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1
‘Development of a technologycommercialisation toolbox for publicly
funded food research’.
WORKING PAPER TITLE:
‘Determinants of EffectiveTechnology Transfer
Research conducted by:
Dr. Maeve Henchion, Ashtown Food Research Centre, Teagasc, Dublin
Ms. Marie Buckley, Ashtown Food Research Centre, Teagasc, Dublin
Mr. Paul O’Reilly, School of Management, Dublin Institute of
Technology
First Report in a study funded by the Department of Agriculture and Food under the Food InstitutionalResearch Measure entitled ‘Development of a technology commercialisation toolbox for publicly funded food
The TOOLBOX Project
2
DETERMINANTS OF EFFECTIVE TECHNOLOGY TRANSFER
Abstract
The paper presents a review of some literature pertaining to research
commercialisation and technology transfer. Specifically it identifies the key
determinants of successful technology transfer as identified by previous research
undertaken in the area. It considers concepts and research under the following
headings: (i) transfer agent; (ii) transfer medium; (iii) transfer object; (iv) transfer
recipient; and (v) demand environment. Technology uptake constraints are also
explored. The latter include: (i) lack of awareness of the output, technology or
innovation; (ii) lack of credibility associated with the technology or innovation; (iii)
poor fit of the innovation with user requirements; (iv) lack of understanding of the
product/output; (v) lack of awareness of the problem (or need for a solution); (vi)
inappropriate timing; and (vii) lack of enabling conditions/incentives.
3
DETERMINANTS OF EFFECTIVE TECHNOLOGY TRANSFER
1. INTRODUCTION
If the food industry is to prosper in the future, it is crucial to develop and
commercialise technological knowledge into industrial success (European
Commission, 2000). In order to establish the best route to Ireland’s success in this
regard, it is important to examine the concepts and theories underlying research
commercialisation and technology transfer. This literature review was undertaken
as part of a FIRM1-funded project entitled ‘Development of a technology
commercialisation toolbox for publicly funded food research’. The overall objective
of this project is to develop a ‘toolbox’ to assist public research organisations
improve technology transfer and research commercialisation of publicly funded food
research through examination of the food innovation system (FIS) in Ireland. For
the purpose of this research, a food innovation system is defined as “the various
actors (policy makers, policy enactors, technology producers, technology users,
technology lobbyists), the environment in which they operate, along with their
interactions that operate in the food industry, and participate in innovation activities
that produce and transfer economically and socially useful tacit and codified
knowledge”.
This review focuses on the process of technology transfer, and key success factors
for and barriers to technology transfer are highlighted. The paper concludes with a
summary of key concepts that require investigation in the context of the food
innovation system in Ireland.
This paper is one of a series of three papers that provide the theoretical
underpinnings to the project. These companion reports are entitled ‘The Case for
Commercialising Publicly Funded Research in the Food Sector’ and ‘Technology
Transfer Defined’.
1 Food Institutional Research Measure – food research programme funded by the Irish Governmentunder the National Development Plan 2000 – 2006.
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2 ASSESSMENT OF TECHNOLOGY TRANSFER
In order to assess the determinants of technology transfer, the authors propose to
use a Contingent Effectiveness Model of Technology Transfer (Bozeman, 2000).
The Contingent Effectiveness Model (Figure 1 and Table 1) draws its name from its
assumption that technology transfer parties have multiple goals and effectiveness
criteria. The model says that impacts of technology transfer can be understood in
terms of who is doing the transfer, how they are doing it, what is being transferred
and to whom. Table 2 describes briefly the effectiveness criteria.
Figure 1 Contingent Effectiveness Model of Technology Transfer
Source: Bozeman, 2000
The model includes the five broad dimensions determining effectiveness: (1)
characteristics of the transfer agent, (2) characteristics of the transfer media, (3)
characteristics of the transfer object, (4) the demand environment and (5)
characteristics of the transfer recipient. These dimensions are thought to be broad
enough to include most of the variables examined in studies of university and
government technology transfer activities. Arrows in the model indicate relations
among dimensions (broken lines indicate weaker links).
5
Table 1 Dimensions of the Contingent Effectiveness Model
Dimension Focus Examples
Transfer agent The institution or organization
seeking to transfer the technology
Government agency, university, private
firm, setting characteristics, culture,
organization, personnel
Transfer
medium
The vehicle, formal or informal, by
which the technology is transferred
License, copyright, person-to-person, formal
literature
Transfer object The content and form of what is
transferred, the transfer entity
Scientific knowledge, technological device,
process, know-how and specific
characteristics of each
Transfer
recipient
The organization or institution
receiving the object
Firm, agency, organization, consumer,
informal group, institution and associated
characteristics
Demand
environment
Factors (market and non-market)
pertaining to the need for the
transferred object
Price for technology, substitutability,
relation to technologies now used, subsidy,
market shelters
Source: Bozeman, 2000
6
Table 2 Technology transfer effectiveness criteria
Effectiveness
criterionFocus
Relation to research and
practice
Key question Theory base Major advantage and
disadvantage
“Out-the-door”
Based on the fact that one
organization has received the
technology provided by another, no
consideration of its impact
Very common in practice,
uncommon as evaluation
measure (except in studies
measuring degree of
participation in tech transfer)
Was technology
transferred?
Atheoretical or classical
organization theory
A: Does not hold transfer agent
accountable for factors that may
beyond control
D: Encourage cynicism & focus
on activity rather than outcome
Market impact
Has the transfer resulted in a
commercial impact, a product, profit
or market share change?
Pervasive in both practice and
research
Did the transferred
technology have an
impact on the firm’s
sales or profitability?
Microeconomics of the firm
A: Focuses on a key feature of
technology transfer
D: Ignores important public
sector and non-profit transfer;
must accommodate market
failure issues
Economic
development
Similar to market impact but gauges
effects on a regional or national
economy rather than a single firm or
industry
Pervasive in both practice and
research
Did technology
transfer efforts lead to
regional economic
development?
Regional science and public
finance theory
A: Appropriate to public
sponsorship, focus on results to
taxpayer
D: Evaluation aimed always
requires unrealistic assumptions
Political reward
Based on the expectation of political
reward (e.g. increased funding)
flowing from participation in
technology transfer
Pervasive in practice, rarely
examined in research
Did the technology
agent or recipient
benefit politically from
participation?
Political exchange theory,
bureaucratic politics models
A: Realistic
D: Does not yield systematic
evaluation
Opportunity
costs
Examines not only alternative uses
of resources but also possible
A concern among
practitioners, rarely examined
What was the impact of
technology transfer on
Political economy, cost-benefit
analysis, public choice
A: Takes into account foregone
opportunities, especially
7
impacts on other (than technology
transfer) missions of the transfer
agent or recipient
except in formal benefit-cost
studies
alternative uses of the
resources?
alternative uses for scientific
and technical resources
D: Difficult to measure, entails
dealing with the
“counterfactual”
Scientific and
technical human
capital
Considers impacts on enhanced
scientific & technical skills,
technically-relevant social capital &
infrastructures (e.g. networks, user
groups) supporting scientific &
technical work
A concern among
practitioners, rarely examined
in research
Did technology
transfer activity lead to
an increment in
capacity to perform and
use research?
Social capital theory (sociology,
political science), human capital
theory (economics)
A: Treats technology transfer
and technical activity as an
overhead investment
D: Not easy to equate inputs
and outputs
Source: Bozeman, 2000
8
2.1 Transfer Agent
In terms of the characteristics of the transfer agent much of the literature on
university research commercialisation activities focuses on the culture of the
university or the research institution. This includes investigations of the resistance
of researchers to becoming involved in commercialisation activities. McFarlane
(1999) found evidence in Australia that there is a conflict of interest between the
views of industry towards research information and that held by academic
researchers. The latter have traditionally been motivated to publish research
findings as soon as possible for reasons relating to status and career development,
whereas the former are more restrictive in disclosing research findings even if the
final commercial result is not absolutely clear. However, Etzkowitz (1998) found
that considerable changes in the norms of academic science are taking place that are
resulting in an environment that is much more conducive to applied research with
commercial potential. Etzkowitz found that much of this change was due to the
emergence of new forms of linkages with industry both through university
initiatives and R&D programmes. Cultural issues impacting on technology transfer
performance included researcher interaction with industry (Rahm, 1994) and
previous industry experience of researchers (Fischer, 1994).
Rahm et al (1988) found those involved in basic research were less likely to engage in
technology transfer compared to those focusing on technology development. The
negative relationship was much stronger in public research centres than universities.
For both settings, the strongest predictor of technology transfer was having
diversity in research missions. Those who were narrowly focused, regardless of the
nature of their focus, were less likely to be engaged in technology transfer than those
centres with diverse multiple missions. Brown (1994) noted that HEIs and public
research centres seeking to capitalise on intellectual property assets through
commercialising research face a common set of problems. First and foremost is that
they lack business and commercial skills. Their management structure is wrong and
they are risk averse. They cannot make timely decisions and their reward system is
inappropriate for business goals. The second problem for HEIs is that they cannot
correct these deficiencies without compromising their ability to carry out their
primary missions of teaching and research.
9
According to Jones-Evans et al (1999), a major problem in increasing the
collaboration between academia and industry, in all countries, was the difference in
the organisational and institutional cultures of universities and industrial firms. In
many cases, this was due a lack of appreciation of the differences, by universities in
the development of academic research as opposed to industrial research, especially in
terms of time conception, priorities and bureaucracy. Industrial firms need to ensure
that any R&D project is disseminated from the laboratory to reach the market place
quickly. Therefore, when collaboration takes place with the public research sector,
firms require researchers who are able to work to commercial time-scales. In many
cases, this is an irreconcilable obstacle, because public institution researchers are not
used to working on commercial R&D projects or to commercial time-scales. Jones-
Evans et al concluded that universities tend to follow a model of action which is
directed from supply to demand side whilst many enterprises, on the contrary,
function according to a model directed from demand to supply side. This
contradiction, the authors noted, could prevent the improvement and reinforcement
of co-operation between industry and public research institutions. In a review of
seven EU countries, Jones-Evans et al found that at an individual level, researchers
have increasingly less time to both establish and undertake collaborative projects
with industry in addition to their teaching and administrative duties for the
University. In addition, the continued emphasis on traditional outputs for academic
work, such as publications, has meant that collaborative industrial R&D is not
valued, except as a source of income.
Related to this, a review by Rank (1999) of university research commercialisation in
Canada found consensus among stakeholders that the lack of human resources with
the right skill mix is a major barrier to successful commercialisation. HEIs and
public research centres have difficulty recruiting and retaining individuals with the
right qualifications and experience. The best researchers are often overworked and
their first loyalties lay with their basic research and their students. There is a
reluctance to put additional time into commercialisation activities. There was
recognition that a variety of skills are needed for effective technology transfer and
specialists rather than generalists are required.
A major issue faced by universities is whether researchers have sufficient incentives
to disclose their inventions and to induce researchers’ co-operation in further
10
development following license agreements (Debackere and Veugelers, 2005). There
has been a weak trend in patenting activities by public research institutions due to
insufficient incentives to disclose, protect and actively commercialise intellectual
property (OECD, 2003). In many cases, scientists are rewarded on the basis of
publication rates and commercialisation efforts do not tend to be recognised in
promotions (Gascoigne and Metcalfe, 1999). In terms of incentive mechanisms, the
management of intellectual property rights and the evaluation system are important.
The ownership of intellectual property rights creates strong incentives for
universities to look for commercial applications of their research. Evaluations of
research should not be solely based on research criteria, but should take into account
that excellence in research and teaching has become, at least partly, more tied to
applications in industry (Debackere and Veugelers, 2005)
Carr (1992) considered the capability of the transfer agent to actually do technology
transfer. This capability is influenced by the nature of the main mission of the
institution and its experience in the technology transfer process. Colwell (2002)
found that technology transfer was more successful where the researcher remained
actively involved in all steps of the development and commercialisation process than
in situations where the researcher was excluded from the transfer process.
2.2 Transfer object
A significant feature of R&D activity in most countries is that the majority of
research resources are directed towards the early stages of R&D, rather than the
later stages, which are more closely linked with commercialisation. In Australia,
government agencies and universities perform around 60% of the country’s research
and about 85% of this is concentrated in the research stage rather than the
development stage (McFarlane, 1999). The result of this bias in funding is
completion of projects that have not yet reached the stage of development that they
are suited for commercialisation. While Richardson et al (1990, cited in Lyall et al
2004) also found that some research may prove of no immediate or direct use, they
argue that it is still appropriate to look for ways of making all research as fully useful
and utilised as possible, particularly where funded by a government department.
11
Other research has found that techniques used by researchers in the completion of
their work are often of more interest to industry audiences than the results of their
research. Papadakis (1992) also found that companies were generally more
interested in the technical expertise, resources and knowledge found in government
research laboratories than in specific products or licenses. Issues regarding the
suitability of research for commercialisation raise questions on research project
objectives and researchers’ comprehension and understanding of industry
requirements. They also highlight a need to address the lack of information
available on the influence of publicly funded research on industrial R&D activities.
In relation to the use of research results, it is important to distinguish between
“conceptual” use, which brings about changes in levels of understanding, knowledge
and attitudes and “instrumental” or direct use, which results in changes in practice
and policy making (Walters et al., 2003). A wide range of forms of research impact
may be identified and include changes in access to research, changes in the extent to
which research is considered, referred to or read, citation in documents, changes in
knowledge and understanding, changes in attitudes and beliefs and changes in
behaviour. Furthermore, a number of mechanisms have been identified to enhance
research impact – dissemination, education, social influence, collaborations between
researchers and users, incentives, reinforcement of behaviour and facilitation.
Walters et al. (2003) also identified a number of barriers to effective research impact
from both the researcher and user standpoints. Barriers to researchers engaging in
research impact activities include lack of resources (money and time), lack of skills
and lack of professional credit from disseminating research.
Barriers to users’ engagement with researchers include the following: lack of time –
to read journals, attend presentations or conduct their own research; low priority in
relation to internal and external pressures; poor communication of research within
organisations; perceptions of research; research is not timely or relevant; research is
less likely to be used where findings are controversial or upset the status quo; other
sources of information may be valued more highly; individual resistance to research;
and, failure to value research at an organisational level.
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2.3 Transfer Medium
A technology transfer mechanism describes “any specific form of interaction between
two or more social entities during which technology is transferred” (Autio and
Laamanen, 1995, p. 648). Transfer media include open literature, patents,
copyrights, licenses, informal and personal exchange, on-site demonstration and
researcher mobility (Bozeman, 2000).
Also important is the organisational structure of technology transfer activities
within research institutions (Bercovitz et al., 2001; Debackere and Veugelers, 2005).
Bercovitz et al. (2001) discussed a decentralised model of technology transfer,
whereby the responsibilities for transfer activities are positioned close to research
groups and individual researchers. Sufficient administration support is provided
which allows the researcher to focus on R&D efforts and knowledge exchange. The
establishment of a technology transfer office is also an inherent component in the
decentralised model.
Rogers et al. (2001) presented five channels through which technology transfer may
occur. A spin-off is a new company that is formed by individuals who were former
employees of the parent organisation, and with a core technology that is transferred
from a parent organisation (Rogers and Steffensen, 1999). Smilor (1990) defined
research based spin-offs as ventures created on the basis of formal and informal
technology transfer or knowledge created by public research organisations (cited in
Mustar et al., 2006). Licensing grants permission or rights to make, use and/or sell
a particular product, design or process, or to perform certain other actions, by a
party that has the right to give such permission. Licensing royalties may earn
substantial income for a research university or for a national R&D laboratory.
Publications in the form of articles published in academic journals are another
means of technology transfer. Unfortunately, journal articles are primarily written
for fellow scientists, rather than for potential users of a research-based technology.
Meetings involve person-to-person interaction through which technical information
is exchanged. Co-operative R&D agreements transfer technologies from federal
R&D laboratories to private companies who collaborate in R&D with the federal
laboratory (Rogers et al., 1998). Collaborative R&D agreements are comprehensive
legal agreements for sharing research personnel, equipment and intellectual
13
property rights in joint government-industry research. Difficulties may arise
because of the different organisational cultures in private companies and government
bodies (Rogers et al., 1999).
Schartinger et al. (2002) identified a number of knowledge interactions that occur
between universities and industry (Table 4). The term knowledge interaction
describes all direct and indirect, personal and non-personal interactions between
organisations and/or individuals from the firm side and the university side, directed
at exchanging knowledge within innovation processes. The channels used for
transferring knowledge depend on characteristics of knowledge, such as the degree
of codification and tacitness in technological artefacts. The potential economic value
of knowledge affects the way knowledge is exchanged between actors, which may
demand knowledge interactions which ensure secrecy, increase trust between actors
and allow for exclusive appropriation of knowledge (Saviotti, 1998).
There are a variety of channels and mechanisms through which academic knowledge
can be transformed into productive knowledge – ranging from direct use of
knowledge inputs, to instruments, tools, techniques and background knowledge, to
highly qualified human resources – and channels appear to have different relevance
in different research fields and industrial sectors (Fontes, 2005). There are inherent
difficulties in the direct industrial use of knowledge inputs generated in research
organisations. Knowledge can be complex, systemic, tacit, person embodied and
context-related (Pavitt, 1991), which makes disembodied ‘transfer’ more difficult and
absorption in different contexts dependent on the level of prior-related knowledge
(Cohen and Levinthal, 1990). Even when knowledge is fully codified in publications
or patents, its full exploitation will require the transfer of a component of tacit
knowledge that is only possessed by the producer(s) of such knowledge (Dasgupta
and David, 1994). The effective translation of knowledge into products and
processes requires a number of complementary scientific and technological activities.
This implies both the presence of enough competencies in the user organisation and
intensive interactions with the knowledge source. Information asymmetries between
knowledge producer and user can be an obstacle for its effective exploitation and
substantial effort may be necessary to transform such knowledge into products and
services (Fontes, 2005). The transformation process involves devising application
for new scientific concepts and/or tuning technologies and prototypes into viable
14
products or services. It also entails an uncertainty-reducing element (crucial from
the adopter’s viewpoint). The transformation process may involve integration
between knowledge coming from different areas – both scientific and functional. In
this regard, personal mobility, shared contexts, integration of knowledge, and trial
and error experiments are key elements. It may also require an element of
translation between the different objectives and languages prevalent in academia and
industry.
Chiesa and Piccaluga (1998, cited in Fontes 2005) highlighted the role of spin-off
entrepreneurs as taking technologies that are often ‘shelved’ in research
organisations and testing them to industrially-related issues – such as production,
market and regulatory aspects – thus uncovering their commercial potential. Spin-
offs allow simultaneous transfer to the new firm of people involved in development,
thus reducing problems associated with the tacit aspects of knowledge and facilitate
the establishment of interdisciplinary teams. Stankiewicz (1994) expressed that
what is normally spun off from universities are R&D and problem-solving
capabilities rather than technologies-as-products. Factors that influence the mode of
commercialisation may be classified as technological and institutional.
Technological factors include maturity of the technology, length of development
cycle, technological and market uncertainty. Table 3 summarises potential sources
of information for the innovation process (Veugelers and Cassiman, 1999).
Table 3 Information sources for innovation
Internal information sources Information within the company
Information within the group
External information sources
From other firms
From research institutes
Freely available
information
Information from suppliers raw materials/components
Information from equipment suppliers
Information from customers
Information from close competitors
Information from universities
Information from public research institutes
Information form technical institutes
Patent information
Specialized conferences, meetings, publications
Trade conferences, seminars
Source: Veugelers and Cassiman, 1999
15
There are three main types of transformation function (Fontes, 2005):
1. Bring to the market (directly or indirectly) results from research conducted
at research organisations, in the form of technologies, products or services.
2. Improve accessibility to industry-oriented knowledge, being exploited by
research organisations below its potential, by increasing the quality of supply
and/or expanding the range of applications or users.
3. Actively intermediate in knowledge and/or technology transfer from
research organisations and its absorption by particular users, by identifying
knowledge that can answer to specific needs and assisting in its adjustment
to particular contents.
Inzelt (2004) stated that the crucial point in the course of innovation relates to
interaction and partnership among firms and between firms and other actors such as
universities and research and development institutes. Inzelt (2004) compiled a list of
types of interaction that may occur. These include:
1. Ad hoc consultations of firm employees at universities
2. Lectures of firm employees held at universities
3. Lectures of faculty members held at firms
4. Regular (informal) discussions between faculty members and firm employees
at meetings of professional associations, conferences and seminars
5. Buying university research results (patents) on an ad hoc basis
6. Employing faculty members as regular consultants
7. Coaching of firm employees by university researchers
8. Training of firm employees by university professors
9. Joint publications by university professors and firm employees
10. Joint supervision of Ph.D. and masters theses by university and firm
members
11. Joint IPRs by university professors and firm employees
12. Access to special equipment of firm/university with or without assistance of
owner’s organisations
13. Investment into university facilities
14. Regularly acquiring university research
15. Formal R&D co-operations such as contract research
16. Formal R&D co-operations such as joint research projects
16
17. Knowledge flows through permanent/temporary mobility universities to
firms
18. Knowledge flows through spin-off formations of new enterprises
Technology transfer institutions are one channel through which new technologies
are funnelled from knowledge producers, science and research, to users, society at
large and enterprises in particular (European Commission, 2004). One success factor
for technology transfer institutions is the awareness of researchers at the public
research organisation. Awareness concerns on the one hand technology transfer in
general, and on the other visibility of technology transfer institutions for personnel
at the public research organisation. The most important condition for successful
technology transfer is availability of high-quality research results or technology to
be transferred. The potential of a public research organisation can however be fully
exploited only if researchers are conscious of commercialisation, have sufficient
incentives to engage in commercialisation and industry co-operation, and thus
actively disclose inventions, contribute to the patenting process, and engage in
contract research (European Commission, 2004).
Shama (1992) described four types of technology transfer strategy. A passive
technology transfer strategy focuses on information dissemination and uses a single
measure to document its performance i.e. the number of disseminations or responses
to inquiries. An active technology transfer strategy seeks to efficiently move
technology to the marketplace, through information dissemination and the licensing
of technology to the private sector. An entrepreneurial technology transfer strategy
seeks to market laboratory-developed technology with emphasis on taking an active
role in new venture formations. A national competitiveness strategy seeks to
enhance social and economic well-being.
17
Table 4 Key indicators of technology transfer activity
Study Measures Study Measures
Carter and
Williams (1959,
cited in
Digman, 1977)
Good information sources; readiness to seek information & knowledge of
practice externally; willingness to share & acquire knowledge on license &
enter joint ventures; effective internal communication & co-ordination;
deliberate surveying of potential ideas; consciousness of costs & profits in
R&D department; routine procedure for costing project investment decisions;
management techniques; high status of science & technology in firm; scientists
& technologists on board of directors; high quality in chief executive; ability
to attract talented people; sound policy of recruitment for management
positions; willingness to arrange for effective training of managerial &
technical staff; good quality in intermediate managers; ability to stimulate
managers; effective selling policy; good technical service to customers;
ingenuity in adapting material & equipment shortages; policy for anticipated
developments; high rate of expansion; rapid replacement of machines.
Shama (1992)
Debackere and
Veugelers
(2005)
Rogers et al.
(2001)
Number of disseminations, number of licenses, royalty
income, licensees sales, companies created, scope of
research paths
Spin-off activities
Licensing of innovations produced in universities
Citations to academic patents
Science parks
References to scientific publications in patents
University-industry collaborative research
Spin-off, Licensing, Publications
Meetings; Co-operative R&D agreements
Schmiemann
and Durvy
(2003)
S&E graduates; population with tertiary education; participation in life-long
learning; employed in med/high-tech manufacturing/services; public
R&D/GDP; Business R&D/GDP; High-tech EPO patents/population; High-
tech US PTO patents/population; SMEs innovating in-house; SMEs
innovation co-operation; innovation expenditure/total sales; innovation
expenditure/total sales; high-tech venture capital/GDP; new capital
raised/GDP; sales of new to market products; home internet access; high tech
value added manufacturing.
Rappert et al.
(1999)
Consultants to companies
Sponsored university positions; Studentships
Use of university equipment; Customer links
R&D contracts; Testing
Part-time teaching
Business support
Collaborative R&D; Teaching company scheme
European
Commission,
• Start-up of technology-oriented enterprises by researchers from the science
base generated at the research institute;
Schartinger et
al. (2002)
Employment of graduates by firms
Conferences or other events with firm and university
18
(2001) • Collaborative research, i.e. defining and conducting R&D projects jointly by
enterprises and science institutions, either on a bi-lateral or consortium basis;
• Contract research and know-how based consulting by science commissioned
by industry;
• Development of intellectual property rights (IPRs) by science both as a tool
indicating their technology competence as well as serving as a base for
licensing technologies to enterprises;
• Co-operation in graduate education, advanced training for enterprise staff,
systematic exchange of research staff between companies and research
institutes (personnel mobility), graduate mobility;
• Prototypes; informal contacts, personal networks
participation; New firm formation by university members
Joint publications; Informal meetings, talks,
communications; Training of firm members; Joint
supervision of Ph.D. and Masters theses; Mobility of
researchers between research & firm; Sabbatical periods for
university members; Collaborative research/joint research
programmes; Lectures at universities, held by firm
members; Contract research and consulting; Use of
university facilities by firms; Licensing of university
patents by firms; Purchase of prototypes, developed at
universities; Reading of publications and patents
Source: Compiled by author
19
2.4 Transfer recipient
The absorptive capacity of the company, the industry and the innovation system
plays an important role in the technology transfer process (Salter and Martin, 2001).
According to Amesse and Cohendet (2001), the quality of the transfer process is
heavily dependent on the absorptive capacities of companies. While trying to
measure the impact of public research, Molas-Gallart et al (1999) found that the
outputs of research may not be taken up, not because of any shortcomings in the
research results or dissemination strategy, but because potential users are unwilling
or unable to exploit the opportunities presented to them. Moreover, they caution
that the transformation of research into successful innovations is not simply a
function of the technical merits of the research but depends on the absorptive
capacity of firms with an interest in this knowledge. Lin et al (2002) concluded that
the transfer process involves not only co-operation, communications and learning
among firms, but also management, resource allocation, and culture creation issues
within the firms.
Cohen and Levinthal (1990) explain that absorption capacity may be developed as a
by-product of a firm’s R&D investment and manufacturing operations.
Furthermore, according to Joly and Mangematin (1996) industry research activity
has two complementary facets: it naturally contributes to the creation of information
and knowledge, but it is also a learning process, which helps to increase absorptive
capacity. Roessner (1993) found that interest in working with federal laboratories
increased as companies’ own internal R&D support decreases. Roessner also found
that companies that worked with federal laboratories were more likely to be larger in
terms of budgets and personnel, and were motivated by the opportunity to access
unique technical resources available at federal laboratories.
R&D increases according to the size of the company and therefore enables companies
to ‘plug in’ to external sources of scientific and technological expertise (Cohen,
1996). This plugging in only becomes possible because the firm is equipped with a
stock of knowledge in a particular domain that condition its ability to evaluate and
exploit extra firm sources of knowledge, i.e. its absorption capacity. In order to
transfer knowledge from universities to firms, firms need the capacity to absorb
knowledge. This absorption capacity (Cohen and Levinthal, 1989, 1990) is highly
20
dependent on learning experiences in the past, which are likely to increase with a
higher research orientation of a firm. The concept of absorption capacity implies
that in order to have access to a piece of knowledge, developed elsewhere, it is
necessary to have experience in R&D on something similar (Saviotti, 1998). Thus,
R&D may be viewed as serving a dual, but strongly interrelated role: firstly,
developing new products and production processes and secondly, enhancing the
learning capacity (Fischer, 2000). Critical indicators for the orientation of a firm
within a sector are its R&D ratio and its share of R&D personnel (Schartinger et al.,
2002).
The importance of technology compatibility with the organisation and its tasks is a
crucial factor in successful technology implementations (Tornatsky and Klein, 1982;
Cooper and Zmud, 1990. Kwon and Zmud (1987) identified a number of factors as
being important in implementing a new technology: characteristics of the user, the
organisation, the technology, the task to which the technology is being applied, and
the organisational environment.
Lin (1997) proposed that companies that receive technology during the technology
transfer process require a particular level of technological capability in order to
successfully incorporate the technology. He proposed that technology capability is a
multidimensional concept, defined as the capability of recipients to receive external
new technology. Six measures of company-level technology capability were
identified: experience, budget, equipment, output, information, and management
capabilities. A technology capability model was subsequently developed (Table 5).
21
Table 5 Measurement dimensions and indicators of technology capability
1st Level
Focus
2nd Level
Dimensions
3rd level
Measurement indicators
Experience
capability
% of technology staff to total staff
Annual turnover rate of employees
Similar experience in technology development or introduction
Budget
capability
R&D budget in the year of transfer
% of R&D budget to sales in year of technology transfer
Extent of management emphasis on technology transfer
Equipment
capability
The newness of the current physical equipment
Capability to measure the production or quality parameters
Degree of automation in equipment, machinery and facility
Number of new product introductions compared with
competition
Output
capability
Ratio of successful new product introductions
The sales value per employee in year of technology transfer
Accumulation of past experience in problem-solving activities
Information
capability
Degree of updating information
Degree of ease in accessing and retrieving information
Technology
capability
Management
capability
Experience and capability of the transfer project manager
Relative bargaining power of the technology source
Quality of management and operational capability of the
recipient
Source: Lin (1997)
The behaviour of SMEs concerning contact and co-operation with science
determines the absolute level of industry science relations in Ireland. However,
SMEs are often said to lack absorptive capacities in order to recognise, adopt and
process new knowledge and technologies produced in public science (European
Commission, 2001).
Kingsley et al. (1996) defined technology absorption as use by contractors, sub-
contractors, or co-sponsors participating in a research, development and
demonstration contract of the technology or knowledge developed in the
government-sponsored project. Technology transfer is the use by an external party
of technology or technical information developed by a publicly sponsored contract.
The flow-chart depicted in Figure 2 presents the technology transfer and technology
absorption models developed by Kingsley et al. (1996).
22
Figure 2 Technology transfer and technology absorption and definitions
of stages in the transfer and absorption processes
Source: Kingsley et al., 1996
23
Kingsley et al. (1996) found that the most common reason for transfer or absorption
not occurring is that a key actor in the project withdrew support at a critical time.
Withdrawal was stimulated by a number of forces:
1. Differing market assessments between the project’s principal contractors and
sponsors that led the latter to withdraw.
2. A breakdown of inter-organisational co-ordination among contractors in
performing the project.
3. Differences between state and local government sponsors concerning support
for the project.
4. Project results were not transferred because the technical solution offered
was no better than existing alternatives.
Deeds (2001) developed a measure of absorptive capacity based on co-citation
analysis of a firm’s scientific publications and indicators of technical capabilities are
used to develop early and late stage measures of a firm’s technical capabilities. The
relative amount of expenditures on research and development has traditionally been
used as an indicator of a firm’s innovative activity in many industries (Scherer, 1980
cited in Deeds, 2001). One of the key challenges in innovation is not simply the
discovery of the new idea, process, or means of organising, but in technically
developing the product or process to the point where it can be produced and/or
replicated at a commercially viable level. The concept of absorptive capacity evolved
from prior research on organisational learning. Organisational learning has been
defined as the growing insights and successful restructuring of organisational
problems (Simon, 1969), the process of improving actions through better
understanding (Fiol and Lyles, 1985) and the ability of the firm to assess and act
upon internal and external stimulus in a cumulative, interactive and purposeful
manner. There is a similarity between these definitions and the definition of
absorptive capacity; however, the distinguishing feature of absorptive capacity is that
it is a function of the level of a firm’s prior related knowledge, which enables it to
recognise valuable new information, assimilate it and apply it to commercial ends.
Absorptive capacity is qualitatively different from technology development.
Absorptive capacity is qualitatively different from technology development.
Absorptive capacity involves learning and acting on the scientific discoveries and
technical activities occurring outside the boundary of the firm. The information
gathered from outside the firm is then used to redirect scientific discovery and
24
technology development activities. In essence, absorptive capacity enhances a firm’s
ability to judge the probability of successfully turning a given piece of basic research
into a profitable product. Firms with greater absorptive capacity are more likely to
pursue projects with a higher probability of success due to their superior knowledge
(Deeds, 2001). Deeds (2001) demonstrated that the market rewards firms that focus
on R&D. While the early stages of technology development provide the foundation
for the later stages of technology development, it is the later stage where
entrepreneurial wealth is realised.
Caloghirou et al. (2004) investigated the extent to which existing internal
capabilities of firms and their interaction with external sources of knowledge affect
their level of innovativeness. Part of these capabilities result from a prolonged
process of investment and knowledge accumulation within firms and form the
absorptive capacity of firms. There are however other efforts of firms that enhance
the absorptive capacity as defined by Cohen and Levinthal (1990) and these relate to
the way firms interact with their environment. Interaction is a key concept for
knowledge creation and innovation. Openness of firms to external knowledge
sources is another important element when evaluating their innovative potential.
Efforts for establishing channels of knowledge flows and linkages can be
distinguished into two broad categories (Souitaris, 2001):
1. Scanning external information (technical reports, use of patent databases,
attendance at conferences, scientific publications, use of Internet) and
2. Co-operating with external organisations, which refers to co-operations with
other firms or with actors from the academic and research sector. The
interacting capability refers to the ability of the firm to create and exploit
linkages with other entities.
Nieto and Quevedo (2005) proposed a model to measure the innovative efforts of
companies (Figure 3). Industry structure was measures by technological
opportunity and knowledge spillovers; an analysis of the relationships between
structural variables and firms innovative behaviour could be enriched with the
inclusion of some internal variable embodying the learning capacity with which
firms face the opportunities that the close environment provides. With this aim, the
variable absorption capacity was selected, this being a variable that represents the
25
linkage between know-how generated outside the firm and the knowledge obtained
internally.
Figure 3 Model of impacts on innovative effort of a company
Source: Nieto and Quevedo, 2005
Giuliani and Bell (2005) defined four components of absorptive capacity: (a) the level
of education attained of the technical personnel employed in the firm, (d) each
professional’s months of experience in the industry, (c) the number of firms in which
each professional has been previously employed, and (d) the type and intensity of
R&D undertaken by the firm.
2.5 Demand Environment
The question of market push or market pull is an important consideration in
technology transfer. Piper and Naghshpour (1996) noted that many public sector
technology transfer practitioners adopt an attitude of “if we build it they will come”.
They also argue for a stronger market push approach and the adaptation of
contemporary marketing practices by HEIs and public research centres to diffuse
technology.
If there is a large gap between the knowledge level in industry and the public
research community, then the possibilities of a knowledge transfer from the public
research community to private firms are limited (Drejer and Jørgensen, 2004). But
even if the necessary absorptive capacity exists in industry, other barriers may
hamper the transfer of knowledge between public research and industry. Among
such barriers are differences in organisational set-up in public research institutions
and private firms. In the traditional, linear description of the innovation process
26
science and research appear only at the beginning of the process. In reality matters
are more complex, since it is often necessary to draw on research and the science
base, and thus learn and create new knowledge, throughout all phases of the
innovation process. Therefore formal collaboration between public research units
and private firms may turn out to be a precondition for applying effectively public
knowledge in industry-based innovation projects (Drejer and Jørgensen, 2004).
The low direct significance of science in industrial innovation may be explained by
looking at the type of knowledge typically offered by science and the demand for
such knowledge in the innovation cycle (Figure 4). Science institutions initially offer
new technical and methodical knowledge, which is needed mainly in innovation
activities that are oriented towards developing new technologies, new materials, new
devices and products that are very new to the market. These activities take place in
the early stages of the innovation process i.e. before market entry and in a stage of
low competition. As such innovation activities are characterised by high uncertainty
and low demand for the outcomes of innovation activities, only a few pioneering
firms are engaged in such activities. In part, these pioneers are start-ups by
researchers who wish to commercialise a new product, technology or business
method. But there may also be well-established enterprises that use new scientific
knowledge in order to establish new business activities by acquiring licenses, or by
adopting new scientific knowledge via joint research activities or researcher
mobility. However, the vast majority of innovation activities are located in latter
stages of the cycle i.e. in the redesign of already existing products to market needs,
in the diffusion of new technology to new areas of application, and in the adoption of
new technologies invented elsewhere to own production and organisation. For all
these activities, heavy interaction with clients and suppliers and careful observation
of market developments, particularly that of competitors, are critical success factors
(European Commission, 2001).
27
Figure 4 Science as a source for innovation in the innovation cycle
Source: European
Commission, , 2001
Huberman (1994 cited in Traore and Rose 2003) identified factors (from the demand
perspective) that influence knowledge dissemination and its ultimate use and are
classified as the user context and the predictors of local use. The user context
includes among other things, the users’ perceived worth of the piece of knowledge,
or the research results, the perceived links to their needs and priorities, the quality of
relationships with research staff, the research staff’s credibility and reputation, and
the administrator’s commitment. For companies, additional factors of the user
context are the regulatory system, the political and economic environments, and the
norms of the social system. The predictors of local use are the users’ understanding
of the findings, the compatibility with organisational needs and priorities and the
resources devoted to use. According to the supply perspective, factors that are
important in explaining knowledge utilisation are (i) the researcher context, namely
the study characteristics, the presence of a dissemination strategy, the time and/or
resource commitment to dissemination, and the user-centeredness of research; (ii)
the dissemination effort and competence, and (iii) the quality of the written products
accompanying or explaining the research results of know-how (Huberman, 1994;
Landry et al., 2001a,b; Frambach, 1993; Frambach et al., 1998). Another emphasis
may be on the role of linkages in explaining knowledge use. In this respect, both
formal and informal contacts between researchers and users are important elements
in explaining knowledge utilisation. In addition, the involvement of users in data
28
collection and their interim feedback may increase the use of research results. For
firms, the appropriation of knowledge and its efficient use may be influenced by the
financial and human resources devoted to R&D, the commitment of senior
management, and the firm’s absorptive capacity. Also important is the learning
capacity of the firm (Traore and Rose, 2003).
Intellectual property rights and patenting systems are part of the regulatory
framework which influences the transfer activities of public research organisations
and innovations by enterprises (European Commission, 2004). Patent applications
are registered to achieve temporary protection of technologically new products or
processes in the market place. Thus, patents show interest in commercial
exploitation of a new technology. A patent only makes sense for a scientific
institution if it is interested in the commercial exploitation of a new finding and a
collaboration with an industrial partner is aimed at or already exists. Therefore, a
high share of patents on the part of scientific institutions can be considered a good
indicator for a close relationship of scientific and industrial laboratories in the
technology field analysed (Meyer-Krahmer and Schmoch, 1998).
3. DETERMINANTS OF EFFECTIVENESS OF TECHNOLOGY TRANSFER
PROCESSES
Although referred to already in the discussion heretofore, this section brings
together the key determinants of effectiveness in the process of technology transfer.
Siegel et al. (2004) concluded that several obstacles to efficiency exist in
university/industry technology transfer – cultural and informational barriers,
technology transfer office staffing and compensation practices and inadequate
rewards for faculty involvement in university/industry technology transfer.
`Marshall (1985) proposed that the long lag between the discovery of new
knowledge at the university and its use by companies could seriously impair global
competitiveness. Siegel identifies a number of barriers to university/industry
transfer:
Lack of understanding regarding university, corporate, or scientific norms
and environments
Insufficient rewards for university researcher
29
Bureaucracy and inflexibility of university administrators
Insufficient resources devoted to technology transfer by universities
Poor marketing/technical/negotiation skills of technology transfer offices
University too aggressive in exercising intellectual property rights
Faculty members/administrators have unrealistic expectations regarding the
value of their technologies
“Public domain” mentality of universities
Speed is a crucial factor in technological and global competition (Amesse and
Cohendet, 2001). Competitive pressures and the drive to achieve first-mover
advantage have reduced development times (Smith and Reinersten, 1998) and have
had a huge impact on the dynamics of technology transfer (Amesse and Cohendet,
2001). Rogers (1983, cited in Spilsbury and Nasi, 2006) described the intrinsic
attributes of innovations that are central to the decision to adopt them: relative
advantage, compatibility, complexity, trialability, observability, reversibility and
decision processes. Markman et al. (2005) discussed the importance of innovation
speed in transferring university technology to market. Kessler and Chakrabarti
(1996 cited in Markman et al, 2005) defined innovation speed as the elapsed time
between an initial discovery and its commercialisation. Sonnenberg (1993 cited in
Markman et al. 2005) proposed that the capability of innovation speed can yield
substantial competitive advantage to a company, when mixed with core processes.
Markman et al. (2005) found a positive relationship between commercialisation time
and licensing revenues.
There are often large uncertainties associated with using new technologies and
innovations as the benefits may only be yielded over long timescales (Spilsbury and
Nasi, 2006). Some barriers to effective technology transfer and uptake of research
are independent of the innovations themselves. Uptake may be impeded by a
number of constraints as presented in Table 6.
30
Table 6 Technology uptake constraints
Constraint Common causes of constraint
Lack of awareness of the
output, technology or
innovation
Poor dissemination of outputs
Lack of adequate ‘marketing’
Inadequate user involvement in the research process
Large supply of competing or contradictory information
Lack of credibility associated
with the technology or
innovation
Lack of influential partners or clients
Lack of familiarity with the ‘supplier’
Science credibility (who? Published where?)
Research findings contrary to conventional wisdom
The innovation has a poor fit
with user requirements
Research product address a problem of low priority for users
Research products inflexible or difficult to adapt
Presentation or format of research product inappropriate
Lack of user group involvement or feedback in development of research
product
Lack of understanding of the
product/output
Purpose/application of product or innovation unclear
Low user capacity
Product format and presentation
Lack of awareness of the
problem (or need for a solution)
Lack of access to information about the problem
Lack of capacity to diagnose/analyse the problem
User group disregards research problem focus
Inappropriate timing Product ‘ready’ but conventional wisdom/current ideas of ‘best practice’
in conflict
Limited window of opportunity for which the output is relevant
Lack of enabling
conditions/incentives
Lack of capacity to implement
Inadequate ‘incentives’ for adoption
Source: Spilsbury and Nasi, 2006
In terms of knowledge development, achieving scientific excellence in research is
essential for the development of industry science linkages. Attractiveness for
industrial partners demands competence at research institutions both in short-term
oriented R&D and in long-term oriented strategic research. Personnel qualifications
and capabilities as well as a clear research mission are also important. In relation to
knowledge transfer capacities, organisations that implement industry science
linkages as a central component of the institutions’ mission are shown to be
successful in attempts to improve industry science links. Research approaches that
seek direct engagement with users reduce the gap between innovation suppliers and
innovation users by making them part of the same process and allowing two-way
communications in the development of research-based solutions. Thus, ‘technology
31
transfer’ should be regarded as the process by which research solutions and
innovations can be modified and adapted to better meet the needs of the intended
users, as illustrated in Figure 4 (Spilsbury and Nasi, 2006).
Figure 4 Innovation and uptake processes
Source: Spilsbury and Nasi, 2006
Risk and difficulties appropriating returns create barriers to technology, and as a
result, there may be an underinvestment in or underutilisation of a technology. The
premise that markets may fail to undertake socially optimal amounts of R&D has
long been accepted by economists. Link and Scott (2001) identified eight factors that
create barriers to technology and thus lead to a private underinvestment in R&D:
1. High technical risk associated with the underlying R&D
2. High capital costs to undertake the underlying R&D
3. Long time to complete the R&D and commercialise the resulting technology
4. Underlying R&D spills over to multiple markets and is not appropriable
5. Market success of the technology depends on technologies in different
industries
6. Property rights cannot be assigned to the underlying R&D
7. Resulting technology must be compatible and interoperable with other
technologies
8. High risk of opportunistic behaviour when sharing information about the
technology
32
Chiesa and Piccaluga (1998, cited in Fontes, 2005) described the need for translators
between academic and industrial contexts that makes knowledge accessible to
different cognitive contexts. Walters et al. (2003) also highlighted the need for
research to be translated in order to have an impact.
Wong et al. (2002) referred to the inherent paradox in commercialising public sector
research. Historically, the rationale of market failure provided the justification for
knowledge generated from public research institutions being placed in the public
domain, in line with public good rationale. In recent years, there has been a policy
shift towards greater commercialisation of public sector research due to a number of
reasons. Firstly, the changing view in respect of the nature and attributes of
information as a consequence of the recent recognition of knowledge as a valuable
commodity leading to greater appreciation of intellectual property originating from
the public sector. Secondly, the increasing role of the private sector in working with
public sector institutions in R&D and getting into research areas previously
unattractive due in part to the widening scope of intellectual property protection.
Finally, the belief that commercialisation is both an important and effective way to
extend and transfer the knowledge products of public sector research to the
marketplace. The commercialisation of public sector research has important
implications not only in respect of the institutional roles and conventions under
which research takes place (Dasgupta and David, 1993) but also in respect of the
complementary relations between open and commercial research and the processes
that have enabled long-term exploitation of the public stock of knowledge (Rappert
et al, 1999). If the historical reason for government’s initial involvement in R&D was
to address the public good, then its current engagement in commercialisation raises
a paradox. True paradoxes, by their nature are not solvable, but must be managed
within the organisation (Handy, 1994).
Bizan (2003) studied what the best criteria for project selection would be. Project
success can be defined in three contexts: technical success if the firm conducting the
project achieved the goals set at the beginning of the project; commercial success if
the project generates some sales; and, financial success if the firm conducting the
project made positive net profits on the project. Bizan found that both size and
organisational form affect the probability of technical success and duration to
33
commercialisation. Specifically, the probability of success increases when (1)
duration of the project increases, (2) firms are related through ownership, and (3)
firms possess complementary abilities.
Commercialisation of research results has also been referred to in the literature as
‘entrepreneurial science’ (Rasmussen et al., 2006). The challenge from the university
perspective in relation to the increasing importance of commercialisation activities is
threefold: to increase the extent of commercialisation, to visualise the contribution to
economic development, and to manage the relationship between commercialisation
and other core activities. As commercialisation activities may affect both teaching
and research, there is a potential for conflict and resistance, as well as mutual
benefits, among the activities. Traditionally, teaching and research have been the
university’s main missions. This has gradually changed with the emergence of
disciplines like biotechnology, increased globalisation, reduced basic funding, and
new perspectives on the role of the university in the system of knowledge
production. Some argue that commercial activities may be a threat to traditional
academic freedom and basic research (e.g. Nelson, 2004). More frequent are worries
about shorter time horizons in research and tensions related to impartiality and
conflicts of interest (Etzkowitz, 1998). Reitan (1997) concluded that researchers
involved in commercialisation need to perceive it both as a desirable and a
manageable activity. This perception is influenced by factors such as work
experience from industry and training in business administration and
entrepreneurship. Klofsten and Jones-Evans (2000) suggested that three basic
activities for stimulating entrepreneurship should be found at a university: (1) the
creation and maintenance of an enterprising culture on the whole at the university,
(2) separate courses in entrepreneurship and, (3) specific training programmes for
individuals who wish to start their own enterprise. Rasmussen et al. (2006) discussed
four initiatives to commercialise university knowledge – establishment of offices for
patenting and licensing, incubator facilities, access to seed or venture capital and IP
ownership. A commercialisation system may include elements ranging from
motivation and education to initiatives to support specific commercialisation projects
such as innovation centres, incubators, patenting offices, and seed capital funds.
Common output indicators of university commercialisation are the number of
licenses and spin-off companies. Spin-off company formation imply not only a
transfer of research results, but also more permanent links between publicly funded
34
research organisations and the market. There are three main reasons for a
university to focus on creating new firms rather than collaborating with existing
ones. First, companies that are created out of activities at the university will most
often start out as partners who acknowledge the university’s competence, financial
situation, and special long-term mission. The companies may thus be important
future contractors. Second, collaboration with existing industry can be highly
influenced by the existing economic cycle. In economically rough periods, attempts
at creating new firms could be made relatively easier and receive public attention
and support. Most countries would also be highly interested in universities
contributing to new economic activity and jobs, particularly if the alternative is to
enter a negative ‘lock-in’ relationship with existing industry, where the universities
cease to be a source of more radically new knowledge and innovations. The third
reason is the visibility of spin-off firms. The impact of collaborative interaction with
existing industry in terms of job creation or innovative new products is difficult to
measure. The establishment of new firms is a more visible output of university
activity and may be used in the struggle for public funding. Roberts and Malone
(1996) stated that spin-offs generate the following advantages: positive influences on
research and teaching, a more exciting atmosphere in the organisation due to new
career opportunities that are evident, and an enhanced reputation and role in the
region.
The technology acquisition performance of firms is influenced by a variety of
institutional factors which include access to R&D personnel, access to external
sources of knowledge (firms and research institutions), the political, legal and
administrative environment and the organisation of knowledge transfer (Hemmert,
2004). Technology acquisition can be broadly defined as the acquisition of
technological knowledge for the development of new products and processes
(Hemmert, 2004).
Absorptive capacity is embodied in the firm’s communication capabilities – spanning
both internal and external communication (Cohen and Levinthal, 1990). Essential
for such communication is the existence of an appropriate knowledge differential
between senders and receivers of information (Cuellar and Gallivan, in press).
Determinants of absorptive capacity that were examined in a study by the latter
authors included prior related knowledge, combinative capabilities,
35
motivation/aspirations, organisational form, culture match with “teacher” firm,
communication channel.
Owing to rapid technological changes, shorter product life-cycles, and increasing
global competition, acquiring new technology becomes crucial to enable firms to
develop new products more quickly (Lin et al., 2002). In addition to conducting
internal R&D activities, firms can reinforce their technological competence by
importing external technologies, and then diffusing, assimilating, communicating
and absorbing them into their organisations i.e. technology transfer (Hamel and
Prahalad, 1990). An organisation’s technology absorptive capacity involves a
number of dimensions and variables (Lin et al, 2002). These include: organisation
culture (innovative, supportive, bureaucratic, effective); technology diffusion channel
(formal versus informal); interaction mechanism (intra- or inter-organisation); R&D
resources (asset and capability); technology absorptive ability (adaptation,
application, production); and, technology transfer performance (execute, strengthen,
profit).
Richardson et al. (1990, cited in Lyall et al., 2004) broadly defined the concept of
research use as gaining information, clarification and illumination and translating
research directly into policy or practice and recognises indirect and long-term
changes as a result of research as well as more immediate use. They noted that
measuring the use and dissemination of research is not a simple issue. Molas-Gallart
et al. (2002) emphasised the indirect and non-linear nature of research impacts and
distinguished between indicators of activity and indicators of impact. Bechhofer et al.
(2001, cited Faulkner and Senker, 1995) argued that the user’s capacity to exploit
public sector research depends partly on the user’s readiness and ability to absorb
externally generated knowledge – it is a two way process. Users are not passive
recipients of research output; they use the knowledge in combination with their
existing technical and social knowledge. Bechhofer et al. (2001, cited Faulkner and
Senker (1995) who stress the relative importance of informal over formal channels
for knowledge transfer. Mollas-Gollart et al. (1999) pointed out that the outputs of
research may not be taken up, not because of any shortcomings in the research
results or dissemination strategy, but because potential users are unwilling or unable
to exploit the opportunities presented to them. Moreover, they caution that the
transformation of research into successful innovations is not simply a function of the
36
technical merits of the research but depends on the absorptive capacity of firms with
an interest in this knowledge.
Technology transfer ranges from simply the transfer of any equipment to the
transfer of know-how about an industrial process (Canadian International
Development Agency, no date). The successful transfer of technology involves more
than simply providing some technology to a partner. The sustainable transfer of
technology often means: modifying the technology to meet local conditions,
recognising the need for appropriate skills to put the technology to use, ensuring
appropriateness of the technology to the local culture, and ensuring that it can be
maintained. Other factors include the nature of the regulatory and societal context
in which the project will be delivered, accessibility to raw materials and the need for
a local partner to take ownership. Technology transfer includes the transfer of
industrial and/or information processes and equipment, the skills and knowledge
necessary to use and exploit the technology, and any associated strategies and
policies necessary to support a developmental goal. The key success factors for
technology transfer are:
1. Technological readiness of the transferee
2. The design is consistent with the transferee’s needs and capabilities
3. The use of appropriate technology
4. The transferee country must have an appropriate enabling and regulatory
environment relative to the technology being transferred
5. The technology is supportive of market needs
6. Long-term mutually beneficial partnership arrangements are established
7. The identification of a local “change” agent as a champion for the technology
8. The society has the necessary infrastructure elements to support the diffusion
of the technology.
Agapitova (2005) argued that studies of innovative activities of individual actors and
related institution-building processes are incomplete without taking into account the
social structures that underlie economic actions. Levels of trust and mutual
forbearance frequently exist within a social network and social networks provide
access to information available to those outside of the network (Granovetter, 1985;
Powell, 1990 cited in Deeds, 2001).
37
There are a number of factors influencing the decision for university researchers to
interact with industry (D’Este and Patel, 2005). Bercovitz and Feldman (2003)
argued that the main reason for focusing on this issue is that it is necessary to
improve the understanding about who in academia interacts with industry and why.
This is particularly important for the design of policies aimed at facilitating and
fostering university knowledge transfer. D’Este and Patel (2005) highlighted five
broad categories of interaction: creation of new physical facilities, consultancy and
contract research, joint research, training, and meetings and conferences. Individual
characteristics are extremely important factors in explaining a university researcher
involvement in a greater variety of interactions with industry. In particular,
previous experience of collaborative research plays a very significant role: those
university researchers with a higher record of past interactions are more likely to be
involved in a greater variety of interactions at a given point in time. Also, age,
professional status and the involvement in patenting activities are important
individual features. Characteristics of the department to which the researcher is
affiliated also have an impact (e.g. departmental research income).
Laperche (2002) identified the following factors as influencing research
commercialisation: legislation (civil service status of researchers, university mission,
intellectual property rights), technical progress (financing of R&D, leadership in
potentially marketable fields), university strategy (development of strategic
approaches, interest of researchers in commercialising research) and economic
environment and entrepreneurship (incentives, demand for science and technology).
Lin and Om (1996) identified four factors (comprising 14 items) that influenced the
selection of a research and development projects. These factors included market
characteristics (size/growth potential of market, degree of understanding of
consumer needs, market competitiveness, opportunity for new technology/market,
interest of top management group), diffusion effect (patentability, diffusion to
science/engineering/industry, relatedness to previous R&D), technological
characteristics (uniqueness of technology/product, quality of technology/product)
and technological success (existence of champions, suitability of R&D support
capabilities, clarity/rationality of goals/plans, appropriateness of R&D period).
Results from research conducted by Lee and Om, using these factors, illustrated that
market characteristics are more important at private R&D institutions in selecting a
38
development project; the diffusion effect factor is more important at public institutes.
Technological characteristics and technological success were considered equally
important at public and private institutes.
Table 7 Key determinants of technology transfer
Study Measures
Stock and Tatikonda
(2000)
(a) Technology uncertainty subdimensions (and factors)
Novelty (technological familiarity, technology newness, radical/incremental
innovation, discontinuous change, platform/derivative innovation)
Complexity (internal system interdependence, external system
interdependence, scope)
Tacitness (tacit knowledge, physical embodiment, codification, invisibility,
structuredness)
(b) Organisational interactions subdimensions (and factors)
Communication (communication methods, magnitude and frequency of
communication, nature of information exchanged)
Co-ordination (quality of planning, relationship formality and structure,
length of time horizon)
Co-operation (trust, willingness to share information, goal congruence,
commitment)
Amesse and Cohendet
(2001)
Speed
Rogers (1983, cited in
Spilsbury and Nasi,
2006)
Relative advantage
Compatibility
Complexity
Trialability
Observability
Reversibility
Decision processes
Kumar and Jain (2003) (a) Decision to commercialise a technology
Status of technology
Source of technology
Market potential for end product
Business philosophy of company
Financial status of company
Tie-up for technical backup support
Patentability of the technology
Entrepreneurial experience of the proposer
Educational background of the entrepreneur
Import-export policy
39
Fiscal policies
Capacity of the company to expand in the future
Geographical location of the company
Size of the industrial firm
(b)Factors that influence commercialisation success
Availability of funds
No repayment during development period
Nil or low interest rate during development period
Optimisation of technology at pilot plant
In-advance completion of engineering and design, including
instrumentation
Commitment and sincerity of entrepreneur/company
Technology supplier support
Concurrent engineering
Product engineering to market needs
Efficient assembly and commissioning
Use of easily available inputs
Training of technical and market staff
Pricing
Product positioning and product launch
Aesthetics of product and packaging
Low interest rate during repayment period
Longer repayment period
Source: Compiled by author
4. MEASUREMENT OF TECHNOLOGY TRANSFER
Schartinger et al. (2002) commented on the measurement of knowledge interactions.
Several analyses focus on those aspects of knowledge which are relatively easily
measured due to their explicit, codified character, such as citations of university
publications in patents or publications by firms, licensing of university patents by
firms, joint publications by university and firm members. A major shortcoming of
these approaches is the limited scope of knowledge flows covered. Various forms of
personal contacts and the associated flows of tacit knowledge are not considered in
this type of analyses. Another approach to measure knowledge interaction is to ask
researchers at industry and university about the types of interactions they use to
exchange knowledge and about the significance of these types. By using a variety of
indicators, different aspects of knowledge interactions and the corresponding flows
40
of knowledge can be identified. A major shortcoming of this approach is the high
degree of subjectivity (Schartinger et al, 2002).
CONCLUSION
This paper sought to provide the reader with an overview of key concepts that will
be used to examine the food innovation system in Ireland. Several themes emerge in
the review of the literature regarding common characteristics in effective, or
ineffective, technology commercialisation. These include characteristics of the
transfer agent (i.e. the university or the public research centre), the suitability of
research for commercialisation, characteristics of the transfer media, characteristics
of the demand environment and the transfer recipient’s absorption capacity. While
there has been some research conducted on the perspectives of Irish researchers on
commercialisation issues (O’Reilly et al, 2001; Jones-Evans et al, 1999) including the
level of awareness of the commercialisation process and researcher-perceived
barriers and obstacles to research commercialisation, there is limited understanding
of these issues in a food research context. There is no information available on
preferred transfer media for Irish food manufacturers. Forfas (2003) identify several
skill gaps in the area of innovation management and technology transfer at industry
level. This project will quantify industry human capital in the R&D area and
develop qualitative insights that will lead to specific human resource development
recommendations to increase the absorptive capacity of the Irish food industry.
41
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