Hitotsubashi University
Institute of Innovation Research

Management of Science, Serendipity, and Research
Performance: Evidence from Scientists' Survey

Kota Murayama
Makoto Nirei
Hiroshi Shimizu

IIR Working Paper WP#13-13

May 2013

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Management of Science, Serendipity, and Research
Performance: Evidence from Scientists’ Survey
Kota Murayama∗

Makoto Nirei
Hitotsubashi University

Hiroshi Shimizu

Key words: science, serendipity, productivity, research management

Abstract
This study investigates the impact of management style on research performance inscience. If a managerial role is played by a leading scientist in the research team, thatis considered management-research integration. If not, we consider that management
and research are separated. We found that separating the managerial and researchrole has a positive eﬀect on the number of papers published for that research project.
In contrast, management-research integration is positively associated with the qualityof the paper through allowing researchers to pursue serendipitous ﬁndings. These
results show the trade-oﬀ between research eﬃciency and quality in science via whoplays the managerial role and the leading research role.

1

Introduction

Would Alexander Fleming have discovered penicillin if he had been part of a large researchteam? Would he have changed his research plan on inﬂuenza to explore a culture contaminated with a fungus in 1928 if his research project had been managed by an eﬃcient
project manager?
Using the scientists’ survey in the U.S. and Japan, this paper explores managementresearch separation and its eﬀect on serendipity and productivity in science. Serendipity plays an important role in science. Alexander Fleming’s discoveries of the enzyme
lysozyme in 1923 and penicillin from the mould Penicillium notaum in 1928 are frequently
cited examples of serendipity. The cosmic background radiation identiﬁed by Bell Lab
scientists Arno Penzias and Robert Wilson, the circular structure of benzene discoveredby Friedrich Kekulé, X-rays developed by Antoine Henri Becquerel, and Hans ChristianØrsted’s ﬁnding that electric currents create magnetic ﬁelds are also well-quoted examplesof serendipity. Many major discoveries have been made by people who were looking for∗

Correspondence to: Kota Murayama, Graduate School of Economics, Hitotsubashi University, 2-1
Naka, Kunitachi, Tokyo, 186-8603, Japan. (murayama0209@gmail.com)

1something very diﬀerent. As will be discussed in the next section, serendipity seemingly
happens in a random manner, implying that serendipity is not manageable. However, oneof the objectives of this paper is to explore how serendipity can be managed in a researchorganization.
The pattern of scientiﬁc research, often called “Big Science,”1 has changed since WorldWar II. Research projects have become increasingly larger in scope and size. The numberof researchers on a research project has increased as well (Adams et al. 2005). Advanced
research instruments require large budgets and a wide range of speciﬁc expertise. Thus,
large-scale, inter-disciplinary, and inter-organizational research has been of signiﬁcance(Agrawal and Goldfarb 2008, Austin et al. 2012). The importance of priority in scientiﬁcdiscovery has also increased (Ellison 2002, Stephan and Levin 1992). Since research is increasingly accomplished in teams across nearly all ﬁelds (Wuchty et al. 2007), managementof the research team becomes signiﬁcant to research performance in science.
Exploring the scientists’ survey, this paper investigates three points. The ﬁrst point
examines whether serendipity has a positive eﬀect on the quality of research. Much of
the anecdotal evidence suggests that serendipity does indeed have a positive eﬀect on thequality of research. However, this point, which is reviewed in the next section, has notbeen empirically examined.
The second point considers the eﬀect of research management on serendipity. Whena scientist makes a serendipitous ﬁnding, he or she is faced with an important choice: toﬂexibly change the research plan to pursue the serendipitous events or to stick closely
to the initial plan. The serendipitous ﬁnding comes unexpectedly in the form of a very
crude and nascent condition. Thus, the scientist is forced to make an intuitive decision
to pursue or not to pursue. As is reviewed in the following section, the choice would be
diﬃcult, particularly when the scientist is working as part of a research team managed by
a competent and eﬃcient project manager. The situation is seen not only in science but
also in business management. The point is related to the classical managerial challengeof top down or bottom up management. If managerial power is transferred to the on-the
spot director, he or she can fully desterilize uncodiﬁed and tacit knowledge and utilize
managerial resources in the context of the actual situation. However, if a hierarchicalmanagerial role is played top down, ﬁndings based on ground level intuition are seldom
utilized. A centralized bureaucracy cannot readily adopt new ideas or easily adapt toenvironmental changes due to its formalization (Gouldner 1954, Merton 1957, Selznick1949).
The third point concerns the eﬀect of division of labor in research and management
on research productivity. One of the advantages of a division of labor is the increased
eﬃciency resulting from specialization and concentration on a single subtask. Thus, if a1

Criticism has been presented from diﬀerent perspectives. For instance, the massive scale of defense
related funding channeled research in physics from basic to applied (Forman 1987). This paper does notsuppose that the Big Science is favorable to progress in science and technology.

2

leading scientist is separated from a managerial role, he or she could focus on researchand increase productivity. A specialized project manager can also be fully responsible
for the progress of a research project. The top down hierarchical management facilitatescompletion of the original research goal. In other words, the second and third points touch
the dilemma in management between exploration and exploitation (March 1991).
Through exploring the scientists’ survey, this paper investigates the eﬀects of sciencemanagement division on serendipity and productivity and presents the following. First,
serendipity has a positive eﬀect on citation. This is consistent with the anecdotal evidence suggesting that major scientiﬁc discoveries are likely to be serendipitous. Second,
the integration of a managerial and leading research role has a positive eﬀect on serendipity. However, this positive eﬀect diminishes as the size of the research project increases.
This implies that integration reduces coordination costs between management and actual
research and provides ﬂexibility in research to scientists, while the advantage of the division of labor in management and science increases as the project gets larger. Third,
the separation of management from research has a larger positive eﬀect on the number of
papers as the project becomes bigger. These empirical results suggest a tradeoﬀ between
serendipity and productivity in science via who plays the managerial and leading researchroles in research management.
In the next section, this paper deﬁnes serendipity and reviews previous literature on
management of science, serendipity, and productivity. Next, it introduces the estimation
strategy and data, which come from the scientists’ survey, in detail. Then, it shows theestimated results, summarizes the ﬁndings, and shows policy and managerial implications.

2Management and Serendipity

Research is rarely done in isolation; rather, research is increasingly being done by a team.
For example, the mean number of authors per paper increased from 2.8 in 1981 to 4.2 in1999 (Adams et al. 2005). Adams et al. (2005) observed that team size increased by 50%
over a 19-year period. This trend continues. In the 1980s, the growth rate was 2.19%; thatrate rose to 2.57% in the 1990s. By showing that scientiﬁc output and inﬂuence increase
with team size, the authors imply that research productivity increases with the divisionof labor in research.
There are several factors behind this increasing trend in team size. Several studies have shown that collaborative research produces better outcomes with higher citation
rates (Andrews 1979, Presser 1980, Sauer 1988, Wuchty et al. 2007). This suggests that interdisciplinary research has become increasingly important. The Internet and institutionalchange have decreased communication costs and promoted increasing team size (Agrawal
and Goldfarb 2008). The increase of team size in scientiﬁc research in the U.S. has been
attributed to the deployment of the National Science Foundation’s NSFNET and its connection to networks in Europe and Japan after 1987 (Adams et al. 2005). Advancement of3

research equipment (e.g., cyclotron, particle accelerators, and high-ﬂux research reactors)
increases both collaboration and team size. The experimental design has also changed
from table-top experiments to large-scale projects. This, too, accompanies changes in thepattern of collaborations among researchers because the new experimental tools require
much diﬀerent expertise.
Many researchers have suggested that diversity in a research team can lead to a greaterlevel of creativity (Allen 1977, Garvey 1979, Kasperson 1978, Pelled et al. 1999). S. andF. (2010) suggested that collaboration reduces the probability of very poor outcomes because of more rigorous selection processes and greater recombinant opportunity in creative
searches. Zuckerman (1977) showed that nearly two-thirds of the 286 Nobel Prize winners named between 1901 and 1972 were honored for work they did collaboratively. By
investigating the conditions under which major discoveries or fundamental new knowledge occur in science, Hollingsworth (2006) stated that scientists are likely to develop
new and alternative ways of thinking if they interact with scientists with diverse expertiseand backgrounds. With the advancement of information and communication technology
and institutional changes, scientists could obtain relevant but diﬀerent knowledge by collaborating with other scientists in areas outside of their specialties. Accessing external
complementary knowledge and expertise through networking becomes signiﬁcant when
promoting innovation not only in business, but also in science (Fleming et al. 2007, Hagedoorn 2002, Heinze et al. 2009, Powell et al. 1996).
The increase of team size and diversity suggests that management becomes importantin science. Managing and coordinating research processes and diﬀerent expertise and synchronizing eﬀorts into a team goal do not happen naturally (Barnard 1938, Simon 1976).
If the research team becomes larger and the research becomes more inter-disciplinary andinter-organizational, the role played by research management will be greater. For example, it is important to manage a certain space, called a “trading zone,” in which groupsof diﬀerent expertise learn to interact and deliver breakthroughs in science (Collins andGorman 2007, Galison 1997).
Furthermore, competition in science becomes ﬁercer. The importance of priority inscientiﬁc discovery has risen (Ellison 2002, Stephan and Levin 1992). There is competition
not only for priority in scientiﬁc discovery, but also for research funding. Thus, it isincreasingly important for a research team to choose a research area and method and to
set a research goal to minimize the threat of being “scooped” (Dasgupta and David 1994,
Stephan and Levin 1992).
As research increasingly becomes large scale and requires a high level of technical andscientiﬁc knowledge, and competition becomes ﬁercer, management of science is becoming
increasingly important. Following the division of labor and coordination costs framework(Becker and Murphy 1992), this paper, which considers the research team closely linked
with specialization and the division of labor, explores the impact of management on performance in scientiﬁc research.

4

The word “serendipity” was coined by the novelist Horace Walpole, who was inspiredby the Persian fairy tale, Three Princes of Serendip. Merton and Barber (2004) explored
how the word unexpectedly won publicity without clear deﬁnition and fared from its
1754 coinage to the twentieth century. In scientiﬁc circles, the word has been used since
the nineteenth century, when the importance of unplanned and accidental factors in themaking of scientiﬁc discovery gained increasing recognition (Merton and Barber 2004).
Serendipity has been often noted for its role in the work of inventors and entrepreneursby persons such as George W. Merck, a president of Merck & Co., and Willis Whitney,a director of research of the General Electric Laboratories, the Pﬁzer Company (Merton
and Barber 2004).
In the colloquial sense, serendipity is the making of happy and unexpected discoveries. Many anecdotal stories reveal how unintentional ﬁndings have yielded unexpectedly
fortunate results. Many great discoveries, such as penicillin, X-rays, celluloid, and artiﬁcial sweetener, have been utterly fortuitous, making the concept of serendipity not
well-operationalized (Roberts 1989, Shapiro 1986). It is uncertain whether the accidental
nature of serendipity is linked with the nature of the discovery process or with the unexpected impact of the discovery. However, upon closer examination, it is obvious that theunplanned and accidental nature of serendipity has to do only with the discovery process.
This is reﬂected in oﬃcial deﬁnitions of the word. For instance, The American Heritage
Dictionary of the English Language, fourth edition, deﬁnes serendipity as “the faculty ofmaking fortunate discoveries by accident.” Furthermore, distinguishing between the unexpected and the accidental is diﬃcult, especially when research involves exploration of theunknown.
Therefore, in order to operationalize the concept of serendipity, it is appropriate tothink of serendipity as “the act of ﬁnding answers to questions not yet posed” (Stephan2010). This deﬁnition focuses on not only the discovery process, but also on the relationship between discovery and the speciﬁc research question. Even though this deﬁnition
directs its attention speciﬁcally to the extent that the discovery answers a question not yet
posed, this paper adopts this deﬁnition and explores the relationship between management
and serendipity in science.

3

Estimation Strategy

3.1

Hypotheses

This paper directs its attention to the managerial role played in a research team in order
to explore the eﬀect of management on serendipity and productivity in science research,focusing on three aspects. The ﬁrst aspect examines the relationship between serendipity
and research performance. Based on anecdotal evidence, we assume that serendipity
improves the quality of research. Hence, our ﬁrst hypothesis to be explored is the following:5H-1: The existence of serendipity has a positive eﬀect on quality of research
The second aspect is related to the discussion about information asymmetry between
management and research, which is closely related to the discussion on serendipity. Scientists possess specialized and domain-speciﬁc expertise. As previous literature on scientiﬁcdiscovery has expressed, the nature of scientiﬁc discovery is highly unpredictable (Polanyi1962), and tacit and uncodiﬁed knowledge plays an important role in research, even though
the outcomes of research are usually codiﬁed and published (Collins and Harrison 1975,
Polanyi 1967). Learning is highly situated in the on-site context (Brown and Duguid 1991,
Kogut and Zander 1992, Lave and Wenger 1991). When scientists are committed to actual research, they encounter unexpected observations and ﬁndings. Thus, if a managerialrole on the research project team and a leading role in the actual research are playedby diﬀerent individuals, the research project will have information asymmetry between
management and actual research. When a scientist observes unexpected but potentiallycreative serendipitous ﬁndings or encounters a serendipitous idea, he or she needs to encourage the person who plays a managerial role to change the initial research plan inorder to pursue serendipity. Presenting a serendipitous encounter to a manager may berisky, particularly when the new idea or observation is contrary to accepted ways of doingor thinking about things (Pelz and Andrews 1966). Thus, even if a surprising fact orrelation is observed, there may be a case in which it is not (optimally) investigated by
the discoverer (Barber and Fox 1958, Van Andel 1992). In contrast, if a core scientist isalso responsible for project management, the coordination and communication costs for
shifting research to pursure serendipitous ﬁndings will be decreased. Hence, this paper
investigates the following hypothesis:
H-2: Serendipity is positively related to the integration of core-scientists from management
However, if a core scientist plays a managerial role, the advantage of division of labor inscience will not be fully realized. Eﬃciency is increased by specialization and concentration
on a single subtask. Managing a research team and conducting research require diﬀerent
sets of expertise. Thus, it is possible to suppose that if a core scientist is separated froma managerial role, he or she can focus on the research. This is important, particularly fora large scale research project, which requires many bureaucratic procedures, paper work,and managerial tasks. This paper, therefore, explores the following hypothesis:
H-3: Research productivity is positively related to the separation of core-scientists frommanagement

3.2

Data Description

We use the data from the scientists’ survey conducted in the U.S. and Japan between 20092011 jointly by Hitotsubashi University; the National Institute of Science and Technology
Policy (NISTEP) of the Ministry of Education, Culture, Sports, Science, and Technology;

6and the Georgia Institute of Technology.2 This survey collected approximately 2,100
responses from scientists in Japan and 2,300 responses from scientists in the U.S. regarding
their research projects.3 The population of the survey was comprised of articles and lettersin the Web of Science database of Thomson Reuters. Review papers were excluded fromthe population. The time window of the papers for the survey was from 2001 to 2006
(database year), during which time the published papers were recorded in the Web of
Science database. The bibliographic information and the number of citations as of the endof December 2006 were used in the survey. This survey selected two sets of focal papers
from the population. The ﬁrst was “Highly Cited” Papers (H paper), which consistedof the top 1% highly cited papers in each journal ﬁeld (22 ﬁelds in total) and from eachdatabase year. The other set was “Normal” Papers (N paper), or randomly selected papers
in each journal ﬁeld and in each database year from the population of the survey, excluding
highly cited papers. Roughly one-third of the samples were from highly cited papers (top
1% in the world) in each science ﬁeld; the rest were from randomly selected papers.
The survey asked questions about the following topics: the knowledge sources which
inspired the projects; uncertainty in the knowledge creation process; research competition;
composition of the research team; sources of research funding; the research outputs, including papers, patents, and licenses; and the proﬁle of scientists. The survey speciﬁcallyaddressed managerial roles and serendipity, to which this paper directs its attention, as
well. The survey asked the corresponding author of the focal paper his role in managementof the research project and research implementation. As for the exitence of serendipity,
this survey asked: “Has the research output found the answers to questions not originallyposed (in other words, was the research output serendipitous)?” Approximately 55% of
respondents answered his/her main ﬁnding was obtained through serendipity.4 In the
Appendix, we provide the list of these threee questions and responses reported in thesurvey.

3.3

Estimation Methodology

To examine our three hypotheses, we employ the following estimation methods respectively.
H-1 model investigates the relationship between serendipity and research quality. Remarkably, a simple regression shows no signiﬁcant relation between serendipity and performance. However, we argue that this insigniﬁcant relationship is caused by the endogeneity
between serendipity and research performance. A research team pursues publication only
when it considers it to be valuable. Hence, the observed citation rate might reﬂect not
only the fact that a serendipitous event has uncovered in research, but also the research
2

See Nagaoka et al. (2011) for the detailed results.
The overall response rate was 27% in Japan and 26% in the U.S.
4
Approximately 61% of respondents in the ﬁeld of Computer Science reports serendipity, which is thelargest fraction among all the ﬁleds. In contrast, only 42% of social scientists reports serendipity.
3

7

team’s ex ante evaluation of that discovery. For this reason, we use a Two-Stage Least
Squares (2SLS). In addition, since the survey oversampled the top 1% highly cited papers
and our dependent variable, the number of citations, correlates with this biased sampling,
we need to appropriately weight each sample. These two problems are explained in detailin the following section.
We conduct probit regression for the H-2 model since the dependent variable, theexistence of serendipity, is a binary. One crucial problem with probit regression is its
fragility to heteroskedasticity of the error term. Hence, we test whether our results arerobust to the misspeciﬁcation of the error term, and claim that our hypothesis still holds.
For the H-3 model, in which the relationship between research productivity and thedegree of management-research integration is examined, we use a Negative Binomial (NB)
regression with assuming that the variance of dependent variable takes quadratic form.5
Since research productivity is measured by the number of papers produced by the entire
research project, our dependent variable is necessarily discrete, and its empirical distribution concentrates at 1. For these reasons, we choose the NB regression model.6 Since theestimate of the NB regression coincides with that of quasi-maximum likelihood, the estimator is robust to misspeciﬁcation of the distribution of the dependent variable. That is, the
NB regression yields a consistent estimator as long as the speciﬁcation of the conditional
expectation of regressand is correct.7

3.4

Definition of Variables

This section introduces the deﬁnitions of variables used in our models. Table 2 presents
a complete list of variables and their deﬁnitions, and Table 3 shows summary statistics ofall variables.
3.4.1

Dependent Variables

Diﬀerent models adopt diﬀerent dependent variables, respectively. To measure research
productivity, we use the number of articles published by the entire research project. Research quality is measured by the total number of citations by 2009.8 H-2 model uses theexistence of serendipity as a dependent variable. This variable takes one if a respondent
answered that his main ﬁndings were obtained through serendipity.

5

More speciﬁcally, we use a zero-truncated NB regression, in which the dependent variable is truncatedat zero. This is because we only have data on research projects that published at least one paper.
6
Poisson regression may not be suitable in our case since the equi-dispersion hypothesis is strongly
rejected (at the 1% signiﬁcant level).
7
See Cameron and Trivedi (2005) for its textbook treatment. Also, see Ding et al. (2010) for an
application in a related context.
8
Some papers might considerably increase citation numbers after 2009. But since 70% of papers in 2008
increased by less than 10 in their citation number from 2007, we think that the total number of citations
by 2009 can be a reasonable approximation.

8

3.4.2

Independent Variables

Management structure is measured by two mutually exclusive variables. Integration isa dummy variable that indicates one only if the researcher takes a leading role in the
research management, designing the research project, organizing the research team, and/or
acquiring research funds. On the other hand, separation is a dummy variable that indicates
one only if the researcher plays no management role.9
Variables that describe a research project’s characteristics are project size, project duration, fund size, competitor threat, and inter-organizational communication. Project size
is a number of people involved in the project, which includes corroborative researchers(excluding coauthors), graduate students, undergraduates, and technicians. Since not all
projects had been terminated by the time of the survey, project duration is calculated
by subtracting the year when the project started from the year of the most recent corresponding publication. Fund size is the total sum of research funds prepared for the
project. Competitor threat is a binary variable and takes one only if the researcher considered the possibility of competitors who may have had priority over the research results.
If the project developed a community of researchers beyond the original laboratory, interorganizational communication indicates one, otherwise it takes zero.
Scientists are classiﬁed by the following characteristics: age, degree, past awards, pasttransfers, past publications, aﬃliation, and country. Age is respondent’s age at the timeof the survey. Degree is shown as one if the researcher has a Ph.D. or equivalent degree.
Award is a binary variable that takes one if the researcher received a distinguished paper
award or a conference award. If the respondent had changed academic or research position
across organizations before the survey, past tranfers takes on the value of one. Past
publication measures the number of referred papers in English published from 2006 to
2008. Aﬃliation equals one if the respondent works for universities. Country shows one
for the respondents in the U.S. and shows zero for the respondents in Japan.
We control the respondent’s research ﬁeld based on the survey’s classiﬁcation. Table
1 shows a correspondence between its classiﬁcation and the 22 ESI journal ﬁelds. All
scientiﬁc areas are divided into ten ﬁelds: Chemistry, Materials Science, Physics & Space
Science, Computer Science & Mathematics, Engineering, Environment/Ecology & Geosciences, Clinical Medicine & Psychiatry/Psychology, Agricultural Sciences & Plant &Animal Sciences, Basic Life Sciences, and Social Sciences.10 We also take into accounteach researcher’s research skill, as well as his or her specialty in theory or experimentation.

9

Hence, both variables taking zero means that a researcher is involved in management to some extent.
Social Sciences may be a fairly broad ﬁeld compared to other categories. However, since about 95%
of respondents are natural scientists, this makes no signiﬁcant diﬀerence.
10

9

3.5

Estimation Issues

3.5.1

Endogeneity Bias

Serendipity is likely to be endogenous in the following sense. It is reasonable to assume
that a scientiﬁc ﬁnding is pursued only when the researcher evaluates its quality and thenbelieves that it is worth pushing forward. If so, there can be two directions of endogenouseﬀects from the measured citation to the reported serendipity. The ﬁrst possibility is thatthe researcher may be less experienced in the ﬁeld in which serendipity occurs than in his
or her ﬁeld of expertise; hence, the ﬁnding seems more novel to the researcher, who overestimates the value of the serendipitous ﬁnding. The second possibility is that the researcher
pursues his/her serendipitous ﬁnding only if it is highly valuable, since shifting research
direction seems more risky. We cannot determine which path of endogenity dominates theother, but both hypotheses suggest that, without crowding out the correlation between theexistence of serendipity and the researcher’s ex-ante evaluation of serendipitous ﬁnding,we underestimate or overestimate the eﬀect of serendipity on research quality. Indeed, in
H-1 model, the result of the variable addition test rejects the hypothesis that serendipity
is an exogenous variable at the 5% signiﬁcance level.
We use instrumental variables to deal with this problem.11 Here, instrumental variables must correlate with the existence of serendipitous ﬁndings, but they must not aﬀect
the ex-ante evaluation of ﬁndings. With this criterion, we instrument serendipity withtwo variables, skill diversity and knowledge inﬂow. Skill diversity is a dummy variable
that becomes one if the researcher stated that it was very important to communicate
with researchers who have diﬀerent research skills, for example, experimental researchers
communicating with theorists. Knowledge inﬂow is also a dummy variable that becomes
one if the researcher stated that it was very important to communicate with visiting researchers or postdoctoral researchers in his or her organization. Serendipity is highly likely
to correlate with these two variables, since complementarity in knowledge and skills are
key to improving creativity. Our argument is supported, for example, by Heinze et al.
(2009), who observed that the most important types of communication to inspire a researcher’s creativity includes specialists who are equipped with knowledge or skills thatthe researcher himself does not possess. We assume that these instruments and the ex-anteevaluation of ﬁndings are uncorrelated, since the former is related to the entire project,
not only to the focal paper.12
3.5.2

Sampling Bias

One-third of the samples are randomly chosen from researchers who wrote a top 1% highly
cited paper. Hence, our samples are not randomly drawn from the entire population. We
11Angrist and Pischke (2008) provides a lengthy treatment on how to take advantage of instrumental
variables from a practitioner’s view.
12
As a robustness check, we also examine other instrument variables. Results are reported in Table 8.

10

must consider this problem to yield a consistent estimator for the H-1 model, since thestratiﬁcation depends on the regressand (i.e., the number of citations). Ignoring thisendogenous stratiﬁcation yields biased estimator.
A straightforward provision for this problem is to introduce a weighting matrix whosei th diagonal element is Qji /Hji , where the numerator is the probability that a randomly
drawn observation from the population falls into stratum j and the denominator is thefraction of observations in stratum j for each observation i.13 Under reasonable regularity conditions, this weighted least squares estimator is ensured to be consistent andasymptotically normally distributed.14 Also, with a slight modiﬁcation on White’s (1980)
heteroskedasticity-consistent covariance matrix, a consistent estimator of covariance matrix can be obtained.

4

Results

4.1

Baseline Estimates

We ﬁrst summarize results on the H-1 regression. In this model, our unit of analysis isa focal paper, not the entire research project. Moreover, we only use observations forwhich the respondent is the ﬁrst author of the focal paper. Table 4 shows the estimation
result. We observe that serendipity has a positive eﬀect on the number of citations at
the 5% signiﬁcance level in the 2SLS regression. This conﬁrms our hypothesis H-1. We
also note in Table 4 that the research quality is high when the researcher perceives thethreat from competition, has experienced transfer between institutions, or has a strong
publication record. In addition, the integration of management and research may have apositive eﬀect on the research quality by oﬀsetting the negative eﬀect of increasing projectsize. Along with the estimated eﬀect of past transfer, this observation may be consistent
with the recent literature which argues that researchers who have more opportunities tocommunicate with other researchers tend to show higher creativity (Fleming et al. 2007,
Powell et al. 1996).
Model H-2 examines the connection between management structure and serendipity.
In Table 5, the column labeled “Model 2-1” exhibits the result of the probit regression for
H-2. We observe that the integration of management has a positive eﬀect on serendipity.
However, the eﬀect tends to attenuate as the project size increases. That is, the integrationof management encourages researchers to pursue the serendipitous idea more often, only
when management tasks are not too intense. Moreover, estimates in Table 5 indicatethat serendipity is reported more often when the researchers are more open to knowledge
inﬂow from visiting researchers and post-doctoral researchers, or to communication withresearchers with diﬀerent skill sets and outside their own laboratories. This result also
13

In our regressions, we have only two stratum, and weights are 0.032 for samples from the highly citedgroup, and 1.433 for others.
14
For the formal treatment, see Wooldridge (2010a,b).

11

justiﬁes our use of instrumental variables.
The regression results of model H-3 are shown in Table 6. The unit of analysis here is
the entire research project, unlike previous regressions. This is because H-3 is concernedwith the eﬀect of management structure on the productivity of the entire project. We use
observations for which the respondent was the researcher who took a central role of theresearch and contributed the most. Our hypothesis is that the separation of managementfrom research increases research productivity. The estimates reported in Table 6 conformto the hypothesis. Note that all coeﬃcients in Table 6 can be interpreted as a semielasticity.
The estimate of the interaction term between separation of management and project sizeis negative. However, since the estimate of the interaction term between separation ofmanagement and the squared project size is positive, as the project size gets bigger,
separation increases the number of papers. That is, separating management from researchis beneﬁcial only when management is strongly needed.

4.2

Robustness Checks

We conduct various robustness checks. First, since the variance of citation number can besigniﬁcantly diﬀerent across ﬁelds, we standardize the citation by dividing the dependent
variable of H-1 model, ln(cite2009), by its standard deviation by ﬁeld. The results shownin Table 7 indicate that the estimates are qualitatively unchanged. In addition, we test thevalidity of our choice of instrumental variables. Four diﬀerent choices are examined: skill
diversity (Model 1-1), knowledge inﬂow (Model 1-2), inter-organizational communicationand skill diversity (Model 1-3), and inter-organizational communication and knowledge
inﬂow (Model 1-4). Table 8 shows the results respectively. Though some estimates show
somewhat weaker coeﬃcient, all of the four models imply that serendipity and the numberof citations are positively correlated, so that our hypothesis is maintained.
Second, for the H-2 probit, we test the robustness to the heteroskedasticity of the error
term. Now the error term is assumed to be heteroskedastic with a variance of
σi2 = exp(zi δ),
where zi is an exogenous variable. We choose two candidates for zi , project duration andresearch fund. In both cases, the likelihood-ratio test rejects the hypothesis that the error
term is homoskedastic at the 5% signiﬁcance level. Models 2-2 and 2-3 in Table 5 showthese results, and all the results are still consistent with our hypothesis.
Finally, we alter the dependent variable in model H-3 to the number of referred papers
in all languages instead of only in English. The estimates in Table 6 show that all theimportant results of this paper are still qualitatively unchanged.

125Conclusion

This paper has investigated the inﬂuence of management-science integration on serendipity and productivity in scientiﬁc research. The major estimated results show that the
integration of a managerial and a leading research role has a positive eﬀect on serendipity.
However, this eﬀect diminishes as the project increases in size and scope. This implies
that integration reduces coordination costs between management and research and provides ﬂexibility in research to scientists, while the advantage of the division of labor inmanagement and science increases as the project size increases. It also shows that the
separation of management from research has a larger, positive eﬀect on the number of
papers as the project becomes bigger. These results show the tradeoﬀ between serendipity
and productivity in science via who plays the managerial role and leading research role inresearch management.
Serendipity plays an essential role in discoveries not only in science, but also in technology, management, business practices, art, and daily life (Jacobs 2010, Svensson andWood 2005, Van Andel 1992). This paper examines management and research in science,and its ﬁndings have implications for corporate R&D and university research administrators. The ﬁndings of this paper imply that bureaucratic coordination, which enlarges
information asymmetry and incommensurability between management and research, profits from serendipitous encounters. It is quite consistent with contingency theory between
complexity of environment (e.g., demand, strategic positioning, and technology) and organizational structure (Burns and Stalker 1961, Lawrence and Lorsch 1967, Scott 1981).
Decentralized and less formalized management that allows a high degree of ﬂexibility issuitable when an organization faces many exceptional problems and problem solving is noteasy (Perrow 1967, Woodward 1965). This suggests that decision-making should be done
where important information is gathered and knowledge is created if environmental change
is uncertain but highly frequent. The more embedded the knowledge, the greater autonomy of the R&D unit (Birkinshaw et al. 2002). However, operational administrators areusually trained to complete the project’s goal. In fact, they attempt to manage in a waythat will eliminate uncertainty in their aﬀairs so that they can meet budgets and target
deadlines (Udwadia 1990). This may be one of the reasons that it is diﬃcult for corporate
R&D overseen by a central business manager to proﬁt from serendipitous ﬁndings at thelaboratory. It suggests that Alexander Fleming would have faced diﬃculties in changing
his original research plan to pursue the serendipitous ﬁndings if he had been working ina corporate laboratory and his research had been led by a competent project manager.
In other words, Fleming would have not pursued the serendipitous ﬁndings, but he would
have delivered more papers concerning the original research project if a managerial role
had been played by a specialized director. It also suggests that it is quite important fora university research administrator to fully understand the nature of discovery in scienceand the tradeoﬀ between serendipity and productivity in science via who plays the man-13

agerial and leading research roles in research management (Kaplan 1959, Kulakowski and
Chronister 2006).
To conclude this paper, we mention some limitations that future research should address more explicitly. A key result suggests that if scientiﬁc research is bureaucraticallycontrolled in a research organization, serendipitous encounters will not be realized. In
other words, even when a managerial role and a leading research role are played by diﬀerent
people, serendipity will be realized if a manager shares tacit and domain-speciﬁc knowledge with leading scientists and understands the nature of scientiﬁc discovery. This paper
presupposes a certain degree of incommensurability, which was proposed by “Khunianparadigm arguments” (Kuhn 1970) between a manager and leading scientists. However,the degree of incommensurability depends on a manager’s expertise and capabilities. Since
the scientists’ survey does not allow investigating a manager’s capabilities, this paper does
not explore the quality of managers in a research organization. Organizations for university research administrators such as SRA (Society of Research Administrators), NCURA(National Council of University Research Administrators) in the U.S., and ARAM (Association of Research Managers and Administrators) in the U.K, has been established since the1960s. And not only these organizations but also governments（e.g., the Development ofa Research Administration System program launched by Ministry of Education, Culture,
Sports, Science, and Technology-Japan) are beginning to consider that a managerial roleshould be played by a specialist who can share tacit and domain-speciﬁc knowledge withleading scientists; scientists could then focus on large-scale research projects, which could
have managerial ﬂexibility for realizing serendipitous encounters. Previous literature on
how scientists with diﬀerent expertise and diﬀerent paradigms communicate has indicatedthat scientists communicate in groups called “trading zones” where they can agree on rulesof exchange, learn language, and share tacit knowledge (Collins and Gorman 2007, Galison
1997, 1999). However, since the extent to which managers and scientists can reduce thedegree of incommensurability depends on a manager’s ability, it is important to explore amanager’s expertise and capabilities on the research outcome in detail.

14

Table 1: List of Fields
22 ESI Journal Fields
Agricultural Sciences
Plant & Animal Science
Biology & Biochemistry
Immunology
Microbiology
Neuroscience & Behavior
Pharmacology & Genetics
Chemistry
Clinical Medicine
Psychiatry/Psychology
Computer Science
Mathmatics
Economics & BusinessSocial Science, general
Engineering
Environment/Ecology
Geosciences
Material Science
Multidisciplinary

10 Fields
Agricultural Sciences &Plant & Animal Science
Basic Life Science

Number of Papers
349

Percentage
7.9

910

20.6

Chemistry
Clinical Medicine &Psychiatry/Psychology
Computer Science &Mathmatics
Social Sciences

441
710

10.0
16.1

208

4.7

250

5.7

Engineering
Environment/Ecology &Geosciences
Material Science

368
308

8.3
7.0

214
13

4.9
0.3

639

14.5

4410

100

(Journal ﬁeld was assigned based on
the analysis of the backward citations)Physics
Space Science

Physics &Space Science

Total

15

Table 2: Deﬁnitions of Variables
Variable
serendipity# of published papers# of citation
project size
project duration
research fundseparation

integrationcompetitor threatskill diversity

knowledge inﬂow

inter-org commpast publication
degreeaward
ageyear in paperaﬃliation
country
theoryexperiment

Definition
Equals one if his/her main ﬁndings are obtained through serendipity.
The number of papers published in a research project.
Cumulative number of citations in 2009.
Sum of the number of corraborative researchers, graduate students,
undergraduates, and technicians involved in the project.
Years since the research project was launched.
The total sum of research funds prepared for the project.
Takes one if the researcher takes a leading role in the research management, designing the research project, organizing the research team,
and/or acquiring research funds.
Takes one if the researcher plays no managment role.
Takes one if the researcher considered the possibility of competitors
who may have had priority over the research results.
Takes one if the researcher states that communication with researchers
who have diﬀerent research skills was important for conceiving the
research project.
Takes one if the researcher states that communication with visiting
researchers or postdoctoral researchers was important for conceiving
the research project.
Takes one if the researcher built a research community beyond ownlaboratory.
The number of referred papers in English that the researcher publishedfrom 2006 to 2008.
Takes one if the researcher has a Ph.D. or equivalent degree.
Takes one if the researcher received a distinguished paper award or aconference award.
Respondent’s age at the time of survey.
The amount of years to publish the paper.
Takes one if the researcher works for universities.
Takes one for the respondents in the U.S. and shows zero for the respondents in Japan.
Takes one if the researcher specializes in theoretical work.
Takes one if the researcher specializes in experiments.

16

Table 3: Summary Statistics
Variable
serendipity# of published papers# of citation
project size
project duration
research fund ($)
separation
integrationcompetitor threatskill diversity
knowledge inﬂow
inter-org commpast publication
degreeaward
agecountry
year in paperaﬃliation
theoryexperiment

Mean
0.558
15.01259.0094.386
6.988
1894156
0.062
0.696
1.089
0.589
0.552
0.555
25.8530.134
0.377
51.452
0.528
2.956
0.731
0.193
0.652

Std. Dev.
0.497
39.889108.288
19.7064.903
11167946
0.241
0.46
1.878
0.492
0.497
0.497
46.7860.628
0.485
10.264
0.499
3.302
0.444
0.395
0.476

17

Min.
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
16
0
0
0
0
0

Max.
1
900
2034
600
47
300 × 106
1
1
5
1
1
1
750
4
1
91
1
38
1
1
1

N
4401
4334
4343
3804
3420
4288
4408
4408
4408
4408
4408
4408
4330
4337
4249
4286
4408
4045
4408
4408
4408

Table 4: Eﬀect of Serendipity on Research Quality
serendipity
project size
separation
separation×pj size
separation×(pj size)2
integration
integration×pj size
integration×(pj size)2
year in paper(year in paper)2
competitor threatln(fund)age(age)2degreeaward
past move
past publication
countryaﬃliation
theoryexperiment
Observations
F-statistic

OLS
0.0341(0.0727)-0.0305** (0.0128)-0.2654(0.1939)-0.0084(0.0908)0.0042(0.0065)-0.0699(0.1124)0.0307** (0.0133)-0.0000(0.0000)-0.0132(0.0255)-0.0005(0.0015)0.0786*** (0.0202)
0.0498** (0.0210)-0.0647** (0.0294)
0.0005*(0.0003)-0.0515(0.0454)0.0140(0.0769)
0.0944(0.0758)
0.0030*** (0.0009)
0.2944*** (0.0883)-0.1675** (0.0849)
0.0315(0.1339)0.1508(0.1140)1405
25.9377

Standard errors in parentheses

2SLS
1.4209**(0.6224)-0.0401*** (0.0150)-0.3046(0.2160)-0.0309(0.1054)0.0055(0.0078)-0.1832(0.1370)0.0381**(0.0155)
0.0000(0.0000)-0.0345(0.0304)-0.0003(0.0017)0.0586**(0.0245)0.0304(0.0260)-0.0361(0.0360)
0.0002(0.0003)-0.0372(0.0519)-0.0061(0.0897)0.2075**(0.0980)
0.0023**(0.0010)0.6487***(0.1873)-0.1968**(0.0989)
0.0618(0.1588)
0.1671(0.1370)1405
5.5658* p < 0.10, ** p < 0.05, *** p < 0.01

Note: Researcher’s ﬁelds are controlled but results are not reported.
fund is the average amount of fund per paper.

18

Table 5: Eﬀect of Management Structure on Serendipity
project size
separation
separation×pj size
integration
integration×pj size
knowledge sharing
skill diversity
inter-org commyear in paper(year in paper)2
competitor threatln(fund)age(age)2degreeaward
past publication
past movecountryaﬃliation
theoryexperiment
Observations
Log Likelihood

Model 2-1
0.0077(0.0049)
0.0438(0.0616)0.0076(0.0128)
0.0769**(0.0387)-0.0082*(0.0050)
0.0720**(0.0284)
0.1106*** (0.0266)
0.0926*** (0.0289)
0.0101(0.0085)0.0001(0.0005)
0.0115*(0.0069)
0.0077(0.0071)-0.0142(0.0113)0.0002(0.0001)-0.0024(0.0167)
0.0147(0.0273)0.0005(0.0004)-0.0391(0.0262)-0.3117*** (0.0330)
0.0092(0.0294)-0.0182(0.0495)-0.0046(0.0414)1435-881.1193

Standard errors in parentheses

Model 2-2
0.0073(0.0060)
0.0097(0.0705)
0.0225(0.0187)
0.0886**(0.0392)-0.0076(0.0060)
0.0773***(0.0262)
0.0627**(0.0290)0.0914***(0.0308)-0.0174*** (0.0065)
0.0008***(0.0003)
0.0042(0.0070)0.0145**(0.0068)-0.0080(0.0097)0.0001(0.0001)-0.0012(0.0172)-0.0055(0.0261)-0.0002(0.0002)-0.0127(0.0258)-0.2429*** (0.0422)
0.0065(0.0261)
0.0121(0.0484)0.0158(0.0382)1265-768.8073* p < 0.10, ** p < 0.05, *** p < 0.01

Note: All coeﬃcients are average marginal eﬀect.
Researcher’s ﬁelds are controlled but results are not reported.
fund is the average amount of fund per paper.

19

Model 2-3
0.0077(0.0066)
0.0541(0.0604)
0.0074(0.0146)
0.0744*(0.0389)-0.0082(0.0066)
0.0625**(0.0291)
0.1117*** (0.0255)
0.0948*** (0.0293)
0.0122(0.0089)-0.0001(0.0005)
0.0114*(0.0067)
0.0148*(0.0083)-0.0124(0.0108)
0.0001(0.0001)-0.0013(0.0162)0.0064(0.0267)0.0006(0.0005)-0.0278(0.0262)-0.3006*** (0.0326)
0.0151(0.0293)-0.0182(0.0495)-0.0021(0.0415)1435-878.7751

Table 6: Eﬀect of Management Structure on Research Productivity
project size
separation
separation×pj size
separation×(pj size)2
integration
integration × pj size
integration × (pj size)2
project duration(project duration)2
competitor threatinter-org commln(fund)past publication
country
award
degreeaﬃliation
Observations
Log Likelihood

English Papers-0.0075(0.0154)0.1898(0.1966)-0.2530***(0.0980)0.0246***(0.0075)-0.0051(0.1120)0.0245(0.0156)-0.294×10−4 *** (0.0000)
0.0940***(0.0166)-0.0009(0.0006)
0.0681***(0.0161)0.2555***(0.0778)0.2060***(0.0168)
0.0056***(0.0009)-0.5821***(0.0863)
0.0960(0.0679)-0.0426(0.0487)-0.1516**(0.0759)1731-5933.2799

Standard errors in parentheses

All Referred Papers
0.0090(0.0134)
0.2454(0.1843)-0.2336***(0.0907)0.0224***(0.0070)0.0268(0.1035)
0.0086(0.0136)-0.297×10−4 *** (0.0000)
0.1099***(0.0157)-0.0014**(0.0006)
0.0562***(0.0151)0.3154***(0.0730)0.1739***(0.0154)0.0047***(0.0008)-0.8596***(0.0811)0.1467**(0.0646)-0.0638(0.0456)-0.2236***(0.0707)1731-6241.8738* p < 0.10, ** p < 0.05, *** p < 0.01

Note: All coeﬁcients are semielasticity.
Researcher’s ﬁelds are controlled but results are not reported.

20

Table 7: Eﬀect of Serendipity on Research Quality with Standardization
serendipity
project size
separation
separation×pj size
separation×(pj size)2
integration
integration×pj size
integration×(pj size)2
year in paper(year in paper)2
competitor threatln(fund)age(age)2degreeaward
past move
past publication
countryaﬃliation
theoryexperiment
Observation
F-statistic

OLS
0.0155(0.0437)-0.0173** (0.0074)-0.1562(0.1135)-0.0102(0.0532)
0.0028(0.0038)-0.0351(0.0669)
0.0175** (0.0077)-0.0000(0.0000)-0.0084(0.0154)-0.0003(0.0009)
0.0471*** (0.0120)
0.0325** (0.0127)-0.0384** (0.0176)
0.0003*(0.0002)-0.0262(0.0270)0.0118(0.0464)0.0568(0.0457)0.0018*** (0.0005)
0.1721*** (0.0532)-0.0986*(0.0507)0.0195(0.0818)
0.0925(0.0695)1405
18.4617

Standard errors in parentheses

2SLS
0.8285**(0.3707)-0.0230*** (0.0087)-0.1792(0.1259)-0.0234(0.0617)0.0036(0.0046)-0.1015(0.0813)0.0218**(0.0090)0.0000(0.0000)-0.0209(0.0182)-0.0002(0.0010)0.0353**(0.0146)
0.0212(0.0154)-0.0216(0.0215)0.0001(0.0002)-0.0178(0.0307)0.0000(0.0537)
0.1231**(0.0586)0.0013**(0.0006)
0.3798*** (0.1111)-0.1158** (0.0587)
0.0372(0.0960)
0.1020(0.0827)1405
6.7746* p < 0.10, ** p < 0.05, *** p < 0.01

Note: Researcher’s ﬁelds are controlled but results are not reported.
fund is the average amount of fund per paper.

21

Table 8: Robustness Test Using Alternative Instruments
serendipity
project size
separation
sep×pj sizesep×(pj size)2
integration
inte×pj size
inte×(pj size)2
year in paper(year in paper)2
competitor threatln(fund)age(age)2degreeaward
past move
past publication
countryaﬃliation
theoryexperiment
Observations
F-statistic

Model 1-1
1.308*(0.678)-0.039*** (0.015)-0.301(0.211)-0.029(0.103)0.005(0.008)-0.174(0.136)
0.038**(0.015)0.000(0.000)-0.033(0.030)-0.000(0.002)0.060**(0.025)0.032(0.026)-0.038(0.036)0.000(0.000)-0.038(0.051)-0.004(0.088)0.198**(0.100)0.002**(0.001)0.620*** (0.197)-0.194** (0.097)
0.059(0.156)0.166(0.134)1405
5.813

Standard errors in parentheses

Model 1-2
1.721*(1.037)-0.042** (0.017)-0.313(0.231)-0.036(0.113)0.006(0.008)-0.208(0.160)0.040** (0.017)
0.000(0.000)-0.039(0.034)-0.000(0.002)0.054*(0.028)0.026(0.030)-0.030(0.041)0.000(0.000)-0.034(0.055)-0.010(0.096)0.232*(0.121)0.002*(0.001)0.725** (0.288)-0.203* (0.107)
0.068(0.170)0.171(0.145)1405
4.728

Model 1-3
1.122**(0.530)-0.038*** (0.014)-0.296(0.205)-0.026(0.100)0.005(0.007)-0.159(0.127)0.037**(0.015)-0.000(0.000)-0.030(0.029)-0.000(0.002)
0.063*** (0.023)
0.035(0.024)-0.042(0.033)0.000(0.000)-0.040(0.049)-0.002(0.085)0.183**(0.092)0.002**(0.001)0.572*** (0.162)-0.191** (0.094)
0.055(0.150)0.164(0.130)1405
6.453* p < 0.10, ** p < 0.05, *** p < 0.01

Note: Researcher’s ﬁelds are controlled but results are not reported.
fund is the average amount of fund per paper.

22

Model 1-4
1.186*(0.625)-0.038*** (0.015)-0.298(0.208)-0.027(0.101)
0.005(0.007)-0.164(0.131)0.037**(0.015)0.000(0.000)-0.031(0.029)-0.000(0.002)0.062*** (0.024)
0.034(0.025)-0.041(0.034)0.000(0.000)-0.040(0.050)-0.003(0.086)0.188**(0.096)0.002**(0.001)0.589*** (0.184)-0.192** (0.095)
0.057(0.152)0.164(0.131)1405
6.194

Appendix: Selected Survey Questions and Responses
Question: Has the research output found the answers to questions not originally posed?
Response Rates
Highly Cited Normal
59.9%
54.0%
40.1%
46.0%

Answers
Yes
No

Question: Please indicate which of the following best describes your role in the management of
the research project.
Answers(1) A leading role in the research management, designing the research
project, organizing the research team, and/or acquiring research funds(2) A member of the research management, but a role less than thatof the leader(3) No managerial role(4) Management was not necessary(5) Other

Response Rates
Highly Cited Normal
70.9%
69.2%
14.1%

14.8%

7.2%
5.8%
2.1%

5.8%
8.0%
2.3%

Question: Please indicate which of the following best describes your role in the research implementation.
Answers(1) I executed the central part of the research and contributed themost to the research output(2) I took part in the central part of the research, but my contribution
was not as substantial as that of the central researcher(3) I implemented the research under the guidance of the above members(4) I contributed to the research through the provision of materials,
data, equipment, or facilities(5) Other

23

Response Rates
Highly Cited Normal
64.4%
65.5%
20.8%

21.9%

2.1%

3.0%

2.7%

2.8%

10.0%

6.8%

References
Adams, J. D., C. B. Grant, R. J. Clemmons, P. E. Stephan. 2005. Scientiﬁc teams and institutional
collaborations: Evidence from U.S. universities. 1981-1999. Res. Policy 34(3) 259–285.
Agrawal, A., A. Goldfarb. 2008. Restructuring research: Communication costs and the democratization of university innovation. Amer. Econom. Rev. 98(4) 1578–1590.
Allen, T. J. 1977. Managing the ﬂow of technology: technology transfer and the dissemination oftechnological information within the R&D organization. MIT Press, Cambridge, Mass.
Andrews, F. M. 1979. Scientiﬁc productivity : the eﬀectiveness of research groups in six countries.
Cambridge University Press, Cambridge.
Angrist, J. D., J. S. Pischke. 2008. Mostly Harmless Econometrics - An Empiricist’s Companion..
Princeton University Press, Princeton, New Jersey.
Austin, M. A., M. S. Hair, S. M. Fullerton. 2012. Research guidelines in the era of large-scale collaborations: An analysis of genome-wide association study consortia. Amer. J. Epidemiology
175(9) 962–969.
Barber, B., R. C. Fox. 1958. The case of the ﬂoppy-eared rabbits: An instance of serendipity
gained and serendipity lost. Amer. J. Sociol. 64(2) 128–136.
Barnard, C. I. 1938. The Functions of the Executive. Harvard University Press, Cambridge, Mass.
Becker, G. S., K. M. Murphy. 1992. The divison of labor, coordination costs, and knowledge.
Quart. J. Econom. 107(4) 1137–1160.
Birkinshaw, J., R. Nobel, J. Ridderstråle. 2002. Knowledge as a contingency variable: Do thecharacteristics of knowledge predict organization structure? Organ. Sci. 13(3) 274–289.
Brown, J. S., P. Duguid. 1991. Organizational learning and communities-of-practice: Toward a
uniﬁed view of working, learning, and innovation. Organ. Sci. 2(1) 40–57.
Burns, T. B., G. M. Stalker. 1961. The management of innovation. Tavistock Publications.
Cameron, C. A., P. K. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge
University Press, Cambridge, Mass.
Collins, Evans R., H., M. Gorman. 2007. Trading zones and interactional expertise. Stud. in Hist.
and Philo. of Sci. 38 657–666.
Collins, H., R. G. Harrison. 1975. Building a tea laser: The caprices of communication. Social
Stud. of Sci. 5(4) 441–450.
Dasgupta, P., P. David. 1994. Toward a new economics of science. Res. Policy 23(5) 487–521.
Ding, W. W., S. G. Levin, P. E. Stephan, A. E. Winkler. 2010. The impact of information
technology on academic scientists’ productivity and collaboration patterns. Management
Sci. 56(9) 1439–1461.
Ellison, G. 2002. Evolving standards for academic publishing: A Q-R theory. J. Polit. Econom.
110(5) 994–1034.
Fleming, L., S. Mingo, D. Chen. 2007. Collaborative brokerage, generative creativity, and creative
success. Admin. Sci. Quart. 52(3) 443–475.
Forman, P. 1987. Behind quantum electronics: National security as basis for physical research in
the united states, 1940-1960. Hist. Stud. in the Phys. and Bio. Sci. 18(1) 149–229.

24

Galison, P. 1997. Image and logic : a material culture of microphysics. University of Chicago
Press, Chicago, Ill. ; London.
Galison, P. 1999. Trading Zone: Coordinating Action and Belief . Routledge, New York, 137–160.
Garvey, W. D. 1979. Communication : the essence of science : facilitating information exchange
among librarians, scientists, engineers and students. Pergamon, Oxford.
Gouldner, A. W. 1954. Patterns of Industrial Bureaucracy. Free Press, Glencoe, Ill.
Hagedoorn, J. 2002. Inter-ﬁrm R&D partnerships: an overview of major trends and patterns since1960. Res. Policy 31(4) 477–492.
Heinze, T., P. Shapira, J. M. Senker. 2009. Organizational and institutional inﬂuences on creativity
in scientiﬁc research. Res. Policy 38(4) 610–623.
Hollingsworth, R. J. 2006. The Dynamics of American Science: An Institutional and Organizational Perspective on Major Discoveries. Campus Verlag, Frankfurt, New York, 361–380.
Jacobs, B. A. 2010. Serendipity in robbery target selection. British J. Criminology 50(3) 514–529.
Kaplan, N. 1959. The role of the research administrator. Admin. Sci. Quart. 4(1) 20–42.
Kasperson, C. J. 1978. An analysis of the relationship between information sources and creativity
in scientists and engineers. Human Comm. Res. 4(2) 113–119.
Kogut, B., U. Zander. 1992. Knowledge of the ﬁrm, combinative capabilities, and the replicationof technology. Organ. Sci. 3(3) 383–397.
Kuhn, T. S. 1970. The Structure of Scientiﬁc Revolutions. 2nd ed. International encyclopedia of
uniﬁed science. Foundations of the unity of science, vol. 2, no. 2, University of Chicago Press,
Chicago.
Kulakowski, E. C., L. U. Chronister. 2006. Research administration and management. Jones andBartlett, Sudbury, Mass. ; London.
Lave, J., E. Wenger. 1991. Situated learning : legitimate peripheral participation. Cambridge
University Press, Cambridge.
Lawrence, P. R., J. W. Lorsch. 1967. Organization and Environment; Managing Diﬀerentiation
and Integration. Division of Research Graduate School of Business Administration Harvard
University, Boston.
March, J. G. 1991. Exploration and exploitation in organizational learning. Organ. Sci. 2(1) 71–87.
Merton, R. K. 1957. Social theory and social structure. revised & enlarged ed. ed. The Free Press,
Glencoe.
Merton, R. K., E. G. Barber. 2004. The travels and adventures of serendipity : a study in sociological semantics and the sociology of science. Princeton University Press, Princeton, N.J. ;Oxford.
Nagaoka, S., M. Igami, J. P. Walsh, T. Ijichi. 2011. Knowledge Creation Process in Science: Key
Comparative Findings from the Hitotsubashi-NISTEP- Georgia Tech Scientists’ Survey inJapan and the US. IIR Working Paper No.11-09.
Pelled, L. H., K. M. Eisenhardt, K. R. Xin. 1999. Exploring the black box: An analysis of workgroup diversity, conﬂict and performance. Admin. Sci. Quart. 44(1) 1–28.
Pelz, D. C., F. M. Andrews. 1966. Scientists in organizations: productive climates for research anddevelopment. John Wiley & Sons, New York.

25

Perrow, C. 1967. A framework for the comparative analysis of organizations. Amer. Sociol. Rev.
32(2) 194–208.
Polanyi, M. 1962. The republic of science. Minerva 1(1) 54–74.
Polanyi, M. 1967. The tacit dimension. Routldge & Kegan Paul, London.
Powell, W. W., K. W. Koput, L. Smith-Doerr. 1996. Interorganizational collaboration and the locus
of innovation: Networks of learning in biotechnology. Admin. Sci. Quart. 41(1) 116–145.
Presser, S. 1980. Collaboration and the quality of research. Social Stud. of Sci. 10(1) 96–101.
Roberts, R. M. 1989. Serendipity : accidental discoveries in science. Wiley.
S., Jasjit, Lee F. 2010. Lone inventors as sources of breakthroughs: Myth or reality? Management
Sci. 58(1) 41–56.
Sauer, R. D. 1988. Estimates of the returns to quality and coauthorship in economic academia. J.
Polit. Econom. 96(4) 855–866.
Scott, W. R. 1981. Organizations: rational, natural and open systems. Prentice-Hall, Englewood
Cliﬀs; London.
Selznick, P. 1949. TVA and the Grass Roots. A study in the sociology of formal organization.
University of California Press, Berkeley; Los Angeles.
Shapiro, G. 1986. A Skeleton in the Darkroom: Stories of Serendipity in Science. 1st ed. Harper& Row, San Francisco.
Simon, H. A. 1976. Administrative Behavior: A Study of Decision-Making Processes in Administrative Organization. 3rd ed. Free Press, New York.
Stephan, P. E. 2010. The Economics of Science, vol. 1. Elsevier, Amsterdam, 217–273.
Stephan, P. E., S. G. Levin. 1992. Striking the mother lode in science : the importance of age,place, and time. Oxford University Press, Oxford.
Svensson, G., G. Wood. 2005. The serendipity of leadership eﬀectiveness in management andbusiness practices. Management Decision 43(7) 1001–1009.
Udwadia, F. E. 1990. Creativity and innovation in oragnizations: Two models and managerial
implications. Tech. Forecasting and Social Change 38(1) 65–80.
Van Andel, P. 1992. Serendipity “expect also the unexpected”. Creativity and Innovation Management 1(1) 20–32.
White, H. 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test forheteroskedasticity. Econometrica 48(4) pp. 817–838.
Woodward, J. 1965. Industrial Organization: theory and practice. Oxford University Press, London.
Wooldridge, J. M. 2010a. Asymptotic properties of weighted m-estimators for standard stratiﬁedsamples. Econometric Theory 17(02) 451–470.
Wooldridge, J. M. 2010b. Econometric analysis of cross section and panel data. 2nd ed. MIT
Press, Cambridge.
Wuchty, S., B. F. Jones, B. Uzzi. 2007. The increasing dominance of teams in production ofknowldge. Science 316(5827) 1036–1039.
Zuckerman, H. 1977. Scientiﬁc Elite : Nobel Laureates in the United States. Free Press, New York.

26