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

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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.

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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).

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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

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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

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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

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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.

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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

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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.

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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

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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

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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

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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

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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.

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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

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(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

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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

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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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