and goals. It can be wise to
start with smaller projects, such as student internships or thesis research, to gain
collaboration experience and to learn about the capabilities of a partner. Drawing up project plans
and the use of management tools can help to make differences in goals visible over the
course of the project. If they are regularly updated they help to keep goals and research
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