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The role of intellectual capital anduniversity technology transfer officesin university-based technology transferHui-I Feng a , Chia-Shen Chen a , Chuan-Hung Wang a & Hsueh-Chiao Chiang ba Graduate Institute of Business Administration , National TaiwanUniversity , College of Management Floor 9, No. 1, Sec. 4,Roosevelt Road, 10617 , Taipei , Taiwan, Republic of Chinab National Science Council , Taipei , Taiwan, Republic of ChinaPublished online: 28 Jan 2011.

To cite this article: Hui-I Feng , Chia-Shen Chen , Chuan-Hung Wang & Hsueh-ChiaoChiang (2012) The role of intellectual capital and university technology transfer offices inuniversity-based technology transfer, The Service Industries Journal, 32:6, 899-917, DOI:10.1080/02642069.2010.545883

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The role of intellectual capital and university technology transferoffices in university-based technology transfer

Hui-I Fenga∗, Chia-Shen Chena, Chuan-Hung Wanga and Hsueh-Chiao Chiangb

aGraduate Institute of Business Administration, National Taiwan University, College ofManagement Floor 9, No. 1, Sec. 4, Roosevelt Road, 10617, Taipei, Taiwan, Republic of China;

bNational Science Council, Taipei, Taiwan, Republic of China

(Received 2 May 2010; final version received 19 November 2010)

This study develops a theoretical model to explain the relationships among intellectualcapital, research outcomes, and technology transfer (TT) performance, investigatingthe role of university TT offices (UTTOs) in the innovation process. The authorsexamined these relationships by sampling 49 Taiwanese universities within a 2-yearperiod. It is concluded that universities with specialized UTTOs indeed promote TTperformance (TTP) based on university–industry cooperation. Furthermore, theresults indicate that human capital is positively associated with research outcomesand relational capital. The greater the amount of relational capital, which representsthe degree of university–industry cooperation, the more significant is the positiveeffect on research outcomes and TTP. The more research outcomes are produced,the more academic research and patent technology will be transferred to industry.

Keywords: intellectual capital; relational capital; technology transfer; highereducation; case study

Introduction

The role of universities’ technology transfer offices (UTTOs) in influencing the techno-

logy transfer (TT) performance of universities has recently been a central research topic

in the industrial organization literature. It has been an accepted fact that UTTOs can

promise success in the TT performance (TTP) of universities (Carlsson & Fridh, 2002;

Friedman & Silberman, 2003; Graff, Heiman, & Zilberman, 2002; Macho-Stadler,

Perez-Castrillo, & Veugelers, 2007; O’Shea, Allen, Chevalier, & Roche, 2005; Owen-

Smith & Powell, 2001; Siegel, Waldman, & Link, 2003). However, previous studies

have ignored the roles of intellectual capital (IC) in universities as promoting the TTP

and that of UTTO as a moderator to strengthen the relationship between IC and TTP.

Furthermore, there have been several recent studies on IC in non-profit organizations

(NPOs) (Benevene & Cortini, 2010; Kong, 2007, 2008, 2010). Therefore, the present

research aims to empirically investigate this pending issue with Taiwanese universities

as the research sample.

As Taiwan continues to march further into the age of knowledge-based economies, the

capitalization of knowledge has become an essential driving force for Taiwan’s economic

development, and universities are eager to act as the key leaders of the movement. In

recent years, the government of Taiwan has been encouraging universities to devote

their research capacity to industry. In 1999, Taiwan introduced the Fundamental

ISSN 0264-2069 print/ISSN 1743-9507 online

# 2012 Taylor & Francis

http://dx.doi.org/10.1080/02642069.2010.545883

http://www.tandfonline.com

∗Corresponding author. Email: d94741003@ntu.edu.tw

The Service Industries Journal

Vol. 32, No. 6, May 2012, 899–917

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Science and Technology Act, which allows universities to retain those of their inventions

that are derived from government-funded research. Along with this policy, the ‘science-

government environment’ mode, wherein universities often involved in knowledge-

based activities, gradually gave way to the ‘science-economy environment’ mode for

better competency. Under these developments in Taiwan, universities have increasingly

grown and reformed. Therefore, Taiwanese universities offer an ideal setting for the

purpose of the present study because, as an emerging country, Taiwan represents an

ideal case of the coexistence of universities with specialized and non-specialized

UTTOs, which impart significant differences in the TTP of universities (National

Science Council [NSC], 2006).

The role of the university, therefore, is that of mediating knowledge transfer and

assisting in avoiding possible market failure (OECD, 1996). There are more university–

industry interactions, not only in scale but also in scope (Morgan & Strickland, 2001).

Universities not only disseminate knowledge to companies but also assist companies in

terms of practical applications. Conceiving of the university as a contributor to the

knowledge-based economy, this study focuses on factors affecting university-based TT

and the moderators of these university–industry relations. We have built a conceptual

model that can test and verify the IC in the innovation process of TT and probe the

moderating role of UTTOs.

Structural equation modeling (SEM) was employed with reference to a sample of 49

Taiwanese universities in the period 2001–2005. The results of path effects show that

human capital (HC) and relational capital (RC) owned by universities are positively

related to research outcomes (ROs) and TTP. The results of the moderating effect further

illustrate that the UTTOs moderate the relationship among HC, RC, ROs, and TTP, such

that the relationship will be more positive for universities with specialized UTTOs.

The purpose of the study is to provide empirical evidence to clarify the influence of IC

and ROs on universities’ TTP in emerging economies. The approach adopted in this study

is thus a pragmatic one. The central research question is as follows: ‘how much does the IC

and UTTOs of universities matter?’ In other words, the effect of IC and UTTOs can be

appropriately evaluated through empirical work in emerging countries in which univer-

sity-based TT is growing.

This paper is structured as follows. First, we provide a brief review of the literature on

TT, IC, and UTTOs. Then, we develop research hypotheses and present our theoretical

model, after which we illustrate our research methods and present our results. Finally,

we conclude and indicate some implications of our conclusions.

Literature review

TT in university

Through entrepreneurship education, individuals can develop, knowledge can be trans-

mitted and commercialized, and economic development can ultimately occur (Kirby &

Ibrahim, 2010; Marques, Ferreira, Rodrigues, & Ferreira, 2010; Naktiyok, Karabey, &

Gulluce, 2010; Sanchez, 2010; Yusof & Jain, 2010), and knowledge transfer plays an

important role in this case. It has been regarded as one of the most important sources of

comparative strategic advantage (Grant, 1996; Gupta & Govindarajan, 2000; Kogut &

Zander, 1992; Nonaka & Takeuchi, 1995). Potential channels of knowledge transfer can

be either research organizations or other companies. In this paper, we are particularly

interested in the knowledge flow between industry and academia in the form of univer-

sity-based TT.

900 H. Feng et al.

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According to the past literatures discussing the functions of TT for universities and

industries, this study categorized the definitions of university-based TT as the following

three different points of view: purpose, knowledge, and platform. From the purpose

point of view, university-based TT is the transfer of the results of research from univer-

sities to industries and then commercial goods are produced by this process (Bremer,

1999). It may also be more narrowly defined as ‘the process whereby inventions or intel-

lectual property (IP) from academic research is licensed or conveyed through use rights to

industry’ (Association of University Technology Managers [AUTM], 1998). The TT

between universities and industries is a form of cooperation and thus has different particu-

lar goals for cooperating with each other. From the industrial standpoint, the goal is to

quickly obtain the potential technology, combine it rigidly with academic resources,

quickly transplant academic capabilities and knowledge via an external knowledge trans-

fer, which is the movement of organization members, tools, or technology from one unit to

another to make them compatible (Argote & Ingram, 2000), from academia to industry,

and immediately gain human resources. Conversely, from the academic standpoint, the

goal is to obtain practical information that can contribute to the research and gain necess-

ary funding and a chance to verify the outcomes of and knowledge culled from the

research. Furthermore, the staff participating in the program can expand their opportu-

nities for career development (Valentin, 2000).

From the knowledge point of view, TT is an active process that spreads or facilitates

the acquisition of related knowledge, experience, and information from one organization

to another, strengthens the accepter’s constitution, and then increases its competitiveness.

Therefore, a successful TT must include the accepter’s development and use of the

technology, which constitutes the accepter’s learning process (Hameri, 1996; Lambe &

Spekman, 1997). Some empirical studies have further noted that the contributions of

university-based research tend to be geographically concentrated, which facilitates the

knowledge transfer and TT activities (Friedman & Silberman, 2003). Therefore, the

process of TT indicates the transfer of a set of knowledge to a business unit, which can

lead to the application of knowledge to technology or to the application of a technology

for a new purpose.

From the platform point of view, TT programs are important to the academic insti-

tutions’ mission of education, research, and public service in that they provide the follow-

ing: a mechanism for important research results to be transferred to the public; service for

faculty and inventors in dealing with industry arrangements and TT issues; a method of

facilitating and encouraging additional industrial research support; a source of unrestricted

funds for additional research; a source of expertise in licensing and industrial contract

negotiations; a method by which the institution can comply with the requirements of

laws such as the Bayh-Dole Act (AUTM, 1998); and a marketing tool to attract students,

faculty, and external research funding (Carlsson & Fridh, 2002). Therefore, the primary

purpose of a TT program is to assist the institution, on behalf of its faculty and inventors,

in the dissemination of research results for the public good (Yusof & Jain, 2010).

As indicated in the valuation model for TT licensing created by the AUTM in 2010, the

net present value of universities from 2010 to 2026 is $271,852–$1,737,591, and the

incremental cash of the company for the same period is $32,472–54,271,879 (AUTM,

2010), demonstrating that there is an increasing trend of TT from universities to industry.

The TT plays an important role in starting an enterprise and creating a new work (Harmon

et al., 1997). In some scholars’ opinions (Carlsson & Fridh, 2002; Thursby & Kemp,

2002), the number of licenses can be considered the sole evaluation indicator that reflects

the TTP. However, the AUTM suggests two more indicators: the income from licensing

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and the number of spin-offs (AUTM, 2004). Because TT is still in its infancy in Taiwan,

there are few spin-offs. Therefore, we select the number of licenses and the income from

licensing as the indicators of TTP in this study.

Intellectual capital and TTP

Before proceeding with TT, universities must achieve abundant research results and garner

talent and establish the potential for more of both. They must also maintain a good

relationship with industry. If this is the case, the university will have sufficient resources

to transfer technology to industry. Therefore, IC, which is often represented as consisting

of three basic and strongly interrelated components, HC, structural capital (SC), and RC

(Benevene & Cortini, 2010; Bontis, 1998; Dzinkowski, 2000; Stewart, 1997), is obviously

a critical factor in this process. However, IC is more than just simply the sum of these three

elements; it is about how to let the knowledge of a firm work for it and have it create value

(Roberts, 1999), and it is the combination of intangible resources and activities that

‘allows an organization to transform a bundle of material, financial, and human resources

in a system capable of creating stakeholder value’ (European Commission, 2006, p. 4).

Therefore, universities need to utilize IC to produce their ROs, cooperate with industry,

and then promote TTP.

The definitions of IC, although initially established for companies, can be easily adapted

for universities and research institutions (Benevene & Cortini, 2010; Del-Palacio, Sole, &

Berbegal, 2010; Kong, 2007, 2008, 2010; Ramırez, Lorduy, & Rojas, 2007):

. HC is defined as the knowledge that human resources (academics, researchers, PhD

students, and administrative staff in this case) would take with them if they left the

institution.. SC is defined as the knowledge that stays within the institution at the end of the

working day. It includes intangible principles, such as governance, organizational

routines, procedures, systems, cultures, and IP, and tangible principles, such as

budget, funds, or databases, which can optimize the former intangible principles.. RC is defined as all resources linked to the external relationships of the institution,

such as those with customers, suppliers, R&D partners, and the government.

University TT offices

The UTTOs characterized by intangibility, inseparability of production and consumption,

heterogeneity, and perishability are service NPOs. UTTOs connect academia and

industries to support the mechanisms of TT and commercialization, by which a broadly

skilled workforce demanded by the marketplace is created. The establishment and

development of UTTOs have become an important goal of many universities in recent

years, and it is clear that the institutions of higher education are a key service industry

(Del-Palacio, Sole, & Batista-Foguet, 2008).

In the last 20 years, and particularly since the passage of the Bayh-Dole Act in 1980,

there has been a proliferation of IP right policy and organizational changes at US univer-

sities, with the creation of centralized TT offices introducing legal formalization and an

institutional focal point for the flow of technologies out of the university system and

into industry. Until now, it has been common in developed countries to set up UTTOs

capable of TT in universities such that teaching will not interfere with academic research;

on the contrary, it is beneficial for the interactions between academia and industry (Siegel,

902 H. Feng et al.

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Waldman, Atwater, & Link, 2004). As the findings from scholars’ empirical research have

noted, universities with UTTOs in developed countries have indeed shown better appli-

cations of technologies and more income from TTs than those without them (Carlsson

& Fridh, 2002; Thursby & Kemp, 2002; Thursby & Thursby, 2001).

TT involves at least two parties. The quality and quantity of interaction are determined

not only just by the interaction between the two parties but also by what each of the players

brings to the game. The knowledge, preparedness, organization, culture, and attitudes of

both sides are important for a successful interaction, but the motivations of the two

sides are often quite different; the main objective of basic research is almost never to

create inventions, whereas the main objective of commercial side, of course, is the profit-

able exploitation of an innovation or an idea (Carlsson & Fridh, 2002). During the process

of TT, the maturity level of technologies developed in the universities will influence

evaluations of the level of commercialization of those technologies and the willingness

to transfer these technologies to industry. Furthermore, according to the Community

Innovation Survey (CIS), an enterprise’s having an academic partnership is positively

correlated with performance, which is a multi-dimensional measurement that includes

the growth rate of the product, the quality improvement, the unit cost, and the market

share (Archibugi, Cohendet, Kristensen, & Schaffer, 1995; Battisti & Stoneman, 2010;

Lucking, 2004; Tether et al., 2001). However, cooperation between two organizations

from different disciplines is difficult, and it is anticipated that there will be obstacles

that must be overcome. Therefore, the gap between the two sides must be bridged via

the establishment of industrial–academic partnerships and legal organizations providing

mediation, namely UTTOs, are established. The UTTOs aim to facilitate knowledge

transfer and are keys to the knowledge flow from university into industry. The successful

introduction of the abundant accumulated knowledge capital of the academy into industry

is the main goal of UTTOs (Siegel et al., 2004).

According to the literature mentioned above, this study addresses the five main factors

affecting TTP and generally identified them as follows:

. HC, signifying abundant and qualified human resources, is the determining factor

for the success of TT (Thursby & Thursby, 2001).. SC, in terms of software and hardware resources and the amount of research funds

(RFs), is related to the commercialization of invention activities. (Carlsson & Fridh,

2002).. RC, in terms of university–industry cooperation, can enhance the success rate for

TT (Lee & Win, 2004).. ROs, in terms of publications and patents, will increase the opportunities for TT

(Thursby & Thursby, 2001).. TT services, in terms of the existence of specialized UTTOs, can transform univer-

sity inventions into profit through licensing (Friedman & Silberman, 2003).

Hypotheses

Intellectual capital and research outcomes

RC comes from the relationships between academics, faculty involved in facilitating TT

inside universities, and staff outside universities. The relationship includes the cooperation

between academics, TT faculty, and factory owners, and it also includes the interactions

between academics, TT faculty, the government sector, and research units (Kneller,

1999). Because the knowledge interaction between industry and the academy does not

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always follow a certain pattern, different academic areas have diverse levels of interaction

frequency (Schartinger, Rammer, Fischer, & Frohlich, 2002). The stronger the motives of

the industries participating in the TT, the higher the success rate of the transfer will be (Lee

& Win, 2004). In particular, the personal connection between the R&D personnel in

enterprises and the universities is the most important transfer channel (Thursby,

Thursby, & Jensen, 2001), and it is also the key to successful TTs, which means that

HC is the essential factor in enhancing RC. For universities, human resources are the

key to developing proactive technology (O’Shea et al., 2005) and are also an important

resource in university–industry cooperation (Siegel et al., 2003). American scholars

have processed research on 480 graduate students in engineering departments and

discovered that senior professors are able to obtain more research project funding from

industry; however, among those professors who have less seniority, the research projects

pursued are mostly funded by the government or universities (Behrens & Gray, 2001).

The following hypothesis is thus presented:

H1: Universities that possess great capacity of HC tend to possess great capacity of RC.

HC indicates the faculty who possess R&D abilities and are valuable to organizations.

R&D ability includes the knowledge, experiences, and skills that the faculty possess (Bassi

& McMurrer, 1998; Del-Palacio et al., 2010). From the perspective of a national inno-

vation system (NIS), faculty with R&D abilities are the key element of economic

growth (Lipsey, 2002) and are also the most valuable assets in the organization (Luu,

Wykes, Williams, & Weir, 2001). For universities, the major drivers of R&D and inno-

vation are the academics there. In particular, to attain more funds, universities must

express their R&D ability and hence must maintain a large-scale R&D team (Bontis,

1998; Roos, Roos, & Edvinsson, 1998). Scholars also view R&D human resources as

the major indicator of whether universities are devoted to research (Thursby & Kemp,

2002). Therefore, the research and work experience of a university’s academics are not

only the main way in which universities present their innovation ability (Stewart, 1997)

but also their major source of research results (Thursby & Kemp, 2002). Consequently,

the following hypothesis is presented:

H2: Universities that possess great capacity of HC tend to promote great capacity of ROs.

There is no doubt that academics can perform well in their research when they work at

a well-established university, which provides good equipments, abundant RFs, and plenty

of book and journal databases. Scholars have investigated the effect of RFs on research

results. They discovered that the investment of more RFs has a positive influence on

the results that are obtained (Carlsson & Fridh, 2002; Del-Palacio et al., 2010), and

further proposed that the key to extending the benefits of university results was the attitude

with which funds were invested (Langford, Hall, Josty, Matos, & Jacobson, 2006).

Besides, the amount of resources devoted to R&D will influence the commercialization

of academic research (Geuna & Nesta, 2006; Thursby & Kemp, 2002). These empirical

studies inferred that the RFs do indeed have a positive influence on ROs. Additionally,

to promote R&D ability, the government of Taiwan invests significant funds in research

or related software and hardware facilities such as libraries or equipment, all with the

intention of improving academics’ R&D benefits with a well-equipped environment.

The following hypothesis is thus presented:

H3: Universities that possess great capacity of SC tend to promote great capacity of ROs.

The statistical information used here is provided by the Taiwan Ministry of Education

and the NSC. The average percentage of studies that were a result of university–industry

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cooperation before the year 1999, when the Fundamental Science and Technology Act was

announced, is only 3.84%. In contrast, the average percentage is 12.1% after 1999. These

figures indicate that academics often scrupulously work within their fields of specializ-

ation and seldom communicate with the industry, with the result that ROs at universities

do not match the requirements of the industry. Therefore, university–industry cooperation

is often viewed as the key factor to consider in measuring the interactions between univer-

sity research faculty and external actors (Thursby & Kemp, 2002), and it is also the main

indicator of the strength of RC. By providing technical consults or related services, univer-

sities can increase the interactions between academics and industries or other institutions,

thereby improving their research results and increasing the benefits of that research

(Carlsson & Fridh, 2002; Fritsch & Schwirten, 1999). University–industry cooperation

is an evident indication of the interactions between universities and industries. The

more university–industry cooperation exists, the more likely industry actors will be to

recognize the R&D abilities of university academics, which will encourage the former

to cooperate with the latter. Additionally, the greater the industry demand for academic

technology, the more effective the TT from universities will become (Thursby &

Kemp, 2002). The following hypothesis is thus presented:

H4: Universities that possess great capacity of RC tend to achieve great capacity of ROs.

Intellectual capital and TTP

HC is the most important asset for organizations and can provide techniques, products, and

services for customers who need them to solve problems. SC is the foundation for HC and

provides a platform for the functioning of HC. RC is, however, a relational network of

organizations (Luu et al., 2001). The foregoing says that HC is the core and the starting

point in IC. Thus, it is obvious that TTP, which is indirectly related to university structure

(the dimension of SC) and directly related to university–industry cooperation (the dimen-

sion of RC) must be driven by HC. HC is the determining factor of the success of TT

(Thursby & Thursby, 2001).

Since the Bayh-Dole Act was enacted in 1980, universities in the USA have actively

improved their administrations to increase the efficiency of knowledge industrialization in

universities. In the evolution of the USA’s universities in the past 20 years, three main

factors resulted in the industrialization of knowledge: the universities’ orientation

toward R&D, excellent IP, and the power of the administration in universities. Each of

these factors depended on HC in universities (Gregorio & Shane, 2003). This study and

the phenomenon in the USA indicate that human resources have played an important

role in universities’ TTP. The hypothesis in this section is as follows:

H5: Universities that possess great capacity of HC tend to achieve high-level TTP.

RC, in terms of university–industry cooperation, relies on group activities by univer-

sity researchers, TT service staff, and corporate personnel (Etzkowitz & Leydesdorff,

2000). Thus, the number of instances of university–industry cooperation indicates how

thirsty the industry is for technical support from universities, and university performance

with regard to TT can also be depicted using this figure (Lee & Win, 2004; Thursby &

Kemp, 2002).

University–industry cooperation is a beneficial way to harness the academic capacity to

solve industrial issues and can help us to further understand the industries and transfer tech-

nologies at play for the academy. It has often been viewed as a way of measuring of the

interaction between research faculties and external actors (Thursby & Kemp, 2002).

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Some scholars discovered that most TTs had taken place in situations in which the indus-

tries and universities had cooperated or interacted (Harmon et al., 1997). To effect TT, the

universities and industries had to capitalize on each other’s strengths, achieve different pur-

poses, and diminish obstacles using industrial–university cooperation, which essentially

meant working via the push of science and the pull of market forces to innovate (Valentin,

2000). Especially, during the process of transferring the academic research results, the

cooperative research projects that the industries provided enabled the universities to partici-

pate in co-R&D and effectively increased the desire to transfer the results to industry (Lee

& Win, 2004). The research of O’Shea et al. (2005) has also shown that the more

cooperation exists between industry and academia, the more resources the industries will

provide and the more effective the TTs will be. The hypothesis in this section is as follows:

H6: Universities that possess great capacity of RC tend to achieve high-level TTP.

Research outcomes and TTP

Publication is a popular way for university professors to share new knowledge or ideas

with other people and for knowledge itself to evolve. It can also be an indication of

research performance. Three indicators, the number of publications, the growth rate of

publications, and the number of citations, are often used in research performance

studies done by research centers in the European Union, Sweden, or the UK. In

Taiwan, the number of publications and the number of patents are used as the primary

measurement categories for both the Research Performance Evaluation of Nationwide

Universities and the National Statistical Survey of Research Performance (NSC, 2006).

In the trend of intelligence capitalization, IP rights became the main way for an

organization to improve its competitiveness as well as the achievement of innovation

activities (Ernst, 2001; Reitzig, 2003). The world begins to evaluate one country’s strength

of technological development based on its number of patents or applications for patents.

Industries also utilize the patents they are awarded to establish their competitive advantage

and obtain greater operational benefits. Universities also are awarded patents based on the

research results of their academics, and this can serve to help one evaluate transfer to

industries. Universities also gain substantial economic benefits in this way (Carlsson &

Fridh, 2002).

As patents normally contribute to the evaluation of universities’ academic perform-

ance (Azagra Caro, Lucio, & Gracia, 2003; Carlsson & Fridh, 2002; Meyer, Sinilainen,

& Utecht, 2003), publications with potential commercial applications published by

research professors will be much more easily identified for TT (Thursby & Thursby,

2001). It is obvious that any increase in either of patents and publications, which are

always encouraged by universities, will facilitate TT (Azoulay, Ding, & Stuart, 2009;

Geuna & Nesta, 2006; Owen-Smith & Powell, 2001). The hypothesis in this section is

as follows:

H7: Universities that possess great capacity of RO tend to achieve high-level TTP.

The moderating roles of UTTOs

As mentioned in the preceding section, Thursby et al. (2001) have shown that TTP at

universities with UTTOs is better than that at those without them. After analyzing more

than 100 cases of TT in five American universities in 1999, the scholars found that the

technology inventor is the key factor in the successful transfer of technologies from

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the viewpoint of TT offices. Additionally, they concluded that about 56% of the TT cases

were completed efficiently by the technology inventor. With the assistance of UTTOs, the

technology inventor can easily achieve TT. Moreover, technology marketing by the staff

of TT offices is another important source of TT (Jansen & Dillon, 1999). Again, we see

that TT offices can moderate the relationship between HC and TTP. The hypothesis in

this section is as follows:

H8: The positive influence of HC on TTP will increase when a university has a specializedUTTO.

RC, a manifestation of university–industry cooperation, relies on the group activities

of university researchers, corporate personnel, and TT service staff (Etzkowitz & Leydes-

dorff, 2000). For some of the already existing modes of university–industry cooperation,

UTTOs have ‘created many new opportunities for technology commercialization and

have made university–industry relationships more transparent and efficient’ (Graff

et al., 2002, p. 90). The hypothesis in this section is as follows:

H9: The positive influence of RC on TTP will increase when a university has a specializedUTTO.

Even if research faculty or academics have achieved strong ROs, they are constantly

limited by their specialties and are unable to fully market their results to industries. If this

task can be assigned to the UTTOs that were set up in the universities, no matter whether

the issue is signing contracts, collecting premiums, or related communications and coordi-

nation, the process will be more effective, and there may be greater benefits obtained. The

purpose of UTTOs is not only to coordinate with the governmental policy of transferring

technologies to industries but also to promote the organization of ROs accumulated for

many years at universities and then transform this intangible asset into derivative

income and economic benefits (Friedman & Silberman, 2003). The hypothesis in this

section is as follows:

H10: The positive influence of RO on TTP will increase when a university has a specializedUTTO.

Methodology

Data

The sample is drawn from the Taiwan Ministry of Education and the NSC database. The

two databases contain comprehensive information related to the outcome of using scientific

and technologic resources, information, and knowledge exchange at Taiwan universities.

After the screening process, we eliminated incomplete data including missing data and

data from military, nursing, and police college. Our sample finally included 49 Taiwanese

universities from which we could examine data from a 2-year period. The final sample

included 86 observations. While the transfer of academic ROs to practical usage does not

happen immediately, a lagged structure was used to improve our ability to make causal

inferences.

Pfeffer (1992, 1995) considers the workforce as a source of strategic advantage,

suggesting that firms adopting this perspective are often able to successfully outmaneuver

and outperform their rivals. Pfeffer demonstrates that a firm’s workforce can perform well

when managed effectively and that such management requires time. For this reason, we

apply this time-lag concept to our study and indicate the impact of IC on ROs a few

years later.

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Ravenscraft and Scherer (1982) studied the lag structure of returns to industrial

research and development and find that the lag structure is roughly bell-shaped, with a

mean lag of 4–6 years. On this basis, we adopt this concept of the lag structure and

investigate the impact of ROs on TTP in the future.

As indicated in statistical data from the NSC and the Taiwan Ministry of Education, it

takes 1 or 2 years on average for an academic to complete a paper or obtain a patent.

Likewise, it takes about 1 or 2 years for ROs transferring into technology licensing and

commodities or knowledge. Therefore, in our study, IC requires 1 or 2 years to have an

effect on ROs and also requires another 1 or 2 years to affect a successful TT.

Model

A conceptual model incorporates all of the latent variables displayed in Figure 1. We

developed 10 hypotheses to describe the relationships among IC (i.e. HC, SC, and RC),

ROs, TTP, and UTTOs, which were regarded as the moderator.

Variables

IC is usually measured using variables within companies. For example, the HC, SC, and

RC can be measured by employees’ satisfaction, the degree of enterprise hierarchy, and

customers’ satisfaction, respectively (Bontis, 1998). For universities focussing on

teaching, research, and services, however, the variables mentioned above are not entirely

suitable. Consequently, we measure IC, ROs, and TTP from the university standpoint.

The implementation of research projects involves academics and administrative staff

(Bontis, 1998; Edvinsson, 1997; Mouritsen, Larsen, & Bukh, 2001; Roos et al., 1998).

Therefore, HC in our study was measured using the number of full-time academics (FA)

and the ratio of professors and associate professors to full-time academics (RA). Full-time

academics include professors, associate professors, and assistant professors (Bueno, 2003).

In our study, SC was measured using equipment and instrument budgets (NT$) (EBs),

books, journals, and database budgets (NT$) (BBs), and RFs (NT$) (RF). EB and BB

Figure 1. The proposed theoretical model. Solid lines indicate direct effects. Dashed lines indicatemoderating effects.∗p , 0.05.∗∗p , 0.01.

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indicate the amount of hardware resources intended to support research development, and

RF indicates what funds are coming from the government, industry, or other research insti-

tutions (Behrens & Gray, 2001; Bueno, 2003; Edvinsson, 1997; Thursby & Kemp, 2002).

RC was measured using a number of cooperative university–industry partnerships

(NUIC) and their corresponding university–industry cooperation budgets (NT$)

(UICB). NUIC and UICB indicate the density and scale, respectively, of the cooperative

research conducted by academics and industries (Bueno, 2003; Carlsson & Fridh, 2002;

Fritsch & Schwirten, 1999; Lee & Win, 2004; Thursby & Kemp, 2002; Thursby &

Thursby, 2001; Zieminski & Warda, 1999).

ROs were measured using the number of publications (PU) and the number of patents

(PA) achieved. PU represents the number of journal papers published by academics.

Academics present their experiences with teaching and research by publishing papers in

journals and magazines or by writing books. For research centers in Taiwan, the European

Union, Sweden, and England, the number of published papers, the growth in the number of

papers published, and the number of times published papers are cited are usually used to

measure the performance of research institutions. PA represents the patents produced

using academic research (Azagra Caro et al., 2003; Meyer et al., 2003). Currently, the

number of patents is usually used to evaluate the performance of research. In Taiwan,

the Ministry of Education evaluates the performance of research at universities using

this index, and the NSC determines technological innovation or enterprise competitive

capacity using this index, too.

TTP was measured using income from technology licensing (NT thousand $) (ITL)

and the number of technology licenses (NTLs). According to research by the AUTM,

most universities in the USA designate NTL as the output of TT and research expenditures

as the input of TT (Carlsson & Fridh, 2002). Patents are meant to be licensed and trans-

ferred to firms to produce cash income (Thursby & Kemp, 2002). Thursby and Thursby

(2001) have addressed the most important five items to consider in measuring TTP:

income from technology licensing, support from RFs, NTLs, the number of patents

obtained, and the extent of commercialization. Thursby and Kemp (2002) also used

ITL, NTL, and information on supporting RFs provided by industry as a performance

index for ROs. Likewise, we measured TTP using ITL and NTL.

We used a dummy variable to indicate whether a specialized UTTO has been estab-

lished at a university. Specialized UTTOs are the specific units and staff that universities

designate to work on TT. Correspondingly, when a university has no specialized UTTOs,

this indicates that the university has not established specific protocols and staff members

or has only employed part-time staff people to assist in the TT process (Carlsson & Fridh,

2002; Dzinkowski, 2000; Edvinsson, 1997; Thursby & Kemp, 2002; Van Buren, 1999). Of

49 universities, there are 15 that have set up specialized UTTOs.

The latent factors and the measured variables used in the study are displayed in

Table 1. Each latent factor was measured using at least two variables. To reduce the var-

iance in the data, which may cause estimation bias, we use the transformation of the

natural logarithm to deal with all of the measured variables besides the ratio of professors

and associate professors to full-time academics (RA) and UTTOs.

Structural equation modeling

We used SEM techniques to test our hypotheses via path analysis. Using Lisrel 8.51, we

estimated the parameters of our research model, which identified various relationships, as

shown in Figure 1. We used these statistical techniques for the following two reasons.

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First, it is suggested by scholars that the use of such an approach allows for the estimation

of latent (i.e. unmeasured) factors that underlie measured (i.e. observable) variables.

Second, Lisrel will allow us to model many relationships to be included in a broader

context that includes TTP, ROs, and IC (Marques et al., 2010). The model illustrated in

Figure 1 also develops the results of the relationships between the latent factors. The

model illustrates the hypothesized relationships among IC, ROs, and TTP. The sample

(n ¼ 86) was used to test the hypothesized relationships.

Results

Table 2 presents the descriptive statistics and correlations for all of the variables analyzed

in this study.

We assessed the overall fit of our research model using several fit indices: the ratio of

the x2 to df (x2/df), the goodness-of-fit index (GFI), the normed fit index (NFI), the non-

normed fit index (NNFI), the comparative fit index (CFI), the incremental fit index (IFI),

and the standardized root mean square residual (SRMR). All of the fit indexes are

represented in Table 3. The ratio of the x2 to df is smaller than 3, and most of the fit

indices are more than 0.9 (NFI, NNFI, CFI, and IFI). In general, the results suggest that

our model fit the data very well.

Table 4 represents our research model with maximum-likelihood parameter estimates.

Five of the seven direct predicted links (H1–H7) were significant.

HC had a significantly positive effect on RC (b ¼ 0.63, p , 0.01) and ROs (b ¼ 0.49,

p , 0.05), indicating that the more abundant, qualified human resources universities have,

the better university–industry cooperation and ROs they will achieve. Therefore, H1 and

H2 were supported.

RC is also positively associated with ROs (b ¼ 0.24, p , 0.01). The better the

cooperation that exists between university and industry, the more publications and

patents the universities will produce. H4 is supported. RC and ROs both had significant,

positive effects on TTP (b ¼ 0.26, p , 0.05; b ¼ 0.75, p , 0.01), indicating that more

Table 1. Latent factors and measured variables.

Latent factors Measured variables Data period

TTP The income of technology licensing (NT thousand $) (ITL) 2004–2005The number of technology licensing (NTL)

ROs The number of publications (PU) 2002–2003The number of patents (PA)

HC The number of full-time academics (FA) 2001–2002The ratio of professor and associate professor to full-time academics

(RA)

SC The amount of equipment and instrument budget (NT$) (EB) 2001–2002The amount of book, journal, and database budget (NT$) (BB)The amount of research funds (NT$) (RF)

RC The number of university–industry cooperation (NUIC) 2001–2002The amount of university–industry cooperation budget (NT$) (UICB)

UTTOs Dummy variable 20041: specialized UTTOs0: no specialized UTTOs

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Table 2. Descriptive statistics and correlations among variables (n ¼ 86).

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12

1. ITL 6.28 1.75 1.0002. NTL 1.95 1.16 0.939∗∗ 1.0003. PU 4.84 1.57 0.758∗∗ 0.726∗∗ 1.0004. PA 2.10 1.47 0.687∗∗ 0.723∗∗ 0.767∗∗ 1.0005. FA 5.82 0.66 0.633∗∗ 0.622∗∗ 0.683∗∗ 0.567∗∗ 1.0006. RA 58.16 16.63 0.414∗∗ 0.402∗∗ 0.581∗∗ 0.467∗∗ 0.553∗∗ 1.0007. EB 18.84 0.82 0.649∗∗ 0.628∗∗ 0.688∗∗ 0.645∗∗ 0.777∗∗ 0.606∗∗ 1.0008. BB 17.44 0.76 0.564∗∗ 0.522∗∗ 0.658∗∗ 0.539∗∗ 0.789∗∗ 0.506∗∗ 0.711∗∗ 1.0009. RF 18.34 1.31 0.757∗∗ 0.738∗∗ 0.922∗∗ 0.758∗∗ 0.771∗∗ 0.706∗∗ 0.774∗∗ 0.748∗∗ 1.000

10. NUIC 1.45 1.01 0.683∗∗ 0.711∗∗ 0.612∗∗ 0.665∗∗ 0.435∗∗ 0.292∗∗ 0.497∗∗ 0.408∗∗ 0.605∗∗ 1.00011. UICB 14.51 1.27 0.693∗∗ 0.692∗∗ 0.671∗∗ 0.628∗∗ 0.493∗∗ 0.308∗∗ 0.549∗∗ 0.438∗∗ 0.652∗∗ 0.914∗∗ 1.00012. UTTOs 0.56 0.50 0.630∗∗ 0.648∗∗ 0.622∗∗ 0.491∗∗ 0.392∗∗ 0.295∗∗ 0.384∗∗ 0.331∗∗ 0.546∗∗ 0.532∗∗ 0.522∗∗ 1.000

∗∗p , 0.01.

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intensive university–industry cooperation and more academic ROs will lead to better

TTP. Thus, H6 and H7 were supported.

However, contrary to our predictions in H3 and H5, there is no evidence supporting the

positive effect of SC on ROs or the positive effect of HC on TTP (b ¼ 0.28, p . 0.1; b ¼

20.12, p . 0.1). Even the results related to H5 showed the negative effect of HC on TTP,

which is surprising to us, although these results are not significant. Regarding the results

of H3, we think that this is because of information technology (IT) such as the internet,

online learning, web resources that are shared across universities, and convenient access

to e-paper, which lifts the restriction on utilizing tangible resources among different

universities. Thus, SC, as measured by the EB; the book, journal, and database budget

(BB); and the amount of RFs has no significant effect on ROs.

Regarding the results for H5, we note that the positive effect of HC on TTP must be

mediated by ROs. This result means that an indirect effect of HC on TTP exists (H2

and H7 were both supported) but that a direct effect does not (H5 was not supported).

The result illustrates that the effect of HC on TTP needs time to manifest. HC needs

time to produce ROs and time to facilitate TTP. Thus, HC can be reasonably said to

have no significant direct effect on TTP.

H8–H10 about moderating effects were all supported, as we expected. H8 is sup-

ported, implying that the moderating role of UTTOs in the relationship between HC

and TTP is significant (Dx2(1) = 13.44, p , 0.01). Interestingly, we found that although

the path effect of HC on TTP was not significant (H5 was not supported), the moderating

Table 3. Goodness-of-fit measures of the SEM.

Model x2 df x2/df GFI NFI NNFI CFI IFI SRMR

Theoretical model 79.68 36 2.213 0.854 0.922 0.926 0.952 0.953 0.0483

Table 4. Results of hypothesized model.

Path effectsHypotheses Parameter estimates t-Value ResultsH1: HC � RC 0.63∗∗ 6.20 SupportedH2: HC � RO 0.49∗ 2.54 SupportedH3: SC � RO 0.28 1.27 Not supportedH4: RC � RO 0.24∗∗ 3.61 SupportedH5: HC � TTP 20.12 20.59 Not supportedH6: RC � TTP 0.26∗ 2.03 SupportedH7: RO � TTP 0.75∗ 2.55 SupportedModerating effectsHypotheses Unconstrained model Constrained model p-Value ResultsH8: UTTOs

moderatingHC � TTP

x2 ¼ 136.81 df ¼ 73 x2 ¼ 150.25 df ¼ 74 Dx2(1) = 13.44,p , 0.01

Supported

H9: UTTOsmoderatingRC � TTP

x2 ¼ 136.81 df ¼ 73 x2 ¼ 151.03 df ¼ 74 Dx2(1) = 14.22,p , 0.01

Supported

H10: UTTOsmoderatingRO � TTP

x2 ¼ 136.81 df ¼ 73 x2 ¼ 150.13 df ¼ 74 Dx2(1) = 13.32,p , 0.01

Supported

∗p , 0.05.∗∗p , 0.01.

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effect of HC on TTP was significant (H8 was supported). We have inferred that H5 is not

supported because of the time lag, and we have also inferred that H8 is supported because

of UTTOs’ ability to shorten the time spent on the process of HC having effects on TTP.

This conclusion means that HC has a positive direct effect on TTP among universities with

specialized UTTOs, while it has no such effect at universities with no specialized UTTO.

H9 is supported, meaning that the moderating role of an UTTO in the relationship

between RC and TTP is significant (Dx2(1) = 14.22, p , 0.01). Specialized UTTOs will

enhance the results of university–industry cooperation for the purpose of TT. Further-

more, there is evidence supporting a moderating effect of UTTOs on the relationship

between ROs and TTP (Dx2(1) = 13.32, p , 0.01), such that H10 is supported. This

result indicates that the process of TT from academic ROs to industry will be influenced

by the existence of specialized UTTOs.

The above three results about moderating effects have illustrated that UTTOs are the

catalyst of benefits from IC (HC, RC) and ROs through TTP.

Table 5 presents additional information concerning the direct, indirect, and total effects

of IC on ROs and TTP. As seen in Table 5, the total effect of HC (HC) on RO is 0.64 (direct

effect ¼ 0.49, indirect effect ¼ 0.15). This represents the strongest effect of any form

of IC on RO. The total effect of HC on TTP is 0.52 (direct effect ¼ 20.12, indirect

effect ¼ 0.64), which shows that HC still has an indirect influence on TTP, even though

the direct effect is not significant (H5 is not supported). These effects clearly suggest

that HC can play a much more important role in TTP than SC and RC.

The total effect of SC on RO is 0.28 (direct effect ¼ 0.28, no indirect effect), whereas

its effect on TTP is 0.21 (no direct effect, indirect effect ¼ 0.21); neither result is signifi-

cant. This illustrates that the tangible equipment and resources that universities possess

have a limited impact on their ROs and TTP.

The total effect of RC on RO is 0.24 (direct effect ¼ 0.28, no indirect effect), whereas

the effect on TTP is 0.44 (direct effect ¼ 0.26, indirect effect ¼ 0.18); both results are

significant. This proves that ROs and TTP require cooperation and coordination between

universities and industries.

Conclusions and implications

Using the hypotheses we have detailed, we conducted a statistical analysis using SEM and

data collected from 49 universities in Taiwan. The conclusions that we have drawn based

on this data analysis procedure can be summarized as follows.

Table 5. Direct, indirect, and total effects of IC on RO and TTP†.

IC RO TTP Direct effect Indirect effect Total effect

HC RO 0.49∗ (2.54) 0.63 × 0.24 ¼ 0.15∗∗ (3.004) 0.64∗∗ (3.088)SC 0.28 (1.27) – 0.28 (1.271)RC 0.24∗∗ (3.61) – 0.24∗∗ (3.614)HC TTP 20.12 (20.59) 0.49 × 0.75 + 0.63 × 0.26 + 0.63

× 0.24 × 0.75 ¼ 0.64∗∗ (2.897)0.52∗∗ (2.970)

SC – 0.28 × 0.75 ¼ 0.21 (1.171) 0.21 (1.171)RC 0.26∗ (2.03) 0.24 × 0.75 ¼ 0.18∗ (2.064) 0.44∗∗ (4.855)

∗p , 0.05.∗∗p , 0.01.†The number in brackets is the t-value.

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First, HC and RC are critical factors for ROs. Those universities exhibiting strong human

resources and university–industry cooperation are expected to produce better ROs. A large

body of academics with rich work experience are the chief source of the research results

(Thursby & Kemp, 2002). University–industry cooperation is the key factor in the

interactions between university research faculties and external actors (Thursby & Kemp,

2002). The more university–industry cooperation occurs, the greater chance there is of

combining academia and practice and facilitating better ROs.

Secondly, RC and ROs have a significant influence on TTP. University–industry

cooperation is critical to such performance. When university professors must answer to

their cooperative partners in carrying out cooperative research projects, these cooperative

partners tend to offer better opportunities for cooperation and to transfer more techno-

logical ROs. When a university has achieved a greater volume of publications and

patents, there will be a better chance that these ROs will be transferred to industry.

Finally, the existence of specialized UTTOs at a university will improve and moderate

the following three relationships: the relationship between HC and TTP, the relationship

between RC and TTP, and the relationship between ROs and TTP. UTTOs will fill the

gap between industry and universities. UTTOs will also create new opportunities for

the commercialization of technology, and they have made university–industry relation-

ships more transparent and efficient (Graff et al., 2002).

Based on this study, we have developed some implications for universities. First, uni-

versities should concentrate on recruiting outstanding researchers. University evaluations

of researcher performance in terms of R&D output should have a great deal of weight in

commercializing that output. Commercialization-oriented research projects and patent

filing should be encouraged.

Secondly, universities should encourage university–industry cooperation by using it as

an important factor in determining promotions and awards. Universities should encourage

academics to understand technology trends, market demand, and the existing circumstances

in relevant industries. Furthermore, they should encourage academics to help resolve

technological dilemmas or develop new products using their professional knowledge. In

this way, double revenue can accrue through improvements in the operating capability of

industry and through the additional RFs and TT revenue that universities will receive.

Thirdly, although SC has no significant effect on ROs in our study, this is not to say

that basic infrastructure (including bibliographic resources and software and hardware

at universities) does not influence ROs and TTP. On the contrary, SC still plays an impor-

tant role. Because of IT, resource-sharing between universities is becoming much more

popular. With this in mind, in tandem with IT development, universities should also

improve their infrastructure and widen it using IT, thereby strengthening each other’s

SC. This will allow them better ROs and TT.

Finally, there is no doubt that TT is a matter of strategic and policy importance at the

highest level within universities. The establishment of UTTOs has a positive effect on TTP.

UTTOs require a great deal of know-how and marketing power. While academics possess

abundant professional knowledge, they often lack extensive knowledge of marketing or

negotiation. Thus, specialized UTTOs are especially important in helping academics to

popularize their ROs, create industry demand, diminish the gap between the supplier and

customer, and ultimately bring the two parties together for the benefit of both. With this

in mind, universities should set up specialized UTTOs to facilitate the TT process.

This study presents some limitations. First, this study addresses only one side of the

TT relationship. A more comprehensive study would also consider the equally important

actors on the commercial side and would investigate those environments that support

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transfer activity. Secondly, it might be helpful for researchers to conduct a wider-ranging

study, one that would also consider the complexities arising and the differences between

these types of interactions in different technology areas and environments.

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