research collaboration in universities and academic ......aimed chieﬂy at expanding the base of...

Research collaboration in universities and academicentrepreneurship: the-state-of-the-art

Barry Bozeman • Daniel Fay • Catherine P. Slade

Published online: 28 November 2012� Springer Science+Business Media New York 2012

Abstract There is abundant evidence that research collaboration has become the norm in

every field of scientific and technical research. We provide a critical overview of the

literature on research collaboration, focusing particularly on individual-level collaborations

among university researchers, but we also give attention to university researchers’ collab-

orations with researchers in other sectors, including industry. We consider collaborations

aimed chiefly at expanding the base of knowledge (knowledge-focused collaborations) as

well as ones focused on production of economic value and wealth (property-focused col-

laborations), the latter including most academic entrepreneurship research collaborations.

To help organize our review we develop a framework for analysis, one that considers

attributes of collaborators, collaborative process and organization characteristics as the

affect collaboration choices and outcomes. In addition, we develop and use a ‘‘Propositional

Table for Research Collaboration Literature,’’ presented as an ‘‘Appendix’’ to this study. We

conclude with some suggestions for possible improvement in research on collaboration

including: (1) more attention to multiple levels of analysis and the interactions among them;

(2) more careful measurement of impacts as opposed to outputs; (3) more studies on

‘malpractice’ in collaboration, including exploitation; (4) increased attention to collabo-

rators’ motives and the social psychology of collaborative teams.

Keywords Research collaboration � Knowledge transfer � Technology transfer � Research

productivity � Academic entrepreneurship � Contributorship � Research effectiveness

JEL Classification O31 � O32 � O38 � L23 � M38

B. Bozeman (&) � D. FayUniversity of Georgia, Athens, GA, USAe-mail: [email protected]

D. Faye-mail: [email protected]

C. P. SladeGeorgia Regents University Augusta, Augusta, GA, USAe-mail: [email protected]

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J Technol Transf (2013) 38:1–67DOI 10.1007/s10961-012-9281-8

1 Introduction

While the term ‘‘additionality’’ has as yet failed to enter standard dictionaries, it has

increasingly made its way into the parlance of innovation and research and development

(R&D) studies (Aerts and Schmidt 2008; Clarysse et al. 2009; Gulbrandsen and Etzkowitz

1999; Luukkonen 2000). One definition of the term is provided by Buisseret et al. (1995,

p. 588): ‘‘Additionality in R&D performance is defined as a measure of the extent to which

public support stimulates new R&D activity as opposed to subsidizing what would have

taken place anyway.’’(Buisseret et al. 1995).

We are concerned here with a literal human additionality, the addition of research

collaborators. There is abundant evidence that research collaboration has become the norm

in every field of scientific and technical research. One recent study (Gazni and Didegah

2011) examining 22 different fields of science shows that in all these fields, at least 60 %

of publications are co-authored. The fact of increasing collaboration is well known, but

what difference does it make if a researcher collaborates as opposed to working in the

solitary mode more common in decades past? It is perhaps fair to say that there is a pro-

collaboration bias in the research, technology and innovation literatures and, indeed, one

that may be warranted. Collaboration tends to increase productivity (Subramanyam 1983),

though the relationship is more nuanced than it appears at first glance. One reason that the

relationship of research collaboration to productivity is not at straightforward is that not

everyone means the same thing by ‘‘research collaboration.’’

2 What is research collaboration?

Studies on research collaboration sometimes have utterly different meanings for the focal

concept and, thus, there is some degree of conceptual ambiguity one must be concerned

about, especially in a literature review. For our purposes, one major type of conceptual

ambiguity in research collaboration is easily dispensed with—differences in level of

analysis. The term ‘‘research collaboration’’ is used to describe relationships between

individuals but also relationships between organizations (as well as relationships of indi-

viduals with organizations). As we explain in more detail below, the focus here is on

individual collaboration. To be sure, it is not always easy to distinguish individual col-

laborations from organizational collaborations. After all, when organizations collaborate, it

is actually individuals who are relating to one another. Organizations are such a part of

daily life that it is sometimes easy to forget that ‘‘organization’’ organizations are con-

venient social constructs based on patterns of human behavior. Nevertheless, when

researchers are asked to identify their collaborators, unless they are explicitly asked about

organizations they tend to identify individuals.

The chief distinction, and a source of ambiguity, in identifying individual collaborators

is the breadth of meaning for collaboration. Many university researchers tend to think of

collaboration in terms of co-authorship. For this reason, and also because co-authorship is

conveniently measured, much of the published work about research collaboration focuses

on co-authorship. As Katz and Martin (1997) point out, in one of the best known and most

comprehensive reviews of research collaboration, the co-author concept of collaboration

has several advantages, including verifiability, stability over time, data availability and

ease of measurement. However, they note that co-authorship is at best a partial indicator of

collaboration.

2 B. Bozeman et al.

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While agreeing fully that co-authorship-as-collaboration has many advantages, we

would go even beyond the limitations noted by Katz and Martin to suggest that

co-authorship is not so much a partial indicator of collaboration as just one of many possible

outcomes of the social processes encompassed by collaboration. In our view, co-authorship

is neither necessary nor sufficient to collaboration. We define collaboration as ‘‘social

processes whereby human beings pool their human capital for the objective of producing

knowledge.’’ By this definition, collaboration need not be focused on publishing articles

and, indeed, collaborations often are more concerned with technology development, soft-

ware or patents and may have no publication objective at any point. Notably, collaboration

requires no direct, person-to-person interaction. Increasingly very large teams of specialists

interact to produce research and publications and, in some cases at least, some of the

collaborators never meet or even interact with one another. Still, this seems to us a col-

laboration since it is a bringing together of talents for the purpose of knowledge creation and

usually results in an identifiable knowledge product (e.g. scientific paper, patent).

By our definition, there is no implication that a collaboration will succeed or even that it

will be brought to full term, resulting in a knowledge product. Our definition requires

activities aimed at ‘‘the objective of producing knowledge.’’ We do not require that the

objective be achieved. Research collaborations, even useful ones, sometimes go down

blind alleys. Researchable phenomena are inherently unpredictable, otherwise there would

be no need for the research. Collaborations can collapse for social reasons as well

including, for example, exhausted resources, choices to redirect energies to a study viewed

as more promising, or incompatibility and disagreements among collaborators. There are

many reasons why research collaborations do not bear fruit.

In our definition, collaboration is about human capital, not other resources. Obviously,

financial resources are vital to the success of many research collaborations, but our defi-

nition suggests that one who only provides resources is not a collaborator but a patron.

Sometimes patrons become co-authors, and sometimes this co-authorship without human

capital contribution can present problems, but by our definition patrons are not collabo-

rators. But our idea of human capital is not a sharply limited one. Thus, a person who has

knowledge of laboratory equipment may bring that form of human capital to a relationship

and, by our definition, be a collaborator, whether or not recognized as a co-author or

whether or not assigned any patent rights. Stokes and Hartley (1989) showed that some-

times a researcher might be listed as a co-author because he or she has provided material or

performed an assay. In some cases an individual may make a major contribution to

research and neither obtain nor desire co-author credit. For example, a mentor may help

shape a vital part of a doctoral student’s dissertation, perhaps even providing the core idea.

Such a relationship can be a true collaboration, but it is not conventional for the advisor to

be credited other than in an acknowledgment (but often the advisor becomes a co-author on

a later publication).

Broader notions of collaboration are not often easy to measure. Focusing on

co-authorship alleviates many measurement problems and, thus, many useful studies (e.g.

Heffner 1981; Vinkler 1993; Wagner 2005; Heinze and Bauer 2007; Mattsson et al. 2008)

of research collaboration begin and end with the co-authored publication.

Despite the challenges of research with a broader concept of research collaboration,

several studies do examine collaborations with measures seeking to tap more than

co-authorship. Most of these studies (e.g. Melin 2000; Bozeman and Corley 2004;

Bozeman and Gaughan 2011) rely on researchers to nominate their collaborators or pro-

vide a broader definition of collaboration (Jeong et al. 2011) and then develop indicators

based on the broader definition.

The-state-of-the-art 3

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3 Knowledge-focused and property-focused collaborations

We examine two different types of R&D productivity here, each quite relevant, even

crucial, to research collaboration. Increments to knowledge are generally measured in

terms of scientific and technical articles produced, cited or, more rarely, demonstrably

used. Increments to wealth are typically measured in terms of patents, new technology,

new business start-ups, and, more rarely, profits. The categories are not mutually exclusive.

In particular, most property-focused collaborations at some point have a knowledge-

focused phase or aspect. Still, the terminology is helpful in delineating research work

according to its primary objectives and in helping gauge whether the collaboration has

contributed to the objective.

A number of studies employing different data and methods have provided strong evi-

dence that collaboration tends to enhance productivity of scientific knowledge (Pravdic and

Oluic-Vukovic 1986; Lee and Bozeman 2005; Wuchty et al. 2007; Huang and Lin 2010).

In the case of collaboration’s effects on profits, wealth and economic development, the

models tend to be more complex and over determined, but here too the preponderance of

evidence is that research collaboration has salutary effects (Franklin et al. 2001; Shane

2004; Dietz and Bozeman 2005; Link and Siegel 2005; Perkmann and Walsh 2009).

If research collaboration enhances productivity, or, to put it another way, if researchers

give rise to additionality, then there are good and practical reasons for describing, orga-

nizing and assessing the state-of-the-art. Indeed, if research collaboration fails to contribute

additionality then there may be even better reason for its close scrutiny. We feel that the

evidence is clear that collaboration provides benefits. However, countless resources and

human energies are invested in facilitating, inducing, and managing collaboration (Allen

1977; Hagedoorn et al. 2000; Sonnenwald 2007) and, thus, the question is not whether it

provides benefits but whether those benefits are sufficient to warrant the prodigious

investment of resources. In addition to the ‘‘is it worth it?’’ question, it is certainly the case

that some collaborations are highly productive and other less so, ergo the ‘‘what works

best?’’ question.

Even if one easily accepts rationales for a review and critique of the research collab-

oration literature, where does one begin and where does one draw the boundary? Several

excellent reviews have already been produced (Melin and Persson 1996; Katz and Martin

1997; Melin 2000). One begins, of course, with these reviews and thus we focus dispro-

portionately on more recent work for this rapidly growing research topic, i.e. work pub-

lished since 2000.

Most R&D collaboration takes place in private industry because most researchers work

in these firms. We know that the objectives, composition, and content of research in

industry tend to be quite different from those found in universities, government or non-

governmental organizations (NGOs) (Crow and Bozeman 1998; Cohen et al. 2002; Guellec

et al. 2004). Much of industrial research draws from public domain research produced in

universities (Mansfield 1995). Our primary focus is on university research. We do not

ignore university researchers’ collaborations with those in industry or other sectors, but in

this review academic researchers are the collaborators of interest.

Focusing on university research, we examine two quite different strains of research

outputs including: (1) work focused on collaborations aimed chiefly at expanding the base of

knowledge and enhancing academic researchers’ reputation and careers; and (2) work

focused on collaborations dedicated, at least in part, on producing economic value and wealth

for the researchers. The former literature we refer to as knowledge-focused research col-laborations and the other as property-focused research collaborations, including most

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academic entrepreneurship research collaborations. Naturally, these are not hard and fast

categories inasmuch as a great deal of the work on academic entrepreneurship considers the

impacts and practical uses of knowledge-focused research. Likewise, there is a growing

literature, mostly sharply critical, examining the effects of flows in the other direction, the

impact of academic entrepreneurship (pejoratively referred to as ‘‘academic capitalism’’) on

product-focused research (Slaughter and Leslie 1997; Rhoades and Slaughter 1997). The

chief arguments of the academic capitalism critique are (1) industrial involvement has

unduly affected university researchers’ choice of research topics and, perhaps more

important (Mendoza 2007; Cooper 2009), (2) led to an exploitation of graduate students, who

have become ‘‘tokens of exchange between academe and industry’’ (Slaughter et al. 2002).

The focus on both knowledge-focused and property-focused output and impacts of

research collaborations may seem at first blush to encompass everything pertaining to

research collaboration. However, the academic entrepreneurship and capitalism literature

is actually much larger than its research collaboration component. We do not address

academic entrepreneurship unless the studies focus explicitly on the research role and the

contributions of research from academia. As such we give no attention to studies focused

on such important topics as start-ups, spin-offs, patenting strategies and venture capital.

Another major boundary condition for our review is that we examine (with just a few

conspicuous exceptions) literature about individual-level researcher collaborations. A

great deal of work, especially in industrial and organizational economics, focuses on

institutional level research partnerships (Poyago-Theotoky et al. 2002). Naturally, insti-

tutions deeply affect the nature of individual collaborations, but we do not consider studies

unless the individual researcher and his or her research knowledge products (e.g. publi-

cations, citations, patents) is the focus of theory or empirical evidence. This restriction

excludes the majority of work on university–industry partnerships and cooperative R&D,

most of which does not operate at the individual level of analysis. Much of this work has

been critiqued in an excellent review paper by Hagedoorn et al. (2000).

We also acknowledge that many of the articles reviewed here do not fit neatly within the

conceptual framework we employ (presented in detail below). Much of the literature on

research collaboration examines multiple categories and subcategories presented in Fig. 1

below. As such, our review will discuss a given article in every category and subcategory

appropriate in our conceptual framework. For instance an article may focus on commer-

cialization of university research, but could also focus on the personal attributes of the

participants involved. The articles addressed therefore receive ample attention below in

both the personal attributes and property-focused research sections.

Finally, in a bit of a departure from previous work reviewing the research collaboration

literature, we pay special attention here to what we refer to as the ‘‘dark side of research

collaboration’’ (Bozeman et al. 2012). Our focus here is a bit different from the academic

capitalism literature. Whereas most of the academic capitalism literature is concerned with

ways in which institutions adversely affect individuals, we focus on the possibilities for

individuals to adversely affect other individuals through unethical or exploitative actions in

research collaborations.

4 An organizing framework

Even with the many limitations we adopt, the research collaboration literature remains

quite extensive. Thus, one contribution of our study is to provide an organizing framework


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for the research collaboration literature. The depiction of the framework is presented in

Fig. 1.

As can be seen in Fig. 1 we organize the literature on research collaboration based on

the topical foci of the relevant published research papers. We identify three main attribute

categories that are consistently analyzed in the literature including collaborator attributes,

attributes about the collaboration in general, and specific organizational or institutional

attributes. Each of these categories contains subcategories that further organize the liter-

ature into a cohesive framework that contributes to understanding of the relationship

between additionality and R&D impacts and we include articles from each subcategory.

We begin with a systematic review of articles focusing on each of our attribute categories

and subcategories, focusing heavily on articles published after Katz and Martin’s 1997

review.

Let us also note that much of this analysis is supported by the ‘‘Propositional Table for

Research Collaboration Literature’’ we developed for this study. The table appears here as

an ‘‘Appendix’’.

4.1 Collaborator attributes

The first category in our conceptual organization framework concerns studies focusing on

individual collaborator attributes in the collaboration process. Many articles in the field of

science and technology policy have addressed questions about research collaboration

concerning the scientists involved in collaborative groups. It is understandable that many

aim to answer the fundamental question of ‘‘Who collaborates with whom?’’ A number of

articles answer this question by identifying the personal attributes of collaborators such as

age, gender, race or national origin. Others focus more on the human capital aspects such

as training or experience that collaborators bring to the collaboration team. Still other

articles focus on the career stages of the collaborators as the important factor of collab-

oration. Within our organizational framework we can classify these articles into three

Knowledge Focused

Indeterminate

Property Focused

Outputs

Collaborator Collaboration Organizational

Personal•Gender•Race•Nationalorigin

Human Capital•Degree•Field of Training•Work experience•Tacit knowledge•Network ties

Career•Career stage•Trajectory•Administrative Role

Process•Periodicity•Meeting type,frequency•Openness•Management Style, structure

Composition•Labor mix, fields•Roles•Organizational home•Statuses•Demographic mix

Actor Type•Single or Multi-Organization•Center orDepartment•Number Organizationactors

External Actors•Resourceproviders•Regulators•Competitors

PublicPolicy

Context

MarketContext

Research Collaboration Attributes

I

M

PA

C

T

S

Fig. 1 Framework for organizing the research collaboration literature

6 B. Bozeman et al.

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subcategories of collaborator attributes: Personal, Career Development and Human

Capital.

4.1.1 Personal attributes and research collaboration

In our organizational framework we define personal attributes as demographic character-

istics of collaborators that either contribute to or hinder the collaboration process with

other researchers. These attributes include, but are not limited to race, gender and national

origin. It is important to note that these are objective measures of individual identity for

scientists involved in collaborative work. One would expect that researchers collaborate

more frequently with scientists who share similar demographic characteristics. Our sub-

categories of collaborator attributes are not mutually exclusive. As such we include articles

that discuss human capital and career attributes, but we also focus on personal charac-

teristics of scientists such as age and gender.

4.1.2 Age

Age is certainly one of the most apparent personal factors one might expect to have an

effect on collaborations. Thus, it is surprising that relatively few studies have examined the

effects of age and career age on collaboration. Perhaps some assume that the effects are

‘‘obvious,’’ that those who are older will have more collaborators and a richer and more

diverse collaboration network. That expectation seems intuitively appealing. However, the

few studies focused on the relation of age to collaboration show mixed results.

Ponomariov and Boardman (2010), examining academic faculty affiliated with uni-

versity research centers, find no significant relationship between researchers’ career age

and their number of publications with industrial collaborators, at least not after controlling

for a number of potentially confounding variables. This finding is perhaps less counter-

intuitive than it might seems. In the first place, the percentage of faculty publishing with

industry-based researchers is a minority (11.4 % for those not affiliated with centers,

20.7 % for those affiliated), whether young or old. Second, many of those who do work

with centers develop early acquaintance with industry personnel, contacts that might

otherwise (for those not so affiliated) take years.

Another study perhaps confounding expectations is Bercovitz and Feldman’s (2008)

study of the commercial activities of medical school faculty. They find that the likelihood

being involved in patenting and licensing diminishes with career age. In contrast,

Haeussler and Colyvas (2011), studying life scientists in German and the United Kingdom,

find that older scientists are more likely to be engaged in a variety of commercial activities,

including not only patenting and licensing, but also consulting and founding a firm.

Lee and Bozeman (2005), examining more than 600 academic scientists in the U.S., find

that career age mitigates the relationship between collaboration and productivity. Those

who are younger have considerable productivity (publications per collaboration) pay-off as

do those who are mid-career. However, at a certain threshold, older researchers begin to

have less ‘‘bang for the buck,’’ that is, having more collaborations and collaborators has a

lesser effect on enhancing productivity. A more recent study of faculty in one university in

The Netherlands (Rijnsoever and Hessels 2011) finds that research experience is positively

related to university faculty both disciplinary and interdisciplinary collaboration.

Aschoff and Grimpe (2011) look at age effects in a somewhat different way, investi-

gating possible early ‘‘imprinting’’ effects of young researchers working with industry.

Their study, employing citation data from 343 German academic scientists in


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biotechnology, finds that those who have co-authors who have publications with industry

personnel are themselves more likely to be involved with industry, suggesting the strong

effects of one’s scientific peer group. Similarly, they find that those in academic depart-

ments with a relatively high percentage of academic faculty co-authoring with industry

have a stronger likelihood themselves of industry involvement. Age does not moderate the

effect of personal peers but does matter to the relationship between age and the industrial

activity of department peers. The authors develop an imprinting hypothesis suggesting that

those who engage with industry at a younger age are likely to have more intense and

continuing industry relations.

When we consider the diversity of findings of these studies relating age to collaboration

we might despair at the lack of consensus. But what appears a lack of consensus is in

actuality different findings from quite different studies. Each of the studies cited above

examines age but the dependent variables considered vary greatly. There is no reason to

expect, for example, that age would have the same effects on engagement with industry as

on the number of research collaborations. When we also consider the intermingling of age,

career age, aging and cohort effects then we can see that the chief lesson from the research

on age and collaboration is that more research is required to address a topic that is much

more complex than it seems initially. There is every reason to believe that age has ubiq-

uitous effects, especially as it interacts with careers and career trajectories, considered

below.

4.1.3 Gender

Much of the work of Bozeman and colleagues (Bozeman and Corley 2004; Bozeman and

Gaughan 2011) focuses on personal attributes, examining personal attributes, especially

gender, in relation to collaboration patterns and accumulated ‘‘scientific and technical

human capital’’ (Bozeman et al. 2001). Bozeman and Corley (2004) for example, argue

that collaboration is part and parcel to human capital. Collaboration is therefore measured

as a proxy to human capital and we are able to understand the determinants of collabo-

ration levels, including personal attributes of the collaborators themselves. The authors

constructed five regression models to examine collaboration patterns among academic

scientists. One of these models analyzed the impact of tenure, grants, gender and field on

the percent of female collaborators of an individual scientist (Bozeman and Corley 2004).

Bozeman and Corley argue that, ‘‘female researchers who hold the rank of non-tenure track

faculty, research faculty, tenure track faculty, research group leader or tenured faculty

collaborate with a higher percentage of other females than male researchers in the same

ranks do’’ (Bozeman and Corley 2004, p. 607). Although the determinants of female

collaboration can generally be described as career attributes of the scientists, the outcome

of female collaboration is highly personal.

Gender is obviously one of the most personal and salient issues in one’s life, especially

in groups where women and minorities are underrepresented, such as academic science

(Pollak and Niemann 1998; Johnson and Bozeman 2012). Gender is therefore an important

personal collaborator attribute in the scientific community. The authors go on further to

say, ‘‘Especially noteworthy is the extent to which non-tenure track females collaborate

with other females (83.33 %)’’ (Bozeman and Corley 2004, p. 607). Evidence from these

findings supports the idea that collaboration patterns vary by gender.

Although this article does not directly address the concept of ‘‘additionality’’ it is useful

to understand collaboration patterns. One could assume that more collaborators or more

female collaborators is positively associated with our definition of additionality. Bozeman

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and Corley are limited in their analysis of gender and collaboration because the authors are

only able to measure gender through objective measures of collaboration patterns. For

instance, they draw conclusions based on the collaboration patterns of male and female

academic scientists. The analysis does not offer any subjective analysis as to whether

gender differences or similarities influenced the collaborative group or collaboration

process. Although collaborative patterns offer useful conclusions, this analysis only shows

half of the gender/collaboration picture. We must therefore understand what factors

increase the number of collaborators. Of increasing concern to research collaboration

literature is the nation of origin of members in a collaborative project.

In a more recent study, Bozeman and Gaughan (2011) examine gender as their primary

focus in research collaboration, seeking explicitly to determine whether previously

observed differences in men’s and women’s collaboration patterns are owing to actual

difference in gender or to spurious relations related more to poorly specified models than to

actual differences (such as, for example, the fact that in most samples of academic

researchers, women tend to be younger than men and models not allowing for this can

distort results). Having developed a new database under the U.S. National Survey of

Academic Scientists, data including more than 1,700 respondents weighted by field and by

gender, the study focused specifically on research collaborations with industry and col-

laboration motivations. The authors found that men and women differed considerably in

the collaboration strategies with men being more oriented to collaborations based on

instrumentality and previous experiences. The study found that simply having a coherent

collaboration strategy was associated with having more collaborators. Perhaps most

important, the Bozeman and Gaughan study was the first to give evidence of women have

slightly more collaborators, at least if one controls for tenure, age, family status, and field.

Another study shows that traditional gender patterns in collaboration seem to be

changing. Rijnsoever and Hessels (2011), in their study comparing disciplinary and

interdisciplinary collaboration patterns find that women are more likely than men to engage

in interdisciplinary collaborations. However, the findings must be treated with caution

inasmuch as it is based on survey data from a single university in The Netherlands and

though there are more than 300 respondents the response rate is only 17 %.

With respect to gender and interactions with industry, Bozeman and Gaughan (2011)

employed the ‘‘industrial involvement index’’ (Lin and Bozeman 2006; Bozeman and

Gaughan 2007; Gaughan and Corley 2010; Ponomariov and Boardman 2008, 2010) to

compare men’s and women’s collaboration with industry. The industrial involvement

index is a weighted gradient (see Bozeman and Gaughan 2007 for detailed explanation)

that aggregates a variety of types of interaction, ranging from modest and low effort (e.g.

providing research papers upon request) to intensive (e.g. co-development of patents).

Bozeman and Gaughan found that even in a more fully specified model men continue to be

more involved with industry but that women’s affiliation with multidisciplinary research

centers tended to mitigate the effect (a finding complementing Gaughan and Corley 2010).

4.1.4 S&T human capital and research collaboration

Especially with regards to collaborations between universities and industry research shows

that knowledge is transferred from universities to the business sector largely through

human capital (Schartinger et al. 2001). As shown in Fig. 1 above we conceptualize human

capital as the degree, field of training, experience, tacit knowledge, or network ties that an

individual collaborator brings to the collaborative group.


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‘‘Scientific and technical human capital’’ (S&T human capital) has been defined as the

sum of researchers’ professional network ties and their technical skills and resources

(Bozeman et al. 2001). There are multiple articles that focus not only on the personal

attributes of the collaborators, but also S&T human capital attributes such as network ties

and field of training. We can also see evidence in the literature that prior experience in

industry negatively influences career publications of academic scientists (Lin and Bozeman

2006), though such experience may increase the propensity for interdisciplinary collabo-

rations (Rijnsoever and Hessels 2011).

Lee and Bozeman’s study (2005) illustrates the importance of personal S&T human

capital attributes. Using a sample of 443 research scientists at university research centers, Lee

and Bozeman examine the impacts of collaboration on research productivity. Research

productivity is measured by the ‘‘normal count’’ of articles published (total number), but also

‘‘fractional count’’ of articles published (total number divided by number of co-authors).

Findings indicate that collaboration is positively and significantly related to the ‘‘normal

count’’ of research productivity. This article also suggests that there is not a significant

relationship to research collaboration and ‘‘fractional count’’ research productivity. We can

see that collaboration influences one possible definition of ‘‘additionality’’, but does not

influence another equally valid, yet distinct, measure of ‘‘additionality.’’

By performing a two stage least squares analysis, Lee and Bozeman were able to

examine determinants of collaboration and then subsequently determine how each influ-

ences measures of productivity. We can therefore see evidence that human capital attri-

butes of individual researchers influence collaboration, despite the mixed evidence

regarding productivity measures. It is important to note that this study is knowledge-

focused; the authors only consider publication counts and not patents or property measures.

The authors find significant field effects for scientific collaboration. Lee and Bozeman

control for field by identifying researchers as either ‘‘basic’’ or ‘‘applied’’. ‘‘Basic’’ fields

include physics, chemistry and biology whereas ‘‘applied’’ includes all engineering sci-

entists. The authors find a significant and positive relationship for applied scientists and

research collaboration, indicating that engineering scientists are more collaborative. It is

important to note that this analysis is limited to collaboration of U.S. scientists.

Duque et al. (2005) find alternative evidence for scientists in Ghana, Kenya and the

State of Kerala in southwestern India. These authors find that ‘‘(1) collaboration is not

associated with any general increment in productivity; and (2) while access to email does

attenuate research problems, such difficulties are structured more by national and regional

context than by the collaborative process itself’’ (Duque et al. 2005, p. 755).

Another example of how research productivity is not necessarily related to the total

number of publications is Cronin’s 2001 article Hyperauthorship: A Postmodern Perver-sion or Evidence of a Structural Shift in Scholarly Communication Practices. Cronin

describes a different meaning of authorship in the biomedical literature in which there is a

large number of authors listed on a particular article, but not necessarily a large number of

writers (Cronin 2001). Cronin terms this practice ‘‘hyperauthorship’’. He states, ‘‘[there has

been a] bifurcation of authorship into contributorship and guarantorship, since what is

implied by a byline in these cases is typically a very precise, often specialized input to a

complex, multidisciplinary project’’ (Cronin 2001, p. 567). Cronin’s version of hyperau-

thorship begs the question, why are these scientists receiving authorship credit when they

have not worked on the project in question (a question we consider in a later section of this

paper)? These possibly peripheral authors are often senior researchers with large amounts

of human and physical capital that contributes to the research, or at least to its publication

and its impact, regardless of the individual’s involvement (Cronin 2001).

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Garg and Padhi (2001) examine hyperauthorship in laser science and technology. The

authors find that hyperauthorship accounts for a substantial number of papers published in

laser science and technology journals, with authors based in Japan, France, Italy and The

Netherlands. Arguing that hyperauthorship (termed mega-authorship in this analysis) is

largely regional, the authors find a greater proportion of hyperauthored papers for scientists

in Japan, France, Italy, and The Netherlands, but in Canada, China, and Australia there is a

greater proportion of single authored papers. The authors also examined the international

nature of collaborations finding more extensive international collaboration in China, Israel,

The Netherlands, and Switzerland and lesser international collaboration in the USA, Japan,

France and Australia (Garg and Padhi 2001, p. 415). Hyperauthorship is an excellent

example of how network ties and science and technology human capital can increase

publish counts without much effort from a scientist who already possesses an extensive

amount of S&T human capital. The implications for enhancing scientific outcomes, as

opposed to career outcomes, remain unclear.

Bozeman and Corley’s (2004) study is among those most focused on the relation of

research collaboration to S&T human capital (see also Jeong et al. 2011). They examine

how research collaboration can contribute to human capital for academic research scien-

tists. The authors consider collaboration as part and parcel of S&T human capital. They

argue, ‘‘that it is a particular sort of social tie that both draws from human capital

endowments and enriches them, that collaboration enables and is re-enforced by other sorts

of ties’’ (Bozeman and Corley 2004, p. 630). Examining the determinants of research

collaboration, Bozeman and Corley develop a series of models that cover most aspects of

this paper’s organizational framework and this is of course no accident inasmuch as the

current model was strongly influenced by that earlier work. Thus, as shown above, the

authors include personal collaborator attributes in the models to analyze relationship

between these attributes and the number of collaborators with whom individuals interact.

Dietz and Bozeman (2005) also examine the influence of human capital on research

productivity, here focusing on property-focused research collaborations.

They analyze the impact of changes in job sectors throughout one’s career on research

productivity as an academic scientist. Using curriculum vitae of 1,200 research scientists

and engineers and information on patents from the U.S. Patent and Trademark Office, the

authors are able to examine both knowledge-focused research and property-focused

research in the form of patents. The core question in this study centers on the diversity of

job experiences for academic scientists. According to the authors, S&T human capital

theory ‘‘implies that a diversity of job experiences will affect collaborative patterns and the

exchange of human capital through the building of a wider variety of network ties and

social capital’’ (Dietz and Bozeman 2005, p. 350). The authors classify job transitions by

the origin sector (academic, industry, government, consulting or medical) and the desti-

nation sector (academic, industry, government, consulting or medical). The most common

job transition was academic-to-academic (62.5 %) followed by industry-to-academic

(8.2 %). The authors find a weak positive relationship between research precocity and

career homogeneity and research productivity (as measured by publications), but did not

find a statistically significant relationship between career homogeneity and patent pro-

ductivity (Dietz and Bozeman 2005). The authors argue that the evidence supporting the

hypotheses that inter and intrasectoral changes in jobs throughout the career will result in

higher research productivity (due to the opportunity provided to build S&T human capital)

(Dietz and Bozeman 2005, p. 362). Evidence supporting this hypothesis is shown in the

descriptive statistics, specifically that higher patent rates are found among those with a

higher percentage of their career in industry (Dietz and Bozeman 2005, p. 362).


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The authors also acknowledge that this career diversity may be more relevant to patent

productivity rather than publication productivity.

Our operationalization of S&T human capital includes network ties, which are explicitly

examined in contemporary R&D research. Some empirical studies focus on general net-

work behavior (e.g. Audretsch et al. 2002) and other studies explicitly examine the dif-

ference between active and passive networking among scientists (e.g. Faria and Goel 2010;

Goel and Grimpe 2011). The former paper (Faria and Goel 2010) focuses on active and

passive networking between academic scientists and journal editors and not network

activity in the research process. The paper’s approach is a game theory model of the

publication process, related to our focus on collaboration but not directly on point.

Audretsch et al. (2002) provide a comprehensive examination of science and technology

invention and innovation in the 2002 article The Economics of Science and Technology.

Relevant to the current discussion of networks ties in collaboration and our chosen context

of academic scientists is the authors’ discussion of collaboration with universities. The

authors argue that ‘‘the literature on universities as research partners is sparse’’ (Audretsch

et al. 2002, p. 181), but they do offer some general conclusions. First, they offer that firms

with network ties to universities tend to have greater R&D productivity as well as a higher

level of patenting (Audretsch et al. 2002, p. 181). They go further positing that firms are

motivated to maintain these network ties with universities in order to have access to the

human capital from faculty and students at universities (Audretsch et al. 2002). These

positive outcomes suggest that private industry network ties with universities are beneficial

to additionality with current research as well as future endeavors, because the network ties

provide access to human capital.

Goel and Grimpe (2011) distinguish between active and passive networking among

German academic scientists. The authors operationalize active networking as participation

in academic conferences. Although this is somewhat problematic because it assumes that

conference participants engage and ‘‘network’’ with other participants, it is nonetheless a

useful finding. One can assume that conferences allow researchers to meet other

researchers with similar interests or from complementary fields. The researchers examine

the difference between this ‘‘active’’ networking and other ‘‘passive’’ networking behavior.

‘‘Active’’ networking involves conscious effort from the researcher and often requires the

individual to spend time and money to engage in such behavior. The authors provide

conference attendance as an example of ‘‘active’’ networking. ‘‘Passive’’ networking

requires no conscious effort from the researcher. ‘‘Passive’’ networks usually develop from

one’s degree granting institution or collaboration with a scholar that has a highly developed

network (Goel and Grimpe 2011). The latter example suggests that researchers may

assimilate the network ties of a prominent scholar without conscious effort. The authors

also examine geographic factors and research bottlenecks that influence on networking.

Findings indicate that passive networking is both complementary and substitutes for

active networking depending on the type of passive networking (Goel and Grimpe 2011).

The authors argue that research group leadership and university employment, which both

represent passive networking are complements to conference participation. However, the

authors also find that ‘‘passive networking by being associated with a particular academic

discipline, appears to aid some types of conference participation in certain disciplines,

while discouraging participation in conferences closer to home’’ (Goel and Grimpe 2011,

p. 14). Other determinants of active network behavior among academic scientists include

scholarly publications and patenting. The authors also present interesting non-findings in

terms of active networking. Contrary to what one would expect in terms of S&T human


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capital theory, experience, career age and gender do not have significant effects on active

network behavior.

Evidence does show that network ties between industry and universities are often very

strong bonds. Mattias Johansson, Merle Jacob and Tomas Hellstrom provide empirical

results that indicate that ‘‘relations are characterized by a small number of strong ties to

universities, which a high degree of trust and informality’’ (Johansson et al. 2005, p. 271).

Variables pertaining to trust and informal relationships present themselves often in the

literature surrounding research collaboration (e.g. Clark 2011; Ubfal and Maffioli 2011;

Bruneel et al. 2010). In Bruneel et al.’s (2010) survey research-based study shows the

importance of having previous collaboration experience to engendering the trust required

for success in collaboration and, related, that collaboration experience reduces barriers to

subsequent collaboration, suggesting important learning effects from collaboration.

Martinelli et al. (2008) emphasize the importance of external relations to collaboration,

arguing that academic researchers who have few external ties (i.e. S&T human capital)

have especial difficulties developing collaborations. Another study (Nilsson et al. 2010)

discusses the importance of a supportive infrastructure in universities, especially with

respect to bolstering the researcher’s confidence that he or she has sufficient social capital

to partner with industry. Ponomariov and Boardman (2008) find that informal interactions

between university scientists and private sector companies trigger more formal and more

intense collaborations with industry. We can also see the importance of individual beliefs

about the proper role of universities in the dissemination of knowledge which, according to

Renault (2006) is influential in determining collaboration patterns with industry.

Human capital analysis is very well represented in the literature surrounding research

collaboration. The examination of human capital is not, however, complete. A more

complete body of knowledge needs to be developed surrounding the positive and negative

consequences of collaboration with disparate levels of human capital. We can see the

beginnings of this body of work through the analyses of hyperauthorship.

Authorship on a collaborative work is increasingly a name game of including the most

prominent scientist on the final product, but it is unclear how this process affects other

team members. We can expect from Merton’s (1968, 1995) classic work on the ‘‘Matthew

Effect’’ that credit will inevitably be disproportionate to more senior researchers, regard-

less of the particular nature or extent of their contribution compared to less well known

collaborators. One would expect that the collaborators and co-authors who receive less

recognition from a given co-authorship would in some cases feel exploited, especially on

those instances where they perceive their own contribution to be more significant than that

of a more senior and well known researcher. Empirical studies are needed to sort out these

dynamics and possible negative consequences.

Another topic worthy of study is the effect of different levels of human capital. It would

be useful to know if ‘‘star scientists’’ tend to rest on their laurels in collaborative groups or

if they work as long and hard as junior colleagues. This issue cannot be clarified looking

only at publication and co-authoring data. (We briefly discuss the issue of contributorship

later in this paper). Related, it would also be interesting to know is collaborators with less

human capital are motivated or demotivated by the interaction with these big name sci-

entists. Do they feel that an association with a famous research is rewarding even if their

recognition is lessened? Or do they feel that simply having their name closely associated

with a famous colleague provides sufficient reward to recompense any possible diminution

in their own credit? In sum, although the human capital aspect of collaboration has by now

received considerable attention, there is still more to be done to truly understand how S&T

human capital influences collaboration.


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4.1.5 Researcher career and research collaboration

As previously discussed, we are also concerned with the career stage, trajectory and

administrative roles in the collaborative group. Although individual career attributes could

certainly contribute to one’s human capital, the two are distinct and thus should be

examined separately. Here we review studies that analyze career attributes such as net-

works and mobility, and career advancement. Many of the same studies that provide

analysis of personal attributes also consider careers and career trajectories.

4.1.6 Researcher mobility

Recent literature on collaboration, networks and mobility tends to focus on relatively few

parts of the globe and particularly collaborations between US and European Union sci-

entists and organizations. Scholars argue that collaboration in the scientific community is

increasingly global in nature (Carayannis and Laget 2004). Not only do collaboration

projects often span national borders, but increasing span sectors as well (i.e. government,

industry and academic) as well (Carayannis and Laget 2004).

At the same time, some recent studies, including Ponds (2009) suggest that the research

collaboration globalization trend has reached its peak and is no longer growing. Ponds also

finds that international collaboration is most likely to occur between academic organiza-

tions rather than academic and industry organizations.

One issue in collaboration studies is differences between ‘‘organic,’’ voluntary collab-

orations and ones developed chiefly through the efforts of administrators and policy-

makers. Some studies have argued that collaboration works best as a voluntary process, and

individuals involved in a collaborative project must commit to the process and the group

structure (Chompalov et al. 2002; Melin 2000). There must therefore be a latent demand

within individual scientists to work with other scientists thus forming collaborative groups.

Melin (2000) argues that there are background causes for individual collaboration that are

either structural or personal. Although Melin touches on it, there is a large hole in the

literature addressing the personal and social attributes of the individual scientists that

influence collaboration.

Certainly we can see many studies on the effects of demographic characteristics on the

adoption of collaborative groups, but what about the intangible characteristics of the

researchers and the interpersonal relationships amongst them? It seems plausible that

interpersonal relationships would have a great influence on the development of a collab-

orative research team. If an individual scientist cannot interact with a colleague outside of a

research team, he or she would probably be less likely to seek out that individual to work

on a project that would last months or even years. However, it is also unclear if these

personal relationships are somewhat secondary to the resources (i.e. funding, lab equip-

ment, or human capital) that an individual brings to the collaboration. Personal skills, in

particular, are difficult to research in connection with collaboration because there are no

easy and direct measures. Thus, research often examines productivity (publications, cita-

tions) as a proxy. However, it is likely that in many collaborations there is a recognition

and assessment of the skills of one’s colleagues and it seems equally likely that these

assessments would have a bearing on collaboration choices. The literature remains largely

silent on this point.

Although our review focuses chiefly on individual collaboration, a study by Chang and

Dozier (1995) is relevant in that it assesses the effects of a technology transfer experiment

that Hughes Electronics Corporation conducted with California State University,


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Los Angeles (a large minority-serving institution). Although this experimental program

(and, thus, the research focus) is at the organizational rather than individual level, it is

useful for our review because it illustrates the influence of racial diversity on collaboration

patterns. Hughes Electronics Corporation had a history of racial diversity as a top orga-

nizational priority. As such, they developed a research collaboration entitled ‘‘Presence on

Campus’’ with a large minority institution to pair racial minorities within the corporation

with racial minorities within the institution.

Focusing on racial diversity, the authors conclude that the ‘‘culture gap between

industry and academia is real, although not as great as sometimes claimed’’ (Chang and

Dozier 1995, p. 94). Although not explicitly stated in the article, the authors allude to the

fact that pairing minority students and faculty with minority industry researchers will

increase positive outcomes for the partnerships. The authors do argue that the program

yielded ‘‘encouraging results’’ for collaborative research projects that were funded and

initiated. These findings suggest that race is an important personal attribute for individual

collaborators.

4.1.7 University careers

Understandably, given its implications for job security and career progress, tenure is a

factor examined in many studies of research collaboration. Tenure is one of the most

significant aspects of the academic reward structure and discussions of collaboration often

take into account the need for one or more collaborators to obtain tenure (Boardman and

Ponomariov 2007). However, not all studies find tenure to be a major determinant of

collaboration choices. For example, Bozeman and Corley (2004) find no statistically sig-

nificant relationship between tenure status and either the number of collaborators or the

percentage of female collaborators for a given year. Nor, at least in this study, does tenure

seem to weigh heavily in collaboration strategies. The authors do not find statistically

significant relationship between tenure status and the proximity of collaborators nor do

they find that the untenured are more ‘‘tactical’’ in their collaboration choices and strat-

egies. However, tenure status does have a significant and positive relationship to a

‘‘mentor’’ collaboration strategy (Bozeman and Corley 2004). That is, among those who

say that their collaboration choices are in part based on a desire to mentor, persons

expressing that choice are, not surprisingly, more often tenured. The authors argue that the

relationship shown may be a result of mentoring opportunity in that tenured faculty have

the most opportunity to serve as mentors (Bozeman and Corley 2004).

Job satisfaction is one of the most influential attributes for research collaboration, but

not necessarily for research productivity. Lee and Bozeman (2005) find that job satisfaction

has a significant positive relationship to collaborations for researchers, but does not have a

significant relationship to productivity (measured in terms of ‘‘fractional count’’ of total

publications). This suggests that the more satisfied a scientist is with his or her position, the

more he or she collaborates. It is possible, of course, that the causality is in the other

direction (or both directions, i.e. reciprocal causality), that collaboration increases job

satisfaction. The research is not presently sufficient to provide a confidence inspiring

parsing of causality between job satisfaction and collaboration.

4.2 Collaboration attributes

Central to our review of research collaboration literature are the studies that examine

research collaboration processes and composition. In this section we review literature that


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examines how the attributes of the collaborative groups interact and affect collaboration

activities and outcomes.

4.2.1 Collaboration process and research collaboration

Cummings and Kiesler’s (2005) research on cross-disciplinary boundaries provides

important insights into the ways in which collaboration processes can diverge according to

context. The authors find that collaborations across disciplinary boundaries report as many

positive outcomes as single-discipline projects, but projects spanning university boundaries

more often have negative outcomes. Multiple university collaborations tend to be improved

when collaborators are interacting face-to-face (Cummings and Kiesler 2005). These

findings suggest that physical meetings and other coordination mechanisms increase the

productivity of collaborations. The authors offer suggestions for mechanisms to assist in

the coordination of projects that span organizational boundaries:

• tools to manage and track the trajectory of tasks over time;

• tools to reduce information overload;

• tools for ongoing conversation (perhaps some version of instant messages for

scientists);

• tools for awareness with reasonable interruption for spontaneous talk;

• tools to support simultaneous group decision-making;

• tools to schedule presentations and meetings across distance. (Cummings and Kiesler

2005, pp. 718–719)

These suggestions indicate that physical interaction and communication are crucial for

the collaboration process. Research collaboration is a continuous process with ongoing

concerns that require frequent interactions among project members. This implies, of

course, that disparate inter-organizational research collaborations may be prone to prob-

lems owing to lesser communication and interaction. Collaborators often have more suc-

cess traversing discipline boundaries than they do geographic and institutional boundaries.

Several studies (e.g. Abramo et al. 2011) have emphasized the role of geographic prox-

imity in promoting collaboration.

Beaver (2001) offers a comprehensive examination of research collaboration in his

review article. Among other aspects of collaboration, Beaver examines research collabo-

ration processes, including synergy, feedback, dissemination, recognition and visibility, all

of which he views as advantages of collaboration (Beaver 2001). While his points gen-

erally seem on the mark he may be a bit too sanguine about benefits of research collab-

oration. For example, he argues in the case of ‘‘synergy’’ that multiple viewpoints enhance

the project outcomes, which seems likely in most cases, but only if all viewpoints in the

collaboration process are heard. Less experienced or less powerful collaborators may be

overlooked, especially in larger and more hierarchical collaborations. Similarly, Beaver

(2001) suggests that research collaboration allows more feedback, dissemination, recog-

nition and visibility in that each member of the collaboration brings to the collaboration a

network of colleagues who will likely be attentive to the research. Again, this assumes that

each member of the collaboration is invested in the project is a visible member of the team.

It also assumes that collaborators bring a favorable reputation to the project. Recent

research (Liao 2011) suggests that it is not the number of collaborators that is important to

productivity but the intensity of collaborations and the degree to which collaborators are

embedded in the network.


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Chompalov et al. (2002) provide some especially useful findings about the impact of

management style in research collaboration. They identify four basic structures for col-

laboration including bureaucratic collaborations, leaderless collaborations, non-specialized

collaborations, and participatory collaborations. Bureaucratic collaborations are most

successful when the project involves multiple organizations and there must be a clear

hierarchy to ensure that no one organization’s interests are disproportionately served

(Chompalov et al. 2002). Leaderless collaborations are similar to bureaucratic structures in

that both are highly formalized and differentiated (Chompalov et al. 2002). This organi-

zational structure defines specific roles and responsibilities for each of the members of the

collaboration, but does not identify a hierarchy of leadership and responsibility. This

structure could prove effective in some collaborations because it forces each member to be

accountable for his or her responsibilities to the rest of the collaboration team. However,

the leaderless collaboration seems to require experienced collaborators and valued, spe-

cialized roles.

In collaborations that involve scientists with disparate levels of human capital, the non-

specialized organizational structure may be the most appropriate. Chompalov et al. (2002)

identify this organizational structure as similar to a bureaucratic structure in terms of

hierarchy, but less formal in terms of roles and responsibilities. This structure can clearly

be seen in academic collaborations that involve a principal investigator (PI) that is awarded

a large grant to conduct a specific research project. If the PI brings on other collaborators to

the project he or she is still ultimately responsible for the project and thus has a clear

leadership role. Other roles and responsibilities can be informal and less differentiated, but

there is still a clear hierarchy, at least at the top.

The final organizational structure defined by Chompalov et al. (2002) is participatory

collaboration. This organizational structure is egalitarian in nature, with team members

having similar status, at least in the project, and a high degree of autonomy. According to

the authors, participatory collaboration is especially common in particle physics (the

domain to which they give greatest attention). While participatory collaboration can be

quite effective, it requires that participants hold egos in check and respect one another’s

opinion.

Although Champolov and colleagues create a typology of organizational structure for

scientific research collaborations, they argue that hierarchy is not the defining characteristic

of collaboration. Consensus, they argue, is the true defining characteristic of collaboration

because regardless of the level of hierarchy involved in a project, participation is always

voluntary and a collaborator is rarely coerced to accept a given hierarchy.

Beyond organizational structure, some literature focuses on the roles of collaborators

within a project or more specifically the role conflict and ambiguity associated with col-

laborations (Duque et al. 2005). In their study of 918 scientists in three developing nations,

Duque and colleagues found a paradox—the factors that undermine the productivity of

collaborations (e.g. transactions costs) are not mitigated by and may even be exacerbated

by new information and communication technologies and remote collaboration.

Garrett-Jones et al. (2010), in a recent contribution to this journal, provide empirical

evidence of how the management of collaborations can affect the actors and the output of

the process. Based on both questionnaire data and interviews of researchers in Australian

Cooperative Research Centers, the ‘‘important management issues of trust, governance,

and competition between functional domains, which emerge from IOR and which have

been inadequately recognized in the context of collaborative R&D centers’’ (Garrett-Jones

et al. 2010, p. 527). The authors specifically stress the role of trust between different actors

in the collaborative arrangements. We can see from these findings that management


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behavior is crucial to develop a productive and encouraging environment for scientists

(Turpin et al. 2011). Although intangible, fostering a sense of trust among research sci-

entists collaborating with other scientists across sectors contributes to effective research.

An important aspect of the collaboration process pertaining to management is the set of

incentives pursued in choosing collaborators. Bozeman and colleagues (Bozeman and

Corley 2004; Lee and Bozeman 2005; Bozeman and Gaughan 2011) have given special

attention to the management style of individuals within the research team. Based on

questionnaire data, the researchers identify archetypal collaboration motives and strategies

as follows:

• The ‘‘Taskmaster’’ tends to choose a collaborator based on work ethic attribution and

whether or not the person sticks to a schedule.

• ‘‘Nationalist’’ collaborators, the least common archetype among the STEM researchers

in the sample, is drawn to collaborators who are fluent in their language or who are of

the same nationality.

• ‘‘Mentors,’’ more common among senior researchers, are motivated to a large degree

by their interest in helping junior colleagues and graduate students by collaborating

with them and, in the process, mentoring them.

• The ‘‘Follower’’ chooses collaborators mostly because someone in administration

requested that they work in the collaboration or, in some instances, because they wish

to partner with a collaborator who has a strong science reputation.

• The ‘‘Buddy’’ chooses collaborators based on the length of time they have known the

person, the quality of previous collaborations and whether or not the collaborator is fun

and entertaining.

• Finally, the ‘‘Tactician’’ chooses collaborators based on whether or not the collaborator

has skills complementary to their own (Bozeman and Corley 2004, p. 610).

Bozeman and Corley (2004) aim to understand which factors predict collaboration

strategies for individual scientists. They find that tenure status, percent of female collab-

orators, number of graduate student collaborators, and the scientists’ cosmopolitan scale all

were significantly and positively related to the ‘‘mentor’’ collaboration strategy. Amount of

grant money is significantly and positively associated with the ‘‘tactician’’ strategy. Evi-

dence also suggests that women are less likely to adopt a ‘‘tactician’’ strategy (Bozeman

and Gaughan 2011).

Lee and Bozeman (2005) take a different approach and use the collaboration strategies

to predict the extent of research collaboration and collaboration productivity through a two

stage least squares analysis of questionnaire data. The authors find significant relationships

between the roles of the academic scientists and productivity, providing evidence that

strategies matter to ‘‘additionality.’’ The authors report significant and negative relation-

ships between a ‘‘nationalist’’ collaboration motivation and number of collaborators. The

STEM researchers who engage in collaboration in order to mentor junior scholars and

doctoral students (mentor strategy) have more collaborators. Those that collaborate

because of a shared national identity or language tend to collaborate less Taken together,

these findings indicate that research collaboration strategies and motives to matter in terms

of collaboration outcomes. In terms of productivity, the authors only find a significant and

positive relationship between a ‘‘tactician’’ strategy and both the normal and fractional

count of research productivity (Lee and Bozeman 2005).

While there is a good deal of research on the topic of research management and motives

and their relation to research effectiveness (e.g. Carayol and Matt 2004) relatively little

research focuses on the interrelation of management, collaboration and effectiveness


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(Vasileiadou 2012). Many important topics receive scant attention. For example, we know

little about the relation of collaborations’ management structure to problem choice or to the

sustaining of longer term and serial collaborations. Nor is there research on the actual costs

(in terms of money but also career costs and foregone opportunities) of managing research

collaborations. Everyone who has participated in research collaborations is well aware to

the time and energy required to management them but there are not rigorous research

studies documenting these costs.

4.2.2 Collaboration composition and research collaboration

Our conceptualization of collaboration includes the labor mix, the organizational home,

status and demographic mix of the collaborators in the collaborative group. Although our

focus of this review centers on individual collaboration, we cannot ignore the larger

organizational context of the collaborative group. This includes not only the organization

of the group itself, but also the organizational ties and associations that individual group

members bring into the collaborative group.

Beaver (2001) discusses the typical structure of a collaborative project at a university,

including the demographic composition of collaborative academic groups. He states that

‘‘The typical group structure at a major research university consists of: A Principal Inves-

tigator (PI), together with postdocs, graduate students (and perhaps undergraduates)—or—A

senior professor, perhaps an assistant or junior professor, postdocs, graduate students (and

perhaps undergraduates)’’ (Beaver 2001, p. 369). Beaver’s description rings true, but is based

on anecdotal evidence, developed chiefly through discussion with his colleagues at Williams

College.

Beaver (2001) also offers his views about the advantages and disadvantages to this basic

collaborative composition. Beaver argues (2001, p. 370) that because of the collaboration

composition the, ‘‘PI loses touch with direct research.’’ This disadvantage occurs because

the PI has subordinate members of the group to perform the basic tasks of the project and,

as a result, the PI loses tacit knowledge of how things work in practice and, instead, invests

more time in routine project administration. Later research (Bozeman and Gaughan 2007)

based on questionnaire data from more than a thousand U.S. university researchers, pro-

vides convergent evidence that the most common personnel configuration of research

projects may channel too many resources to line administration. When asked to identify the

single most important problem in their research work, respondents mentioned time writing

and administering grants as the most important obstacles.

In terms of organizational home, it is important to consider how the geographic location

of the organization contributes to collaboration. A recent study examines the influence of

geographic proximity on increased collaboration between universities and private industry

organizations (Abramo et al. 2011). Defining collaboration as any partnership between

universities and industry that result in a coauthored scientific article, the authors conclude

that collaboration is often ‘‘exclusive’’ between universities and companies, but often can

involve multiple universities collaborating with a single company. Findings suggest that

significant inefficiencies occur in the market between university and private firm collab-

orations, but these inefficient patterns provide useful insight into how the organizational

home can influence collaboration behavior. Abramo et al. (2011) find that private com-

panies are more likely to partner with universities in close proximity rather than the most

qualified institution. The authors argue that 93 % of collaborations within the study could

have been conducted with a ‘‘higher ranking’’ academic partner. This suggests that

researchers tend to collaborate with those who are in closer proximity.


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Lee and Bozeman (2005) provide complementary findings. In terms of nationality and

composition of a collaboration team, Lee and Bozeman (2005) offer findings indicating

that researchers engaged in collaborations with scholars outside of normal work groups,

including other nations, tend to collaborate more than their peers. Referred to as the

individual’s ‘‘cosmopolitan scale’’, this suggests that those scholars collaborating with

more distant scholars collaborate more than do those who chiefly collaborate with persons

in their immediate environment. However, most university scientists (more than 60 %)

collaborate almost exclusively with persons in their university research center or laboratory

(Bozeman and Corley 2004).

A more recent study shows the importance of distinguishing research collaboration

according to the level of development of the researchers’ national locale. Focusing on

developing nations, Ynalvez and Shrum (2011) indicate that collaboration has no especial

productivity pay off for researchers in developing nations and that collaborations, when

they are undertaken at all, often are done so in the face of major impediments and insti-

tutional barriers. Research by Toivanen and Ponomariov (2011) focuses on collaboration in

Africa and, similarly, notes important structural impediments to collaboration.

4.3 Organizational/institutional attributes of research collaboration

Our conceptualization of organizational and institutional attributes is distinct from the

previous discussion of collaboration composition in that we are here concerned with the

macro level organizational and institutional attributes rather than the individual organi-

zational attributes of the collaborators. Here we review studies concerned with both

internal and external actors of the research project. Organizational and institutional

arrangements of collaborative research projects have received a great deal of attention in

the literature in recent years. Many scholars are concerned with the process of university/

industry partnerships and how the organizational arrangement influences research policy.

Our chief focus in this section is the role of individual academic scientists and their roles in

university/industry partnerships. Other papers are focused less on university/industry

partnerships than on issues emerging from collaborations among researchers working in

different universities or hybrid settings.

4.3.1 Organizational actors and research collaboration

Within our organizational framework we include actor type as a subcategory of organi-

zational/institutional attributes in research collaboration. Actors within a collaborative

project can certainly influence the process and outcome of the research project and

therefore it is vital to provide an appropriate review of literature addressing actor type

issues.

We begin with further discussion of Cumming and Kiesler’s (2005) paper on collab-

oration across disciplines and organizations. The authors examine ‘‘collaborations across

disciplinary and university boundaries to understand the need for coordination in these

collaborations and how different levels of coordination predict success’’ (Cummings and

Kiesler 2005, p. 703) and find that collaborative projects across multiple disciplines

report positive outcomes whereas collaborations across multiple universities face higher

coordination costs. Cummings and Kiesler (2005) argue that the problems pertaining to

principal investigators associated with multiple universities could be alleviated with more

coordination mechanisms. However, they also find that ‘‘having PI universities involved in

a project significantly reduced the likelihood that PIs would actually employ sufficient


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coordination mechanisms’’ (Cummings and Kiesler 2005, p. 715). The authors offer

some implications for practice discussed above as they address collaboration process

attributes.

In their study of the relation of organizational and work characteristics to researchers’

publication productivity, Fox and Mohapta (2007) examine the relevant literature and their

own questionnaire data. They ask four chief questions about the relationship of university

researchers’ productivity to ‘‘social-organizational characteristics’’ of the work setting.

Specifically, they consider ‘‘What are the effects upon publication productivity of (1) team

composition (number of persons/positions, by gender, on a research team); (2) collabo-

ration (inside and outside of department and university); (3) work practices; and (4)

workplace climate?’’ (Fox and Mohapta 2007, p. 543). The authors use data from a survey

conducted in 1993–1994 of 1,215 faculty in doctoral-granting departments of computer

science, chemistry, electrical engineering, microbiology, and physics. They measure

publication productivity as the number of articles published or accepted for publication

reported by respondents in the 3 years prior to the survey. Team composition focuses on

the number of and gender of graduate students and the gender of the faculty member on the

research team. Collaboration is measure by the number of collaborations with faculty

within one’s own department, outside one’s own department but on the same campus and

those on other campuses. Work practices and departmental climate, while important, are

not relevant to the current review of literature.

The authors find a significant and positive relationship between being a male faculty

member together with having higher numbers of male graduate students and research

productivity. Evidence does not show a significant relationship between gender of faculty

and productivity or number of female student and productivity.

Fox and Mohapta (2007) find significant and positive relationships to collaborations

with other faculty within one’s own department or at another institution. Evidence shows

no significant relationship between collaboration with faculty outside one’s own depart-

ment, but within the same campus and research productivity. These results are distinct

from the Cummings and Kiesler (2005) article that suggests that collaborations across

organizational boundaries increase coordination costs and are therefore problematic. In

contrast, Fox and Mohapatra find that such collaborations are the ones with the highest

productivity. The respective findings are not inherently contradictory. It is certainly pos-

sible for collaborations to at the same time be highly productive and to exert a major toll in

terms of coordination costs.

Just as important as inter-university collaboration is international collaboration. In

that regard, Carayannis and Laget (2004) suggest that researchers are increasingly

configured into collaboration networks that are global in nature. Academic scientists are

more likely to collaborate with scientists from other countries and in other sectors.

According to Beaver (2001, 2004) global collaborations could prove problematic in

terms of coordination, but the evidence for management of such cross-national col-

laborations remains scant.

Whereas academics seem to be increasingly drawn to international collaborations, this

is not necessarily the case for industry–university research collaborations, as suggested by

Abramo et al. (2011). Industrial groups partnering with academics seem to have a pref-

erence for collaborations with nearby universities and academic researchers. This seems in

line with both intuition and previous research findings. Whereas knowledge development,

as embodied in publications, seems to have few significant geographic boundaries,

industries partnering with academic researchers in property-focused collaborations con-

tinue to prefer nearby research partners. Technology transfer often works best when


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supported by regular face-to-face interaction that enables tailoring of research and appli-

cation (see Bozeman 2000 for a review of research relevant to this topic).

Katz and Hicks (1997) provide evidence about the value of research collaboration.

Using bibliometric data, the authors analyze collaboration’s value in terms of citation rates.

They find that, compared to single-authored papers, collaboration with domestic

researchers increases the citation rate by .75 citations, whereas collaboration with

researchers in other countries increases by 1.6 citations.

Beyond the individual scientists involved in the collaboration process, it is also

important to consider the organizational environment in which these individuals interact

(Liao and Yen 2012). Katz (2000) specifically addresses this concern. He argues that

conventional measures used to evaluate research do not account for the non-linear rela-

tionship between the size institutions associated with collaborations and the research

performance (Katz 2000). He goes further to argue that traditional measures of size and

performance result in an exponential power-law relationship between size of the research

group, institution or nation and the perceived research performance. Katz (2000, p. 24)

makes his argument based on a variety of performance-related measures including number

of published papers, number of citations to papers, citations per paper, and number of

co-authored papers.

Katz makes arguments about the size of institutions and the propensity to collaborate

among the members of the institution, noting that ‘‘smaller educational institutions have

a greater propensity than larger ones to collaborate domestically, particularly with

industrial partners and other educational institutions’’ (Katz 2000, p. 29). He also

reports that larger institutions are more likely to engender collaborations, both internal

ones and international collaborations and that collaborations tend to exhibit linear

increases as the size of institutions increase. One recent paper focused on Taiwan

suggests that international collaborations tend to have greater impact, but that level of

impact from international collaboration is field specific (Liu et al. 2012). However, a

study (Gazni and Didegah 2011) of U.S. researchers shows that publications with U.S.

and foreign collaborators tend to receive fewer citations than publications by U.S.

collaborators only.

Returning to Katz’s (2000) study, we can see that additionality can vary by organiza-

tional size. Larger academic institutions have the infrastructure and human capital nec-

essary to provide internal collaboration networks. Scientists at large institutions may not

need to go far to collaborate. This would explain the increased internal collaborations at

larger institutions. Larger academic institutions generally have the resources to attract

researchers with large amounts of human capital. By the same token, the propensity for

researchers in smaller institutions to collaborate domestically with industry partners and

other academic institutions more than likely has to deal with the resource limitations of

smaller institutions.

4.3.2 External actors and research collaboration

In our conceptual model, external actors of particular importance include resource pro-

viders, regulators and competitors. We can see evidence of external actors negatively

contributing to the collaboration process (e.g. Hall et al. 2001), but also evidence that

external actors positively contribute to collaborative R&D (e.g. Johnson 2009). Funding

from different sources can strongly influence the process of collaboration (Bozeman and

Gaughan 2007; Matt et al. 2011). However even if it is clear that external actors affect

collaboration and effectiveness, the specific causal paths are not always obvious.


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The research reviewed in this section helps clarify the ways in which external actors affect

research collaboration.

Geisler (1986) provides one of the most important studies of external actors in the

research collaboration process. The external actors of interest to Geisler (1986, p. 33) are

persons on Industrial Advisory Boards (IAB), individuals who have ‘‘a significant, yet

sometimes underestimated role in the transfer of technology between universities and

industry, in the context of university–industry collaborative arrangements.’’ Findings

suggest that IABs facilitate the transfer of technology by creating a locus for the interaction

university researchers and industrial personnel.

In the United States and many other nations, the national government is among the

most significant and pervasive external parties to research (as well as an internal actors

inasmuch as many nations maintain extensive government personnel engaged in R&D

activities). Inevitably, institutions that shape research generally also affect patterns of

research collaboration. In their study of the relationship of academic research to

industrial performance, Grossman et al. (2001) consider, among other factors, the ways

in which funding can affect research collaboration patterns. As is well known, the U.S.

federal government has been the primary funder for academic research and industry

financial support has been modest, at least on a percentage basis. Despite this funding

disparity, industry has proved to be an important actor for research collaboration. The

authors (Grossman et al. 2001, p. 151) argue ‘‘industry has provided a large stimulus to

fundamental long-term research in many fields, posing new questions to academic

researchers and exposing gaps in knowledge through their innovative activities.’’ The

authors also suggest that many industrial actors are unsatisfied with the federal gov-

ernment’s funding levels to sustain long-term researcher, but they are not increasing their

own funding levels for university–industry collaborations (Grossman et al. 2001). As

funding from government has slowed and that many institutions have come to rely on

alternative funding methods including own source institutional funding and industry

sources (Jankowski 1999; Morgan and Strickland 2001; National Academy of Engi-

neering 2003).

Beyond funding, external actors such as regulators can influence collaboration pat-

terns. Given our focus on academic researchers, an important external actor with a

regulatory role is that of the institutional technology transfer office at most research

universities. In a series of related studies, Siegel et al. (2003a, b, 2004) provide broad-

based empirical analyses of university technology transfer offices. Siegel et al. (2003a)

report results from interviews with university administrators, scientists, and business

professionals. The authors provide examples of numerous barriers to effective technology

transfer and some recommendations for improving the process. As can be seen, most of

these recommendations are specifically targeting the technology transfer offices (TTO) at

institutions.

• ‘‘Universities need to improve their understanding of the needs of their true

‘customers,’ i.e., firms that can potentially commercialize their technologies

• Adopt a more flexible stance in negotiating technology-transfer agreements and

streamline UITT policies and procedures

• Hire licensing officers and TTO managers with more business experience

• Switch to incentive compensation in the TTO

• Hire managers/research administrators with a strategic vision, who can serve as

effective boundary spanners (tie to boundary spanning literature)

• Devote additional resources to the TTO and patenting


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• Increase the rewards for faculty participation in UITT by valuing patents and licenses

in promotion and tenure decisions and allowing faculty members to keep a larger share

of licensing revenue (as opposed to their department or university)

• Recognize the value of personal relationships and social networks, involving scientists,

graduate students, and alumni’’ (Siegel et al. 2003a, p. 122)

These recommendations aim to increase the efficiency and effectiveness of university/

industry collaboration. Although the TTO is not mentioned specifically in all of the above

recommendations, the TTO would most likely be responsible for implementation of the

more general suggestions. We can see from Siegel et al. (2003a) not only an example of an

effective TTO, but more importantly a negative example of how a TTO could discourage

collaboration between universities and industry. Effective TTOs generally have more

business experience and create incentives and procedures that benefit the industry side of

the university/industry collaboration. These findings are consistent with other studies

(Renault 2006) that find that institutional policies negatively influence collaboration with

industry.

It would appear that the empirical evidence shows that university administration

often discourages collaboration between universities and industry. Even in instances

where higher-level university administration is committed to industrial partnership,

middle- and lower-levels of bureaucracy sometimes sabotage these goals (Audretsch

et al. 2002). Nevertheless, industry outcomes often are net positive for university

interactions. Hisrich and Smilor (1988) find positive outcomes for companies as a result

of these university programs, specifically university business incubators. The authors

identify four key factors that must be linked for successful university business

incubators technology transfer to industry: talent, technology, capital and know-how

(Hisrich and Smilor 1988). Findings also suggest that these incubator programs benefit

companies by ‘‘helping them build credibility, shorten the learning curve and solve

problems faster, and by providing access to entrepreneurial networks’’(Hisrich and

Smilor 1988, p. 14). We can see here that not all regulatory external actors negatively

affect industry collaborations with academia. In fact, universities often target businesses

and use resources and human capital to develop the companies further. Evidence shows

that a number of university-associated patents are used in startup companies, but most

are used in established large firms (Meyer 2006).

Drawing from a sample of projects funded by the Advanced Technology Program, Hall

et al. (2001) offer some general conclusions on the effects of regulations on collaboration

patterns between universities and industry. Their research focuses on the barriers that limit

collaboration or partnering behavior between industry and universities, primarily intel-

lectual property concerns. Implicit in this analysis is the notion that universities are subject

to more stringent regulations compared to private industry. According to the authors (Hall

et al. 2001, p. 94) ‘‘we have demonstrated that IP issues between firms and universities do

exist, and in some cases those issues represent an insurmountable barrier which prevents

the sought-after research partnership from ever coming about.’’ The authors also identify

determinants of such situations occurring. IP barriers are more likely to occur when the

research is expected to lead to results that are not easily appropriable and when there is a

lesser degree of public goods aspects to the work. Industry is more likely to avoid part-

nering with university researchers when the research is expected to be short term. These

results are consistent with the (Hagedoorn et al. 2000) of research on industry university

partnerships.


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Researchers have borrowed from other disciplines to understand the nature of inter-

action between internal and external actors, most notably from psychology and the ‘‘Not

Invented Here or NIH’’ construct often examined in industry. Grosse Kathoefer and Leker

(2010) apply the NIH syndrome to knowledge transfer in academia. They identify four

factors related to the syndrome. The first two, the preference for internally generated

knowledge and the perception of the professors on how important outsiders regard internal

knowledge generation, are consistent with the NIH syndrome core of ‘‘having prejudices

against external knowledge’’ (Grosse Kathoefer and Leker 2010, p. 10). The final two

factors deal specifically with the focus of our review: reluctance to collaboration and

reluctance to knowledge sharing. The results presented regarding the determinants of the

NIH Syndrome can therefore be closely linked to a lack of desire for collaboration with

external actors because a perception of competition.

The authors offer some general conclusions as to what produces this distrust of external

actors in the form of NIH syndrome. First, they offer that NIH can be ‘‘regarded as a

psychological issue being individual-based’’ (Grosse Kathoefer and Leker 2010, p. 11).

The respondents examined in the analysis came from two distinct academic fields, physics

and engineering, however the two groups showed no systematic differences in levels of

NIH. The authors therefore conclude the syndrome is individual in nature. Findings do

indicate a systematic difference between scientists focusing on basic research and those

focused on applied research. The latter group shows lower levels of the syndrome. The

authors argue this is due to direct ties with industry to produce research products thus

increasing trust and acceptance of external actors.

4.4 Outputs and impacts from research collaboration

Unavoidably, previous sections of this paper have dealt with the ‘‘dependent variables’’

from collaboration. But in this section the chief focus is on outputs and impacts and we

include some research findings that do not conveniently fit into the categories developed

above.

As noted above, this review is concerned with distinction between knowledge-focused

research and property-focused research. Naturally, different stakeholders in research col-

laborations have different values for various outputs and these differences in values, goals

and perspectives affect the collaboration processes and perceived effectiveness (Siegel

et al. 2003b).

As stated above we define knowledge-focused research as that research that contributes

to the general scientific knowledge of a field but offers little monetary or property gain for

the researchers of the project. We define property-focused research as research that typi-

cally results in some form of monetary or property benefit for the researchers including

patents, new technology, new business start-ups or more rarely monetary profits. We

acknowledge in our conceptualization that these two foci or outputs are not mutually

exclusive. As such we include a third type of output: indeterminate.D’Este and Perkmann (2011) present an excellent analysis of the tension between

knowledge-focused, property-focused and indeterminate-focused research collaborations.

They focus on the motivations of academic researchers to collaborate, both formally and

informally with industry (D’Este and Perkmann 2011). The authors identify four main

motivations that are consistent with our three main outputs of collaboration. The first is

commercialization, which they define as ‘‘commercial exploitation of technology or

knowledge’’ (D’Este and Perkmann 2011, p. 330). Commercialization resembles our

operationalization of property-focused research. The next motivation identified by D’Este


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and Perkmann is learning, defined as ‘‘informing academic research through engagement

with industry’’ (D’Este and Perkmann 2011, p. 330). Learning as a motivation to collab-

orate is consistent with our operationalization of knowledge-focused research. The final

two motivations for academics to engage in collaborative research with industry are less

obvious. Access to funding and access to in-kind resources as a motivation to collaborate

are both indeterminate-focused outputs. Although both are resource related, it is not always

apparent if the resources are devoted to monetary gain or increased knowledge production.

Below we offer further review of articles that discuss the outputs of collaborations.

4.4.1 Knowledge focus and research collaborations

As indicated above, we define knowledge-focused research collaborations as ‘‘collabora-

tions aimed chiefly at expanding the base of knowledge and enhancing academic

researchers’ academic reputation.’’ As can be seen in the literature, knowledge-focused and

property-focused research are not mutually exclusive (Hessels and Van Lente 2008).

Applied studies can contribute to fundamental knowledge and that fundamental studies can

be somewhat applied in nature. Although industry partnership is most often applied,

industry also funds basic research thus contributing to knowledge-focused outputs. Much

of this funding comes to develop equipment that could be used for future applied research

(Nedeva et al. 1999). There is evidence that access to equipment and additional research

resources is a major incentive for many research collaborations (Tartari and Breschi 2011).

This section reviews literature that examines collaborations influence on knowledge-

focused research. It is important to note that often knowledge-focused outputs are not the

motivating factor for collaboration, but an aspect serving researchers’ values and, as such,

an incentive to continue to collaborate even in cases where not all parties to the collab-

oration have exactly the same goals.

Lee (2000) uses data from two surveys of academic research and their industrial

partners to examine the sustainability of collaborations. Findings are distinguished between

expected and unexpected benefits of the collaboration, but also between benefits for

industry and for universities. One might expect industry managers to identify monetary

benefits as the motivating factor for continued collaboration, but Lee reports that the most

important benefit for firms is ‘‘an increased access to new university research and dis-

coveries’’ (Lee 2000, p. 111). The author also finds that faculty engage in research in order

to secure funds for graduate students and lab equipment. Despite the fact that knowledge-

focused outputs are not the motivating factor in the expected group,1 we can still see

empirical evidence that knowledge-focused outputs influence research collaboration. Lee’s

findings are consistent with other research on expectations from industry–university

partnerships (Feller and Roessner 1995; Gray and Steenhuis 2003).

One study focusing explicitly on knowledge-focused research outputs is Landry et al.’s

(2007) analysis of knowledge transfer among Canadian university researchers in natural

sciences and engineering. The authors answer three research questions: the first distin-

guishing between knowledge and technology transfer, the second identifying differences

1 There is evidence that industry collaborations with universities most often produce increased knowledge-focused outputs rather than property-focused outputs (Levy et al. 2009). Given private industry’s profit-maximizing goal, however, we would expect firms to be motivated by increased property-focused outputs toengage in collaborative projects.


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between disciplines in terms of knowledge transfer, and finally discussing the determinants

of knowledge transfer. Their findings indicate that researchers produce knowledge-focused

outputs more actively ‘‘when no commercialization was involved than when there was

commercialization of protected intellectual property’’ (Landry et al. 2007, p 561). The

authors also found significant field effects suggesting that researchers in certain disciplines

were more active in knowledge-focused research than others, engineering being the most

active, followed by earth sciences, mathematics, statistics, physics and space sciences.

Focus on user needs and relationships between researchers and research users positively

influence knowledge transfer across all disciplines, but findings also indicate that deter-

minates of knowledge transfer vary across disciplines. The authors argue that ‘‘different

policies would be required to increase knowledge transfer in different research fields’’

(Landry et al. 2007, p. 561).

Boardman and Ponomariov (2007) develop an empirical model focusing on academic

scientists’ desire to produce knowledge-focused research. In this article the authors are

primarily concerned with the impact of tenure on a desire to produce knowledge-focused

or property-focused research. Boardman and Ponomariov (2007) analyze survey

responses from a sample of 348 tenured or tenure track academic faculty researchers at

university research centers. The authors construct two models, the first based on

responses to a questionnaire item ‘‘Worrying about possible commercial applications

distracts one from doing good research,’’ and the second dependent variable based on the

item ‘‘I am more interested in developing fundamental knowledge than in the near-term

economic or social applications of science and technology’’ (Boardman and Ponomariov

2007, p. 61). Both models include personal attributes of the respondents, including age,

gender, if the respondent is tenured, field of study, if the respondent had a job in industry

prior to current position, especially important for present purposes, the respondents’

proportion of collaborative papers (Boardman and Ponomariov 2007). The authors find a

significant and negative relationship between tenure and both the ‘‘distraction of com-

mercial interests’’ and the ‘‘interest in developing fundamental knowledge’’ variables

(Boardman and Ponomariov 2007). Evidence shows that junior faculty are more likely to

value basic rather than applied research and do not value property-focused research as

highly as senior colleagues (Boardman and Ponomariov 2007). These findings are largely

consistent with another study that suggests that faculty members that are more embedded

in academia value basic research more than property-focused research (Ambos et al.

2008). Evidence is mixed over which output is valued most by senior researchers, but

both studies include measures of collaboration thus suggesting that collaboration does

not drastically affect the research output (either knowledge-focused or property-focused).

It is important to remember that the respondents in the Boardman and Ponomariov article

are all affiliated with university research centers, which could imply that they have a

deeper commitment to industrial partnerships and perhaps property-focused research

collaborations.

In terms of ‘‘additionality’’, a strong argument can be made from the evidence pre-

sented in Beaver’s 2004 article Does Collaborative Research have Greater EpistemicAuthority? This article provides evidence that suggests that collaborative projects do

indeed have greater epistemic authority than individual research projects (Beaver 2004).

The author measures epistemic authority of an individual project with the number of

citations, both in number, probability of citation and length of citation history (Beaver

2004).

As a theoretical introduction to his study, Beaver makes sociological, philosophical and

historical arguments supporting his hypothesis that collaboration produces more cited


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results. One philosophical and sociological argument is that collaboration provides inter-

subjective verifiability to the project, ‘‘the ability to establish or prove truth (or falsity)

through arriving at a free, unforced agreement among many different ‘subjectivities’ or

people’’ (Beaver 2004, p. 401).

This argument seems warranted to the extent that collaborative projects are subjected to

more scrutiny from the multiple authors. The results may be viewed as more valid because

they have survived a rigorous test from multiple rather than a single subjectivity (Beaver

2004). This argument is not compelling in instances of hyper-authorship. If an author is

listed merely because of his or her human capital, then the author may provide no oversight

or expertise at all. In some cases there is a false epistemic authority because the audience

would expect an author to evaluate a project attached to his or her name and this expec-

tation would not be met. However, in cases where each collaborator does provide at least

some attention to the given project, then collaborative nature of the work should provide

added validity to the knowledge product.

Beaver further supports his position by using Kuhn’s (1996) Structure of ScientificRevolutions as a conceptual framework. Kuhn argues that scientific revolutions occur as

the result of challenges to existing paradigms, often played out as a struggle between

young and established scholars. Beaver (2004, p. 404) argues that ‘‘Each collaborator

having something of an ‘outsider’s viewpoint’ increases the likelihood of recognizing

significant novelty, and of detecting important error.’’ Beaver makes an assumption that the

collaborative process is typically a tension between established and not established sci-

entists. Although this tension could possibly improve scientific results, it is unclear if this

tension is always present in scientific collaboration.

Beyond the philosophical issues associated with Beaver’s argument, the author pro-

vides an empirical model assessing the epistemic effects of collaboration. Using quali-

tative analysis of 660 refereed research articles from 33 professors at Williams College,

Beaver examined the collaboration patterns of professors and the resulting citations of

the publications. The evidence suggests that ‘‘collaborative research produces signifi-

cantly more authoritative research, as reflected in acknowledgements through citations,

and in the longer intellectual influence indicated by greater citation lifetimes’’ (Beaver

2004, p. 407). Methodologically, the study is somewhat problematic. Beaver’s study is

limited to professors at Williams College, certainly not a representative group. Never-

theless, despite problems with external validity, the study provides many directions for

future researchers to follow. Beaver’s evidence suggests that additionality is created

through collaboration. The proposition that research involving more than one author has

more epistemic authority than single-authored projects cannot be said to have been

proved by Beaver’s work but he does frame a tantalizing questions and present relevant,

if not conclusive, evidence.

Industry–university cooperation also seems to have many positive effects on knowl-

edge outcomes. While one might assume that industry is primarily motivated by prop-

erty-focused outcomes such as patents or profitable products, Caloghirou et al. (2001)

show that industry partners often are strongly motivated by less targeted work aimed at

generally enriching the knowledge available to them. Although the authors limit their

analysis to cases in Europe, the findings may be broadly generalizable. Their analysis

investigates research joint ventures between privates firms and European Universities.

The authors make several valuable conclusions to research on collaboration. First, they

argue that firms are increasingly turning to universities for R&D collaboration and

present evidence to support this view. Second, they argue ‘‘when collaborating with

universities, firms primarily aim at achieving research synergies, keeping up with major


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technological developments, and sharing R&D costs’’ (Caloghirou et al. 2001, p. 160).

The study concludes that the major benefit to firms from collaborating with universities

is ‘‘enhancing their knowledge base, followed by improvements in production processes’’

(Caloghirou et al. 2001, p. 160). Although the decreased costs and improvements in the

production processes could be viewed as property-focused incentives, their findings

indicate that these industry partnerships are just as often motivated by knowledge-based

incentives.

Much of the empirical research on collaboration uses research outputs as dependent

variables to show how different research patterns and behaviors influence either property-

focused or knowledge-focused outputs. Recent research has used research outputs as

determinants of collaboration or network patterns among academic scientists.

Goel and Grimpe (2011) present findings that suggest that knowledge-focused

outputs in the form of scholarly articles promote conference participation, thus

increasing the network behavior of the scientist. The authors also show that property-

focused outputs in the form of increased patenting positively affects conference

participation. Although Goel and Grimpe are primarily concerned with active versus

passive networking among academics, their findings show that the character of

research outputs can influence networking behavior. We see evidence that collabo-

ration increases both knowledge and property-focused outputs, but also that knowl-

edge and property-focused outputs causally affect active networking among academics

and, thus, the potential for collaboration. It would be useful for future research to

examine this complex relationship more explicitly. Scholars would do well to

examine research output not only as dependent variables but also as possible causes

of collaboration and additionality. The review below focuses on articles that view

research outputs as the dependent variable in question (because that is the most

common approach in the literature), but we look forward to future articles that

examine outputs from different perspectives.

4.4.2 Indeterminate outputs and research collaboration

Empirical studies focus on how collaboration produces both tangible (Tartari and Breschi

2011) and intangible benefits for the research process. Garrett-Jones et al. (2010) examine

Australian academics’ perceptions about collaboration and the management of s conflict

between the career goals of individuals’ and their organizations’ productivity goals.

Findings indicate that the research scientists, including those in research centers, engage in

collaborations because of intangible motivations including ‘‘better access to industry

partners and working with a larger cohort of scholars with similar scientific interests’’

(Garrett-Jones et al. 2010, pp. 534–535). The research centers are also shown to provide

both financial and human resources for the actors. We can therefore see that often

collaboration, even collaboration across universities, industry and government, often focus

on both knowledge and property, with different actors playing somewhat different tech-

nical roles. The authors conclude that often the researchers interviewed saw the benefits of

their collaboration first in terms of effects on their own career and second in terms of how

potential benefit to the larger scientific community (Garrett-Jones et al. 2010, p 542).

Although the authors warn against generalizing their findings to other countries and

research center networks, they do contend that the findings could well be applicable to the

U.S.


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4.4.3 Property-focused outputs and research collaboration

For at least 30 years, many researchers have studies the role of universities as providers of

knowledge and technology to industry (Niosi 2006). Some argue that there has been an

increase in patenting at universities due to biotechnology, but others claim that patent

statistics could be an erroneous indicator of productivity or (Saragossi and van Pottelsberghe

de la Potterie 2003).

Research has examined the effect of university licensing on research behavior,

including collaboration (Thursby et al. 2001). Thursby and colleagues provide some

general conclusions about licensing behavior at the university level. The authors conclude

(Thursby et al. 2001, p. 59) ‘‘Patent applications grow one-to-one with disclosures, while

sponsored research grows similarly with licenses executed. Royalties are typically larger

the higher the quality of the faculty and the higher the fraction of licenses that are executed

at latter stages of development.’’ Although these findings are not specifically related to

collaboration, some could inform collaboration research by helping understand incentives

for partnering and the possible outcomes of collaboration.

Property-focused research in our conceptualization is research that provides economic

benefits (or has the potential to do so) to researchers or research that may provide com-

mercial benefits to industry, with the academic researcher benefiting either directly or

indirectly through industry’s provision of resources.

Studies of knowledge-focused research often employ bibliometric data and examine

citation or publications rates, but studies of property-focused research more often use

patent, licensing and royalties data. An interesting and illustrative property-focused study

that is comparative in nature is provided by Morgan et al. (2001) who compare the

patenting and invention activity of scientists in the academic sector to counterparts in

industry. They examine patent activity rates, patent activity shares and patent success rates.

These measures could easily be expanded and applied to studies of collaboration

productivity.

A common concern in property-focused research is with the dynamics and costs and

benefits of collaboration between universities and industry. Most studies find a positive

relationship between collaboration and firms’ property-related output (Loof and Brostrom

2008). A excellent paper by Ambos et al. (2008) focuses not only on identifying factors

related to the commercialization of university research but also suggest routs to greater

‘‘ambidexterity’’ for university research. The authors focus on projects that have produced

patents, licenses, spin-off companies or some combination of these and develop four

statistical models pertaining to the commercialization of academic research. Their full

model includes not only individual and organizational-determinants, but also controls for

other factors such as collaboration on the specific project. Interestingly, their findings

indicate that collaboration on a given project has no statistically significant relationship

with research commercialization. Research with multiple authors is no more likely to be

commercialized. The authors do find statistically significant relationships between the

amount of previous grants associated with the researchers, the academic staff time,

organizational-level determinants and individual level determinants and the likelihood that

the research is commercialized. The authors argue in their conclusion that perhaps the

greatest organizational predictor of commercial success is the presence of a technology

transfer office (TTO) within the university. However, the he breadth of support and

experience of the TTO office are not significant predictors, indicating that the mere

presence of a TTO office signals and organizational commitment to commercialization

(Ambos et al. 2008).


123

Ambos and colleagues’ concept of ‘‘ambidexterity’’ pertains to the ability to have

multiple uses for research, a feature Bozeman and Rogers (2002) have pointed to has one

of the main indicators or research value and innovation. According to Ambos et al. (2008),

ambidexterity is the ability simultaneously to produce scientific contributions (i.e.

knowledge-focused research) and commercial contributions (i.e. property-focused

research). A principal investigator’s (PI) ‘‘embeddedness’’ (number of years served) in

academia decreases the probability of commercial output, but the PI’s scientific excellence,

measured in citations, is significantly and positively associated with the probability of

commercialization. The authors argue (p. 1442) that the faculty members ‘‘who are both

motivated to pursue commercial activity and who believe it will not harm their academic

careers are more likely to generate commercial outputs.’’ We can see here an implicit

negative connotation towards commercial or property-focused research in the scientific

community.

Possible negative impacts of university and industry research engagement receives more

attention in Behrens and Gray’s (2001) study of the relationship of industry sponsorship of

university work, especially impacts for graduate students. The authors developed a strat-

ified sample of graduate students from the same two engineering departments at six US

universities and distributed a survey about research experiences. All of these research

experiences were collaborative in that the student was working with a faculty member

within the department. Findings indicate that there is not a significant difference between

students engaged in a collaborative project that is sponsored by industry versus students

engaged in a collaborative project that is not sponsored by industry (Behrens and Gray

2001). Behrens and Gray find significantly different student experiences and outcomes

between students collaborating on projects with external funding compared to students on

unfunded projects. Funded projects produced more positive experiences and outcomes for

graduate students. This is consistent with empirical studies presented above, indicating

that resources can drastically influence the collaboration process and product (Lee and

Bozeman 2005). This indicates that the end goal of the research (i.e. knowledge vs.

property-focus) does not influence the experience and outcomes of collaborators, but rather

funding at the front end of the process can affect collaborators.

Beyond funding, the literature also examines the role of sector switching in patent

productivity (property-focused research). The Dietz and Bozeman (2005) article discussed

above addresses this concern directly. The authors examine the role of inter or intrasectoral

switching throughout the career of an academic scientist. This career diversity concerns

collaboration conceptually because the central theory behind the analysis is that sector

switching will provide researchers with human capital (i.e. network ties, tacit knowledge,

etc.) that will foster productivity. Logically, network ties contribute to more collaboration

possibilities. Evidence does not support the same positive relationship between sector

switching in jobs and publication productivity (i.e. knowledge-focused research).

Another 2008 study that deals with property-focused research is Re-thinking NewKnowledge Production: A Literature Review and a Research Agenda by Hessels and van

Lente (2008). Their article is a systematic review of the Gibbons-Nowotny concept of

‘‘Mode 2 knowledge production’’. Mode 2 knowledge-production deserves attention in a

discussion of collaboration and property focused research. This mode of knowledge pro-

duction is not meant to replace the traditional mode of knowledge production, but rather

supplement it (Hessels and Van Lente 2008).

Hessels and van Lente begin their review by discussing the differences between Mode 1

and Mode 2 knowledge production. Mode 1 is defined by an academic context, maintaining

disciplinary boundaries, homogeneity, autonomy and traditional quality control. Mode 2


123

knowledge production is defined by a context of application, transdisciplinary collabora-

tion, heterogeneity, social accountability and novel quality control (Hessels and Van Lente

2008). Mode 2 knowledge production is therefore consistent with a collaborative property-

focused research process.

Perhaps the most applicable concepts of Mode 2 knowledge production discussed in the

piece to our discussion of knowledge focused research is that of the context of application

and the transdisciplinarity. The context of application goes beyond the distinction between

basic and applied research. The authors argue that the commonly held distinction is fal-

lacious because, ‘‘fundamental research has always been inspired by more applied

knowledge and applied research has always shown interest in fundament understanding of

the relevant phenomena’’ (Hessels and van Lente 2008, p. 750). This is an important

distinction in terms of this study’s focus. It allows researchers examining collaboration to

understand that a project can be property focused, but also concerned with fundamental

knowledge production. The two are not mutually exclusive.

Mode 2 knowledge production literature argues that science is becoming increasingly

transdisciplinary (Hessels and Van Lente 2008). The work of Godin (1998) is used to

illustrated the controversy associated with this assertion. Godin does not agree with the

dichotomy between disciplinary research and interdisciplinary research (Godin 1998).

Godin argues that ‘‘the development of disciplines with specialisations and hybrid for-

mations is typical of any scientific practice’’ (Hessels and van Lente 2008, p. 751).

Transdisciplinary research, on the contrary, implies cooperation of different disciplines,

co-evolution of a common guiding framework and the diffusion of results during the

research process (Hessels and Van Lente 2008). Although Godin is correct that interdis-

ciplinary and hybrid formations of discipline has occurred for a long period, the authors

describe the new developments asserted by Mode 2 production through a discussion of the

rise of transdisciplinary journals. The authors argue that although most scientific pro-

duction is disciplinary in nature, there has also been a rise in transdisciplinary journals

(Hessels and Van Lente 2008; Hicks and Katz 1996).

When discussing property-focused research in the context of academic research it is

important to understand the influence of university pressures to produce property from

research endeavors. Davis et al. (2011) examine the effects of university patenting on

academic researchers perceptions of academic freedom in their article Scientists’ Per-spectives Concerning the Effects of University Patenting on the Conduct of AcademicResearch in the Life Sciences. The authors argue that ‘‘the most important finding of our

analysis is that basic researchers were significantly more skeptical about the impact of

university patenting on academic freedom and highly productive scientists were signifi-

cantly less skeptical’’ (Davis et al. 2011, p. 29). What is significant to the current dis-

cussion of collaboration, however, is the lack of finding between collaboration behavior

and the belief that university patenting negatively affects academic freedom. One would

expect that if an academic researcher felt hindered by collaborating with industry in the

name of increased property-focused outputs for the institution she would view patenting

requirements as negatively effecting academic freedom (Davis et al. 2011). Their findings,

or lack thereof, are somewhat problematic because collaboration is generally voluntary in

nature. If a researcher felt that collaboration negatively influenced academic freedom then

she would simply not engage in collaboration. Despite this limitation it is useful to

understand that collaboration does not influence one’s perception of university patenting.

It is important to note here that not all patents or property-focused research is equal.

Feller and Feldman (2010) offer some important conclusions that aggregate patent data on

patents and licensing provides a limited picture of collaboration between universities and


123

industry. Instead of aggregate data, the authors use the case study approach to examine the

complex interrelated relationships between faculty and firms that result in a university

patent, patent held by the firm, or research that is eventually brought to market. Similar to

the above discussion of TTOs, the Feller and Feldman find that these organizations often

behave in ways that hinder the collaborative process, but unlike Siegel et al. (2003b), the

current study argues that this is often because of the influence from the industry rather than

the institutional culture of the TTO. Whereas Siegel et al. (2003a, b) argue that the TTOs

should be more business-like and use business means to further the collaboration process,

Feller and Feldman argue that often the behaviors of TTOs and other regulatory com-

missions are influenced by the ‘‘strategies and experiences of the firms with which they are

engaged’’ (Feller and Feldman 2010, p. 614). Of course TTOs are seen as barriers to

collaboration in other empirical articles (Siegel et al. 2003b).

Much of the literature that examines property-focused research deals with collabora-

tions between industry and universities. Hanel and St-Pierre (2006) are no exception, but

the authors limit their analysis to Canadian manufacturing firms in their 2006 article.

Findings are consistent with much of the previous literature discussed regarding motiva-

tions for industry to partner with universities and the positive outputs associated with

collaborative behavior. The authors argue that, ‘‘the major incentive to collaborate with a

university is the access to research and critical competencies, which allows firms to reach

the very edge of contemporary technology’’ (Hanel and St-Pierre 2006, p. 496).

In terms of the positive outcomes of collaborative research, the authors provide con-

clusions that are clearly property-focused (as we conceptualize it). The authors conclude,

Collaborations with universities has a positive impact on the originality of innova-

tions and their contribution to the perceived economic performance of the innovating

firm such as to maintain their competitive position, the maintain of their profit

margins, the increase in their share of the international market and their increase of

profitability (Hanel and St-Pierre 2006, p. 496).

This study provides evidence that collaboration is often motivated by a desire to engage

with universities because these institutions provide access to cutting edge technology.

Although this motivation may appear to be knowledge-focus, the desire to access cutting

edge technology is most likely an underlying desire to produce the most innovative product

that has the potential to generate the most profit. Although these motivations and outcomes

seem less than altruistic, we hesitate in making a normative assessment of motivations and

outcomes of these collaborations. Although profitability is a powerful motivator, the result

of these collaborations is often an increase in knowledge base and potential funding to the

academic community. The Hanel and St-Pierre (2006) article provides the most clear

assessment of collaboration on property-focused outputs, but the benefits of these outputs

can also increase the knowledge base of the academic community. We can see that the

research outputs of collaboration are not quite so distinct.

There is a general theme in the literature surrounding research collaborations to

examine how collaborative behavior influences the innovativeness of the firm that partners

with the academic institution. Huang and Yu (2011) examine the effect of competitive

versus non-competitive collaboration on firm innovation. Although the study provides

other important information in terms of collaboration, the key finding for our discussion is

that non-competitive R&D collaboration specifically those collaborations conducted with

universities is positively correlated with innovation. Again, we see empirical findings that

suggest that collaborating with universities provide material benefits for industry in terms

of innovative products that increase the likelihood of profits.


123

5 Research collaboration: the dark side

During the past decade or so, researchers, especially those in the biomedical sciences

(e.g. Rennie et al. 2000; Wainwright et al. 2006; Cohen et al. 2004), have begun to focus

on ethical issues and the ‘‘dark side’’ of collaboration. Lagnado (2003) argues that trust in

the meaning of co-authorship has eroded. Levsky et al. (2007) describe a number of

potentially troubling trends in authorship in medical journals between 1995 and 2005,

including honorary authorship, ghost authorship, duplicate and redundant publications and

most important, authors’ refusal to accept responsibility for their articles despite their

readiness to accept credit for professional purposes. They note that causes of the trends

continue to be unknown but that the relationship between authorship and career pressures

on academic physicians is clear.

Outside of biomedical fields, research on the ethics and socio-political dynamics of

scientific collaboration (Shrum et al. 2001, 2007) remains scarce. Perhaps this scarcity is

owing to the view that such problems are neither as pervasive nor as troublesome in other

science and technology fields as they are in biomedical research. To be sure, biomedical

research is different. Medical research has special hazards resulting from unethical

behavior, in part because of its massive operation of clinical trials (Devine et al. 2005;

Klingensmith and Anderson 2006). Similarly, medical research has ties to pharmaceutical

industry, including some representatives eagerly providing services as ‘‘phantom’’ co-

authors.

Even if medical research poses particular challenges, the potential for unethical

behavior in research and collaboration remains pervasive. Far from being restricted to

biomedical fields, problems in scientific collaboration are ubiquitous in science. Some of

these problems are ethical (Shrum et al. 2001), others practical (Bozeman and Corley

2004), some pertain to collaboration among individuals (Katz and Martin 1997), and some

to collaboration among institutions (Chompalov and Shrum 1999).

The literature on scientific collaboration not only identifies problems in collaboration

but also possible solutions. For example, Marusic et al. (2004) and Pichini et al. (2005)

describe the many international Uniform Requirements for coauthorship information and

the complex but poorly understood relationship among coauthorship, grants, promotion,

and admittance to professional associations. While some articles (e.g. Mullen and Ramirez

2006) provide a conceptual analysis of coauthorship and collaboration issues, most do not

provide exacting specification of alleged problems. Most work is case-based or anecdotal

and, as a result, neither the scientific community nor policy-makers have much systematic,

empirically based evidence of the possible pitfalls of collaboration, co-authorship, and the

various social mechanisms created for assigning credit.

The few studies available on the ethics of collaboration tend to focus on questions

associated with scholarly manuscript authorship (Chompalov et al. 2002). This focus is

understandable and welcome. Allocation of credit and responsibility for authorship is an

important issue and it must be resolved if research results are to be managed and used

effectively (Devine et al. 2005). Due to increasingly interdisciplinary work and the

demands of large-scale collaborative work, the assignment of authorship for publication is

complex and sometimes confusing.

Some attribute problems with sorting out co-authorship and crediting to the explosion in

research and the funding imperatives driving collaboration among investigators from

multiple sites and numerous disciplines (Devine et al. 2005; Drenth 1998). Ultimately the

system of scientific authorship is built on trust that the published work reflects the data and

analysis of the authors (Lagnado 2003).


123

While few studies of co-authoring ethics have been undertaken, research on other

ethical issues in collaboration is even scarcer, sometimes non-existent.

Almost as important as co-author credit is the decision process by which co-authorship

is decided. As decision analysts have known for years, often process is the primary

determinant of outcome (Brockner and Wiesenfeld 1996). While there is remarkably little

evidence about collaboration and co-authorship decision processes and norms, most agree

(Katz and Martin 1997; Melin 2000) that these vital processes affect not only scientific

career trajectories and advancement but very course of science. The choice of scientific

topics and the configuration of research teams depend in part on collaborative and

co-authorship norms. In the vast majority of instances, researchers have considerable

autonomy in their collaboration choices and collaboration strategies are based in part of

judgments about the conferring of co-authorship and status (Heffner 1981). The issue is

who decides.

Partly because of a high level of threat, biomedical researchers and editors have taken

the lead in identifying and moving to resolve ethical problems in collaboration. The

International Committee of Medical Journal Editors (ICMJE, known as the Vancouver

Group) created a set of ‘‘Uniform Requirements’’ for authorship in 1985. But by the mid-

1990s, these protocols were still employed by only a handful of journals. Drummond

Rennie, a deputy editor of the Journal of the American Medical Association, and a strong

proponent of collaboration policies, acknowledged this deficit in an editorial colorfully

subtitled, Guests, Ghosts, Grafters, and the Two-sided Coin (Rennie and Flanagin 1994).

Rennie uses the term ‘‘contributorship’’ to refer to the process entail as authors declare in

detail, usually at time of submission, their individual contributions to scholarly papers in

the spirit of scientific transparency (Rennie 2001, p. 1274). Following a series of articles

that describe a growing problem with irresponsible authorship of medical research

articles, Rennie proposed a major change in instructions to authors to JAMA (2000,

p. 89). These changes in contributorship requirements have provided clear signals where

none were given before and, presumably, have enhanced collaborators’ ability to com-

municate effectively with one another about contribution and credit. However, even in

journals adopting contributorship policies, we still know little about the validity of

contributorship statements or the social and potential power dynamics entailed in

developing them. To date, no research systematically assesses the effects of contribu-

torship statements despite the fact that they have been widely adopted in medical and

health sciences fields.

One research ethics problem that has received a good deal of attention is conflict of

interest (McCrary et al. 2000; see Mowery and Sampat 2001 for an overview). We only

note this ethical issue in passing, however, inasmuch as they occur in many cases in single

researcher work or, when occurring in collaborations, are sometimes individual separable

breaches of ethics even if occurring in a collaborative context.

As mentioned above, some ‘‘dark side’’ aspects of collaboration have received very

little attention. While there is widespread concern about the possibilities for student

exploitations in collaborations, especially collaborations to which industrial firms are

partners (e.g. Slaughter et al. 2002), most of the evidence thus far is anecdotal or unsys-

tematic. The few systematic case studies (e.g. Baldini 2008) available suggest problems but

give no clues about the extensiveness of student exploitation in collaborations. Moreover,

there is some evidence that collaborations rooted in industry–university partnerships often

have salutary effects for students including early publication, job offers and mentoring (see

Welsh et al. 2008; Bozeman and Boardman, in press).


123

It certainly seems plausible that collaboration, including collaborations involving

industry, sometimes has negative consequences for students and postdoctoral researchers

but it seems just as likely that they often benefit tremendously from such experiences. The

real issue is the balance of benefits and costs and the factors that govern the quality of the

collaboration experience. More evidence is needed.

6 Conclusion

We began this overview and assessment asking whether the literal ‘‘addtionality’’ of

research collaboration, additions in the sense of adding other researchers beyond the single

investigator, enhances additionality in the usual sense of that term (Buisseret et al. 1995,

p. 268) as ‘‘enhancing what would have taken place anyway.’’ We have not been able to

provide a precise answer to that question since a valid study would likely require an

experiment, not yet performed as far as we know, comparing researchers, some in teams

and some working individually, on an identical research project. Since such a study seems

unlikely (who is going to convince researchers to enter themselves and their life’s work

into treatment and control groups?), we have strived for a second best, examining the

extant literature on research collaboration.

At first glance it would appear that scientific research collaboration studies are

incredibly disjointed and somewhat ambiguous in focus. The conceptual model we employ

here in no was improves upon the fragmentation of the literature but, rather brings it into

relief. The literature, variant as it is not only in findings but in its theoretical approaches,

methods, analytical tool and most basic purposes, is not yet rife for any true meta-analysis.

However, the more conventional literature review we have provided at least shows the foci

of the field, some basis and sometimes consensual findings and, perhaps most important,

shows in its omissions research gaps needed more attention. In this concluding section we

focus on some of those gaps as and provide the broad outline of a research agenda for

future study of research collaboration.

1. Level of analysis. We begin this review noting that our primary attention would be

focused o the individual level of analysis, relationships among individual researchers,

rather than relationships among organizations are the relationship of individuals 2

institutions. Naturally, that has not been entirely possible to do. In the 1st place, it is

often the case that studies work at more than one level of analysis at the same time, but

without necessarily making this explicit or worse, at least a few studies are sufficiently

ambiguous that it is not possible to truly determine the level of analysis. The literature

were would surely be improved with an increasing number of studies simultaneously

working at different levels of analysis, with research designs explicitly addressing

interactions whose design allowed them explicitly to address interactions among

nested variables. This prescription is not methodological nitpicking. One of the major

limitations of current research is the tendency for researchers to either (1) focus

intensely on either the world of the individual researcher while, unfortunately,

ignoring the larger context within which the researcher operates, or (2) focus on

collaborating organizations at a level of abstraction sufficiently general as to permit no

consideration of the role of individual dynamics that may shape the outcomes of

collaborating organizations. We understand of course, why this strategy is not often

employed. The analytical requirements in the data requirements generally are


123

prohibitive. But one of the nice things about research assessments is that authors have

the chance to consider the ideal.

2. Beyond outputs. A good deal of the research on collaboration includes reasonable and

valid output measures. Indeed, this is an improvement over much of the work in fields

related to technology and knowledge management. Less, and are studies that go

beyond such outputs as patents and licenses produced to examine whether, in fact, the

volume of such outputs made any difference at all. This is a problem one finds more

often in property focused studies of collaboration. For those interested in knowledge

focused collaborations advances and bibliometric techniques have been useful for at

least determining one sort of impact, citations.

3. Worst practice. Very few studies focus on failed collaborations ones that bore little

fruit. Perhaps even more of an oversight, few studies systematically compare

nonproductive and productive collaborations (at least beyond running straightforward

regression models with output variables). This is entirely understandable area and most

realms there is usually a great deal more demand for what works and what does not

work. But it is also a limitation. It is especially problematic that so many studies began

with high-performing collaborations, putting a de facto limit on their ability to say

much about the population of collaborations.

4. The other ‘‘dark side.’’ As we see from the section above, there has of late been a

considerable increase and studies focusing on the dark side of collaboration, including

exploitation, negative impacts on students, and unethical behavior. At least to our

knowledge, there have been few such studies examining product-focused collabora-

tions (an exception being studies of the impact on university–industry collaboration on

graduate students and junior faculty careers). Studies that have been performed that are

examining product-focused collaborations tend to be case oriented or historical in

form, understandable given the difficulties of gathering data about bad behavior. Still,

we might expect that bad behavior on the industrial side of university–industry

partnerships is no less common than on the university side. It would be good for this

perspective to catch up.

5. Methods? As discussed above many knowledge-focused collaboration studies use

bibliometric techniques to examine the citations of published works to measure the

impact of the collaboration. This process also gives extra weight to studies with

significant lag time to allow for more citations. A successful collaborative project

could be recent, but highly revolutionary and contributing a great deal to the

fundamental knowledge in a scientific field. Collaboration research must find a better

way to measure the impact to fundamental knowledge beyond citation rates. Presently,

using citation rates and impact factors is appealing because it has the seduction of

convenience.

Another concern we have in regards to collaboration research is that few studies

examine the personal relationships between collaborators and the collaboration process in

general. This is a theory problem but also one of method. In order to truly examine

interpersonal relationships and the comprehensive process of collaboration, researchers

must move beyond simple demographic measures of subjects. Large surveys and inter-

views of academic scientists must be conducted so we can understand if these demographic

factors are actually salient in the decision-making process when considering a collabora-

tive project. We must also understand the intangibles that cannot be measured by biblio-

metric analysis of published works and the demographic characteristics of the authors. We

must in particular understand more about the psychological antecedents to research


123

collaboration choice. Collaboration research should examine the positive and negative

outcomes of collaboration, but also the positive and negative aspects of the processes of

collaboration.

6. Collaboration motives. In studies of research collaboration, and especially those

centered on product-focused collaborations, it is easy to lose sight of the fact that the

objects of are flesh and blood human beings pursuing multiple, complex and often

conflicting motives. It would certainly be convenient if collaborations could be

understood fully as efforts to maximize economic pay-off from research, but

qualitative studies show us that even in those cases where economic gain is paramount,

there is nonetheless much more going on than that. In some cases, the unraveling the

motives behind collaboration may be exceedingly difficult. Thus, for example, the

young researcher working at a university research center may, all at the same time, be

pushed by the center to cooperate with industry on technology development, be pushed

by her academic department to develop the type of publications that are the currency

of academic reputations, be concerned for the futures of the students and postdocs in

her lab, be thinking about the next job, whether in a university or in industrial setting,

and making choices based on the perceived competence, fairness, and the comple-

mentarities of potential collaborators. Throw in such factors as the need to maintain

access to scarce and expensive research equipment, pressures and commitments

related to grants-writing and funding, geographic proximity of collaborators and well-

documented competition among collaborators and we see a volatile mix of conditions

possibly affecting collaboration motives, with the outcomes playing out an enormous

variety of different ways.

While some research attempts to get at motives, usually either through surveys or

interviews, but motive-centered, researcher-centered studies are not the norm. But there

is now greater need to understand the complexity of collaboration calculus. Conflicts

among centers, industries and traditional academic departments were not so important

decades ago. Abundant resources, no longer the norm, also had a tendency to reduce

complexity. But with declining grant money and fewer academic positions in most fields,

competitive dynamics intercede to a degree not common in the past. It seems to us that

the research has had a very difficult time keeping pace with the changes in the

researchers’ environment.

Despite these and other limitations of the literature on research collaboration, it seems to

us that considerable strides have been made. If we take as a chronological benchmark Katz

and Martin’s (1997) excellent review of collaboration literature, one that was in some

respects similar to our own review, we can see signs of progress. The research proposition

table provided in the ‘‘Appendix’’ of this paper well verifies this point. Not only have we

seen many more studies since the turn of millennium, but also entirely new aspects of

collaboration have been examined, often with entirely new analytical tools. Summary

assessment: much progress, much work remaining.

Appendix

See Table 1.


123

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pro

per

ty-f

ocu

sed

rese

arch

Coll

abora

tor

attr

ibute

sA

schoff

and

Gri

mpe

(2011

)A

schoff

,B

.&

Gri

mpe,

C.

(2011).

Loca

lize

dnorm

sand

aca

dem

ics’

indust

ryin

volv

emen

t:T

he

moder

ati

ng

role

of

age

on

pro

fess

ional

impri

nti

ng

.U

npubli

shed

pap

er

dow

nlo

aded

Feb

ruar

y3,

2012

from

htt

p:/

/ftp

.zew

.de/

pub/

zew

-docs

/ver

anst

altu

ngen

/innovat

ionpat

enti

ng2011/p

aper

s/

Gri

mpe.

pdf

Young

facu

lty

are

‘‘im

pri

nte

d’’

by

thei

r

coll

abora

tion

wit

hin

dust

ryper

sonnel

Coll

abora

tion

attr

ibute

sA

udre

tsch

etal

.(2

002

)A

udre

tsch

,D

.B

.,B

oze

man

,B

.,C

om

bs,

K.

L.,

Fel

dm

an,

M.,

Lin

k,

A.

N.,

Sie

gel

,D

.S

.,&

Wes

sner

,C

.(2

002).

The

econom

ics

of

scie

nce

and

tech

nolo

gy.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

27(2

),155–203

Net

work

beh

avio

rin

S&

Thum

anca

pit

al

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Bal

din

i(2

008

)B

aldin

i,N

.(2

008).

Neg

ativ

eef

fect

sof

univ

ersi

typat

enti

ng:

myth

san

dgro

unded

evid

ence

.Sci

ento

met

rics

,75(2

),

289–311

Ther

eis

no

evid

ence

of

the

exte

nt

of

studen

t

explo

itat

ion

inre

sear

chco

llab

ora

tions,

only

clues

that

itex

ists


123

http://ftp.zew.de/pub/zew-docs/veranstaltungen/innovationpatenting2011/papers/Grimpe.pdf



Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sB

eaver

(2001

)B

eaver

,D

.D

.(2

001).

Refl

ecti

ons

on

scie

nti

fic

coll

abora

tion

(and

its

study):

Pas

t,pre

sent,

and

futu

re.

Sci

ento

met

rics

,52(3

),365–377

Syner

gy,

feed

bac

k,

dis

sem

inat

ion,

reco

gnit

ion

and

vis

ibil

ity

are

the

advan

tages

of

coll

abora

tion

Coll

abora

tion

attr

ibute

sB

eaver

(2004

)B

eaver

,D

.D

.(2

004).

Does

coll

abora

tive

rese

arch

hav

e

gre

ater

epis

tem

icau

thori

ty?

Sci

ento

met

rics

,60(3

),

399–408

This

arti

cle

pro

vid

esev

iden

ceth

at

coll

abora

tive

rese

arch

pro

ject

shav

egre

ater

epis

tem

icau

thori

tyth

anin

div

idual

rese

arch

pro

ject

s

Coll

abora

tion

attr

ibute

sB

ehre

ns

and

Gra

y(2

001

)B

ehre

ns,

T.

R.,

&G

ray,

D.

O.

(2001).

Unin

tended

conse

quen

ces

of

cooper

ativ

ere

sear

ch:

Impac

tof

indust

ry

sponso

rship

on

clim

ate

for

acad

emic

free

dom

and

oth

er

gra

duat

est

uden

toutc

om

e.R

esea

rch

Poli

cy,

30(2

),

179–199

Ther

eis

not

asi

gnifi

cant

dif

fere

nce

bet

wee

n

studen

tsen

gag

edin

aco

llab

ora

tive

pro

ject

that

issp

onso

red

by

indust

ryver

sus

studen

ts

engag

edin

aco

llab

ora

tive

pro

ject

that

isnot

sponso

red

by

indust

ry

Coll

abora

tor

attr

ibute

sB

erco

vit

zan

dF

eldm

an(2

008)

Ber

covit

z,J.

&F

eldm

an,

M.

(2008).

Aca

dem

ic

entr

epre

neu

rs:

Org

aniz

atio

nal

chan

ge

atth

ein

div

idual

level

.O

rganiz

ati

on

Sci

ence

,19,

69–89

Pat

enti

ng

and

lice

nsi

ng

acti

vit

ies

of

med

ical

school

facu

lty

dec

reas

ew

ith

age

Coll

abora

tion

attr

ibute

sB

oar

dm

anan

dC

orl

ey(2

008)

Boar

dm

an,

P.

C.,

&C

orl

ey,

E.

A.

(2008).

Univ

ersi

ty

rese

arch

cente

rsan

dth

eco

mposi

tion

of

rese

arch

coll

abora

tions.

Res

earc

hP

oli

cy,

37(5

),900–913

This

isone

of

man

yuse

ful

studie

sof

coll

abora

tion

wher

eco

auth

ors

hip

isa

unit

of

mea

sure

Coll

abora

tor

attr

ibute

sB

oar

dm

anan

dP

onom

ario

v

(2007

)

Boar

dm

an,

P.

C.,

&P

onom

ario

v,

B.

L.

(2007).

Rew

ard

syst

ems

and

NS

Funiv

ersi

tyre

sear

chce

nte

rs:

The

impac

t

of

tenure

on

univ

ersi

tysc

ienti

sts’

val

uat

ion

of

appli

ed

and

com

mer

cial

lyre

levan

tre

sear

ch.

The

Journ

al

of

Hig

her

Educa

tion

,78(1

),51–70

The

auth

ors

dev

elop

anem

pir

ical

model

focu

sing

on

acad

emic

scie

nti

sts

des

ire

to

pro

duce

know

ledge-

focu

sed

rese

arch

.T

hey

find

tenure

impac

tsco

llab

ora

tion

pat

tern

s

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Boze

man

(2000

)B

oze

man

,B

.(2

000).

Tec

hnolo

gy

tran

sfer

and

publi

c

poli

cy:

are

vie

wof

rese

arch

and

theo

ry.

Res

earc

hP

oli

cy,

29,

627–655

This

arti

cle

sum

mar

izes

the

lite

ratu

reon

indust

ry-a

cadem

icco

llab

ora

tions

and

tech

nolo

gy

tran

sfer

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Boze

man

and

Boar

dm

an

(in

pre

ss)

Boze

man

,B

.&

C.

Boar

dm

an.

(In

Pre

ss)

Aca

dem

icfa

cult

y

work

ing

inuniv

ersi

tyre

sear

chce

nte

rs:

Nei

ther

capit

alis

m’s

slav

esnor

teac

hin

gfu

git

ives

.T

he

Journ

al

of

Hig

her

Educa

tion

Coll

abora

tions

roote

din

indust

ry–univ

ersi

ty

par

tner

ship

soft

enhav

esa

luta

ryef

fect

sfo

r

studen

tsin

cludin

gea

rly

publi

cati

on,

job

off

ers

and

men

tori

ng


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Contr

ibuto

rat

trib

ute

sB

oze

man

and

Corl

ey(2

004

)B

oze

man

,B

.,&

Corl

ey,E

.(2

004).

Sci

enti

sts’

coll

abora

tion

stra

tegie

s:Im

pli

cati

ons

for

scie

nti

fic

and

tech

nic

al

hum

an

capit

al.

Res

earc

hP

oli

cy,

33(4

),599–616

Coll

abora

tion

isan

aspec

tof

hum

anca

pit

al.

Fem

ale

rese

arch

erco

llab

ora

tion

ishig

hly

per

sonal

.T

enure

impac

tsco

llab

ora

tion,

per

hap

sas

are

sult

of

the

men

tori

ng

pro

cess

Coll

abora

tor

attr

ibute

sB

oze

man

etal

.(2

001

)B

oze

man

,B

.,D

ietz

,J.

S.,

&G

aughan

,M

.(2

001).

Sci

enti

fic

and

tech

nic

alhum

anca

pit

al:

An

alte

rnat

ive

model

for

rese

arch

eval

uat

ion.

Inte

rnati

onal

Journ

al

of

Tec

hnolo

gy

Managem

ent,

22(7

),716–740

Sci

enti

fic

and

tech

nic

alhum

anca

pit

al(S

&T

hum

anca

pit

al)

has

bee

ndefi

ned

asth

esu

mof

rese

arch

ers’

pro

fess

ional

net

work

ties

and

thei

rte

chnic

alsk

ills

and

reso

urc

es

Coll

abora

tion

attr

ibute

sB

oze

man

and

Gau

ghan

(2007

)B

oze

man

,B

.,&

Gau

ghan

,M

.(2

007).

Impac

tsof

gra

nts

and

contr

acts

on

acad

emic

rese

arch

ers’

inte

ract

ions

wit

h

indust

ry.

Res

earc

hP

oli

cy,

36(5

),694–707

Dev

elopm

ent

of

the

indust

rial

involv

emen

t

index

Coll

abora

tion

attr

ibute

sB

oze

man

and

Gau

ghan

(2011

)B

oze

man

,B

.,&

Gau

ghan

,M

.(2

011)

How

do

men

and

wom

endif

fer

inre

sear

chco

llab

ora

tions?

An

anal

ysi

sof

the

coll

abora

tive

moti

ves

and

stra

tegie

sof

acad

emic

rese

arch

ers,

Res

earc

hP

oli

cy,

40(1

0),

Dec

ember

2011,

1393–1402.

Men

and

wom

endif

fer

inre

sear

ch

coll

abora

tion

stra

tegie

s.

Coll

abora

tion

attr

ibute

sB

oze

man

and

Roger

s(2

002

)B

oze

man

,B

.&

J.R

oger

s(2

002)

Ach

urn

model

of

know

ledge

val

ue:

Inte

rnet

rese

arch

ers

asa

know

ledge

val

ue

coll

ecti

ve.

Res

earc

hP

oli

cy,

31(4

),769–794

Hav

ing

mult

iple

use

sfo

rre

sear

chis

an

indic

ator

for

rese

arch

val

ue

and

innovat

ion

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Boze

man

etal

.(2

012

)B

oze

man

,B

.,Y

outi

e,J.

,S

lade,

C.

P.

&G

aughan

,M

.

(2012).

The

‘‘dark

side’

’of

aca

dem

icre

searc

hco

llabora

tions:

Case

studie

sin

explo

itati

on,

bull

ying

and

unet

hic

al

beh

avi

or.

Pap

erpre

par

edfo

rth

eA

nnual

Mee

ting

of

the

Soci

ety

for

Soci

alS

tudie

sof

Sci

ence

(4S

)

Oct

ober

17–20,

2012,

Copen

hag

enB

usi

nes

sS

chool,

Fre

der

iksb

erg,

Den

mar

k

Indiv

idual

sca

noft

enad

ver

sely

infl

uen

ceoth

er

indiv

idual

sto

the

det

rim

ent

of

rese

arch

coll

abora

tions

and

thei

routp

uts

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Bro

ckner

and

Wie

senfe

ld

(1996

)

Bro

ckner

,J.

&W

iese

nfe

ld,

B.

M.

(1996).

An

Inte

gra

tive

Fra

mew

ork

for

Expla

inin

gR

eact

ions

toD

ecis

ions:

Inte

ract

ive

Eff

ects

of

Outc

om

esan

dP

roce

dure

s,

Psy

cholo

gic

al

Bull

etin

,120(2

),189–208

Dec

isio

nm

akin

gpro

cess

isth

epri

mar

y

det

erm

inan

tof

outc

om

e


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tor

attr

ibute

sB

runee

let

al.

(2010)

Bru

nee

l,J.

,D

’Est

e,P

.&

Sal

ter,

A.

(2010).

Inves

tigat

ing

the

fact

ors

that

dim

inis

hth

ebar

rier

sto

univ

ersi

ty–

indust

ryco

llab

ora

tion.

Res

earc

hP

oli

cy,

39(7

),858–868

Surv

eyre

sear

chsh

ow

sth

eim

port

ance

of

hav

ing

pre

vio

us

coll

abora

tion

exper

ience

to

engen

der

ing

the

trust

requir

edfo

rsu

cces

sin

coll

abora

tion

Coll

abora

tion

attr

ibute

sB

uis

sere

tet

al.

(1995

)B

uis

sere

t,T

.J.

,C

amer

on,

H.

M.,

&G

eorg

hio

u,

L.

(1995).

What

dif

fere

nce

does

itm

ake

addit

ional

ity

inth

epubli

c

support

of

RD

inla

rge

firm

s.In

tern

ati

onal

Journ

al

of

Tec

hnolo

gy

Managem

ent,

10,

4(5

),587–600

This

arti

cle

defi

nes

addit

ional

ity

inR

&D

asth

e

exte

nt

tow

hic

hpubli

csc

ruti

ny

and

dis

cours

e

pro

duce

more

than

what

would

hav

ebee

n

pro

duce

dw

ithout

publi

cin

put

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Cal

oghir

ou

etal

.(2

001

)C

aloghir

ou,

Y.,

Tsa

kan

ikas

,A

.,&

Vonort

as,

N.

S.

(2001).

Univ

ersi

ty–in

dust

ryco

oper

atio

nin

the

conte

xt

of

the

Euro

pea

nfr

amew

ork

pro

gra

mm

es.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

26(1

),153–161

Fir

ms

are

incr

easi

ngly

turn

ing

touniv

ersi

ties

for

R&

Dco

llab

ora

tion.

When

coll

abora

ting

wit

huniv

ersi

ties

,fi

rms

pri

mar

ily

aim

at

achie

vin

gre

sear

chsy

ner

gie

s,kee

pin

gup

wit

hm

ajor

tech

nolo

gic

aldev

elopm

ents

,an

d

shar

ing

R&

Dco

sts.

The

maj

or

report

ed

ben

efit

tofi

rms

from

coll

abora

ting

wit

h

univ

ersi

ties

isen

han

cing

thei

rknow

ledge

bas

e,fo

llow

edby

impro

vem

ents

in

pro

duct

ion

pro

cess

es

Coll

abora

tion

attr

ibute

sC

aray

annis

and

Lag

et(2

004

)C

aray

annis

,E

.G

.,&

Lag

et,

P.

(2004).

Tra

nsa

tlan

tic

innovat

ion

infr

astr

uct

ure

net

work

s:P

ubli

c-pri

vat

e,E

U–

US

R&

Dpar

tner

ship

s.R

&D

Managem

ent,

34(1

),17–31

Coll

abora

tion

inth

esc

ienti

fic

com

munit

yis

incr

easi

ngly

glo

bal

innat

ure

Coll

abora

tion

attr

ibute

sC

aray

ol

and

Mat

t(2

004

)C

aray

ol,

N.,

&M

att,

M.(2

004).

Does

rese

arch

org

aniz

atio

n

infl

uen

ceac

adem

icpro

duct

ion?

Lab

ora

tory

level

evid

ence

from

ala

rge

Euro

pea

nuniv

ersi

ty.

Res

earc

hP

oli

cy,

33,

1081–1112

Ther

eis

agre

atdea

lof

rese

arch

on

the

topic

of

rese

arch

man

agem

ent

and

moti

ves

and

thei

r

rela

tion

tore

sear

chef

fect

iven

ess

Coll

abora

tion

attr

ibute

sC

han

gan

dD

ozi

er(1

995

)C

han

g,

D.

B.,

&D

ozi

er,

K.

(1995).

Tec

hnolo

gy

tran

sfer

and

acad

emic

educa

tion

wit

ha

focu

son

div

ersi

ty.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

20(3

),88–95

Rac

ial

div

ersi

tyaf

fect

sco

llab

ora

tion

pat

tern

s

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Chom

pal

ov

and

Shru

m(1

999

)C

hom

pal

ov,

I.,

&S

hru

m,

W.

(1999).

Inst

ituti

onal

coll

abora

tion

insc

ience

:A

typolo

gy

of

tech

nolo

gic

al

pra

ctic

e.Sci

ence

Tec

hnolo

gy

and

Hum

an

Valu

es,

24(3

),

338–372

Som

epro

ble

ms

insc

ienti

fic

coll

abora

tion

hav

e

todo

wit

hth

ein

stit

uti

ons

them

selv

es


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sC

hom

pal

ov

etal

.(2

002

)C

hom

pal

ov,

I.,

Gen

uth

,J.

,&

Shru

m,

W.

(2002).

The

org

aniz

atio

nof

scie

nti

fic

coll

abora

tions.

Res

earc

hP

oli

cy,

31(5

),749–767

This

arti

cle

off

ers

aty

polo

gy

of

org

aniz

atio

nal

stru

cture

s(b

ure

aucr

atic

,le

ader

less

,non-

spec

iali

zed

and

par

tici

pat

ory

)fo

rsc

ienti

fic

coll

abora

tions.

Coll

abora

tion

work

sbes

tas

a

volu

nta

rypro

cess

Coll

abora

tion

attr

ibute

sC

lark

(2011

)C

lark

,B

.Y

.(2

011).

Infl

uen

ces

and

confl

icts

of

feder

al

poli

cies

inac

adem

ic–in

dust

rial

scie

nti

fic

coll

abora

tion.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

36(5

),514–545

Res

earc

hco

llab

ora

tions

are

oft

enbas

edon

info

rmal

rela

tionsh

ips

and

trust

Coll

abora

tion

attr

ibute

sC

lary

sse

etal

.(2

009

)C

lary

sse,

B.,

Wri

ght,

M.,

&M

ust

ar,

P.

(2009).

Beh

avio

ura

l

addit

ional

ity

of

R&

Dsu

bsi

die

s:A

lear

nin

gper

spec

tive.

Res

earc

hP

oli

cy,

38(1

0),

1517–1533

This

man

usc

ript

addre

sses

addit

ionali

tyin

R&

D

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Cohen

etal

.(2

004

)C

ohen

,M

.B

.,T

arnow

,E

.,&

De

Young,

B.

R.

(2004).

Coau

thors

hip

inpat

holo

gy,

aco

mpar

ison

wit

hphysi

cs

and

asu

rvey

-gen

erat

edan

dm

ember

-pre

ferr

edau

thors

hip

guid

elin

e.M

edG

enM

ed,

63(1

),1–5

Appro

pri

ate

auth

ors

hip

assi

gnm

ent

isof

consi

der

able

import

ance

toboth

scie

nti

sts

and

the

publi

c.K

now

ing

who

did

apar

ticu

lar

pie

ceof

work

isim

port

ant—

scie

nti

sts

can

conta

ctth

eap

pro

pri

ate

coll

eague,

ask

ques

tions,

and

obta

indat

aor

reag

ents

,an

d

the

publi

cca

nsh

ift

funds

tobet

ter

scie

nti

sts

and

opti

miz

eit

sre

turn

on

inves

tmen

tin

the

scie

nti

fic

mar

ket

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Cohen

etal

.(2

002

)C

ohen

,W

.M

.,N

elso

n,

R.

R.,

&W

alsh

,J.

P.

(2002).

Lin

ks

and

impac

ts:

The

infl

uen

ceof

publi

cre

sear

chon

indust

rial

R&

D.

Managem

ent

Sci

ence

,48(1

),1–23

Res

earc

hin

indust

ryte

nds

tobe

quit

edif

fere

nt

from

that

found

inuniv

ersi

ties

,gover

nm

ent

or

NG

O’s

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Coll

ins

and

Wak

oh

(2000

)C

oll

ins,

S.,

&W

akoh,

H.

(2000).

Univ

ersi

ties

and

tech

nolo

gy

tran

sfer

inJa

pan

:R

ecen

tre

form

sin

his

tori

cal

per

spec

tive.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

25(2

),

213–222

Unli

ke

know

ledge

dev

elopm

ent

indust

ry–

acad

emia

par

tner

ship

shav

egeo

gra

phic

boundar

ies

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Cooper

(2009

)C

ooper

,M

.H

.(2

009).

Com

mer

cial

izat

ion

of

the

univ

ersi

ty

and

pro

ble

mch

oic

eby

acad

emic

bio

logic

alsc

ienti

sts.

Sci

ence

Tec

hnolo

gy

Hum

an

Valu

es,

34(5

),629–653

Indust

rial

involv

emen

thas

unduly

affe

cted

univ

ersi

tyre

sear

cher

s’ch

oic

eof

rese

arch

topic

s


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sC

ronin

(2001

)C

ronin

,B

.(2

001).

Hyper

auth

ors

hip

:A

post

moder

n

per

ver

sion

or

evid

ence

of

ast

ruct

ura

lsh

ift

insc

hola

rly

com

munic

atio

npra

ctic

es?

Journ

al

of

the

Am

eric

an

Soci

ety

for

Info

rmati

on

Sci

ence

and

Tec

hnolo

gy,

52(7

),

558–569

Dis

cuss

eshyper

-auth

ors

hip

and

the

effe

ctof

coll

abora

tion

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Cro

wan

dB

oze

man

(1998

)C

row

,M

.M

.,&

Boze

man

,B

.(1

998).

Lim

ited

by

des

ign:

R&

Dla

bora

tori

esin

the

US

nati

onal

innova

tion

syst

em.

Colu

mbia

Univ

ersi

tyP

ress

Res

earc

hin

indust

ryte

nds

tobe

quit

edif

fere

nt

from

that

found

inuniv

ersi

ties

,gover

nm

ent

or

NG

O’s

Coll

abora

tion

attr

ibute

sC

um

min

gs

and

Kie

sler

(2005

)C

um

min

gs,

J.N

.,&

Kie

sler

,S

.(2

005).

Coll

abora

tive

rese

arch

acro

ssdis

cipli

nar

yan

dorg

aniz

atio

nal

boundar

ies.

Soci

al

Stu

die

sof

Sci

ence

,35(5

),703

Coll

abora

tions

acro

ssdis

cipli

nar

yboundar

ies

report

asm

any

posi

tive

outc

om

esas

pro

ject

s

wit

hfe

wer

dis

cipli

nes

,but

pro

ject

ssp

annin

g

univ

ersi

tyboundar

ies

are

posi

tivel

y

asso

ciat

edw

ith

neg

ativ

eoutc

om

esfo

r

rese

arch

Coll

abora

tor

attr

ibute

sD

’Est

ean

dP

erkm

ann

(2011

)D

’Est

e,P

.,&

Per

km

ann,

M.

(2011).

Why

do

acad

emic

s

engag

ew

ith

indust

ry?

The

entr

epre

neu

rial

univ

ersi

tyan

d

indiv

idual

moti

vat

ions.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

36(3

),316–339

The

auth

ors

pro

vid

efo

ur

mai

nm

oti

vat

ions

(com

mer

cial

izat

ion,

lear

nin

g,

acce

ssto

fundin

gan

dac

cess

toin

-kin

dre

sourc

es)

that

are

consi

sten

tw

ith

the

thre

em

ain

outp

uts

of

coll

abora

tion

inour

model

Coll

abora

tor

attr

ibute

sD

avis

,L

arse

nan

dL

otz

(2011

)D

avis

,L

.,L

arse

n,

M.

T.,

&L

otz

,P

.(2

011).

Sci

enti

sts’

per

spec

tives

conce

rnin

gth

eef

fect

sof

univ

ersi

ty

pat

enti

ng

on

the

conduct

of

acad

emic

rese

arch

inth

eli

fe

scie

nce

s.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

36(1

),

14–37

Bas

icre

sear

cher

sar

esi

gnifi

cantl

ym

ore

skep

tica

lab

out

the

impac

tof

univ

ersi

ty

pat

enti

ng

on

acad

emic

free

dom

and

hig

hly

pro

duct

ive

scie

nti

sts

are

signifi

cantl

yle

ss

skep

tica

l.T

her

eis

no

rela

tionsh

ipbet

wee

n

coll

abora

tion

beh

avio

ran

dth

ebel

ief

that

univ

ersi

typat

enti

ng

neg

ativ

ely

affe

cts

acad

emic

free

dom

The

dar

ksi

de

of

rese

arch

coll

abora

tion

Dev

ine

etal

.(2

005

)D

evin

e,E

.B

.,B

eney

,J.

,&

Lis

aA

.B

ero,

L.

A.

(2005).

Equit

y,

acco

unta

bil

ity,

tran

spar

ency

:Im

ple

men

tati

on

of

the

contr

ibuto

rship

conce

pt

ina

mult

i-si

test

udy.

Am

eric

an

Journ

al

of

Pharm

ace

uti

cal

Educa

tion

,69(4

),

455–459

Sort

ing

out

co-a

uth

ors

hip

and

cred

itin

gm

ay

resu

ltfr

om

the

explo

sion

inre

sear

chan

dth

e

fundin

gim

per

ativ

esdri

vin

gco

llab

ora

tion

among

inves

tigat

ors

from

mult

iple

site

san

d

num

erous

dis

cipli

nes


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sD

ietz

and

Boze

man

(2005)

Die

tz,

J.S

.&

Boze

man

,B

.(2

005).

Aca

dem

icca

reer

s,

pat

ents

,an

dpro

duct

ivit

y:

indust

ryex

per

ience

as

scie

nti

fic

and

tech

nic

alhum

anca

pit

al.

Res

earc

hP

oli

cy,

34(3

),349–367

Res

earc

hco

llab

ora

tion

may

not

hav

edir

ect

econom

icim

pac

t

The

dar

ksi

de

of

rese

arch

coll

abora

tion

Dre

nth

(1998

)D

renth

,J.

P.

H.

(1998).

Mult

iple

auth

ors

hip

:T

he

contr

ibuti

on

of

senio

rau

thors

.JA

MA

,280(3

),219–221

Sort

ing

out

co-a

uth

ors

hip

and

cred

itin

gm

ay

resu

ltfr

om

the

explo

sion

inre

sear

chan

dth

e

fundin

gim

per

ativ

esdri

vin

gco

llab

ora

tion

among

inves

tigat

ors

from

mult

iple

site

san

d

num

erous

dis

cipli

nes

Coll

abora

tion

attr

ibute

sD

uque

etal

.(2

005

,p.

755)

Duque,

R.

B.,

Ynal

vez

,M

.,S

oory

amoort

hy,

R.,

Mbat

ia,

P.,

Dzo

rgbo,

D.

B.

S.,

&S

hru

m,

W.

(2005).

Coll

abora

tion

par

adox.

Soci

al

Stu

die

sof

Sci

ence

,35(5

),755

Nat

ional

and

regio

nal

conte

xt

affe

cts

the

coll

abora

tive

pro

cess

,due

inpar

tto

dif

fere

nce

sin

acce

ssto

tech

nolo

gy.

The

arti

cle

dis

cuss

esth

ero

leof

confl

ict

in

coll

abora

tions

Coll

abora

tion

attr

ibute

sF

aria

and

Goel

(2010

)F

aria

,J.

R.,

&G

oel

,R

.K

.(2

010).

Ret

urn

sto

net

work

ing

in

acad

emia

.N

etnom

ics,

11(2

),103–117

Expla

inin

gdif

fere

nce

sbet

wee

nac

tive

and

pas

sive

net

work

ing

among

scie

nti

sts

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Fel

ler

and

Fel

dm

an(2

010

)F

elle

r,I.

,&

Fel

dm

an,

M.

(2010).

The

com

mer

cial

izat

ion

of

acad

emic

pat

ents

:B

lack

boxes

,pip

elin

es,

and

Rubik

’s

cubes

.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

35(6

),

597–616

Tec

hnolo

gy

tran

sfer

org

aniz

atio

ns

oft

enhin

der

coll

abora

tion

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Fel

ler

and

Roes

sner

(1995

)F

elle

r,I.

,&

Roes

sner

,D

.(1

995).

What

does

indust

ry

expec

tfr

om

univ

ersi

typar

tner

ship

s?C

ongre

ssw

ants

to

see

bott

om

-lin

ere

sult

sfr

om

indust

ry/g

over

nm

ent

pro

gra

ms,

but

that

’snot

what

the

par

tici

pat

ing

com

pan

ies

are

seek

ing.

Issu

esin

Sci

ence

and

Tec

hnolo

gy,

12(1

)

80–84

Know

ledge-

focu

sed

outp

uts

infl

uen

ce

univ

ersi

ty-a

cadem

icre

sear

chco

llab

ora

tions

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Fox

and

Mohap

ta(2

007

)F

ox,

M.

F.,

&M

ohap

ta,

S.

(2007).

Soci

al-o

rgan

izat

ional

char

acte

rist

ics

of

work

and

publi

cati

on

pro

duct

ivit

y

among

acad

emic

scie

nti

sts

indoct

ora

l-gra

nti

ng

dep

artm

ents

.Jo

urn

al

of

Hig

her

Educa

tion

,78(5

),

542–571

This

arti

cle

focu

ses

on

the

work

of

acad

emic

scie

nti

sts

and

how

thei

rre

sear

chis

affe

cted

by

the

soci

al-o

rgan

izat

ional

char

acte

rist

ics

of

thei

rpro

fess

ional

sett

ing


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sF

rankli

net

al.

(2001

)F

rankli

n,

S.

J.,

Wri

ght,

M.,

&L

ock

ett,

A.

(2001).

Aca

dem

ican

dsu

rrogat

een

trep

reneu

rsin

univ

ersi

tysp

in-

out

com

pan

ies.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

26(1

),127–141

Res

earc

hco

llab

ora

tion

may

not

hav

edir

ect

econom

icim

pac

t

Coll

abora

tor

attr

ibute

sG

arre

tt-J

ones

etal

.(2

010

)G

arre

tt-J

ones

,S

.,T

urp

in,

T.,

&D

imen

t,K

.(2

010).

Man

agin

gco

mpet

itio

nbet

wee

nin

div

idual

and

org

aniz

atio

nal

goal

sin

cross

-sec

tor

rese

arch

and

dev

elopm

ent

cente

rs.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

35(5

),527–546

The

arti

cle

des

crib

eshow

the

man

agem

ent

of

coll

abora

tions

can

affe

ctth

eac

tors

and

the

outp

ut

of

the

pro

cess

.R

esea

rch

scie

nti

sts

engag

ein

coll

abora

tions

bec

ause

of

inta

ngib

lem

oti

vat

ions

incl

udin

gbet

ter

acce

ss

toin

dust

rypar

tner

san

dw

ork

ing

wit

ha

larg

er

cohort

of

schola

rsw

ith

sim

ilar

scie

nti

fic

inte

rest

s

Coll

abora

tion

attr

ibute

sG

arg

and

Pad

hi

(2001

)G

arg,

K.

C.,

&P

adhi,

P.(2

001).

Ast

udy

of

coll

abora

tion

in

lase

rsc

ience

and

tech

nolo

gy.

Sci

ento

met

rics

,51(1

0),

415–427

This

arti

cle

des

crib

eshyper

-auth

ors

hip

inla

ser

S&

Tan

dhow

itvar

ies

by

countr

y

Coll

abora

tor

attr

ibute

sG

aughan

and

Corl

ey(2

010

)G

aughan

,M

.,&

Corl

ey,

E.

A.

(2010).

Sci

ence

facu

lty

at

US

rese

arch

univ

ersi

ties

:T

he

impac

tsof

univ

ersi

ty

rese

arch

cente

r-af

fili

atio

nan

dgen

der

on

indust

rial

acti

vit

ies,

Tec

hnova

tion,

30(3

),215–222

The

arti

cle

use

sth

ein

dust

rial

involv

emen

t

index

and

incl

udes

adis

cuss

ion

of

gen

der

impac

ton

indust

rial

R&

Dac

tivit

ies

Coll

abora

tion

attr

ibute

sG

azni

and

Did

egah

(2011

)G

azni,

A.,

&D

ideg

ah,

F.

(2011).

Inves

tigat

ing

dif

fere

nt

types

of

rese

arch

coll

abora

tion

and

cita

tion

impac

t:a

case

study

of

Har

var

dU

niv

ersi

ty’s

publi

cati

ons.

Sci

ento

met

rics

,87(2

),251–265

Ast

udy

of

man

usc

ripts

from

22

fiel

ds

show

ed

that

60

%of

the

publi

cati

ons

wer

e

coau

thore

d

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Gei

sler

(1986

)G

eisl

er,

E.

(1986).

The

role

of

indust

rial

advis

ory

boar

ds

in

tech

nolo

gy

tran

sfer

bet

wee

nuniv

ersi

ties

and

indust

ry.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

10(2

),33–42

Exte

rnal

acto

rs(i

.e.

the

Indust

rial

Advis

ory

Boar

ds)

hav

ea

signifi

cant,

yet

som

etim

es

under

esti

mat

edro

lein

the

tran

sfer

of

tech

nolo

gy

bet

wee

nuniv

ersi

ties

and

indust

ry

Coll

abora

tion

attr

ibute

sG

odin

(1998

)G

odin

,B

.(1

998).

Wri

ting

per

form

ativ

ehis

tory

:T

he

new

new

Atl

anti

s?Soci

al

Stu

die

sof

Sci

ence

,28(3

),465–483

The

auth

or

does

not

agre

ew

ith

the

dic

hoto

my

bet

wee

ndis

cipli

nar

yre

sear

chan

d

inte

rdis

cipli

nar

yre

sear

ch


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sG

oel

and

Gri

mpe

(2011

)G

oel

,R

.K

.,&

Gri

mpe,

C.

(2011)

Act

ive

ver

sus

pas

sive

acad

emic

net

work

ing:

Evid

ence

from

mic

ro-l

evel

dat

a.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

1–19

This

arti

cle

expla

ins

dif

fere

nce

sbet

wee

nac

tive

and

pas

sive

net

work

ing

among

scie

nti

sts

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Gra

yan

dS

teen

huis

(2003

)G

ray,

D.

O.,

&S

teen

huis

,H

.J.

(2003).

Quan

tify

ing

the

ben

efits

of

par

tici

pat

ing

inan

indust

ryuniv

ersi

tyre

sear

ch

cente

r:A

nex

amin

atio

nof

rese

arch

cost

avoid

ance

.

Sci

ento

met

rics

,58(2

),281–300

Know

ledge-

focu

sed

outp

uts

infl

uen

ce

univ

ersi

ty-a

cadem

icre

sear

chco

llab

ora

tions

Coll

abora

tion

attr

ibute

sG

rim

pe

and

Fie

r(2

010

)G

rim

pe,

C.

&F

ier,

H.

(2010).

Info

rmal

univ

ersi

ty

tech

nolo

gy

tran

sfer

:A

com

par

ison

bet

wee

nth

eU

nit

ed

Sta

tes

and

Ger

man

y,

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

35(6

),637–650

This

arti

cle

expla

ins

dif

fere

nce

sbet

wee

nac

tive

and

pas

sive

net

work

ing

among

scie

nti

sts

Coll

abora

tor

attr

ibute

sG

ross

eK

athoef

eran

dL

eker

(2010

)

Gro

sse

Kat

hoef

er,

D.,

&L

eker

,J.

(2010),

Know

ledge

tran

sfer

inac

adem

ia:

An

explo

rato

ryst

udy

on

the

not-

inven

ted-h

ere

syndro

me.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

1–18

The

auth

ors

apply

the

Not

Inven

ted

Her

e(N

IH)

syndro

me

toknow

ledge

tran

sfer

inac

adem

ia.

They

find

itre

late

sto

indiv

idual

char

acte

rist

ics

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Gro

ssm

anet

al.

(2001

)G

ross

man

,J.

H.,

Rei

d,

P.

P.,

&M

org

an,

R.

P.

(2001).

Contr

ibuti

ons

of

acad

emic

rese

arch

toin

dust

rial

per

form

ance

infi

ve

indust

ryse

ctors

.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

26(1

),143–152

The

auth

ors

off

erso

me

import

ant

rem

arks

regar

din

ghow

fundin

gca

nin

fluen

ce

coll

abora

tion

pat

tern

sw

ithin

and

acro

ss

sect

ors

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Guel

lec

etal

.(2

004

)G

uel

lec,

D.,

&V

anP

ott

elsb

erghe

de

laP

ott

erie

,B

.(2

004).

Fro

mR

&D

topro

duct

ivit

ygro

wth

:D

oth

ein

stit

uti

onal

sett

ings

and

the

sourc

eof

funds

of

R&

Dm

atte

r?O

xford

Bull

etin

of

Eco

nom

ics

and

Sta

tist

ics,

66(3

),353–378

Res

earc

hin

indust

ryte

nds

tobe

quit

edif

fere

nt

from

that

found

inuniv

ersi

ties

,gover

nm

ent

or

NG

O’s

Coll

abora

tor

attr

ibute

sG

ulb

randse

nan

dS

med

by

(2005

)

Gulb

randse

n,

M&

Sm

eby,

J.C

.(2

005).

Indust

ryfu

ndin

g

and

univ

ersi

typro

fess

ors

’re

sear

chper

form

ance

.

Res

earc

hP

oli

cy,3

4(6

),932–950

Indust

ryfu

ndin

gfo

runiv

ersi

tyre

sear

chvar

ies

by

countr

y

Coll

abora

tion

attr

ibute

sG

ulb

randse

nan

dE

tzkow

itz

(1999

)

Gulb

randse

n,

M.,

&E

tzkow

itz,

H.

(1999).

Conver

gen

ce

bet

wee

nE

uro

pe

and

Am

eric

a:T

he

tran

siti

on

from

indust

rial

toin

novat

ion

poli

cy.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

24(2

),223–233

R&

Dre

quir

esin

novat

ion.

Hum

anad

dit

ional

ity

isin

ferr

ed


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sH

aged

oorn

etal

.(2

000

)H

aged

oorn

,J.

,L

ink,

A.

N.,

&V

onort

as,

N.

S.

(2000).

Res

earc

hpar

tner

ship

s.R

esea

rch

Poli

cy,

29(4

–5),

567–586

R&

Dco

llab

ora

tion

pro

vid

esben

efits

,yet

countl

ess

reso

urc

esan

dhum

anen

ergie

sar

e

inves

ted

infa

cili

tati

ng,

induci

ng,

and

man

agin

gco

llab

ora

tion

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Hal

l,L

ink

and

Sco

tt(2

001

)H

all,

B.

H.,

Lin

k,

A.

N.,

&S

cott

,J.

T.

(2001).

Bar

rier

s

inhib

itin

gin

dust

ryfr

om

par

tner

ing

wit

huniv

ersi

ties

:

Evid

ence

from

the

advan

ced

tech

nolo

gy

pro

gra

m.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

26(1

),87–98

Exte

rnal

acto

rssu

chas

regula

tors

neg

ativ

ely

contr

ibute

toth

eco

llab

ora

tion

pro

cess

.

Cer

tain

bar

rier

sli

mit

coll

abora

tion

or

par

tner

ing

beh

avio

rbet

wee

nin

dust

ryan

d

univ

ersi

ties

,pri

mar

ily

inte

llec

tual

pro

per

ty

conce

rns;

univ

ersi

ties

are

subje

ctto

more

stri

ngen

tre

gula

tions

com

par

edto

pri

vat

e

indust

ry

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Han

elan

dS

t-P

ierr

e(2

006

)H

anel

,P

.,&

St-

Pie

rre,

M.

(2006).

Indust

ry–U

niv

ersi

ty

coll

abora

tion

by

Can

adia

nm

anufa

cturi

ng

firm

s.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

31(4

),485–499

The

maj

or

ince

nti

ve

toco

llab

ora

tew

ith

a

univ

ersi

tyis

the

acce

ssto

rese

arch

and

crit

ical

com

pet

enci

es,

whic

hal

low

sfi

rms

to

reac

hth

ever

yed

ge

of

conte

mpora

ry

tech

nolo

gy

(i.e

.pro

per

tyfo

cuse

doutp

uts

)

Coll

abora

tor

attr

ibute

s

Hae

uss

ler

and

Coly

vas

(2011

)H

aeuss

ler,

C.,

&J.

A.

Coly

vas

(2011),

Bre

akin

gth

eIv

ory

Tow

er:

Aca

dem

icE

ntr

epre

neu

rship

inth

eL

ife

Sci

ence

s

inU

Kan

dG

erm

any,

Res

earc

hP

oli

cy,

40(1

),41–54

Old

ersc

ienti

sts

are

likel

yto

be

engag

edin

a

var

iety

of

R&

Dco

mm

erci

aliz

atio

nac

tivit

ies

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Hef

fner

(1981

)H

effn

er,

A.

G.

(1981).

Funded

rese

arch

,m

ult

iple

auth

ors

hip

,an

dsu

bau

thors

hip

coll

abora

tion

info

ur

dis

cipli

nes

.Sci

ento

met

rics

,3(1

),5–12

Res

earc

her

shav

eco

nsi

der

able

auto

nom

yin

thei

rco

llab

ora

tion

choic

esan

dco

llab

ora

tion

stra

tegie

sar

ebas

edin

par

tof

judgm

ents

about

the

confe

rrin

gof

co-a

uth

ors

hip

and

stat

us

Coll

abora

tion

attr

ibute

sH

einze

and

Bau

er(2

007

)H

einze

,T

.&

Bau

er,

G.

(2007).

Char

acte

rizi

ng

crea

tive

scie

nti

sts

innan

o-S

&T

:P

roduct

ivit

y,

mult

idis

cipli

nar

ity,

and

net

work

bro

ker

age

ina

longit

udin

alper

spec

tive.

Sci

ento

met

rics

,70(3

),811–830

This

isone

of

man

yuse

ful

studie

sof

coll

abora

tion

wher

eco

auth

ors

hip

isa

unit

of

mea

sure


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Hes

sels

and

Van

Len

te(2

008

)H

esse

ls,

L.

K.,

&V

anL

ente

,H

.(2

008).

Re-

thin

kin

gnew

know

ledge

pro

duct

ion:

Ali

tera

ture

revie

wan

da

rese

arch

agen

da.

Res

earc

hP

oli

cy,

37(4

),740–760

Know

ledge-

focu

sed

and

pro

per

ty-f

ocu

sed

rese

arch

are

not

mutu

ally

excl

usi

ve.

Mode

2

know

ledge

pro

duct

ion

(tra

nsd

isci

pli

nar

y

rese

arch

)is

not

mea

nt

tore

pla

ceth

e

trad

itio

nal

mode

of

know

ledge

pro

duct

ion,

but

rath

ersu

pple

men

tit

.M

ode

2know

ledge

pro

duct

ion

isco

nsi

sten

tw

ith

coll

abora

tive

pro

per

ty-f

ocu

sed

rese

arch

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Hic

ks

and

Kat

z(1

996

)H

icks,

D.

M.,

&K

atz,

J.S

.(1

996).

Wher

eis

scie

nce

goin

g?

Sci

ence

,T

echnolo

gy

&H

um

an

Valu

es,

21(4

),379

Ther

ehas

bee

nan

incr

ease

intr

ansd

isci

pli

nar

y

journ

als

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

His

rich

and

Sm

ilor

(1988

)H

isri

ch,

R.

D.,

&S

mil

or,

R.

W.

(1988).

The

univ

ersi

tyan

d

busi

nes

sin

cubat

ion:

Tec

hnolo

gy

tran

sfer

thro

ugh

entr

epre

neu

rial

dev

elopm

ent.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

13(1

),14–19

Univ

ersi

ty-b

ased

busi

nes

sin

cubat

ors

enco

ura

ge

tech

nolo

gy

tran

sfer

thro

ugh

entr

epre

neu

rial

dev

elopm

ent

Coll

abora

tion

attr

ibute

sH

uan

gan

dL

in(2

010

)H

uan

g,

M.

H.,

&L

in,

C.

S.

(2010).

Inte

rnat

ional

coll

abora

tion

and

counti

ng

infl

atio

nin

the

asse

ssm

ent

of

nat

ional

rese

arch

pro

duct

ivit

y.

Pro

ceed

ings

of

the

Am

eric

an

Soci

ety

for

Info

rmati

on

Sci

ence

and

Tec

hnolo

gy,

47(1

),1–4

Res

earc

hco

llab

ora

tion

tends

toen

han

ce

pro

duct

ivit

yof

scie

nti

fic

know

ledge

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Huan

gan

dY

u(2

011

)H

uan

g,

K.

F.,

&Y

u,

C.

M.

J.(2

011).

The

effe

ctof

com

pet

itiv

ean

dnon-c

om

pet

itiv

eR

&D

coll

abora

tion

on

firm

innovat

ion.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

36(4

),383–403

Non-c

om

pet

itiv

eR

&D

coll

abora

tion,

spec

ifica

lly

those

coll

abora

tions

conduct

ed

wit

huniv

ersi

ties

,is

posi

tivel

yco

rrel

ated

wit

h

innovat

ion

and

pro

vid

esm

ater

ial

ben

efits

to

indust

ry

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Jankow

ski

(1999

)Ja

nkow

ski,

J.E

.(1

999).

Tre

nds

inac

adem

icre

sear

ch

spen

din

g,

alli

ance

s,an

dco

mm

erci

aliz

atio

n.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

24(1

),55–68

Fundin

gfr

om

gover

nm

ent

has

slow

edan

d

man

yin

stit

uti

ons

hav

eco

me

tore

lyon

alte

rnat

ive

fundin

gm

ethods

incl

udin

gow

n

sourc

ein

stit

uti

onal

fundin

gan

din

dust

ry

fundin

g


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sJe

ong

etal

.(2

011

)Je

ong,

S.,

Choi,

J.Y

.&

Kim

,J.

(2011)

The

det

erm

inan

tsof

rese

arch

coll

abora

tion

modes

:ex

plo

ring

the

effe

cts

of

rese

arch

and

rese

arch

erch

arac

teri

stic

son

co-a

uth

ors

hip

.

Sci

ento

met

rics

,89,

3,

967–983

Thes

ere

sear

cher

spro

vid

ea

bro

ader

defi

nit

ion

of

coll

abora

tion

than

our

curr

ent

study

Coll

abora

tion

attr

ibute

sJo

han

sson

etal

.(2

005

)Jo

han

sson,

M.,

Jaco

b,

M.,

&H

ells

tro

m,

T.

(2005).

The

stre

ngth

of

stro

ng

ties

:U

niv

ersi

tysp

in-o

ffs

and

the

signifi

cance

of

his

tori

cal

rela

tions.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

30(3

),271–286

Net

work

ties

bet

wee

nin

dust

ryan

dac

adem

ia

crea

test

rong

bonds

for

coll

abora

tion

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Johnso

n(2

009

)Jo

hnso

n,

W.

H.

A.

(2009).

Inte

rmed

iate

sin

trip

lehel

ix

coll

abora

tion:

The

role

sof

4th

pil

lar

org

anis

atio

ns

in

publi

cto

pri

vat

ete

chnolo

gy

tran

sfer

.In

tern

ati

onal

Journ

al

of

Tec

hnolo

gy

Tra

nsf

erand

Com

mer

ciali

sati

on

,

8(2

),142–158

Exte

rnal

acto

rssu

chas

regula

tors

posi

tivel

y

contr

ibute

toco

llab

ora

tive

R&

D

Coll

abora

tor

attr

ibute

sJo

hnso

nan

dB

oze

man

(2012

)Jo

hnso

n,

J.,

&B

oze

man

,B

.(2

012).

Per

spec

tive:

Adopti

ng

anas

set

bundle

sm

odel

tosu

pport

and

advan

cem

inori

ty

studen

ts’

care

ers

inac

adem

icm

edic

ine

and

the

scie

nti

fic

pip

elin

e.A

cadem

icM

edic

ine,

87

(11),

1488–1495

Min

ori

tyunder

repre

senta

tion

inac

adem

ic

scie

nce

affe

cts

coll

abora

tion

pat

tern

s

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Kat

z(2

000

)K

atz,

J.S

.(2

000).

Sca

le-i

ndep

enden

tin

dic

ators

and

rese

arch

eval

uat

ion.

Sci

ence

and

Publi

cP

oli

cy,

27(1

),

23–36

Bey

ond

the

indiv

idual

scie

nti

sts

involv

edin

the

coll

abora

tion

pro

cess

,it

isal

soim

port

ant

to

consi

der

the

org

aniz

atio

nal

envir

onm

ent

in

whic

hth

ese

indiv

idual

sin

tera

ct

Coll

abora

tion

attr

ibute

sK

atz

and

Hic

ks

(1997

)K

atz,

J.S

.,&

Hic

ks,

D.

(1997).

How

much

isa

coll

abora

tion

wort

h?

Aca

libra

ted

bib

liom

etri

cm

odel

.

Sci

ento

met

rics

,40(3

),541–554

This

arti

cle

anal

yze

show

coll

abora

tion

infl

uen

ces

the

val

ue

of

the

final

pro

duct

thro

ugh

am

easu

reof

cita

tion

rate

s;

coll

abora

tion

wit

ha

dom

esti

csc

ienti

sts

incr

ease

sth

eci

tati

on

rate


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sK

atz

and

Mar

tin

(1997

)K

atz,

J.S

.,&

Mar

tin,

B.

R.

(1997).

What

isre

sear

ch

coll

abora

tion?

Res

earc

hP

oli

cy,

26(1

),1–18

Foundat

ional

revie

wan

dcr

itiq

ue

of

the

rese

arch

coll

abora

tion

lite

ratu

re.

The

co-

auth

or

conce

pt

of

coll

abora

tion

has

sever

al

advan

tages

,in

cludin

gver

ifiab

ilit

y,

stab

ilit

y

over

tim

e,dat

aav

aila

bil

ity

and

ease

of

mea

sure

men

t.H

ow

ever

,co

-auth

ors

hip

isat

bes

ta

par

tial

indic

ator

of

coll

abora

tion

The

dar

ksi

de

of

rese

arch

coll

abora

tion

Kli

ngen

smit

han

dA

nder

son

(2006

)

Kli

ngen

smit

h,

M.

E.

&A

nder

son,

K.

A.

(2006).

Educa

tional

schola

rship

asa

route

toac

adem

ic

pro

moti

on:

Adep

icti

on

of

surg

ical

educa

tion

schola

rs.

The

Am

eric

an

Journ

al

of

Surg

ery,

191(4

),533–537

The

auth

ors

test

whet

her

educa

tional

schola

rship

isa

via

ble

pat

hw

ayto

acad

emic

pro

moti

on

and

the

per

ver

seim

pac

tsit

may

hav

eon

auth

ors

hip

attr

ibuti

on

Coll

abora

tor

attr

ibute

sK

uhn

(1996

)K

uhn,

T.

S.

(1996).

The

stru

cture

of

scie

nti

fic

revo

luti

ons.

Univ

ersi

tyof

Chic

ago

Pre

ss

Bea

ver

(2004)

use

sK

uhn’s

conce

ptu

al

fram

ework

of

rese

arch

par

adig

ms

toad

dre

ss

chal

lenges

inth

eco

llab

ora

tion

pro

cess

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Lag

nad

o(2

003

)L

agnad

o,

M.

(2003).

Pro

fess

ional

wri

ting

assi

stan

ce:

effe

cts

on

bio

med

ical

publi

shin

g.

Lea

rned

Publi

shin

g,

16(1

),21–27

Tru

stin

the

mea

nin

gof

co-a

uth

ors

hip

has

eroded

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Lan

dry

,A

mar

aan

dO

uim

et

(2007

)

Lan

dry

,R

.J.

,A

mar

a,N

.,&

Ouim

et,

M.

(2007).

Det

erm

inan

tsof

know

ledge

tran

sfer

:ev

iden

cefr

om

Can

adia

nuniv

ersi

tyre

sear

cher

sin

nat

ura

lsc

ience

san

d

engin

eeri

ng.

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

32(6

),

561–592

The

auth

ors

answ

erth

ree

rese

arch

ques

tions:

the

firs

tdis

tinguis

hin

gbet

wee

nknow

ledge

and

tech

nolo

gy

tran

sfer

,th

ese

cond

iden

tify

dif

fere

nce

sbet

wee

ndis

cipli

nes

inte

rms

of

know

ledge

tran

sfer

,an

dfi

nal

lydis

cuss

ing

the

det

erm

inan

tsof

know

ledge

tran

sfer

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Lee

(2000

)L

ee,

Y.

S.

(2000).

The

sust

ainab

ilit

yof

univ

ersi

ty–in

dust

ry

rese

arch

coll

abora

tion:

An

empir

ical

asse

ssm

ent.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

25(2

),111–133

The

most

import

ant

ben

efit

for

firm

sfr

om

indust

ryac

adem

iaco

llab

ora

tions

isan

incr

ease

dac

cess

tonew

univ

ersi

tyre

sear

ch

and

dis

cover

ies.

Coll

abora

tor

attr

ibute

sL

eean

dB

oze

man

(2005)

Lee

,S

.,&

Boze

man

,B

.(2

005).

The

impac

tof

rese

arch

coll

abora

tion

on

scie

nti

fic

pro

duct

ivit

y.

Soci

al

Stu

die

sof

Sci

ence

,35(5

),673

Res

earc

hco

llab

ora

tion

tends

toen

han

ce

pro

duct

ivit

yof

scie

nti

fic

know

ledge.

Younger

and

mid

care

ersc

ienti

sts

hav

e

gre

ater

pro

duct

ivit

ypay

off

.Jo

bsa

tisf

acti

on

impac

tsco

llab

ora

tion


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Lev

sky

etal

.(2

007

)L

evsk

y,

M.

E.

Rosi

n,

A.

Coon,

T.

P.

Ensl

ow

,W

.L

.&

Mil

ler,

M.A

.(2

007).

Ades

crip

tive

anal

ysi

sof

auth

ors

hip

wit

hin

med

ical

journ

als,

1995–2005.

South

ern

Med

ical

Journ

al,

100(4

),371–375

Ther

ear

epote

nti

ally

troubli

ng

tren

ds

in

auth

ors

hip

inm

edic

aljo

urn

als

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Lev

y,

Roux

and

Wolf

f(2

009

)L

evy,

R.,

Roux,

P.,

&W

olf

f,S

.(2

009).

An

anal

ysi

sof

scie

nce

–in

dust

ryco

llab

ora

tive

pat

tern

sin

ala

rge

Euro

pea

nuniv

ersi

ty.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

34(1

),1–23

Indust

ryco

llab

ora

tions

wit

huniv

ersi

ties

most

oft

enpro

duce

incr

ease

dknow

ledge-

focu

sed

outp

uts

rath

erth

anpro

per

ty-f

ocu

sed

outp

uts

Coll

abora

tion

attr

ibute

sL

iao

(2011

)L

iao,

C.

H.

(2011)

How

toim

pro

ve

rese

arch

qual

ity?

Exam

inin

gth

eim

pac

tsof

coll

abora

tion

inte

nsi

tyan

d

mem

ber

div

ersi

tyin

coll

abora

tion

net

work

s.

Sci

ento

met

rics

,86(3

),747–761

Itis

not

the

num

ber

of

coll

abora

tors

that

is

import

ant

topro

duct

ivit

ybut

the

inte

nsi

tyof

coll

abora

tions

and

the

deg

ree

tow

hic

h

coll

abora

tors

are

embed

ded

inth

enet

work

Coll

abora

tion

attr

ibute

sL

iao

and

Yen

(2012

)L

iao,

C.

H.

&Y

en,

H.

R.

(2012).

Quan

tify

ing

the

deg

ree

of

rese

arch

coll

abora

tion:

Aco

mpar

ativ

est

udy

of

coll

abora

tive

mea

sure

s.Jo

urn

al

of

Info

rmet

rics

,6(1

),

27–33

Itis

import

ant

toco

nsi

der

the

org

aniz

atio

nal

envir

onm

ent

inw

hic

hre

sear

cher

sin

tera

ct

Coll

abora

tor

attr

ibute

sL

inan

dB

oze

man

(2006

)L

in,

M.

W.,

&B

oze

man

,B

.(2

006).

Res

earc

her

s’in

dust

ry

exper

ience

and

pro

duct

ivit

yin

univ

ersi

ty–in

dust

ry

rese

arch

cente

rs:

A‘‘

scie

nti

fic

and

tech

nic

alhum

an

capit

al’’

expla

nat

ion.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

31(2

),269–290

This

arti

cle

use

sth

ein

dust

rial

involv

emen

t

index

Coll

abora

tion

attr

ibute

sL

ink

and

Sie

gel

(2005

)L

ink,

A.

N.,

&S

iegel

,D

.S

.(2

005).

Univ

ersi

ty-b

ased

tech

nolo

gy

init

iati

ves

:Q

uan

tita

tive

and

qual

itat

ive

evid

ence

.R

esea

rch

Poli

cy,

34(3

),253–257

Res

earc

hco

llab

ora

tion

may

not

hav

edir

ect

econom

icim

pac

t

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Lin

ket

al.

(2007)

Lin

k,

A.

N.,

Sie

gel

,D

.S

.&

Boze

man

,B

.(2

007).

An

empir

ical

anal

ysi

sof

the

pro

pen

sity

of

acad

emic

sto

engag

ein

info

rmal

univ

ersi

tyte

chnolo

gy

tran

sfer

,

indust

rial

&co

rpora

tech

ange.

Res

earc

hP

oli

cy,

16(4

),

641–655

Aca

dem

ics

oft

enpre

fer

info

rmal

indust

ry–

univ

ersi

tyre

sear

chco

llab

ora

tions


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Coll

abora

tion

attr

ibute

sL

iuet

al.

(2012)

Liu

,H

.,C

han

g,

B.

&C

hen

,K

.(2

012)

Coll

abora

tion

pat

tern

sof

Tai

wan

ese

scie

nti

fic

publi

cati

ons

invar

ious

rese

arch

area

s.Sci

ento

met

rics

,29(1

),145–165

Inte

rnat

ional

coll

abora

tions

tend

tohav

e

gre

ater

impac

ton

outc

om

es,

but

that

level

of

impac

tfr

om

inte

rnat

ional

coll

abora

tion

is

fiel

dsp

ecifi

c

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Lo

of

and

Bro

stro

m(2

008

)L

oo

f,H

.,&

Bro

stro

m,

A.

(2008).

Does

know

ledge

dif

fusi

on

bet

wee

nuniv

ersi

tyan

din

dust

ryin

crea

se

innovat

iven

ess?

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

33(1

),73–90

Oft

ena

posi

tive

rela

tionsh

ipis

found

bet

wee

n

coll

abora

tion

and

innovat

ive

pro

per

ty-

focu

sed

outp

uts

for

afi

rm

Coll

abora

tion

attr

ibute

sL

uukkonen

(2000

)L

uukkonen

,T

.(2

000).

Addit

ional

ity

of

EU

fram

ework

pro

gra

mm

es.

Res

earc

hP

oli

cy,

29(6

),711–724

This

man

usc

ript

use

sth

eco

nce

pt

of

addit

ionali

tyin

R&

Dpro

gra

ms

inth

eE

U

Coll

abora

tion

attr

ibute

sM

arti

nel

liet

al.

(2008

)M

arti

nel

li,

A.,

Mey

er,

M.,

&T

unze

lman

n,

N.

(2008).

Bec

om

ing

anen

trep

reneu

rial

univ

ersi

ty?

Aca

sest

udy

of

know

ledge

exch

ange

rela

tionsh

ips

and

facu

lty

atti

tudes

in

am

ediu

m-s

ized

,re

sear

ch-o

rien

ted

univ

ersi

ty.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

33(3

),259–283

Itis

dif

ficu

ltfo

rac

adem

ics

wit

hout

exte

rnal

rela

tions

tobeg

inco

llab

ora

tion

outs

ide

one’

s

ow

norg

aniz

atio

n

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Man

sfiel

d(1

995

)M

ansfi

eld,

E.

(1995).

Aca

dem

icre

sear

chunder

lyin

g

indust

rial

innovat

ions:

Sourc

es,

char

acte

rist

ics,

and

finan

cing.

The

Rev

iew

of

Eco

nom

ics

and

Sta

tist

ics,

77(1

),

55–65

Much

of

indust

rial

rese

arch

dra

ws

from

publi

c

dom

ain

rese

arch

pro

duce

din

univ

ersi

ties

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Mar

usi

cet

al.

(2004

)M

arusi

c,M

.,B

ozi

kov,

J.,

Kat

avic

,V

.,H

ren,

D.,

Klj

akovic

-

Gas

pic

,M

.,&

Mar

usi

c,A

.(2

004).

Auth

ors

hip

ina

smal

l

med

ical

journ

al:

ast

udy

of

contr

ibuto

rship

stat

emen

tsby

corr

espondin

gau

thors

.Sci

ence

And

Engin

eeri

ng

Eth

ics,

10(3

),493–502

Auth

ors

addre

ssunif

orm

requir

emen

tsfo

r

coau

thors

hip

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Mat

tet

al.

(2011)

Mat

t,M

.,R

obin

,S

.,&

Wolf

f,S

.(2

011).

The

infl

uen

ceof

publi

cpro

gra

ms

on

inte

r-fi

rmR

&D

coll

abora

tion

stra

tegie

s:P

roje

ct-l

evel

evid

ence

from

EU

FP

5an

dF

P6.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

1–32.

doi:

10.1

007/s

10961-0

11-9

23

2-9

Fundin

gfr

om

dif

fere

nt

sourc

esca

nin

fluen

ce

the

pro

cess

of

coll

abora

tion

Coll

abora

tion

attr

ibute

sM

atts

son

etal

.(2

008

)M

atts

son,

P.,

Lag

et,

P.,

Nil

sson,

A.

&S

undber

g,

C.

(2008).

Intr

a-E

Uvs.

extr

a-E

Usc

ienti

fic

co-p

ubli

cati

on

pat

tern

sin

EU

.Sci

ento

met

rics

,75(3

),555–574

This

isone

of

man

yuse

ful

studie

sof

coll

abora

tion

wher

eco

auth

ors

hip

isa

unit

of

mea

sure


123

http://dx.doi.org/10.1007/s10961-011-9232-9

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

The

dar

ksi

de

of

rese

arch

coll

abora

tions

McC

rary

etal

.(2

000

)M

cCra

ry,

S.,

Ander

son,

C.,

Jakovlj

evic

,J.

,K

han

,T

.,

McC

ull

ough,

L.,

Wra

y,

N.,

&B

rody,

B.

(2000).

A

nat

ional

surv

eyof

poli

cies

on

dis

closu

reof

confl

icts

of

inte

rest

inbio

med

ical

rese

arch

.T

he

New

Engla

nd

Journ

al

Of

Med

icin

e,343(2

2),

1621–1626

Confl

icts

of

inte

rest

inre

sear

chco

llab

ora

tion

has

rece

ived

agood

dea

lof

atte

nti

on

inth

e

lite

ratu

re

Coll

abora

tion

attr

ibute

sM

elin

(2000

)M

elin

,G

.(2

000).

Pra

gm

atis

man

dse

lf-o

rgan

izat

ion:

Res

earc

hco

llab

ora

tion

on

the

indiv

idual

level

.R

esea

rch

Poli

cy,

29(1

),31–40

Foundat

ional

revie

wan

dcr

itiq

ue

of

the

rese

arch

coll

abora

tion

lite

ratu

reth

atsu

gges

ts

that

coll

abora

tion

work

sbes

tas

avolu

nta

ry

pro

cess

Coll

abora

tion

attr

ibute

sM

elin

and

Per

sson

(1996

)M

elin

,G

.,&

Per

sson,

O.

(1996).

Stu

dyin

gre

sear

ch

coll

abora

tion

usi

ng

co-a

uth

ors

hip

s.S

cien

tom

etri

cs,

36(3

),363–377

Foundat

ional

revie

wan

dcr

itiq

ue

of

the

rese

arch

coll

abora

tion

lite

ratu

re

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Men

doza

(2007

)M

endoza

,P

.(2

007)

Aca

dem

icca

pit

alis

man

ddoct

ora

l

studen

tso

cial

izat

ion:

Aca

sest

udy.

The

Journ

al

of

Hig

her

Educa

tion

,78(1

),71–96

Indust

rial

involv

emen

thas

unduly

affe

cted

univ

ersi

tyre

sear

cher

s’ch

oic

eof

rese

arch

topic

s

Coll

abora

tion

attr

ibute

sM

erto

n(1

968

)M

erto

n,

R.

K.

(1968)

The

Mat

thew

effe

ctin

scie

nce

.

Sci

ence

,159(3

810),

56–63

Auth

ors

hip

isa

nam

egam

ean

dcr

edit

wil

l

inev

itab

lybe

dis

pro

port

ionat

eto

more

senio

r

rese

arch

ers,

regar

dle

ssof

the

par

ticu

lar

nat

ure

or

exte

nt

of

thei

rco

ntr

ibuti

on

com

par

edto

less

wel

lknow

nco

llab

ora

tors

Coll

abora

tion

attr

ibute

sM

erto

n(1

995

)M

erto

n,

R.

K.

(1995)

The

Thom

asT

heo

rem

and

the

Mat

thew

Eff

ect.

Soci

al

Forc

es,

74(2

),379–422

Auth

ors

hip

isa

nam

egam

ean

dcr

edit

wil

l

inev

itab

lybe

dis

pro

port

ionat

eto

more

senio

r

rese

arch

ers,

regar

dle

ssof

the

par

ticu

lar

nat

ure

or

exte

nt

of

thei

rco

ntr

ibuti

on

com

par

edto

less

wel

lknow

nco

llab

ora

tors

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Mey

er(2

006

)M

eyer

,M

.(2

006).

Aca

dem

icin

ven

tiven

ess

and

entr

epre

neu

rship

:O

nth

eim

port

ance

of

star

t-up

com

pan

ies

inco

mm

erci

aliz

ing

acad

emic

pat

ents

.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

31(4

),501–510

Anum

ber

of

univ

ersi

tyas

soci

ated

pat

ents

are

use

din

star

tup

com

pan

ies,

but

most

are

use

d

ines

tabli

shed

larg

efi

rms


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Morg

anet

al.

(2001

)M

org

an,

R.

P.,

Kru

ytb

osc

h,

C.,

&K

annan

kutt

y,

N.

(2001).

Pat

enti

ng

and

inven

tion

acti

vit

yof

US

scie

nti

sts

and

engin

eers

inth

eac

adem

icse

ctor:

Com

par

isons

wit

h

indust

ry.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

26(1

),

173–183

The

auth

ors

com

par

epat

enti

ng

and

inven

tion

acti

vit

yof

scie

nti

sts

inth

eac

adem

icse

ctor

to

counte

rpar

tsin

indust

ry,

inden

tify

ing

sever

al

indic

ators

that

could

be

use

din

futu

re

rese

arch

toex

amin

epro

per

ty-f

ocu

sed

outp

uts

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Morg

anan

dS

tric

kla

nd

(2001

)M

org

an,

R.

P.,

&S

tric

kla

nd,

D.

E.

(2001)

U.S

.univ

ersi

ty

rese

arch

contr

ibuti

ons

toin

dust

ry.

Sci

ence

and

Publi

cP

oli

cy,

28(2

)113–122

As

fundin

gfr

om

gover

nm

ent

has

slow

edm

any

inst

ituti

ons

hav

eco

me

tore

lyon

alte

rnat

ive

fundin

gm

ethods

incl

udin

gow

nso

urc

e

inst

ituti

onal

fundin

gan

din

dust

ryso

urc

es

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Mow

ery

and

Sam

pat

(2001

)M

ow

ery,

D.

C.,

&S

ampat

,B

.N

.(2

001).

Pat

enti

ng

and

lice

nsi

ng

univ

ersi

tyin

ven

tions:

Les

sons

from

the

his

tory

of

the

rese

arch

corp

ora

tion

.In

dust

rial

And

Corp

ora

teC

hange,

10(2

),317–355

Confl

icts

of

inte

rest

inre

sear

chco

llab

ora

tion

has

rece

ived

agood

dea

lof

atte

nti

on

inth

e

lite

ratu

re

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Mull

enan

dR

amir

ez(2

006

)M

ull

en,

P.D

.,&

Ram

irez

,G

.(2

006).

The

pro

mis

ean

d

pit

fall

sof

syst

emat

icre

vie

ws,

Annual

Rev

iew

of

Publi

cH

ealt

h,

27:8

1–102

The

auth

ors

use

thei

rex

per

ience

as

coll

abora

tors

insy

stem

atic

revie

ws

of

the

lite

ratu

reto

dis

cuss

the

fals

ese

nse

of

rigor

impli

edby

the

term

s‘‘

syst

emat

icre

vie

w’’

and

‘‘m

eta-

anal

ysi

s’’

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Nat

ional

Aca

dem

yof

Engin

eeri

ng

(2003

)

Nat

ional

Aca

dem

yof

Engin

eeri

ng

(2003).

The

Impact

of

Aca

dem

icR

esea

rch

on

Indust

rial

Per

form

ance

.

Was

hin

gto

n,

DC

:N

atio

nal

Aca

dem

ies

Pre

ss

As

fundin

gfr

om

gover

nm

ent

has

slow

edm

any

inst

ituti

ons

hav

eco

me

tore

lyon

alte

rnat

ive

fundin

gm

ethods

incl

udin

gow

nso

urc

e

inst

ituti

onal

fundin

gan

din

dust

ryso

urc

es

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Ned

eva

etal

.(1

999

)N

edev

a,M

.,G

eorg

hio

u,

L.,

&H

alfp

enny,

P.

(1999).

Ben

efac

tors

or

Ben

efici

ary—

The

role

of

indust

ryin

the

support

of

univ

ersi

tyre

sear

cheq

uip

men

t.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

24(2

),139–147

Much

of

indust

ryfu

ndin

gco

mes

todev

elop

equip

men

tth

atco

uld

be

use

dfo

rfu

ture

appli

edre

sear

ch

Coll

abora

tion

attr

ibute

sN

ilss

on

etal

.(2

010

)N

ilss

on,

A.

S.,

Ric

kne,

A.,

&B

engts

son,

L.

(2010).

Tra

nsf

erof

acad

emic

rese

arch

:U

nco

ver

ing

the

gre

y

zone.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

35(6

),

617–636

Asu

pport

ive

infr

astr

uct

ure

inuniv

ersi

ties

can

fost

era

bel

ief

that

anin

div

idual

rese

arch

er

has

enough

soci

alca

pit

alto

par

tner

wit

h

indust

ry


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Nio

si(2

006

)N

iosi

,J.

(2006).

Intr

oduct

ion

toth

esy

mposi

um

:

Univ

ersi

ties

asa

sourc

eof

com

mer

cial

tech

nolo

gy.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

31(4

),399–402

Univ

ersi

ties

hav

ebee

nst

udie

das

am

ajor

pro

vid

erof

tech

nolo

gy

toin

dust

ryfo

rar

ound

30

yea

rs

Coll

abora

tion

attr

ibute

sP

erkm

ann

and

Wal

sh(2

009

)P

erkm

ann,

M.,

&W

alsh

,K

.(2

009).

The

two

face

sof

coll

abora

tion:

Impac

tsof

univ

ersi

ty–in

dust

ryre

lati

ons

on

publi

cre

sear

ch.

Indust

rial

and

Corp

ora

teC

hange,

18(6

),

1033

Res

earc

hco

llab

ora

tion

may

not

hav

edir

ect

econom

icim

pac

t

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Pic

hin

iet

al.

(2005)

Pic

hin

i,S

.,P

uli

do,

M.,

&G

arcı

a-A

lgar

,O

.(2

005).

Auth

ors

hip

inm

anusc

ripts

subm

itte

dto

bio

med

ical

journ

als:

anau

thor’

sposi

tion

and

its

val

ue.

Sci

ence

and

Engin

eeri

ng

Eth

ics,

11(2

),173–175

Auth

ors

addre

ssunif

orm

requir

emen

tsfo

r

coau

thors

hip

Coll

abora

tor

attr

ibute

sP

oll

akan

dN

iem

ann

(1998

)P

oll

ak,

K.

I.,

&N

iem

ann,

Y.

F.

(1998).

Bla

ckan

dw

hit

e

token

sin

acad

emia

:A

dif

fere

nce

of

chro

nic

ver

sus

acute

Dis

tinct

iven

ess1

.Jo

urn

al

of

Appli

edSoci

al

Psy

cholo

gy,

28(1

1),

954–972

Wom

enar

eunder

repre

sente

din

acad

emia

Coll

abora

tion

attr

ibute

sP

onds

(2009

)P

onds,

R.

(2009).

The

lim

its

toin

tern

atio

nal

izat

ion

of

scie

nti

fic

rese

arch

coll

abora

tion

.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

34(1

),76–94

Res

earc

hco

llab

ora

tion

isbec

om

ing

more

glo

bal

Coll

abora

tion

attr

ibute

sP

onom

ario

v(2

008

)P

onom

ario

v,

B.

L.

(2008),

Eff

ects

of

univ

ersi

ty

char

acte

rist

ics

on

scie

nti

sts’

inte

ract

ions

wit

hth

epri

vat

e

sect

or:

An

explo

rato

ryas

sess

men

t.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

33,

485–503

Info

rmal

inte

ract

ions

bet

wee

nuniv

ersi

ty

scie

nti

sts

and

pri

vat

ese

ctor

com

pan

ies

incr

ease

both

the

likel

ihood

and

inte

nsi

tyof

coll

abora

tive

rese

arch

Coll

abora

tion

attr

ibute

sP

onom

ario

van

dB

oar

dm

an

(2008

)

Ponom

ario

v,

B.,

&B

oar

dm

an,

P.

C.

(2008).

The

effe

ctof

info

rmal

indust

ryco

nta

cts

on

the

tim

euniv

ersi

ty

scie

nti

sts

allo

cate

toco

llab

ora

tive

rese

arch

wit

hin

dust

ry.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

33(3

),301–313

Info

rmal

inte

ract

ions

bet

wee

nuniv

ersi

ty

scie

nti

sts

and

pri

vat

ese

ctor

com

pan

ies

incr

ease

both

the

likel

ihood

and

inte

nsi

tyof

coll

abora

tive

rese

arch

Coll

abora

tor

attr

ibute

sP

onom

ario

van

dB

oar

dm

an

(2010

)

Ponom

ario

v,

B.

&B

oar

dm

an,

P.

C.

(2010).

Infl

uen

cing

scie

nti

sts’

coll

abora

tion

and

pro

duct

ivit

ypat

tern

sth

rough

new

inst

ituti

ons:

univ

ersi

tyre

sear

chce

nte

rsan

dsc

ienti

fic

and

tech

nic

alhum

anca

pit

al.

Res

earc

hP

oli

cy,

39,

613–624

Age

and

care

erag

edo

not

affe

ctco

llab

ora

tion


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Poyag

o-T

heo

toky

etal

.(2

002

)P

oyag

o-T

heo

toky,

J.,

Bea

th,

J.,

&S

iegel

,D

.S

.(2

002).

Univ

ersi

ties

and

Fundam

enta

lR

esea

rch:

Refl

ecti

ons

on

the

Gro

wth

of

Univ

ersi

ty–In

dust

ryP

artn

ersh

ips.

Oxf

ord

Rev

iew

of

Eco

nom

icP

oli

cy,1

8(1

),10–21

Agre

atdea

lof

work

,es

pec

iall

yin

indust

rial

and

org

aniz

atio

nal

econom

ics,

focu

ses

on

inst

ituti

onal

level

rese

arch

par

tner

ship

s

Coll

abora

tion

attr

ibute

sP

ravdic

and

Olu

ic-V

ukovic

(1986

)

Pra

vdic

,N

.,&

Olu

ic-V

ukovic

,V

.(1

986).

Dual

appro

ach

to

mult

iple

auth

ors

hip

inth

est

udy

of

coll

abora

tion/

scie

nti

fic

outp

ut

rela

tionsh

ip.

Sci

ento

met

rics

,10(5

),

259–280

Res

earc

hco

llab

ora

tion

tends

toen

han

ce

pro

duct

ivit

yof

scie

nti

fic

know

ledge

Coll

abora

tion

attr

ibute

sR

enau

lt(2

006

)R

enau

lt,

C.

S.

(2006).

Aca

dem

icca

pit

alis

man

duniv

ersi

ty

ince

nti

ves

for

facu

lty

entr

epre

neu

rship

.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

31(2

),227–239

Indiv

idual

bel

iefs

regar

din

gth

epro

per

role

of

univ

ersi

ties

inth

edis

sem

inat

ion

of

know

ledge

isver

yin

fluen

tial

indet

erm

inin

g

coll

abora

tion

pat

tern

sw

ith

indust

ry

The

dar

ksi

de

of

rese

arch

coll

abora

tions.

Ren

nie

and

Fla

nag

in(1

994

)R

ennie

,D

.&

Fla

nag

in,

A.

(1994).

Auth

ors

hip

!A

uth

ors

hip

!

Gues

ts,

ghost

s,gra

fter

s,an

dth

etw

o-s

ided

coin

.

JAM

A,2

71(6

),469–471

Unif

orm

stan

dar

ds

for

auth

ors

hip

use

donly

ina

han

dfu

lof

journ

als

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Ren

nie

etal

.(2

000)

Ren

nie

,D

.,F

lannag

in,

A,

&Y

ank,

Y.

(2000).

The

contr

ibuti

on

of

auth

ors

.J.

Am

.M

ed.

Ass

oc,

284:

89–91

Ther

eis

incr

easi

ng

inte

rest

inth

eet

hic

al

dil

emm

asas

soci

ated

wit

hre

sear

ch

coll

abora

tions

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Ren

nie

(2001

)R

ennie

,D

.(2

001)

Who

did

what

?A

uth

ors

hip

and

contr

ibuti

on

in2001.

Musc

le&

Ner

ve,

24(1

0),

1097–4598

Contr

ibuto

rship

refe

rsto

the

pro

cess

enta

ilas

auth

ors

dec

lare

indet

ail,

usu

ally

atti

me

of

subm

issi

on,

thei

rin

div

idual

contr

ibuti

ons

to

schola

rly

pap

ers

inth

esp

irit

of

scie

nti

fic

tran

spar

ency

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Rhoad

esan

dS

laughte

r(1

997

)R

hoad

es,

G.

&S

laughte

r,S

.(1

997).

Aca

dem

icC

apit

alis

m,

Man

aged

Pro

fess

ional

s,an

dS

upply

-Sid

eH

igher

Educa

tion.

Aca

dem

icL

abor,

51,

9–38

Ther

eca

nbe

neg

ativ

eas

pec

tsof

acad

emic

entr

epre

neu

rship

,es

pec

iall

yw

ith

ahyper

focu

son

pro

duct

pro

duct

ion

Coll

abora

tor

attr

ibute

sR

ijnso

ever

and

Hes

sels

(2011

)R

ignso

ever

,F

.J.

&H

esse

ls,

L.

K.

(2011).

Fac

tors

asso

ciat

edw

ith

dis

cipli

nar

yan

din

terd

isci

pli

nar

y

rese

arch

coll

abora

tion.

Res

earc

hP

oli

cy,

40(3

),463–472

Res

earc

hex

per

ience

isposi

tivel

yre

late

dto

univ

ersi

tyfa

cult

yboth

dis

cipli

nar

yan

d

inte

rdis

cipli

nar

yco

llab

ora

tion


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Sar

agoss

ian

dvan

Pott

elsb

erghe

de

laP

ott

erie

(2003

)

Sar

agoss

i,S

.,&

van

Pott

elsb

erghe

de

laP

ott

erie

,B

.(2

003).

What

pat

ent

dat

are

vea

lab

out

univ

ersi

ties

:T

he

case

of

Bel

giu

m.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

28(1

),

47–51

Pat

ent

stat

isti

csco

uld

be

aner

roneo

us

indic

ator

of

pro

duct

ivit

yor

addit

ional

ity

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Sch

arti

nger

etal

.(2

001)

Sch

arti

nger

,D

.,S

chib

any,

A.,

&G

assl

er,

H.

(2001).

Inte

ract

ive

rela

tions

bet

wee

nuniv

ersi

ties

and

firm

s:

Em

pir

ical

evid

ence

for

Aust

ria.

The

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

26(3

),255–268

Know

ledge

istr

ansf

erre

dfr

om

univ

ersi

ties

to

the

busi

nes

sse

ctor

larg

ely

thro

ugh

hum

an

capit

al

Coll

abora

tion

attr

ibute

sS

han

e(2

004

)S

han

e,S

.A

.(2

004).

Aca

dem

icen

trep

reneu

rship

:U

niv

ersi

tysp

inoff

sand

wea

lth

crea

tion

.E

dw

ard

Elg

ar

Publi

shin

g

Res

earc

hco

llab

ora

tion

may

not

hav

edir

ect

econom

icim

pac

t

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Shru

met

al.

(2001

)S

hru

m,

W.,

Chom

pal

ov,

I.,

&G

enuth

,J.

(2001).

Tru

st,

confl

ict

and

per

form

ance

insc

ienti

fic

coll

abora

tions.

Soci

al

Stu

die

sof

Sci

ence

,31(5

),681–730

Outs

ide

of

bio

med

ical

scie

nce

s,re

sear

chon

the

ethic

san

dso

cio-p

oli

tica

ldynam

ics

of

scie

nti

fic

coll

abora

tion

rem

ains

scar

ce

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Shru

met

al.

(2007

)S

hru

m,

W.,

Gen

uth

,J.

,&

Chom

pal

ov,

I.(2

007).

Str

uct

ure

sof

scie

nti

fic

coll

abora

tion

.M

ITP

ress

Outs

ide

of

bio

med

ical

scie

nce

s,re

sear

chon

the

ethic

san

dso

cio-p

oli

tica

ldynam

ics

of

scie

nti

fic

coll

abora

tion

rem

ains

scar

ce

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Sie

gel

etal

.(2

003a)

Sie

gel

,D

.S

.,W

aldm

an,

D.

A.,

Atw

ater

,L

.E

.,&

Lin

k,

A.

N.

(2003).

Com

mer

cial

know

ledge

tran

sfer

sfr

om

univ

ersi

ties

tofi

rms:

Impro

vin

gth

eef

fect

iven

ess

of

univ

ersi

ty–in

dust

ryco

llab

ora

tion.

The

Journ

al

of

Hig

hT

echnolo

gy

Managem

ent

Res

earc

h,

14(1

),111–133

An

import

ant

exte

rnal

acto

rw

ith

are

gula

tory

role

isth

atof

the

inst

ituti

onal

tech

nolo

gy

tran

sfer

offi

ceat

most

rese

arch

univ

ersi

ties

Coll

abora

tor

attr

ibute

sS

iegel

etal

.(2

003b)

Sie

gel

,D

.S

.,W

aldm

an,

D.

&L

ink,

A.

(2003).

Ass

essi

ng

the

Impac

tof

Org

aniz

atio

nal

Pra

ctic

eson

the

Rel

ativ

e

Pro

duct

ivit

yof

Univ

ersi

tyT

echnolo

gy

Tra

nsf

erO

ffice

s:

An

Explo

rato

ryS

tudy,

Res

earc

hP

oli

cy,

32,

27–48

When

dis

cuss

ing

outp

uts

and

asse

ssin

gth

e

focu

sof

said

outp

uts

,dif

fere

nt

stak

ehold

ers

inth

eco

llab

ora

tion

hav

edif

fere

nt

per

spec

tives

and

foci

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Sie

gel

etal

.(2

004)

Sie

gel

,D

.S

.,W

aldm

an,

D.

A.,

Atw

ater

,L

.E

.&

Lin

k,

A.

N.

(2004).

Tow

ard

am

odel

of

the

effe

ctiv

etr

ansf

erof

scie

nti

fic

know

ledge

from

acad

emic

ians

topra

ctit

ioner

s:

Qual

itat

ive

evid

ence

from

the

com

mer

cial

izat

ion

of

univ

ersi

tyte

chnolo

gie

s.Jo

urn

al

of

Engin

eeri

ng

and

Tec

hnolo

gy

Managem

ent,

21,

115–142

This

isone

of

sever

alem

pir

ical

studie

sof

univ

ersi

tyte

chnolo

gy

tran

sfer

offi

ces


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Sla

ughte

ran

dL

esli

e(1

997

)S

laughte

r,S

.&

Les

lie,

L.

L.

(1997).

Aca

dem

icC

apit

ali

sm:

Poli

tics

,P

oli

cies

,and

the

Entr

epre

neu

rial

Univ

ersi

ty.

Johns

Hopkin

sU

niv

ersi

tyP

ress

Ther

eca

nbe

neg

ativ

eas

pec

tsof

acad

emic

entr

epre

neu

rship

,es

pec

iall

yw

ith

ahyper

focu

son

pro

duct

pro

duct

ion

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Sla

ughte

ret

al.

(2002)

Sla

ughte

r,S

.,T

.C

ampbel

l,M

.H

.F

oll

eman

,&

E.

Morg

an

(2002)

The

‘‘tr

affi

c’’

ingra

duat

est

uden

ts:

Gra

duat

e

studen

tsas

token

sof

exch

ange

bet

wee

nac

adem

ean

d

indust

ry,

Sci

ence

,T

echnolo

gy

and

Hum

an

Valu

es,

27(2

),

282–313

Ther

eis

wid

espre

adco

nce

rnth

atst

uden

tsar

e

explo

ited

inre

sear

chco

llab

ora

tions

Coll

abora

tion

attr

ibute

sS

onnen

wal

d(2

007

)S

onnen

wal

d,

D.

H.

(2007).

Sci

enti

fic

coll

abora

tion.

Annual

Rev

iew

of

Info

rmati

on

Sci

ence

and

Tec

hnolo

gy,

41(1

),

643–681

R&

Dco

llab

ora

tion

pro

vid

esben

efits

,yet

countl

ess

reso

urc

esan

dhum

anen

ergie

sar

e

inves

ted

infa

cili

tati

ng,

induci

ng,

and

man

agin

gco

llab

ora

tion

Coll

abora

tor

contr

ibuto

rship

Sto

kes

and

Har

tley

(1989

)S

tokes

,T

.D

.&

Har

tley

,J.

A.

(1989).

Coau

thors

hip

,so

cial

stru

cture

and

infl

uen

cew

ithin

spec

ialt

ies.

Soci

al

Stu

die

sof

Sci

ence

,19(1

),101–125

Som

etim

esre

sear

cher

sli

sted

asco

-auth

ors

sim

ply

bec

ause

he

or

she

pro

vid

edm

ater

ial

or

per

form

edan

assa

y

Coll

abora

tor

attr

ibute

sS

tuar

tan

dD

ing

(2006

)S

tuar

t,T

.E

.&

Din

g,

W.

W.

(2006).

When

do

scie

nti

sts

bec

om

een

trep

reneu

rs?

The

soci

alst

ruct

ura

lan

tece

den

ts

of

com

mer

cial

acti

vit

yin

the

acad

emic

life

scie

nce

s,

Am

eric

an

Journ

al

of

Soci

olo

gy,

112(1

),97–144

This

arti

cle

dev

elops

the

conce

pt

of

acad

emic

entr

epre

neu

rism

Coll

abora

tion

attr

ibute

sS

ubra

man

yam

(1983

)S

ubra

man

yam

,K

.(1

983).

Bib

liom

etri

cst

udie

sof

rese

arch

coll

abora

tion:

Are

vie

w.

Journ

al

of

Info

rmati

on

Sci

ence

.

6(1

),33–38

Sci

enti

fic

rese

arch

isbec

om

ing

anin

crea

singly

coll

abora

tive

endea

vor.

The

nat

ure

and

mag

nit

ude

of

coll

abora

tion

var

yfr

om

one

dis

cipli

ne

toan

oth

er,

and

dep

end

upon

such

fact

ors

asth

enat

ure

of

the

rese

arch

pro

ble

m,

the

rese

arch

envir

onm

ent,

and

dem

ogra

phic

fact

ors

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Tar

tari

and

Bre

schi

(2011)

Tar

tari

,V

.&

Bre

schi,

S.

(2011).

Set

them

free

:S

cien

tist

s’

eval

uat

ions

of

ben

efits

and

cost

sof

univ

ersi

ty–in

dust

ry

rese

arch

coll

abora

tion

.In

dust

rial

and

Corp

ora

teC

hange,

21(5

),1117–1147

Acc

ess

toeq

uip

men

tan

dad

dit

ional

rese

arch

reso

urc

esis

am

ajor

ince

nti

ve

for

man

y

rese

arch

coll

abora

tions


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

Org

aniz

atio

nal

coll

abora

tion

attr

ibute

s

Thurs

by

etal

.(2

001

)T

hurs

by,

J.G

.,Je

nse

n,

R.,

&T

hurs

by,

M.

C.

(2001).

Obje

ctiv

es,

char

acte

rist

ics

and

outc

om

esof

univ

ersi

ty

lice

nsi

ng:

Asu

rvey

of

maj

or

US

univ

ersi

ties

.T

he

Journ

al

of

Tec

hnolo

gy

Tra

nsf

er,

26(1

),59–72

Pro

per

ty-f

ocu

sed

outp

ut

isa

val

idre

sear

ch

topic

for

exam

inin

gco

llab

ora

tion

Coll

abora

tion

attr

ibute

sT

oiv

anen

and

Ponom

ario

v

(2011

)

Toiv

anen

,H

.&

Ponom

ario

v,

B.

(2011)

Afr

ican

regio

nal

innovat

ion

syst

ems:

bib

liom

etri

can

alysi

sof

rese

arch

coll

abora

tion

pat

tern

s.Sci

ento

met

rics

,88(2

),471–493

This

arti

cle

note

sim

port

ant

stru

ctura

l

imped

imen

tsto

coll

abora

tion

Coll

abora

tor

attr

ibute

sT

urp

inet

al.

(2011

)T

urp

in,

T.,

Gar

rett

-Jones

,S

.,W

ooll

ey,

R.

(2011)

Cro

ss-

sect

or

rese

arch

coll

abora

tion

inA

ust

rali

a:T

he

Cooper

ativ

eR

esea

rch

Cen

tres

Pro

gra

mat

the

cross

road

s.

Sci

ence

&P

ubli

cP

oli

cy,

38,

2,

87–97

Man

agem

ent

beh

avio

ris

cruci

alto

dev

elop

a

pro

duct

ive

and

enco

ura

gin

gen

vir

onm

ent

for

scie

nti

sts

Coll

abora

tor

attr

ibute

sU

bfa

lan

dM

affi

oli

(2011

)U

bfa

l,D

.,&

Maf

fioli

,A

.(2

011)

The

impac

tof

fundin

gon

rese

arch

coll

abora

tion:

Evid

ence

from

adev

elopin

g

countr

y.

Res

earc

hP

oli

cy,

40(9

),1269–1279

Res

earc

hco

llab

ora

tions

are

oft

enbas

edon

info

rmal

rela

tionsh

ips

and

trust

Coll

abora

tion

attr

ibute

sV

asil

eiad

ou

(2012

)V

asil

eiad

ou,

E.

(2012).

Res

earc

hte

ams

asco

mple

x

syst

ems:

impli

cati

ons

for

know

ledge

man

agem

ent.

Know

ledge

Managem

ent

of

Res

earc

hP

ract

ice,

10(2

),

118–127

Rel

ativ

ely

litt

lere

sear

chfo

cuse

son

the

inte

rrel

atio

nof

man

agem

ent,

coll

abora

tion

and

effe

ctiv

enes

s

Coll

abora

tion

attr

ibute

sV

inkle

r(1

993

)V

inkle

r,P

.(1

993).

Res

earc

hco

ntr

ibuti

on,

auth

ors

hip

and

team

cooper

ativ

enes

s.Sci

ento

met

rics

,26(1

),213–230

Coau

thors

hip

isnot

the

bes

tm

easu

reof

coll

abora

tion

but

bro

ader

noti

ons

of

coll

abora

tion

are

not

oft

enea

syto

mea

sure

.

Focu

sing

on

co-a

uth

ors

hip

alle

via

tes

man

y

mea

sure

men

tpro

ble

ms

Coll

abora

tion

attr

ibute

sW

agner

(2005

)W

agner

,C

.S

.(2

005).

Six

case

studie

sof

inte

rnat

ional

coll

abora

tion

insc

ience

.Sci

ento

met

rics

,62(1

),3–26

This

isone

of

man

yuse

ful

studie

sof

coll

abora

tion

wher

eco

auth

ors

hip

isa

unit

of

mea

sure

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Wai

nw

right

etal

.(2

006)

Wai

nw

right,

S.

P.,

Wil

liam

s,C

.,M

ichae

l,M

.,F

arsi

des

,B

.,

Cri

bb,

A.

(2006).

Eth

ical

boundar

y-w

ork

inth

e

embry

onic

stem

cell

labora

tory

.Soci

olo

gy

of

Hea

lth

&Il

lnes

s,28(6

),1467–9566

Ther

eis

incr

easi

ng

inte

rest

inth

eet

hic

al

dil

emm

asas

soci

ated

wit

hre

sear

ch

coll

abora

tions


123

Ta

ble

1co

nti

nu

ed

Att

ribute

cate

gory

In-t

ext

cita

tion

Full

cita

tion

Rel

evan

tfi

ndin

gs

The

dar

ksi

de

of

rese

arch

coll

abora

tions

Wel

shet

al.

(2008

)W

elsh

,R

.,L

.G

lenn,

W.

Lac

y,

and

D.

Bis

cott

i(2

008).

Clo

seen

ough

but

not

too

far:

Ass

essi

ng

the

effe

cts

of

univ

ersi

ty–in

dust

ryre

sear

chre

lati

onsh

ips

and

the

rise

of

acad

emic

capit

alis

m.

Res

earc

hP

oli

cy,

37,

1255–1266

Coll

abora

tions

roote

din

indust

ry–univ

ersi

ty

par

tner

ship

soft

enhav

esa

luta

ryef

fect

sfo

r

studen

tsin

cludin

gea

rly

publi

cati

on,

job

off

ers

and

men

tori

ng

Coll

abora

tion

attr

ibute

sW

uch

ty,

Jones

and

Uzz

i(2

007

)W

uch

ty,

S.,

Jones

,B

.F

.,&

Uzz

i,B

.(2

007).

The

incr

easi

ng

dom

inan

ceof

team

sin

pro

duct

ion

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123

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