GraSPP-DP-E-14-001 and STIG-DP-E-14-001

Adaptating Organization in Science:
How Contextual Environment Aﬀects
Task Allocation of Academic Biology Laboratories
Sotaro Shibayama, Yasunori Baba and John P. Walsh
October 2013

GraSPP Discussion Paper E-14-001

STIG Discussion Paper E-14-001

GraSPP-DP-E-14-001 and STIG-DP-E-14-001

Adaptating Organization in Science:
How Contextual Environment Affects
Task Allocation of Academic Biology Laboratories

Sotaro Shibayamaa,b,*, Yasunori Babaa and John P.Walshc
October 2013a

The University of Tokyo, Research Center for Advanced Science and Technology
4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
Phone/Fax: +81-3-5452-5371.
e-mail: shibayama@00.alumni.u-tokyo.ac.jpb
The University of Tokyo, Department of Technology Management for Innovation
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japanc
Georgia Institute of Technology, School of Public Policy*
Corresponding author.

GraSPP Discussion Papers can be downloaded without charge from:http://www.pp.u-tokyo.ac.jp/research/dp/index.htm

Graduate School of Public Policy
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
Phone:+81-3-5841-1349
Discussion Papers are a series of manuscripts in their draft form. They are not intended forcirculation or distribution except as indicated by the author. For that reason Discussion Papers may
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ADAPTATING ORGANIZATION IN SCIENCE: HOW CONTEXTUAL ENVIRONMENT AFFECTSTASK ALLOCATION OF ACADEMIC BIOLOGY LABORATORIES

Sotaro Shibayama a,b, *, Yasunori Baba a, John P. Walsh ca

The University of Tokyo, Research Center for Advanced Science and Technology
4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
Phone/Fax: +81-3-5452-5371.
e-mail: shibayama@00.alumni.u-tokyo.ac.jpb

The University of Tokyo, Department of Technology Management for Innovation
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japanc

Georgia Institute of Technology, School of Public Policy*

Corresponding author.

ABSTRUCT
This study examines the contingency of organizational design in academic organizations drawing on a surveyof biology laboratories in Japanese universities. It investigates the intra-laboratory task allocation between alab head and members (e.g., junior staff, PhD students) in three research phases (i.e., planning, execution, andwriting) and how different patterns of task allocation affect lab-level scientific productivity. Results suggestthat organic organizational structure with limited division of labor suits basic research, where exploratory
approach is common with high unpredictability, while mechanistic structure is fitter for applied research,which is relatively exploitative and predictable. Thus, academic organizations, just as industrial organizations,need to adapt their organizational design to contextual environment to maximize their performance.

KEYWORDS
Laboratory; task allocation; contingency; division of labor; organizational design; biology

JEL CODE
I23, L23, O32, D23
1

1.

INTRODUCTION
Because the modern economy heavily relies on knowledge production from the academic sector,

fostering competitive academic organizations is essential in vitalizing the economy (Etzkowitz andLeydesdorff, 2000; Stephan, 1996). Scientific research in academia is usually undertaken in university
laboratories, where a diverse range of expertise from some researchers is integrated under the supervision of alab head (also called principal investigator or PI) and the process of research is accelerated by division of labor(e.g., Carayol and Matt, 2006; Latour and Woolgar, 1979; Owen-Smith, 2001). The continuous nature oflaboratories, unlike temporary cross-organizational collaboration, enables researchers to pursue ambitious
research goals, and laboratories function as a place of training junior researchers (Delamont et al., 1997;
Knorr-Cetina, 1999). These characteristics of laboratories contribute to scientific production in the long term.
As such, science policy literature has suggested that laboratory is the most appropriate unit in analyzing
scientific production (Carayol and Matt, 2006; Latour and Woolgar, 1979), but few studies have focused on the
lab-level scientific production.
Organizational theory suggests that organizational structure and business processes should be
adapted to a variety of contextual factors (e.g., Burns and Stalker, 1961; Minzberg, 1979; Tidd et al., 1997).
Especially, in the lab context, though in industry, previous literature has shown, for example, that information
flow and communication patterns are contingent on task characteristics (Allen and Cohen, 1969; Tushman,1978). These studies seem to imply that academic organizations also have to adapt their organizational design
depending on circumstances. Though academic organizations at the level of university or college may lackflexibility for this requirement and appear bureaucratic (Minzberg, 1979), university laboratories, given a highlevel of autonomy, can be flexible to adjust themselves to the dynamic nature of science. In fact, someliterature finds an analogy between university laboratories and small startups in that lab heads act as apresident who raises funds and organizes a team of researchers to win the competition and to market new
products in the form of scientific knowledge (Stephan, 2012). Nevertheless, prior literature on organizational
theory has been developed mainly in the industrial context, and it is not totally clear if implications fromindustry can be directly translated into academia.
University laboratories is peculiar in that lab heads supervise members including students and juniorresearchers, who may need training for short of experience (e.g., Knorr-Cetina, 1999; Owen-Smith, 2001).
That is, lab heads have to play a dual role of an educator and a manager in their relationship with young
members. With this regard, sociology literature offers in-depth illustration based on ethnographies of one or a
few university laboratories mostly in life sciences and physics (Fujimura, 1997; Knorr-Cetina, 1999; Latour2and Woolgar, 1979; Lynch, 1984; Owen-Smith, 2001; Salonius, 2008). However, general picture on theorganizational behavior of university laboratories is still lacking, and their implication for scientific production
is limited.
The objective of this study is three-fold. First, it aims to draw a broader picture concerning the natureof organizational behavior of university laboratories especially in terms of task allocation on the basis of astructured survey data, whereby to reinforce the prior findings largely based on ethnographies. Second, in sodoing, this study attempts to identify productive organizational designs under different contexts, whereby toextend the application of organizational theory, mostly formulated in the industrial context, to the context ofacademic organizations. Third, this study aims to answer two managerial questions on task allocation in
university laboratories: first, whether lab members should be engaged only in labor-intensive tasks, and second,
whether lab heads should stay away from the bench as a pure manager or engage also in labor-intensive tasks
as player mangers. That is, prior literature roughly assumes that lab heads are the pure manager and members
are workers, but we challenge this simplistic view. In particular, we hypothesize that different modes of task
allocation should be employed depending on research areas and other contextual factors. To empirically test
our hypotheses, we conducted interviews of 30 researchers and a survey of 396 lab heads from Japanese
universities in the field of biology.

2.

ORGANIZATIONAL ASPECTS OF LABORATORY WORK
Research activities in natural sciences are usually undertaken in laboratories that consist of a labhead and some members under the lab head’s supervision (e.g., Carayol and Matt, 2006; Latour and Woolgar,
1979; Owen-Smith, 2001). Lab heads are usually professors, and members include students, postdocs(postdoctoral researchers), junior faculty members,1 and technicians. Universities provide lab heads withcertain space, where they can install necessary facilities and employ a group of members to solve their researchquestions. Unlike temporary cross-organizational collaboration, laboratories allow a continuous form of
teamwork and lab heads can pursue relatively long-term goals. In this long-term perspective, lab heads arrange
a portfolio of research projects, some of which may be challenging but with potentially great impact and othersof which less novel but likely to succeed, so that they can spread the risk and constantly produce certain output(Knorr-Cetina, 1999). Laboratories allow division of labor. In biology, research techniques are well embedded
in individual researchers (Knorr-Cetina, 1999; Latour and Woolgar, 1979), and thus, coordinating multiple

1

Depending on university systems, junior faculty members (e.g., assistant professors, lecturers) may be supervised as members
under a lab head or may become independent lab heads. In Japan, junior faculty members are usually supervised by a lab head.
3

researchers with different technical expertise is often essential. Lab tasks are also vertically divided. That is,
lab heads are usually responsible for setting up the research environment (e.g., budget, funding, andrecruitment) and coordinating a series of projects, while members commit to executing specific projects(Traweek, 1988). Importantly, laboratories also function as a place of education. Young researchers typicallyconsider their lab experience as opportunities to acquire research technique, which is requisite for their future
employment (Delamont and Atkinson, 2001; Delamont et al., 1997). With this pedagogical function ofuniversity laboratories, the science community can transfer skills and knowledge from old to new generations,stabilizing the practice of science (Delamont et al., 1997).
In terms of task allocation, prior literature has mainly focused on the roles of lab heads and assumed
that they are occupied with fund raising, arrangement of research environment, employment of members, andresearch planning (National Research Council, 1998). For example, Knorr-Cetina (1999) has found that
researchers often stop bench work after becoming lab heads in high energy physics and molecular biology.
National Research Council (1998) mentions in a report on American life scientists’ career design that “[a]principal investigator builds a research group by defining the scientific questions to be addressed, specifying
the methods to be used, obtaining necessary funding, finding the suitable research environment, and attracting
the research personnel…. The research personnel in the group usually work on more specific tasks that pertain
to the construction of research tools or the acquisition and analysis of data.” As for the role of members, on theother hand, much less has been studied. A few studies on sociology of education, focusing on postgraduate
education, have examined the relationship between lab heads and students (Delamont and Atkinson, 2001;
Delamont et al., 1997; Salonius, 2008). An ethnography of British universities suggests that lab heads are
responsible for identifying research projects and assigning them to students (Delamont et al., 1997). Becher etal. (1994) also point out that determining research subjects is rarely the responsibility of students themselves.
For students, mastering tacit lab skills is the most important goal in lab experience (Delamont and Atkinson,
2001). Thus, learning and engaging in technical tasks seems to be regarded as the students’ primary role. Thisdivision of labor between lab heads and members may be particularly clear in biology. Whitley (1984) suggeststhat work techniques in most natural sciences are rather reliable and standardized. In fact, many experimental
techniques in biology are available as commercial kits and outsourcing services. This may allow lab heads todelegate technical work to members easily.
To further the discussion of task allocation, we distinguish three phases of research process. In
general, scientific research starts from setting research questions and developing research plans, then, thequestions are tested by experiments, simulations, and other approaches, and finally, the test results are4

interpreted and used to advance extant knowledge (Nightingale, 1998). This advanced part of knowledge often
raises new questions for future research, and the whole process is repeated. We split this process into twofunctional phases: planning, or determining research subjects and hypotheses, and execution, or testing the
hypotheses usually by experiment and data analyses in biology. In addition, we consider the phase of writing.
The planning and execution are iterated until sufficient findings are accumulated that make up a convincing
story as a scientific paper. For these three phases, prior literature roughly implies that lab heads engage inplanning and members in execution, but it has paid limited attention to task allocation in writing. In what
follows, we describe general qualities of each phase and discuss the rationale of task allocation.
Execution phase. Biology is strongly governed by the notion of empiricism (Bertalanffy et al., 1962:
p.100), and thus, biological research heavily depends on experiment except for a few purely computational andtheoretical subfields. In the execution phase, researchers aim to transform some material substances into
interpretable information, which often takes the form of figures and tables, and part of them are used in thenext writing phase as “Results” in publications (Latour and Woolgar, 1979: Ch. 2). This transformation may beprocessed through some devices and facilities (e.g., typical examples in biology include NMR and DNA
sequencers, etc.) or may be done more manually. Experimental procedures usually follow protocols. Some
procedures are well-established and may be made available as commercial kit and somewhat automated, whileothers are less so and researchers may have to start from developing or optimizing protocols. It is particularly
noteworthy that execution in biology usually draws on living organisms such as bacteria, cultured cells, mice,and even humans in clinical research. Since they often need maintenance on a daily basis, researchers tend to
be chained to laboratories. Experimental procedure could take varying ranges of time from minutes toovernight and from weeks to months, and researchers have to arrange their job schedule depending on the life
cycle of these organisms and the type of experiment. Experimental techniques requires substantial tacitknowledge and generally takes high level of concentration (Knorr-Cetina, 1999; Latour and Woolgar, 1979).
Particular care must be taken, for example, when they use bio-hazardous materials or handle pathogenic agents.
For these reasons, execution tasks are highly labor-intensive and time-consuming. Although efficientexperimental methods have been developed, they simply enable more trials and errors and do not necessarily
mitigate the burden of labor. Therefore, it seems natural that members but not lab heads are regarded as themain player in this phase (Delamont and Atkinson, 2001; Delamont et al., 1997).
Writing phase. The next phase, writing, is the process of producing a scientific paper for publicationfrom given results. Although the process of creating a paper may span all three phases in that literature review
may be done in planning and figures and tables are produced in execution (Latour and Woolgar, 1979: Ch. 2),
5

we define the writing phase in a narrow sense. Still, writing can be more than a mechanical process ofsummarizing experimental results and can be an intellectual process of interpreting results, placing themadequately in the context of concurrent scientific debate, and creating a convincing story that interests peer
researchers. These tasks call for some special skills. First, biological research usually draws on multiple
research techniques, and one project may involve multiple members. Thus, the first step in writing is to selectappropriate results from many results possibly done by many members. Such coordination seems manageable
only by lab heads who have the authority and a holistic viewpoint beyond each member’s. Second, biologicalresearch is often serendipitous and, experimental results are unpredictable (Shimizu et al., 2012). To write apaper with unexpected results, writers may have to start over from literature review and revise the planned
story. With this regard, the quality of papers can be greatly affected by writers’ theoretical knowledge. Third,
the writing process may involve informal communication with other researchers. Before submitting a paper for
peer review, authors attempt to improve the possibility of acceptance by incorporating the knowledge ofleading researchers. Some of our interviewees emphasized that negotiation with journal editors is also
indispensable. Overall, this phase requires intellectual and social skills, which seems better exercised byexperienced lab heads rather than by young members.
Planning phase. The planning phase starts with choosing research subjects and identifying specificquestions. The goal of academic research is to advance knowledge, but the advancement is acknowledged only
after publication, and the credit is awarded basically only to the first discoverers (Merton, 1973). Thus,
researchers have to carefully choose research agenda so that they can win the competition, for which they have
two options: to find new niche ones, or to outperform competitors in extant ones. In either case, outside extant
knowledge, or the status of competitors, is essential information for planning, where even unpublishedinformation may have to be collected. For the latter option, researchers typically extend a line of their own past
research because it tends to be most advanced in a certain area. Thus, their own past research is anotheressential information source for planning. With this regard, the planning and writing phases are closely related.
Once research areas and questions are identified, researchers have to translate their hypotheses into technicallyoperational plan (i.e., the plan of execution). Thus, this phase also takes technical knowledge. Researchers
have to process and integrate these various types of knowledge and develop a strategically feasible plan.
Therefore, it seems natural to assume that lab heads’ supposedly higher-level intellectual capabilities andlonger research experience better serve this phase (Delamont and Atkinson, 2001; Knorr-Cetina, 1999).

3.

VARIATION IN TASK ALLOCATION AND SCIENTIFIC PRODUCTIVITY
6

Although the prior assumption in task allocation seems reasonable, it does not have to imply
complete division of labor. Thus, we ask whether members should concentrate on execution as if they were
technicians or be encouraged to take part also in planning and writing, and whether lab heads should stay awayfrom execution tasks as a pure manager or participate in them as a player manager (Golden et al., 2000), andwe argue that answers to these questions depend on contexts (Burns and Stalker, 1961; Minzberg, 1979; Tidd
et al., 1997).

3.1.

Members’ Engagement in Planning
Based on the assumption that lab heads are primarily responsible for this phase, our question is if

members also should be engaged. The answer to this question from prior literature, though limited, is rather
negative for their observation that members are allowed to decide their own research subjects (Delamont andAtkinson, 2001; Delamont et al., 1997; Salonius, 2008). While this is understandable for members’ limited
research experience, it is exactly why they need training. Perhaps, mastering experimental skills may be thefirst priority for young members (Delamont and Atkinson, 2001), but learning how to design and coordinate
research projects should be indispensable. Thus, one could argue that members must be engaged in the wholeprocess of research for educational purposes even if it may compromise scientific productivity. In the longterm, well-trained researchers should better serve the science community. This argument highlights the conflict
between the two roles of academic organizations, education vs. research, (Hackett, 1990), and lab heads face
the dilemma of whether to prioritize their own research productivity or to give their young subordinates
opportunities for learning even at the sacrifice of productivity. Many of our interviewees referred to this point,suggesting that there are two types of laboratories: one where members are treated like blue-collar workers in afactory, and the other where members are decently trained as would-be researchers. One interviewee
mentioned as follows:In natural sciences, it may be common that young members (especially students) are
exploited to produce experimental results as if they were technicians. Some laboratories
even do attendance management like a factory. However, I believe that such an approachcannot develop good researchers, and that those who obediently follow lab head’s
instructions, if being successful, will not become a leader of whatever world. I believethat universities are the place for education, and thus, students must be respected morethan professors.

7

Although the above argument implies that members need to be given opportunities to experienceother phases than execution, it still maintains the assumption that it has, if any, negative impact on productivity.
However, motivation theory might suggest otherwise. In general, researchers place great emphasis on
autonomy and independence (Amabile and Gryskiewicz, 1987; Hollingsworth and Hollingsworth, 2000). As
such, Roach and Sauermann (2010) imply that academically-trained researchers show strong preference for
freedom to choose research projects. Involving members from the outset of research process can motivate themto seriously engage in later phases. With autonomy in project selection, members attribute their success or
failure to their own actions, and their intrinsic motivation is facilitated (Hackman and Oldham., 1976). Put
differently, it can be counterproductive to keep members away from this upstream phase. A lab head weinterviewed mentioned as follows:
Because the execution phase in life science research is painstakingly laborious, members
would not go through it without strong intrinsic motivation. With this regard, having
members engage in planning is effective. I try to respect members’ choice of research
topics even if they seem likely to fail, hoping them to reach a serendipitous discovery.

Then, the question comes down to the balance between potential loss of productivity due tomembers’ inexperience and potential gain due to reinforced motivation. We argue that this depends on contexts,
particularly, on research areas in terms of being basic vs. applied. Here, we briefly discuss the concept ofbasicness. Biology is a broad discipline and is related to many research fields such as medicine, agriculture,and pharmaceutical. This diversity can be attributable to many aspects, but one that can be relevant incoordinating lab work is the nature of research goals (Sauermann and Stephan, 2012). Some researchers seek
general understanding of certain phenomena (i.e., basic research), while others are guided by consideration forpractical use (i.e., applied research) (Stokes, 1997). Although this distinction of research orientation may becontroversial, researchers show some consensus. Calvert (2004) lays out a few characteristics of basic research.
First, basic research is unpredictable, where researchers aim to find a new concept or push the boundaries ofexisting knowledge. This feature in basic research leads to exploratory approach compared to more
exploitative and confirmatory approach in applied research. Second, basic research is general in that its results
can be used for a wide range of instances and phenomena while applied research helps to solve a specificproblem. Third, basic research is driven by the internal theoretical dynamics of the discipline. This is also
related to the generality because theories involve statements of general principles. We assume that thesefeatures of basic and applied research affect how lab tasks should be allocated.
8

Particularly, in the planning phase, the basicness of research agenda could affect productive modes oftask allocation for two reasons. First, intrinsic motivation is regarded as an indispensable antecedent for
exploratory and divergent thinking (Amabile, 1996), which is more relevant in basic research (Calvert, 2004).
As such, Sauermann and Cohen (2010), based on a survey of corporate researchers, show that intrinsic
motivation contributes to productivity to a greater extent in upstream R&D activities than in downstream.
Second, the exploratory nature of basic research implies that research plan in basic agenda is prone to frequent
update. That is, researchers have to frequently adjust their research plan in accordance with experimental
results. Thus, the feedback loop between planning and execution runs quickly (Nelson, 1959), and it could be
streamlined by members’ engagement in the planning phase. In contrast, this potential benefit seems limited inapplied agenda, where the goal of research is rather clear and members can stick to original plans. Thus, wehypothesize:
Hypothesis 1: Members’ engagement in planning has greater positive effect on productivity in basic
research than in applied research.

3.2.

Lab Head’s Engagement in Execution
The second question is whether lab heads should stay away from the bench as a pure manager. As

above discussed, prior assumption is that members have comparative advantage in this labor-intensive andtime-consuming phase (Delamont and Atkinson, 2001; Delamont et al., 1997). Though we accept thatmembers are the main player in this phase, we further ask if co-participation of lab heads contributes to
productivity. Our interviewees suggested that quite a few lab heads actually engage in bench work, not because
they are in short of labor, but simply because they enjoy it. They mentioned that most researchers have become
researchers for their taste for doing experiment, which allows direct interaction with the nature, so they will notabandon it to become a pure manager. Such laboratories must be sacrificing productivity if the argument ofcomparative advantage is correct. We suppose that this is generally the case but that lab head’s execution canbe justified in a certain situation.
A potential source of benefit from lab head’s execution is the proximity between a lab head andmembers in daily research activities. Through direct supervision, lab heads could find out and help solve
problems in members’ execution tasks in a timely fashion. This could make enormous impact on the progressof members’ work. Biological experiment could easily take weeks or months to produce results, andinexperienced members could be unaware of fatal problems that are obvious to experienced researchers.
Furthermore, collocation can increase the social proximity because lab members spend most of their time at9

bench. Then, communication between a lab head and members is facilitated, contributing to the efficiency ofteam work (Teasley et al., 2002). Shimizu et al. (2012), drawing on a survey sample of natural scientists in the
US and Japan, indicate that the integration of management and execution roles is positively associated with
serendipitous discoveries. When members are separated from a lab head and their communication cost is high,unexpected findings are more likely to be overlooked for members’ lack of holistic viewpoints, and they might
be even deliberately ignored for fear that the lab head might hate to hear unexpected news (Barber and Fox,
1958; Van Angel, 1992).
Another benefit from lab head’s engagement in execution is technological catch-up. A lab head weinterviewed suggested that it is difficult even for experienced lab heads to follow state-of-the-art techniques
without engaging in experiment at all.
When interpreting experimental results, researchers have to distinguish true from false
signs of discoveries and to find out hidden serendipitous signs. They may be obvious forexperimenters but not for non-experimenters. It is not rare that pure-manager lab heads
misinterpret experimental results and make silly instructions to their members.
Unfortunately, members often have to follow the instructions and tend to blindly do so
especially when the lab head is renowned.

This is because most experimental techniques (even commercially available kits) take a great deal of tacitknowledge. Knorr-Cetina (1999) suggests that an experimental technique is a “package” of lab protocol,
material objects, and researchers. Lab heads may be able to understand the theoretical mechanism of newtechniques, but only experimenters know the knack of techniques. Thus, lab heads would become technicallyobsolete if distancing themselves from bench work (Salonius, 2008), which can be a serious problem whenexperimental techniques are rapidly advancing as in biology.
We hypothesize that these points are particularly important in basic research. In basic research,
because of its exploratory nature and abstract goals, research plans tend not to be strictly predetermined andare frequently updated, which requires tight communication between a lab head and members. As autonomoustrials and errors are encouraged, the risk of being stuck in trivial problems and unpromising lines of research
becomes higher, which justifies the cost of lab head’s direct supervision. Furthermore, since the success ofbasic research depends more on unplanned findings, a keen eye for serendipitous signs in experimental results
is essential. Given that young members have limited capabilities with this regard, lab heads need to maintaintheir technical capabilities by catching up with the latest technologies. Thus, we hypothesize:
10Hypothesis 2: Lab heads’ engagement in execution has positive effect on productivity in basic research
while it has less positive or even negative effect in applied research.

3.3.

Task Allocation in Writing
We above argued that writing is more than summarizing experimental results and takes substantial

knowledge, and thus, that lab heads have comparative advantage to members. More precisely, however, thisdepends on how much value is added to papers in this phase. For example, if research goals are practical, notmuch theoretical knowledge may be required, and if experimental results are predictable, the storyline of apaper can be determined before execution. Then, the value added in the writing phase may be limited. Again,
we argue that the basicness of research is relevant. First, the ultimate goal of applied research is, by definition,
application (Calvert, 2004; Stokes, 1997). Thus, publications in applied research can be appreciated if it ispractically useful even if it does not advance theoretical understanding. For example, research on clinicalmedicine can be published if it proves the efficacy of a drug substance but does not elucidate its mechanism.
On the other hand, the goal of basic research is to advance knowledge and tends to refer to general and abstract
concepts (Calvert, 2004; Stokes, 1997). Thus, researchers have to understand up-to-date theoretical debate andincorporate their own findings in the extant knowledge. With this regard, lab heads’ advantage over young
members in writing is more relevant in basic research than in applied research.
The unpredictable nature of basic research could strengthen this tendency. Basic research takes more
exploratory approach and applied research more confirmatory approach (Calvert, 2004). Basic research often
starts from a broad question without having a precisely testable hypothesis, and experimental results might beapplied to a diverse range of scientific discussion. If researchers have a broad range of knowledge not only
about originally intended areas but also about surrounding areas, serendipitous discoveries are more likely tooccur. With this regard, writing in basic research can be more a creative process of generating a novel story.
Our interviewee referred to this point, suggesting the necessity of substantial knowledge and experience for
writing in basic research.
Serendipitous discoveries are important in biological, particularly basic, research. I thinkthat even young researchers could find unintended results if they are careful enough.
However, it does not guarantee a publication. For publication, serendipitous discoveries
must be theorized and proved in accordance with the extant theories in the field. Thistakes substantial knowledge and is not feasible for inexperienced researchers.

11

On the other hand, applied research tends to have a clear focus on application (Calvert, 2004). To the extent
that research goal is specific, the room for creative interpretation is limited. Thus, writing in applied research
can be relatively a process of summarizing experimental results according to the predetermined plan. Then,
members’ inexpensive labor may lend them comparative advantage in this phase as in the execution phase.
Assuming that the skills of writing are better addressed by lab heads, especially in basic research, wemust further ask whether non-involvement in writing demotivates members. With this regard, the motivation
for writing is importantly different from that for the other two phases. That is, researchers are intrinsicallymotivated for curiosity until they make discoveries. However, writing is driven more by extrinsic motivation
for the recognition from the peer and for other incentive systems (Merton, 1973). Thus, as long as members
admit that their lab head is a better writer and they are given authorship, members would not insist on their
engagement in this phase. In sum, we hypothesize:
Hypothesis 3: Lab heads’ engagement in writing has greater positive effect on productivity in basic
research than in applied research, and members’ engagement in writing has greater positive effect on
productivity in applied research than in basic research.

4.

DATA & METHOD

4.1.

Sample and the Context of Japanese Universities
This study draws on a survey of Japanese university laboratories in the field of biology. To prepare asampling frame for the survey, we employed the following criteria. First, we chose researchers currently in theposition of a full professor. Japanese universities have a three-level promotion system with full professor at the
top followed by associate professor and assistant or lecturer. Before obtaining an entrance position (assistant or
lecturer), academics tend to experience a few years of postdoc term. Typical biology laboratories consist ofsenior staff (full and/or associate professor), who are the lab head, and members including a few junior faculty
members (assistant or lecturer), postdocs, students, and technicians. Unlike American universities, juniorfaculty members are often under the supervision of lab heads. Some associate professors have independent
laboratories, and others work in the same laboratory with a full professor, often co-supervising a laboratory.
Second, we chose researchers who have received national grants at least once in the field of biology in the last
three years (2007-2009), which implies that they are active researchers. Drawing on the list of recipients ofGrants-in-Aid for Scientific Research (the primary competitive funding source for Japanese university
scientists),2 we prepared our sampling frame of 1,378 researchers. After re-examining their research fields and2

Japan Society for the Promotion of Science (http://www.jsps.go.jp/j-grantsinaid/index.html).
12affiliations on the Internet, we chose 900 lab heads in 56 universities as a final sample.
The survey instrument was developed based on semi-structured interviews with 10 Japanese
researchers. We mailed the survey to the 900 lab heads and collected 396 responses (response rate = 44%). The
survey was conducted from May through July 2010. We also collected publication data from Web of Science tomeasure scientific productivity. To examine non-response bias, we randomly selected 50 non-respondents andfound no significant difference between the response and non-response groups in publication productivity,
organizational rank, and gender (p > 0.1).
Japan has three types of universities offering four-year undergraduate or postgraduate education:national, public, and private universities. 3 Among them, national universities are the primary player ofacademic research while most private universities are education oriented. For example, while 73% of
undergraduate students were in private schools whereas 22% were in national universities as of 2010. On theother hand, only 36% of graduate students were in private schools and 58% were in national universities.
Among national universities, top seven universities (Universities of Tokyo, Kyoto, Osaka, Tohoku, Hokkaido,
Kyushu, and Nagoya) are designated as pre-imperial universities and have been enjoying exceptionallyprestigious status both in research and in education. For example, they receive approximately 50% of national
research funds (Shibayama, 2011) and produce 30% of PhDs among all universities (Ishibashi and Ohtake,2009). In our sample, 83% of the respondents are from national universities and 43% are from the top seven
universities.

4.2.

Measures and Description
Lab productivity. We prepared two measures of productivity at the lab level. First, we count the

number of publications authored by lab heads (respondents) in the last five years of 2007 through 2011 (pubcount). In biology, lab heads usually become an author for any paper published from their laboratories,
regardless of their extent of engagement. Thus, we assume that publications authored by lab heads should
cover most publications from their laboratory. Second, to gauge more qualitative aspect of scientificproduction, we drew on citation count. To address the age effect of citation count, we summed up
age-weighted citation count for the above publications (citation count).4 Further, we divide pub count andcitation count by the number of lab staff (lab heads and junior researchers) to compute per-staff productivity.

3

As of 2010, Japan has 86 national, 95 public, and 597 private universities (School Basic Survey by Ministry of Education,
Culture, Sports, Science and Technology: http://www.e-stat.go.jp/).
4
Since we collected the publication data in 2012 (1- 5 years after publication), newer papers should have fewer citation counts
than older papers. We divided the citation count of each paper by the number of elapsed years and summed them up.
13

Because these two measures are highly correlated and the regression results are similar, we mainly report theresults based on citation count.
Organizational structure and task allocation. We inquired for the number of senior staff (full andassociate professors), junior researchers (assistant, lecturer, and postdocs), PhD students, and technicians ineach laboratory. The summation of these members is used as a measurement of lab size. On average, alaboratory consists of 1.6 senior staff, 2.6 junior researchers (assistant, lecturer, or postdoc), 2.8 PhD students,
and 1.1 technicians. To examine task allocation, we define six research tasks for the three phases: 1) choosing a
subject, 2) formulating a hypothesis, 3) planning experiment, 4) doing experiment, 5) analyzing data, and 6)
writing papers. We suppose that 1) – 3) corresponds to the planning phase, 4) and 5) to the execution phase,and 6) to the writing phase. For each of these six tasks, we inquired as to the extent of the involvement of full
professor (our respondent), associate professors, junior researchers, and PhD students, respectively. The
response takes three-point scale, 0: no role, 1: supportive role, and 2: leading role.5 Using this instrument, we
prepared measurements of task allocation (detail shown in the result section). In the following analyses, we
focus on laboratories that have at least one junior researchers and one PhD student.6 Those without either
junior researchers or PhDs tend to be very small. Among 396 laboratories, 77% (309 laboratories) satisfy thiscondition with the mean size of 9.2 and the standard deviation of 5.2.
Basic research. To measure research orientation in each laboratory, we asked “which describes yourresearch goal, basic (aiming at advancement of theory and knowledge) or applied (aiming at solving problems
in the real society)?” with five-point scale, 1) mostly basic, 2) more basic than applied, 3) both to a similar
extent, 4) more applied than basic, and 5) mostly applied. Of our respondents, 55% chose 1), implying that
their research goal was completely basic. For these basic laboratories, a dummy variable is coded one, andother laboratories are regarded as applied with the dummy coded zero (basic research). The inclination to basic
research is due to our sampling.7 To validate this subjective measurement, we drew on the type of journals
where the respondents publish their papers. Based on the classification of “basicness” of journals (Narin et al.,1976), we calculated the percentage of respondents’ papers in basic journals and confirmed that basic
laboratories tend to publish in basic journals (r = .29, p < .001). Second, we surveyed the number of patentapplications in 2009–2010 and confirmed that basic laboratories have significantly fewer patents than appliedlaboratories (.31 vs. .85 applications per year; p < .001). Third, we identified a research field in which each
5This measurement may need careful interpretation in that we do not know actual time spent on each task.
There is no laboratory without a full professor. In terms of task allocation, we assume that technicians are not very relevant
because they usually engage only in the execution phase.
7
The majority of our sample are from schools of sciences while the rest are from schools of medicine, agriculture,pharmaceuticals, etc.
614

respondent received the majority of national grants (field). We categorized these fields into basic and appliedfields8 and confirmed that this measure is correlated with basic research (r = .41, p < .001). In addition, we
tested the assumption that basic research is more exploratory and applied research is more confirmatory(Calvert, 2004). We surveyed “which describes the quality of your research, exploratory or confirmatory?”
with a similar five-point scale and found that this is significantly positively correlated with the measurement ofbeing basic vs. applied (r =.23, p < .001).
Control variables. The productivity of individual lab members should affect lab productivity andcould possibly change task allocation. To incorporate lab head’s productivity, we draw on publications
first-authored by lab heads before they obtained the tenure position (i.e., before they opened their own lab).9
We summed up the age-weighted citation count of these papers and averaged it by the number of years for the
pre-tenure term (pre-tenure citation count). As a proxy of members’ productivity, we use the university rank. In
the Japanese academic context, for its admission and employment system, the ability of students and juniorresearchers are strongly affected by the university prestige. We prepared a dummy variable for the top sevenpre-imperial universities (top 7 univ). As measures of research input, we also include per-staff research budget(JPY in million) and lab head’s average hours spent on research activities (time for research). Time forresearch was measured with six-point scale, 1) less than 10 hours, 2) 10-20 hours, 3) 20-30 hours, 4) 30-40
hours, 5) 40-50 hours, and 6) 50 hours or longer per week. Some measures for individual background areincluded. We controlled for the number of years since lab heads opened their own lab (lab age). We asked theexperience of research abroad with six-point scale, 1) none, 2) less than half a year, 3) one year, 4) 2 years, 5) 3
years, and 6) 4 years or more (foreign experience). If the current laboratory is where they obtained their degree,
a dummy variable is coded one (inbred). If a lab head obtained a degree of medical doctor, a dummy variable
is coded one (medical doctor). If a lab head is female, a dummy variable is coded one (female). In addition,
following regressions include fixed-effects of field identified by past national grants. Table 1 presents thedescriptive statistics and correlation matrix of these variables.

5.

RESULTS

5.1.

Variation in Task Allocation
Figure 1 illustrates the extent of engagement by the three ranks of lab constituents (lab heads,10

8

Basic fields include basic biology, biological science, basic medicine, neuroscience, and genome science. Applied fields
include agriculture, pharmaceutical, and medicine.
9
In biology, the first author implies that the author made the most important contribution to the paper.
10
If a laboratory is co-supervised by full and associate professors, the maximum value of full and associate professors’
engagement is used as lab head’s engagement.
15

junior researchers, and PhD students) in six research tasks. It indicates that the planning phase is primarily
conducted by lab heads and execution by members (junior researchers and PhDs). Thus, this confirms theassumption in the previous literature (National Research Council, 1998; Delamont and Atkinson, 2001;
Knorr-Cetina, 1999). However, the extent of lab heads’ engagement in execution and that of members’planning show some variation, suggesting that the division of labor is not completely stringent. As for thewriting phase, lab heads are highly committed but members are also engaged (only about 10% of members
play no role in writing).
We further analyze the extent of deviation from the typical task allocation in Table 2. For threerank-phase combinations (members’ planning, lab head’s execution, and members’ writing), we computed the
proportion of laboratories where the leading role is played by the supposedly atypical rank. In the wholesample, about one-third of laboratories engage members in planning, and about half engage lab heads inexecution and members in writing (Rows 1-3). Again, these results suggest that the division of labor is not very
strict. When the sample is split by research areas, members’ writing is more common in applied than in basic
laboratories (57% vs. 50%, p > .1). In addition, we examine the patterns of task allocation with the eight
possibilities of three rank-phase combinations (Row 4-11). Rows 4 and 5 are the supposedly typical pattern,which accounts for 35%, but other patterns are not rare (12-20%) except Rows 8 and 10. Rows 8 and 10
correspond to members’ planning but members’ not writing, which implies that laboratories engaging members
in planning tend to engage them also in writing.
To further scrutinize the pattern of task allocation, we ran a factor analysis for the 18 (three ranks x
six task types) measures, which yielded a six-factor solution based on the Kaiser-Guttman criterion (i.e.,eigenvalues greater than one) (Table 3). The six factors correspond to 1) member’s planning, 2) researcher’s
full responsibility, 3) lab head’s planning, 4) PhD’s execution, 5) lab head’s execution, and 6) lab head’s
writing (and member’s not writing). This solution is consistent with our instrument design in that items
intended to fall in the same concept belong to the same factor. Based on this result, we prepared three measurescorresponding to factors 1), 5), and 6), taking the average of original measures for easier interpretation as our
key independent variables.11

5.2.

Prediction of Scientific Productivity
Table 4 presents the results of regression analyses. We estimate scientific productivity with

11
For Factor 6), we took the inverse of professor’s writing and averaged them with PhD’s and researchers’ writing. We do notinclude Factors 2), 3), and 4) because they are not significant (Appendix 1). As for 2) and 3), this is possibly because they havevery small variation (Figure 1).

16

fixed-effect ordinary least squares models. We use the logarithms of per-staff citation count and pub count as
the dependent variables in Tables 4A and 4B, respectively. As for citation count (Table 4A), Model 1, based on
the whole sample, shows that members’ planning and members’ writing have significantly positive effect (b= .273, p < .05; b = .285, p <.1, respectively). It shows that lab head’s execution does not have significant
effects (p > .1). As for control variables, pre-tenure citation count as a measure of lab head’s ability shows
positive effect (b = .033, p < .01) and female shows strongly negative effect (b = -.833, p < .01). To investigate
the difference between basic and applied research, we split the sample into basic and applied laboratories(Models 3 and 4). As for the planning phase, members’ engagement show significantly positive effect in bothlab types (b = .418, p < .05 and b = .357, p < .1, respectively), and the effect is slightly weaker in appliedlaboratories. This is supportive to Hypothesis 1. Interestingly, lab head’s execution has opposite effects
between two lab types: significantly positive (b = .465, p < .01) in basic laboratories but significantly negativein applied laboratories (b = -.390, p < .01). This supports Hypothesis 2. This implies that the cost of lab head’s
engagement in execution exceeds its benefit in applied laboratories. As for lab head’s writing, positive effect isobserved in basic laboratories (b = .465, p < .1) and weaker and insignificant effect in applied laboratories (b= .145, p > .1). This is supportive to Hypothesis 3. To further examine the difference between basic and appliedlaboratories, Model 2 introduces interaction terms of task allocation and basic research with the whole sample.
The result shows a strongly significant coefficient for the interaction term for lab head’s execution (b = .833, p< .001), confirming Hypothesis 2. However, the interaction terms for members’ planning and lab head’s
writing do not show a significant coefficient although the direction is as expected.
Table 4B shows qualitatively similar results with pub count as the dependent variable. However, inapplied laboratories, members’ planning and lab head’s writing become rather negative, though insignificant(Model 4). Furthermore, Model 2 shows that the effects of three task allocation measures are significantly
smaller in applied laboratories than in basic laboratories (interaction terms: b = .274, p < .1; b = .500, p < .001;
and b = .333, p < .1, respectively). This supports Hypotheses 1-3. These results imply that the effect of task
allocation could be different for qualitative and quantitative aspects of scientific production. Especially inapplied laboratories, members’ planning contributes to the quality (citation count) but not the quantity (pubcount) of production, and likewise, lab head’s writing improves the quality but not to the quantity. For betterinterpretation, Figure 2 illustrates the effect of task allocation in three phases for two dependent variables.

5.3.

Supplementary Analysis
Sensitivity analysis. We consider three factors for sensitivity analysis. First, task allocation can be17affected by external collaboration. When multiple laboratories participate in a project, each phase is likely to
involve participants from multiple laboratories. Then, lab heads are more likely to be in charge of the phase asa representative of each laboratory, and conversely, members are less likely to be given the leading role. We
surveyed the frequency of external collaboration in recent two years and found that some laboratories havecollaborated more than 10 times (mean = 2.6). Restricting the sample to low-collaboration labs (once a year orless), we re-ran the regressions (Appendix 2). The result is qualitatively similar, but it shows stronger effects ofmembers’ planning and lab heads’ writing (and members’ not writing) (Model 1).
Second, we examine the influence of the ability of members, which should affect both task allocationand productivity. Assuming that organizational ranks affect the ability of members, we split the sample into
two groups of high and low university ranks and ran the regressions (Appendix 3A). Overall, the result shows
weaker statistical significance probably for smaller sample sizes. The effect of members’ planning issignificantly positive in high-ranked universities while it becomes insignificant, though still positive, in
low-ranked universities (Models 1-3 vs. Models 4-6). This implies that the positive effect of members’planning is moderated by their ability, which is confirmed by our interview.
In top universities, students can advance their research on their own. However, in
low-ranked universities, students would be at a loss what to do if autonomy is given. I
believe that lab heads cannot help treating them more as a technician than as anindependent researcher in low-ranked universities.

Third, we analyze the effect of members’ planning with the distinction of PhD students and juniorresearchers; prior ethnographies pointed out that PhD students tend not to engage in planning (Becher et al.,
1994; Delamont et al., 1997), but they had no clear mention of more experienced members. Approximately
20% of laboratories allow the leading role for junior researchers only (but not for PhDs) and another 20%
allow the leading role for both junior researchers and PhDs, while 60% do not allow the leading role forneither of them.12 Appendix 4 compares the productivity with the last type of laboratories as the base group.
Models 1 and 2 show significantly positive effect for the leading role of only junior researchers. Interestingly,
when both junior researchers and PhDs are engaged in planning, the effect becomes slightly weaker. Thissuggests that the engagement of PhD students have no additional effect when junior researchers are alreadyinvolved, but that it has rather negative effect especially in basic laboratories. This implies that members’
research experience matters in the planning phase. In fact, one of our interviewees in basic research referred to
12

Few labs allow the leading role for PhDs but not for junior researchers, and we drop them for this regression analysis.
18this point:
I involve PhD students and junior researchers in the planning phase, and I am trying toallow them to do what they want. However, I do not think that independent planning isplausible for most PhD students. A solid research plan takes minimum experience, andhaving a PhD or not makes a substantial difference with this regard. One of my PhD
students was obsessed with an idea, which I did not believe promising, but I could notchange his mind until later literature proved it was wrong.

In applied laboratories, in contrast, the leading role of both ranks of members shows more significant andgreater effect than that of only junior researchers. This might suggest that learning the planning skills forapplied research takes less time than that for basic research.
Contingency on other contexts. We examine contingency to two additional contextual factors: labsize and lab age. The size is one of relatively well-studied factors though not conclusive (Carayol and Matt,
2006; Horta and Lacy, 2011). As for lab heads’ execution, the positive effect in basic laboratories is stronger inlarger laboratories (Appendix 3B: Models 2 and 5). To the contrary, the effect of lab heads’ writing in basic
laboratories is stronger for smaller laboratories. While larger lab size may cause overcapacity for lab heads’
active participation (Delamont and Atkinson, 2001; Salonius, 2008), it may allow economies of scale andscope. The result implies that the latter effect is dominant in execution but the former in writing.
As for lab age (Appendix 3C), the result shows that members’ planning and lab head’s writing inbasic laboratories is significantly positive in old laboratories but not in young laboratories (Models 2 and 5).
This may be because longer experience of senior lab heads allows them to supervise members better inplanning and to draw on deeper and wider knowledge in writing. In addition, the negative effect of lab head’s
execution in applied laboratories is more evident in old laboratories than in young laboratories (Models 3 and
6). This may be because the opportunity cost of lab head’s execution is greater for senior lab heads than forjunior lab heads.
Training policy and task allocation. Finally, we attempt to examine the drivers behind task
allocation. In the survey, we asked several questions about respondents’ (lab head’s) policies on PhD training,based on which we analyzed what policies are associated with particular types of task allocation (Appendix 5).
The result shows that members’ planning is positively correlated with “giving scientifically important project”
and “monitoring frequently.” Thus, when lab heads are given challenging subjects, members are likely to beengaged in the planning phase and their work is closely monitored. This is especially significant in basic
19

laboratories, and this is consistent with our discussion for the benefit of collocation. In applied laboratories,“giving safe project” (so that PhD students can finish dissertation without delay) is negatively correlated withmembers’ planning. That is, some lab heads assign easy subjects to students without engaging them in planning.
“Giving safe project” is also negatively correlated with lab head’s execution, implying that some lab heads
choose easy projects and avoid participation in execution. With lab head’s writing, “giving independent
project” is negatively correlated; i.e., some PhDs are assigned writing tasks as part of an independent project.
“Giving team project” is positively correlated with lab head’s execution and writing, implying that lab heads
regard the typical division of labor as a form of teamwork.

6.

DISCUSSION AND CONCLUSIONS
Drawing on a survey data of Japanese biology laboratories, this study investigates the variation intask allocation and its impact on scientific productivity under different contexts. First, this study confirms theprior assumption that lab heads primarily engage in planning tasks while members in execution tasks(Delamont and Atkinson, 2001; Delamont et al., 1997; Salonius, 2008). However, this study also shows thatlab heads engage in the execution phase and members in the planning phase to some extent in manylaboratories. Thus, the task allocation in biology laboratories is more flexible than prior literature has assumed.
In addition, this study shows that writing tasks are allocated to both lab heads and members to varying extents.
These results help draw a general picture of organizational behavior in university laboratories. Importantly, thisstudy shows that the variation in task allocation affects scientific productivity, and that this effect is contingent
on environmental contexts, which is consistent to the prediction of organizational theory (Burns and Stalker,
1961; Minzberg, 1979; Tidd et al., 1997).
This study distinguishes three phases of research process and examines the effects of task allocation.
In the planning phase, the result suggests that members’ engagement increases productivity, possibly because
autonomy stimulates members’ intrinsic motivation (Amabile and Gryskiewicz, 1987) and may encourage theireffort in later phases. This effect seems stronger for experienced members (e.g., postdocs) than for students,
consistent to the prior ethnographies (Becher et al., 1994; Delamont et al., 1997). This effect also seems
stronger in high-ranked universities than in low-ranked universities. Comparing basic and applied research,members’ engagement seems more effective in basic research possibly for its exploratory nature and therelevance of intrinsic motivation than in applied laboratories. The result also shows that the effect of task
allocation is stronger for citation count than for publication count, implying that members’ participation inplanning is more relevant for the quality of production than for the quantity.
20

In the execution phase, members are the primary players, and intuitively, lab heads seem too
expensive for the labor-intensive tasks. However, the result indicates that many lab heads actually engage inthis phase. Our interviewees pointed out some rationale for lab heads’ execution, such as catching up latest
technologies and giving members timely input through collocation. Interestingly, the regression results show
opposite effects of lab heads’ execution between basic and applied laboratories. Thus, in basic laboratories, thebenefit of lab heads’ engagement seems to exceed its cost possibly because of exploratory nature of basic
research. That is, in exploratory approaches, sharing workspace with members, having frequent discussion, andupdating research plans in a timely fashion may be more important. In contrast, in applied laboratories, which
tend to take more exploitative and confirmatory approach, research plans are more deterministic and frequent
input from lab heads may be less needed. The positive effect in basic laboratories is even stronger inhigh-ranked universities, which might be due to members’ higher ability.
In the writing phase, the result shows that lab head’s greater engagement and members’ weaker
engagement improves productivity particularly in basic laboratories. Since writing scientific papers is a highly
intellectual task, lab heads’ expertise seems more suitable than members’. This argument is more relevant inbasic research, which is more theory-driven and exploratory and takes theoretical knowledge of experienced
researchers (Calvert, 2004). Consistently, this positive effect is strengthened when lab heads are moreexperienced. Again, this effect is clearer for citation count than for publication count, implying that lab heads’
capabilities contribute more to quality than to quantity.
These results are in line with organizational theory, which argues that flexible organizational designis essential for uncertain tasks (Thompson, 1967; Tidd et al., 1997). Namely, there is an analogy between basic
vs. applied laboratories and organic vs. mechanistic organizations. As above discussed, basic research ischaracterized by exploratory approach and high unpredictability, which is better addressed by organicorganizational structure; on the other hand, applied research is relatively stable with less uncertainty, to whichmechanistic organizational structure is suitable (Burns and Stalker, 1961). Specifically, prior literature suggeststhat non-routinized decision-making requires flexible organizational structure (Perrow, 1967), and thatcomplex tasks do not allow decentralized organizational structure (Marengo and Dosi, 2005). As such, ourresults suggest that division of labor in planning and execution improves productivity in applied research while
it compromises productivity in basic research. Thus, this study offers empirical evidence that the fundamental
prediction of organizational theory is applicable to academic organizations.
From a practical perspective, this study offers implications for two managerial questions inuniversity laboratories. First, we asked whether members should be given training opportunities not only in21

execution but also in planning and writing. In applied laboratories, members’ engagement in planning shows
positive effect, and members’ engagement in writing (opposite of lab head’s writing) shows negative effect on
qualitative aspect of productivity. As long as quality matters, members should be involved in planning both foreducational and for strategic reasons. As for members’ writing, at least, the result does not provide statisticallysignificant evidence that engaging inexperienced members in writing compromises productivity. Thus, one
may well favor members’ writing for educational purposes. In basic laboratories, since the positive effect ofmembers’ planning is more clearly observed, members should be engaged in planning. However, members’
writing shows significantly negative effect. This presents a dilemma for lab heads, who are under pressure for
productivity but have to train their members. With this regard, our data shows that experienced lab heads tend
to give writing tasks to members. This is understandable for their relatively stable status. Apparently, thisdilemma is more serious for younger lab heads, especially when the competition is intensifying and job
security is destabilizing. Second, we asked whether lab heads should stay away from the bench to be a puremanager or engage in experiment as a player manager. The result suggests that lab head’s cost for execution
tasks cannot be justified in applied research, and thus, lab heads should concentrate on managerial roles. In
contrast, in basic research, lab heads’ execution increases productivity possibly for collocation with members
and technology catch-up. In reality, it is often the case that lab heads cannot afford the time for execution tasks
for administrative and other reasons once they attain high-rank positions. Thus, university administrations
should avoid such situations and allow lab heads sufficient time for bench work especially in basic fields.
These results need to be interpreted with reservations for some limitations, which simultaneously
suggest some directions of future research. First of all, our key contextual factor, research areas, needs cautious
interpretation. Although our argument in this study is based on dichotomous distinction between basic andapplied laboratories, task characteristics may be different between, for example, purely basic, purely applied,and basic-applied combinatorial laboratories (Stokes, 1997). In particular, since recent science policies attach
more emphasis to practical application (Etzkowitz, 1983), basic laboratories should be under the pressure toengage also in somewhat applied research. Then, they may need more ambidextrous approach in theirorganizational design to deal with different types of research goals (Raisch et al., 2009). Related to this point,
our analysis is based on a cross-sectional data, but laboratories may change their research goals over time.
Thus, future research should investigate more details of task characteristics (research areas) and their career
trajectory to facilitate the understanding in the contingency and dynamics of organizational design. Also for thenature of cross-sectional data, we cannot rule out the problem of endogeneity. It is likely that task allocation isdetermined by the ability of lab constituents. Particularly, members’ ability is difficult to measure although we22tried to control for the ability of both lab heads and members. Finally, this study draws on a sample of Japanese
university laboratories. Although the Japanese academia, especially in biology, is highly embedded in the
global science community, we cannot rule out the possibility that our results are specific to the context of Japan.
In addition, our findings may be specific to the field of biology. Therefore, further research for generalizationis needed.

ACKNOWLEDGMENTS
We acknowledge financial support from the Konosuke Matsushita Memorial Foundation and Grant-in-Aid for
Research Activity Start-up of the Japan Society for the Promotion of Science (#23810004) and Grant-in-Aid
for Scientific Research (B) Program (#20330077) form The Ministry of Education, Culture, Sports, Scienceand Technology (MEXT) of Japan. We appreciate Ms. Aya Sasaki for technical support.

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Tidd, J., Bessant, J., Pavitt, K. 1997. Managing innovation: Integrating technological, market andorganizational change. New Jersey: John Wiley & Sons.
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27

Figure 1

Research Process and Task Allocation

28

29

Figure 2

Prediction of Task Allocation Effect a

Planning Phase

Execution Phasea

Writing Phase

LH stands for lab heads. Based on regression results (Models 3 and 4 in Tables 4A and 4B), we predicted the lab productivity in terms of per-staff citation count (top row) andpub count (bottom row) for basic and applied laboratories. In each phase, we compare two extreme patterns of task allocation. In planning, since a lab head usually plays theleading role, a lab head’s solo leading vs. co-leading with members is of the focal interest. Similarly, in execution, members’ solo leading vs. co-leading with a lab head is thequestion. In writing, since a lab head’s and members’ roles are negatively correlated, a lab head’s solo leading vs. members’ is the point. For the prediction, the mean values areused for all variables except the focal task allocation variables.
30

Table 1

Descriptive Statistics and Correlation of Variables a

Mean Std.Dev.

Lab Peoductivity
1Per-staff pub count2
Per-staff citation countControl variable3
Pre-tenure citation count4
Time for research
5
Foreign experience
6
Inbred7
Medical doctor
8
Female9Budget/#staff
10
Lab age
11
Lab size
12
Basic research
13
Top7 univ
Task allocation
14
Members' planning15
Lab head's execution
16
Lab head's writing (& members' not writing)

a

Min

Max

1

2

.627

3

4

5

6

7

8

-.071
.120
.103
.028
.016
.059
-.013
-.039

-.016
-.063
-.045
.384
.027
.016
.136

.061
.083
-.049
.265
-.054
-.050

-.059
-.065 -.140-.058 0.13
.022 -0.02-.033 0.12.070
.078 .006 -.065-.007 -.128 .005 .115-.099
.088 .236 .153

.029
.080
-.110

-.051 -.087.130 -.009.154 .099

64.93721.92596.11518.092.000.000

1146.783
115.000

2.738
3.770
3.633
.105
.174
.033
3.960
13.214
9.246
.540
.489

4.929
1.535
1.548
.307
.380
.178
2.882
7.747
5.204
.499
.501

.000
1.000
1.000
.000
.000
.000
.278
1.000
3.000
.000
.000

45.238
6.000
6.000
1.000
1.000
1.000
17.50035.00045.000
1.000
1.000

.295
.063
.087
.007 .116-.110 -.087 -.119 .084.046.108 -.039 -.067.133.035 .186 .068-.075 -.133 -.041 .148.130.084 .133 .242-.055 .132 -.229 -.165.365 .483 .122 .075
.122 -.066.066 .166.062 .133.070 .096

1.179
1.227
1.015

.489
.567
.407

.000
.000
.333

2.000
2.000
2.000

.059
.004
.027

N=309. Bold italic: p<0.05.
31

9

10

11

12

.023
-.097
-.042

.037
.199

.222

.095
.065
.052
.094
.057 -.160

.132
.022
-.017

.035
.037
.163

13

14

.150
.028
.074
-.026 -.359

15

.093

Typologies of Task Allocation a

Table 2

1
2
3
4
5
6
7
8
9
10
11

Members'
planning
YES
NO
NO
NO
NO
YES
YES
YES
YES

Lab head's
execution
YES
NO
NO
YES
YES
NO
NO
YES
YES

Members'
writing
YES
NO
YES
NO
YES
NO
YES
NO
YES
Total

Total
99
148
163
62
45
61
38
11
39
8
41
305

(32%)
(49%)
(53%)
(20%)
(15%)
(20%)
(12%)
(4%)
(13%)
(3%)
(13%)

Research Areas
Basic
Applied55 (34%) 41 (30%)
78 (48%) 67 (49%)
81 (50%) 79 (57%)
30 (19%) 32 (23%)
22 (14%) 23 (17%)
38 (23%) 21 (15%)
17 (10%) 21 (15%)
6 (4%)
5 (4%)
26 (16%) 11 (8%)
7 (4%)
1 (1%)
16 (10%) 24 (17%)
162
138

a
YES: leading role and NO: otherwise (Columns 2-4). We computed the mean of the extent of engagement (0: none, 1:
supportive, and 2: leading role) in related tasks and ranks, and assigned YES if it is 1.5 or greater. Columns 5-10 show thenumber of laboratories and its percentage (parentheses) for each pattern of task allocation.

32

Table 3

Lab head

Juniorresearcher

PhD

Factor Analysis of Task Allocation

Subject
Hypothesis
Planning
Experiment
Analysis
Writing
Subject
Hypothesis
Planning
Experiment
Analysis
Writing
Subject
Hypothesis
Planning
Experiment
Analysis
Writing

Factor1

Factor2

Factor3

Factor4

Factor5

Factor6

M embers'
planning

Junior researcher’s
full responsibility

Lab head’splanning

PhD’s
execution

Lab head’s
execution

Lab head's'
writing

-.077
-.020
.064
-.006
.070
.167
.666
.748
.544
-.036
.154
.174
.675
.812
.698
.052
.303
.261

.133
.088
-.062
.046
.166
.232
.423
.446
.594
.811
.822
.698
-.047
-.010
-.028
.144
.125
.003

.750
.836
.579
-.019
.259
.348
.104
.115
.107
.047
.035
.138
-.184
-.090
-.045
.072
.114
.065

.002
.170
-.007
-.055
.048
.120
-.121
-.071
.060
.115
.206
.030
.163
.323
.404
.884
.787
.273

-.032
.087
.498
.857
.677
.282
.014
.015
-.152
.190
.029
.012
.163
.043
-.048
.001
-.035
.013

.088
-.057
.052
.004
.125
.629
-.087
.051
.034
.122
.118
-.497
-.268
-.104
-.117
-.046
-.175
-.825

33

Table 4

Fixed-effect OLS Models of Scientific Productivity a(A) Dependent Variable = ln(Per-staff Citation Count)

All labs
Model 1

Basic labs
Model 3Model 2

Applied labs
Model 4Control variable
Pre-tenure citation countTime for research.033 **(.012).031 **(.012).034 †(.018).029 †.048(.039).045(.038).024(.054).082(.059)

Foreign experience-.043(.038)-.075 *(.038)-.115 *(.055)-.024(.055)

Inbred.210(.202).255(.197).413(.284).080(.279)

Medical doctor.129(.182).074(.178).045(.263).039(.257)

Female-.833 **(.313)-.775 *(.305)-1.050 *(.419)-.309(.523)

Budget/#staff.009(.021).007(.020).054 †(.032)-.029(.027)

Lab age.003(.009).003(.008)-.007(.012).016(.013)

Basic Research.073(.132)-1.314 *(.541).093(.118).027(.115)-.201(.171).286 †(.163)(.182)

Top7 univ
Task allocation
Members' planning
Lab head's execution
Lab head's writing (& members' not writing)Interaction
Members' planning x Basic research.273 *(.127).289(.184).418 *(.181).357 †-.022(.103)-.415 **(.140).465 **(.161)-.390 **(.138).285 †(.159).166(.215).465 †(.236).145(.214).086(.246)

Lab head's execution x Basic research.833 ***(.202)

Lab head's writing x Basic research.242(.301)

F testLog likelihood
N(.016)

2.838 ***

3.619 ***

3.466 ***

2.044 **

-371.377-361.085-193.073-151.313

292

292

156

136

a

Unstandardized coefficients and standard errors (parentheses). Two-tailed test. † p<0.10; * p<0.05; ** p<0.01;*** p<0.001.
The use of fixed effect model is justified with F-test for joint subfield effect (p < .01) and Hausman test (p < .001) in Model 2.
34

(B) Dependent Variable = ln(Per-staff Pub Count)
All labs
Model 1

Basic labs
Model 3Model 2

Applied labs
Model 4Control variable
Pre-tenure citation countTime for research.000(.008)-.001(.008).004(.011)

-.003

.027

(.026).022(.025).026(.034).044(.012)(.042)

Foreign experience-.023(.025)-.046 †(.025)-.091 **(.034).007(.039)

Inbred.155(.134).194(.131).311 †(.177).023(.199)

Medical doctor-.066(.121)-.085(.118)-.074(.164)-.101(.183)

Female-.387 †(.208)-.334 †(.202)-.369(.261)-.097(.373)

Budget/#staff-.018(.014)-.018(.013)-.007(.020)-.021(.019)

Lab age.010 †(.006).010 †(.006).002(.007).022 *(.009)

Basic Research-.082(.087)-1.372 ***(.359)

Top7 univ.051(.078).004(.076)-.126(.106).161(.116)

Task allocation
Members' planning.079(.085)-.030(.122).281 *(.113)-.032(.130)

Lab head's execution-.099(.068)-.326 ***(.092).206 *(.101)-.271 **(.098)

Lab head's writing (& members' not writing).056(.106)-.107(.143).267 †(.147)-.103(.153)

Members' planning x Basic research.274 †(.163)

Lab head's execution x Basic research.500 ***(.134)

Lab head's writing x Basic research.333 †(.199)

Interaction

F testLog likelihood
N1.496 *

2.588 ***

2.200 **

1.799 *

-252.219-240.938-119.329-105.421

292

292

156

136

35